Sample records for optimal brain function
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Li, Kaiming; Guo, Lei; Zhu, Dajiang; Hu, Xintao; Han, Junwei; Liu, Tianming
2013-01-01
Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain. PMID:22281931
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The association between resting functional connectivity and dispositional optimism.
Ran, Qian; Yang, Junyi; Yang, Wenjing; Wei, Dongtao; Qiu, Jiang; Zhang, Dong
2017-01-01
Dispositional optimism is an individual characteristic that plays an important role in human experience. Optimists are people who tend to hold positive expectations for their future. Previous studies have focused on the neural basis of optimism, such as task response neural activity and brain structure volume. However, the functional connectivity between brain regions of the dispositional optimists are poorly understood. Previous study suggested that the ventromedial prefrontal cortex (vmPFC) are associated with individual differences in dispositional optimism, but it is unclear whether there are other brain regions that combine with the vmPFC to contribute to dispositional optimism. Thus, the present study used the resting-state functional connectivity (RSFC) approach and set the vmPFC as the seed region to examine if differences in functional brain connectivity between the vmPFC and other brain regions would be associated with individual differences in dispositional optimism. The results found that dispositional optimism was significantly positively correlated with the strength of the RSFC between vmPFC and middle temporal gyrus (mTG) and negativly correlated with RSFC between vmPFC and inferior frontal gyrus (IFG). These findings may be suggested that mTG and IFG which associated with emotion processes and emotion regulation also play an important role in the dispositional optimism.
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The association between resting functional connectivity and dispositional optimism
Yang, Wenjing; Wei, Dongtao; Qiu, Jiang; Zhang, Dong
2017-01-01
Dispositional optimism is an individual characteristic that plays an important role in human experience. Optimists are people who tend to hold positive expectations for their future. Previous studies have focused on the neural basis of optimism, such as task response neural activity and brain structure volume. However, the functional connectivity between brain regions of the dispositional optimists are poorly understood. Previous study suggested that the ventromedial prefrontal cortex (vmPFC) are associated with individual differences in dispositional optimism, but it is unclear whether there are other brain regions that combine with the vmPFC to contribute to dispositional optimism. Thus, the present study used the resting-state functional connectivity (RSFC) approach and set the vmPFC as the seed region to examine if differences in functional brain connectivity between the vmPFC and other brain regions would be associated with individual differences in dispositional optimism. The results found that dispositional optimism was significantly positively correlated with the strength of the RSFC between vmPFC and middle temporal gyrus (mTG) and negativly correlated with RSFC between vmPFC and inferior frontal gyrus (IFG). These findings may be suggested that mTG and IFG which associated with emotion processes and emotion regulation also play an important role in the dispositional optimism. PMID:28700613
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How the Public Engages With Brain Optimization
O’Connor, Cliodhna
2015-01-01
In the burgeoning debate about neuroscience’s role in contemporary society, the issue of brain optimization, or the application of neuroscientific knowledge and technologies to augment neurocognitive function, has taken center stage. Previous research has characterized media discourse on brain optimization as individualistic in ethos, pressuring individuals to expend calculated effort in cultivating culturally desirable forms of selves and bodies. However, little research has investigated whether the themes that characterize media dialogue are shared by lay populations. This article considers the relationship between the representations of brain optimization that surfaced in (i) a study of British press coverage between 2000 and 2012 and (ii) interviews with forty-eight London residents. Both data sets represented the brain as a resource that could be manipulated by the individual, with optimal brain function contingent on applying self-control in one’s lifestyle choices. However, these ideas emerged more sharply in the media than in the interviews: while most interviewees were aware of brain optimization practices, few were committed to carrying them out. The two data sets diverged in several ways: the media’s intense preoccupation with optimizing children’s brains was not apparent in lay dialogue, while interviewees elaborated beliefs about the underuse of brain tissue that showed no presence in the media. This article considers these continuities and discontinuities in light of their wider cultural significance and their implications for the media–mind relationship in public engagement with neuroscience. PMID:26336326
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Traumatic Brain Injury Inpatient Rehabilitation
ERIC Educational Resources Information Center
Im, Brian; Schrer, Marcia J.; Gaeta, Raphael; Elias, Eileen
2010-01-01
Traumatic brain injuries (TBI) can cause multiple medical and functional problems. As the brain is involved in regulating nearly every bodily function, a TBI can affect any part of the body and aspect of cognitive, behavioral, and physical functioning. However, TBI affects each individual differently. Optimal management requires understanding the…
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Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.
Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele
2018-01-01
Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.
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Enhancing health leadership performance using neurotherapy.
Swingle, Paul G; Hartney, Elizabeth
2018-05-01
The discovery of neuroplasticity means the brain can change, functionally, in response to the environment and to learning. While individuals can develop harmful patterns of brain activity in response to stressors, they can also learn to modify or control neurological conditions associated with specific behaviors. Neurotherapy is one way of changing brain functioning to modify troubling conditions which can impair leadership performance, through responding to feedback on their own brain activity, and enhancing optimal leadership functioning through learning to maximize such cognitive strengths as mental efficiency, focus, creativity, perseverance, and executive functioning. The present article outlines the application of the concept of optimal performance training to organizational leadership in a healthcare context, by describing approaches to neurotherapy and illustrating their application through a case study of a health leader learning to overcome the neurological and emotional sequelae of workplace stress and trauma.
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Segregation of the Brain into Gray and White Matter: A Design Minimizing Conduction Delays
Wen, Quan; Chklovskii, Dmitri B
2005-01-01
A ubiquitous feature of the vertebrate anatomy is the segregation of the brain into white and gray matter. Assuming that evolution maximized brain functionality, what is the reason for such segregation? To answer this question, we posit that brain functionality requires high interconnectivity and short conduction delays. Based on this assumption we searched for the optimal brain architecture by comparing different candidate designs. We found that the optimal design depends on the number of neurons, interneuronal connectivity, and axon diameter. In particular, the requirement to connect neurons with many fast axons drives the segregation of the brain into white and gray matter. These results provide a possible explanation for the structure of various regions of the vertebrate brain, such as the mammalian neocortex and neostriatum, the avian telencephalon, and the spinal cord. PMID:16389299
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Denis, I; Potier, B; Vancassel, S; Heberden, C; Lavialle, M
2013-03-01
The increasing life expectancy in the populations of rich countries raises the pressing question of how the elderly can maintain their cognitive function. Cognitive decline is characterised by the loss of short-term memory due to a progressive impairment of the underlying brain cell processes. Age-related brain damage has many causes, some of which may be influenced by diet. An optimal diet may therefore be a practical way of delaying the onset of age-related cognitive decline. Nutritional investigations indicate that the ω-3 poyunsaturated fatty acid (PUFA) content of western diets is too low to provide the brain with an optimal supply of docosahexaenoic acid (DHA), the main ω-3 PUFA in cell membranes. Insufficient brain DHA has been associated with memory impairment, emotional disturbances and altered brain processes in rodents. Human studies suggest that an adequate dietary intake of ω-3 PUFA can slow the age-related cognitive decline and may also protect against the risk of senile dementia. However, despite the many studies in this domain, the beneficial impact of ω-3 PUFA on brain function has only recently been linked to specific mechanisms. This review examines the hypothesis that an optimal brain DHA status, conferred by an adequate ω-3 PUFA intake, limits age-related brain damage by optimizing endogenous brain repair mechanisms. Our analysis of the abundant literature indicates that an adequate amount of DHA in the brain may limit the impact of stress, an important age-aggravating factor, and influences the neuronal and astroglial functions that govern and protect synaptic transmission. This transmission, particularly glutamatergic neurotransmission in the hippocampus, underlies memory formation. The brain DHA status also influences neurogenesis, nested in the hippocampus, which helps maintain cognitive function throughout life. Although there are still gaps in our knowledge of the way ω-3 PUFA act, the mechanistic studies reviewed here indicate that ω-3 PUFA may be a promising tool for preventing age-related brain deterioration. Copyright © 2013 Elsevier B.V. All rights reserved.
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Toward an Integration of Deep Learning and Neuroscience
Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P.
2016-01-01
Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses. PMID:27683554
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Joint brain connectivity estimation from diffusion and functional MRI data
NASA Astrophysics Data System (ADS)
Chu, Shu-Hsien; Lenglet, Christophe; Parhi, Keshab K.
2015-03-01
Estimating brain wiring patterns is critical to better understand the brain organization and function. Anatomical brain connectivity models axonal pathways, while the functional brain connectivity characterizes the statistical dependencies and correlation between the activities of various brain regions. The synchronization of brain activity can be inferred through the variation of blood-oxygen-level dependent (BOLD) signal from functional MRI (fMRI) and the neural connections can be estimated using tractography from diffusion MRI (dMRI). Functional connections between brain regions are supported by anatomical connections, and the synchronization of brain activities arises through sharing of information in the form of electro-chemical signals on axon pathways. Jointly modeling fMRI and dMRI data may improve the accuracy in constructing anatomical connectivity as well as functional connectivity. Such an approach may lead to novel multimodal biomarkers potentially able to better capture functional and anatomical connectivity variations. We present a novel brain network model which jointly models the dMRI and fMRI data to improve the anatomical connectivity estimation and extract the anatomical subnetworks associated with specific functional modes by constraining the anatomical connections as structural supports to the functional connections. The key idea is similar to a multi-commodity flow optimization problem that minimizes the cost or maximizes the efficiency for flow configuration and simultaneously fulfills the supply-demand constraint for each commodity. In the proposed network, the nodes represent the grey matter (GM) regions providing brain functionality, and the links represent white matter (WM) fiber bundles connecting those regions and delivering information. The commodities can be thought of as the information corresponding to brain activity patterns as obtained for instance by independent component analysis (ICA) of fMRI data. The concept of information flow is introduced and used to model the propagation of information between GM areas through WM fiber bundles. The link capacity, i.e., ability to transfer information, is characterized by the relative strength of fiber bundles, e.g., fiber count gathered from the tractography of dMRI data. The node information demand is considered to be proportional to the correlation between neural activity at various cortical areas involved in a particular functional mode (e.g. visual, motor, etc.). These two properties lead to the link capacity and node demand constraints in the proposed model. Moreover, the information flow of a link cannot exceed the demand from either end node. This is captured by the feasibility constraints. Two different cost functions are considered in the optimization formulation in this paper. The first cost function, the reciprocal of fiber strength represents the unit cost for information passing through the link. In the second cost function, a min-max (minimizing the maximal link load) approach is used to balance the usage of each link. Optimizing the first cost function selects the pathway with strongest fiber strength for information propagation. In the second case, the optimization procedure finds all the possible propagation pathways and allocates the flow proportionally to their strength. Additionally, a penalty term is incorporated with both the cost functions to capture the possible missing and weak anatomical connections. With this set of constraints and the proposed cost functions, solving the network optimization problem recovers missing and weak anatomical connections supported by the functional information and provides the functional-associated anatomical subnetworks. Feasibility is demonstrated using realistic diffusion and functional MRI phantom data. It is shown that the proposed model recovers the maximum number of true connections, with fewest number of false connections when compared with the connectivity derived from a joint probabilistic model using the expectation-maximization (EM) algorithm presented in a prior work. We also apply the proposed method to data provided by the Human Connectome Project (HCP).
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Nasrallah, Fatima A; Lee, Eugene L Q; Chuang, Kai-Hsiang
2012-11-01
Arterial spin labeling (ASL) MRI provides a noninvasive method to image perfusion, and has been applied to map neural activation in the brain. Although pulsed labeling methods have been widely used in humans, continuous ASL with a dedicated neck labeling coil is still the preferred method in rodent brain functional MRI (fMRI) to maximize the sensitivity and allow multislice acquisition. However, the additional hardware is not readily available and hence its application is limited. In this study, flow-sensitive alternating inversion recovery (FAIR) pulsed ASL was optimized for fMRI of rat brain. A practical challenge of FAIR is the suboptimal global inversion by the transmit coil of limited dimensions, which results in low effective labeling. By using a large volume transmit coil and proper positioning to optimize the body coverage, the perfusion signal was increased by 38.3% compared with positioning the brain at the isocenter. An additional 53.3% gain in signal was achieved using optimized repetition and inversion times compared with a long TR. Under electrical stimulation to the forepaws, a perfusion activation signal change of 63.7 ± 6.3% can be reliably detected in the primary somatosensory cortices using single slice or multislice echo planar imaging at 9.4 T. This demonstrates the potential of using pulsed ASL for multislice perfusion fMRI in functional and pharmacological applications in rat brain. Copyright © 2012 John Wiley & Sons, Ltd.
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Point-based warping with optimized weighting factors of displacement vectors
NASA Astrophysics Data System (ADS)
Pielot, Ranier; Scholz, Michael; Obermayer, Klaus; Gundelfinger, Eckart D.; Hess, Andreas
2000-06-01
The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non-metabolized radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark-generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark- specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset.
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The Myth of Optimality in Clinical Neuroscience.
Holmes, Avram J; Patrick, Lauren M
2018-03-01
Clear evidence supports a dimensional view of psychiatric illness. Within this framework the expression of disorder-relevant phenotypes is often interpreted as a breakdown or departure from normal brain function. Conversely, health is reified, conceptualized as possessing a single ideal state. We challenge this concept here, arguing that there is no universally optimal profile of brain functioning. The evolutionary forces that shape our species select for a staggering diversity of human behaviors. To support our position we highlight pervasive population-level variability within large-scale functional networks and discrete circuits. We propose that, instead of examining behaviors in isolation, psychiatric illnesses can be best understood through the study of domains of functioning and associated multivariate patterns of variation across distributed brain systems. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Exact solution for the optimal neuronal layout problem.
Chklovskii, Dmitri B
2004-10-01
Evolution perfected brain design by maximizing its functionality while minimizing costs associated with building and maintaining it. Assumption that brain functionality is specified by neuronal connectivity, implemented by costly biological wiring, leads to the following optimal design problem. For a given neuronal connectivity, find a spatial layout of neurons that minimizes the wiring cost. Unfortunately, this problem is difficult to solve because the number of possible layouts is often astronomically large. We argue that the wiring cost may scale as wire length squared, reducing the optimal layout problem to a constrained minimization of a quadratic form. For biologically plausible constraints, this problem has exact analytical solutions, which give reasonable approximations to actual layouts in the brain. These solutions make the inverse problem of inferring neuronal connectivity from neuronal layout more tractable.
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Lalani, Jigar; Rathi, Mohan; Lalan, Manisha; Misra, Ambikanandan
2013-06-01
Poly (d,l-lactide-co-glycolide acid) (PLGA) Nanoparticles (NPs) with sustained drug release and enhanced circulation time presents widely explored non-invasive approach for drug delivery to brain. However, blood-brain barrier (BBB) limits the drug delivery to brain. This can be overcome by anchoring endogenous ligand like Transferrin (Tf) and Lactoferrin (Lf) on the surface of NPs, allowing efficient brain delivery via receptor-mediated endocytosis. The aim of the present investigation was preparation, optimization, characterization and comparative evaluation of targeting efficiency of Tf- vs. Lf-conjugated NPs. Tramadol-loaded PLGA NPs were prepared by nanoprecipitation techniques and optimized using 3(3) factorial design. The effect of polymer concentration, stabilizer concentration and organic:aqueous phase ratio were evaluated on particle size (PS) and entrapment efficiency (EE). The formulation was optimized based on desirability for lower PS (<150 nm) and higher EE (>70%). Optimized PLGA NPs were conjugated with Tf and Lf, characterized and evaluated for stability study. Pharmacodynamic study was performed in rat after intravenous administration. The optimized formulation had 100 mg of PLGA, 1% polyvinyl alcohol (PVA) and 1:2 acetone:water ratio. The Lf and Tf conjugation to PLGA NPs was estimated to 186 Tf and 185 Lf molecules per NPs. Lyophilization was optimized at 1:2 ratio of NPs:trehalose. The NPs were found stable for 6 months at refrigerated condition. Pharmacodynamic study demonstrated enhanced efficacy of ligand-conjugated NPs against unconjugated NPs. Conjugated NPs demonstrated significantly higher pharmacological effect over a period of 24 h. Furthermore Lf functionalized NPs exhibited better antinociceptive effect as compared to Tf functionalized NPs.
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Gorelick, Philip B; Furie, Karen L; Iadecola, Costantino; Smith, Eric E; Waddy, Salina P; Lloyd-Jones, Donald M; Bae, Hee-Joon; Bauman, Mary Ann; Dichgans, Martin; Duncan, Pamela W; Girgus, Meighan; Howard, Virginia J; Lazar, Ronald M; Seshadri, Sudha; Testai, Fernando D; van Gaal, Stephen; Yaffe, Kristine; Wasiak, Hank; Zerna, Charlotte
2017-10-01
Cognitive function is an important component of aging and predicts quality of life, functional independence, and risk of institutionalization. Advances in our understanding of the role of cardiovascular risks have shown them to be closely associated with cognitive impairment and dementia. Because many cardiovascular risks are modifiable, it may be possible to maintain brain health and to prevent dementia in later life. The purpose of this American Heart Association (AHA)/American Stroke Association presidential advisory is to provide an initial definition of optimal brain health in adults and guidance on how to maintain brain health. We identify metrics to define optimal brain health in adults based on inclusion of factors that could be measured, monitored, and modified. From these practical considerations, we identified 7 metrics to define optimal brain health in adults that originated from AHA's Life's Simple 7: 4 ideal health behaviors (nonsmoking, physical activity at goal levels, healthy diet consistent with current guideline levels, and body mass index <25 kg/m 2 ) and 3 ideal health factors (untreated blood pressure <120/<80 mm Hg, untreated total cholesterol <200 mg/dL, and fasting blood glucose <100 mg/dL). In addition, in relation to maintenance of cognitive health, we recommend following previously published guidance from the AHA/American Stroke Association, Institute of Medicine, and Alzheimer's Association that incorporates control of cardiovascular risks and suggest social engagement and other related strategies. We define optimal brain health but recognize that the truly ideal circumstance may be uncommon because there is a continuum of brain health as demonstrated by AHA's Life's Simple 7. Therefore, there is opportunity to improve brain health through primordial prevention and other interventions. Furthermore, although cardiovascular risks align well with brain health, we acknowledge that other factors differing from those related to cardiovascular health may drive cognitive health. Defining optimal brain health in adults and its maintenance is consistent with the AHA's Strategic Impact Goal to improve cardiovascular health of all Americans by 20% and to reduce deaths resulting from cardiovascular disease and stroke by 20% by the year 2020. This work in defining optimal brain health in adults serves to provide the AHA/American Stroke Association with a foundation for a new strategic direction going forward in cardiovascular health promotion and disease prevention. © 2017 American Heart Association, Inc.
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Defining Optimal Brain Health in Adults
Gorelick, Philip B.; Furie, Karen L.; Iadecola, Costantino; Smith, Eric E.; Waddy, Salina P.; Lloyd-Jones, Donald M.; Bae, Hee-Joon; Bauman, Mary Ann; Dichgans, Martin; Duncan, Pamela W.; Girgus, Meighan; Howard, Virginia J.; Lazar, Ronald M.; Seshadri, Sudha; Testai, Fernando D.; van Gaal, Stephen; Yaffe, Kristine; Wasiak, Hank; Zerna, Charlotte
2017-01-01
Cognitive function is an important component of aging and predicts quality of life, functional independence, and risk of institutionalization. Advances in our understanding of the role of cardiovascular risks have shown them to be closely associated with cognitive impairment and dementia. Because many cardiovascular risks are modifiable, it may be possible to maintain brain health and to prevent dementia in later life. The purpose of this American Heart Association (AHA)/American Stroke Association presidential advisory is to provide an initial definition of optimal brain health in adults and guidance on how to maintain brain health. We identify metrics to define optimal brain health in adults based on inclusion of factors that could be measured, monitored, and modified. From these practical considerations, we identified 7 metrics to define optimal brain health in adults that originated from AHA’s Life’s Simple 7: 4 ideal health behaviors (nonsmoking, physical activity at goal levels, healthy diet consistent with current guideline levels, and body mass index <25 kg/m2) and 3 ideal health factors (untreated blood pressure <120/<80 mm Hg, untreated total cholesterol <200 mg/dL, and fasting blood glucose <100 mg/dL). In addition, in relation to maintenance of cognitive health, we recommend following previously published guidance from the AHA/American Stroke Association, Institute of Medicine, and Alzheimer’s Association that incorporates control of cardiovascular risks and suggest social engagement and other related strategies. We define optimal brain health but recognize that the truly ideal circumstance may be uncommon because there is a continuum of brain health as demonstrated by AHA’s Life’s Simple 7. Therefore, there is opportunity to improve brain health through primordial prevention and other interventions. Furthermore, although cardiovascular risks align well with brain health, we acknowledge that other factors differing from those related to cardiovascular health may drive cognitive health. Defining optimal brain health in adults and its maintenance is consistent with the AHA’s Strategic Impact Goal to improve cardiovascular health of all Americans by 20% and to reduce deaths resulting from cardiovascular disease and stroke by 20% by the year 2020. This work in defining optimal brain health in adults serves to provide the AHA/American Stroke Association with a foundation for a new strategic direction going forward in cardiovascular health promotion and disease prevention. PMID:28883125
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Finding influential nodes for integration in brain networks using optimal percolation theory.
Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A
2018-06-11
Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.
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Toward a preoperative planning tool for brain tumor resection therapies.
Coffey, Aaron M; Miga, Michael I; Chen, Ishita; Thompson, Reid C
2013-01-01
Neurosurgical procedures involving tumor resection require surgical planning such that the surgical path to the tumor is determined to minimize the impact on healthy tissue and brain function. This work demonstrates a predictive tool to aid neurosurgeons in planning tumor resection therapies by finding an optimal model-selected patient orientation that minimizes lateral brain shift in the field of view. Such orientations may facilitate tumor access and removal, possibly reduce the need for retraction, and could minimize the impact of brain shift on image-guided procedures. In this study, preoperative magnetic resonance images were utilized in conjunction with pre- and post-resection laser range scans of the craniotomy and cortical surface to produce patient-specific finite element models of intraoperative shift for 6 cases. These cases were used to calibrate a model (i.e., provide general rules for the application of patient positioning parameters) as well as determine the current model-based framework predictive capabilities. Finally, an objective function is proposed that minimizes shift subject to patient position parameters. Patient positioning parameters were then optimized and compared to our neurosurgeon as a preliminary study. The proposed model-driven brain shift minimization objective function suggests an overall reduction of brain shift by 23 % over experiential methods. This work recasts surgical simulation from a trial-and-error process to one where options are presented to the surgeon arising from an optimization of surgical goals. To our knowledge, this is the first realization of an evaluative tool for surgical planning that attempts to optimize surgical approach by means of shift minimization in this manner.
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Mura, Marco; Castagna, Alessandro; Fontani, Vania; Rinaldi, Salvatore
2012-01-01
Purpose This study assessed changes in functional dysmetria (FD) and in brain activation observable by functional magnetic resonance imaging (fMRI) during a leg flexion-extension motor task following brain stimulation with a single radioelectric asymmetric conveyer (REAC) pulse, according to the precisely defined neuropostural optimization (NPO) protocol. Population and methods Ten healthy volunteers were assessed using fMRI conducted during a simple motor task before and immediately after delivery of a single REAC-NPO pulse. The motor task consisted of a flexion-extension movement of the legs with the knees bent. FD signs and brain activation patterns were compared before and after REAC-NPO. Results A single 250-millisecond REAC-NPO treatment alleviated FD, as evidenced by patellar asymmetry during a sit-up motion, and modulated activity patterns in the brain, particularly in the cerebellum, during the performance of the motor task. Conclusion Activity in brain areas involved in motor control and coordination, including the cerebellum, is altered by administration of a REAC-NPO treatment and this effect is accompanied by an alleviation of FD. PMID:22536071
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Optimizing real time fMRI neurofeedback for therapeutic discovery and development
Stoeckel, L.E.; Garrison, K.A.; Ghosh, S.; Wighton, P.; Hanlon, C.A.; Gilman, J.M.; Greer, S.; Turk-Browne, N.B.; deBettencourt, M.T.; Scheinost, D.; Craddock, C.; Thompson, T.; Calderon, V.; Bauer, C.C.; George, M.; Breiter, H.C.; Whitfield-Gabrieli, S.; Gabrieli, J.D.; LaConte, S.M.; Hirshberg, L.; Brewer, J.A.; Hampson, M.; Van Der Kouwe, A.; Mackey, S.; Evins, A.E.
2014-01-01
While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain–behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders. PMID:25161891
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Dolcos, Sanda; Hu, Yifan; Iordan, Alexandru D; Moore, Matthew; Dolcos, Florin
2016-02-01
Converging evidence identifies trait optimism and the orbitofrontal cortex (OFC) as personality and brain factors influencing anxiety, but the nature of their relationships remains unclear. Here, the mechanisms underlying the protective role of trait optimism and of increased OFC volume against symptoms of anxiety were investigated in 61 healthy subjects, who completed measures of trait optimism and anxiety, and underwent structural scanning using magnetic resonance imaging. First, the OFC gray matter volume (GMV) was associated with increased optimism, which in turn was associated with reduced anxiety. Second, trait optimism mediated the relation between the left OFC volume and anxiety, thus demonstrating that increased GMV in this brain region protects against symptoms of anxiety through increased optimism. These results provide novel evidence about the brain-personality mechanisms protecting against anxiety symptoms in healthy functioning, and identify potential targets for preventive and therapeutic interventions aimed at reducing susceptibility and increasing resilience against emotional disturbances. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
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Simple wavefront correction framework for two-photon microscopy of in-vivo brain
Galwaduge, P. T.; Kim, S. H.; Grosberg, L. E.; Hillman, E. M. C.
2015-01-01
We present an easily implemented wavefront correction scheme that has been specifically designed for in-vivo brain imaging. The system can be implemented with a single liquid crystal spatial light modulator (LCSLM), which makes it compatible with existing patterned illumination setups, and provides measurable signal improvements even after a few seconds of optimization. The optimization scheme is signal-based and does not require exogenous guide-stars, repeated image acquisition or beam constraint. The unconstrained beam approach allows the use of Zernike functions for aberration correction and Hadamard functions for scattering correction. Low order corrections performed in mouse brain were found to be valid up to hundreds of microns away from the correction location. PMID:26309763
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Intraoperative Functional Ultrasound Imaging of Human Brain Activity.
Imbault, Marion; Chauvet, Dorian; Gennisson, Jean-Luc; Capelle, Laurent; Tanter, Mickael
2017-08-04
The functional mapping of brain activity is essential to perform optimal glioma surgery and to minimize the risk of postoperative deficits. We introduce a new, portable neuroimaging modality of the human brain based on functional ultrasound (fUS) for deep functional cortical mapping. Using plane-wave transmissions at an ultrafast frame rate (1 kHz), fUS is performed during surgery to measure transient changes in cerebral blood volume with a high spatiotemporal resolution (250 µm, 1 ms). fUS identifies, maps and differentiates regions of brain activation during task-evoked cortical responses within the depth of a sulcus in both awake and anaesthetized patients.
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NASA Astrophysics Data System (ADS)
Lin, Juan; Liu, Chenglian; Guo, Yongning
2014-10-01
The estimation of neural active sources from the magnetoencephalography (MEG) data is a very critical issue for both clinical neurology and brain functions research. A widely accepted source-modeling technique for MEG involves calculating a set of equivalent current dipoles (ECDs). Depth in the brain is one of difficulties in MEG source localization. Particle swarm optimization(PSO) is widely used to solve various optimization problems. In this paper we discuss its ability and robustness to find the global optimum in different depths of the brain when using single equivalent current dipole (sECD) model and single time sliced data. The results show that PSO is an effective global optimization to MEG source localization when given one dipole in different depths.
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Watson, Brendon O.; Buzsáki, György
2015-01-01
Sleep occupies roughly one-third of our lives, yet the scientific community is still not entirely clear on its purpose or function. Existing data point most strongly to its role in memory and homeostasis: that sleep helps maintain basic brain functioning via a homeostatic mechanism that loosens connections between overworked synapses, and that sleep helps consolidate and re-form important memories. In this review, we will summarize these theories, but also focus on substantial new information regarding the relation of electrical brain rhythms to sleep. In particular, while REM sleep may contribute to the homeostatic weakening of overactive synapses, a prominent and transient oscillatory rhythm called “sharp-wave ripple” seems to allow for consolidation of behaviorally relevant memories across many structures of the brain. We propose that a theory of sleep involving the division of labor between two states of sleep–REM and non-REM, the latter of which has an abundance of ripple electrical activity–might allow for a fusion of the two main sleep theories. This theory then postulates that sleep performs a combination of consolidation and homeostasis that promotes optimal knowledge retention as well as optimal waking brain function. PMID:26097242
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Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain
Osechinskiy, Sergey; Kruggel, Frithjof
2011-01-01
Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function. PMID:22567290
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Sleep, Memory & Brain Rhythms.
Watson, Brendon O; Buzsáki, György
2015-01-01
Sleep occupies roughly one-third of our lives, yet the scientific community is still not entirely clear on its purpose or function. Existing data point most strongly to its role in memory and homeostasis: that sleep helps maintain basic brain functioning via a homeostatic mechanism that loosens connections between overworked synapses, and that sleep helps consolidate and re-form important memories. In this review, we will summarize these theories, but also focus on substantial new information regarding the relation of electrical brain rhythms to sleep. In particular, while REM sleep may contribute to the homeostatic weakening of overactive synapses, a prominent and transient oscillatory rhythm called "sharp-wave ripple" seems to allow for consolidation of behaviorally relevant memories across many structures of the brain. We propose that a theory of sleep involving the division of labor between two states of sleep-REM and non-REM, the latter of which has an abundance of ripple electrical activity-might allow for a fusion of the two main sleep theories. This theory then postulates that sleep performs a combination of consolidation and homeostasis that promotes optimal knowledge retention as well as optimal waking brain function.
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DHA Effects in Brain Development and Function
Lauritzen, Lotte; Brambilla, Paolo; Mazzocchi, Alessandra; Harsløf, Laurine B. S.; Ciappolino, Valentina; Agostoni, Carlo
2016-01-01
Docosahexaenoic acid (DHA) is a structural constituent of membranes specifically in the central nervous system. Its accumulation in the fetal brain takes place mainly during the last trimester of pregnancy and continues at very high rates up to the end of the second year of life. Since the endogenous formation of DHA seems to be relatively low, DHA intake may contribute to optimal conditions for brain development. We performed a narrative review on research on the associations between DHA levels and brain development and function throughout the lifespan. Data from cell and animal studies justify the indication of DHA in relation to brain function for neuronal cell growth and differentiation as well as in relation to neuronal signaling. Most data from human studies concern the contribution of DHA to optimal visual acuity development. Accumulating data indicate that DHA may have effects on the brain in infancy, and recent studies indicate that the effect of DHA may depend on gender and genotype of genes involved in the endogenous synthesis of DHA. While DHA levels may affect early development, potential effects are also increasingly recognized during childhood and adult life, suggesting a role of DHA in cognitive decline and in relation to major psychiatric disorders. PMID:26742060
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DHA Effects in Brain Development and Function.
Lauritzen, Lotte; Brambilla, Paolo; Mazzocchi, Alessandra; Harsløf, Laurine B S; Ciappolino, Valentina; Agostoni, Carlo
2016-01-04
Docosahexaenoic acid (DHA) is a structural constituent of membranes specifically in the central nervous system. Its accumulation in the fetal brain takes place mainly during the last trimester of pregnancy and continues at very high rates up to the end of the second year of life. Since the endogenous formation of DHA seems to be relatively low, DHA intake may contribute to optimal conditions for brain development. We performed a narrative review on research on the associations between DHA levels and brain development and function throughout the lifespan. Data from cell and animal studies justify the indication of DHA in relation to brain function for neuronal cell growth and differentiation as well as in relation to neuronal signaling. Most data from human studies concern the contribution of DHA to optimal visual acuity development. Accumulating data indicate that DHA may have effects on the brain in infancy, and recent studies indicate that the effect of DHA may depend on gender and genotype of genes involved in the endogenous synthesis of DHA. While DHA levels may affect early development, potential effects are also increasingly recognized during childhood and adult life, suggesting a role of DHA in cognitive decline and in relation to major psychiatric disorders.
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Hu, Yifan; Iordan, Alexandru D.; Moore, Matthew; Dolcos, Florin
2016-01-01
Converging evidence identifies trait optimism and the orbitofrontal cortex (OFC) as personality and brain factors influencing anxiety, but the nature of their relationships remains unclear. Here, the mechanisms underlying the protective role of trait optimism and of increased OFC volume against symptoms of anxiety were investigated in 61 healthy subjects, who completed measures of trait optimism and anxiety, and underwent structural scanning using magnetic resonance imaging. First, the OFC gray matter volume (GMV) was associated with increased optimism, which in turn was associated with reduced anxiety. Second, trait optimism mediated the relation between the left OFC volume and anxiety, thus demonstrating that increased GMV in this brain region protects against symptoms of anxiety through increased optimism. These results provide novel evidence about the brain–personality mechanisms protecting against anxiety symptoms in healthy functioning, and identify potential targets for preventive and therapeutic interventions aimed at reducing susceptibility and increasing resilience against emotional disturbances. PMID:26371336
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An algorithm for automatic parameter adjustment for brain extraction in BrainSuite
NASA Astrophysics Data System (ADS)
Rajagopal, Gautham; Joshi, Anand A.; Leahy, Richard M.
2017-02-01
Brain Extraction (classification of brain and non-brain tissue) of MRI brain images is a crucial pre-processing step necessary for imaging-based anatomical studies of the human brain. Several automated methods and software tools are available for performing this task, but differences in MR image parameters (pulse sequence, resolution) and instrumentand subject-dependent noise and artefacts affect the performance of these automated methods. We describe and evaluate a method that automatically adapts the default parameters of the Brain Surface Extraction (BSE) algorithm to optimize a cost function chosen to reflect accurate brain extraction. BSE uses a combination of anisotropic filtering, Marr-Hildreth edge detection, and binary morphology for brain extraction. Our algorithm automatically adapts four parameters associated with these steps to maximize the brain surface area to volume ratio. We evaluate the method on a total of 109 brain volumes with ground truth brain masks generated by an expert user. A quantitative evaluation of the performance of the proposed algorithm showed an improvement in the mean (s.d.) Dice coefficient from 0.8969 (0.0376) for default parameters to 0.9509 (0.0504) for the optimized case. These results indicate that automatic parameter optimization can result in significant improvements in definition of the brain mask.
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Docosahexaenoic Acid and Cognition throughout the Lifespan
Weiser, Michael J.; Butt, Christopher M.; Mohajeri, M. Hasan
2016-01-01
Docosahexaenoic acid (DHA) is the predominant omega-3 (n-3) polyunsaturated fatty acid (PUFA) found in the brain and can affect neurological function by modulating signal transduction pathways, neurotransmission, neurogenesis, myelination, membrane receptor function, synaptic plasticity, neuroinflammation, membrane integrity and membrane organization. DHA is rapidly accumulated in the brain during gestation and early infancy, and the availability of DHA via transfer from maternal stores impacts the degree of DHA incorporation into neural tissues. The consumption of DHA leads to many positive physiological and behavioral effects, including those on cognition. Advanced cognitive function is uniquely human, and the optimal development and aging of cognitive abilities has profound impacts on quality of life, productivity, and advancement of society in general. However, the modern diet typically lacks appreciable amounts of DHA. Therefore, in modern populations, maintaining optimal levels of DHA in the brain throughout the lifespan likely requires obtaining preformed DHA via dietary or supplemental sources. In this review, we examine the role of DHA in optimal cognition during development, adulthood, and aging with a focus on human evidence and putative mechanisms of action. PMID:26901223
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Optimizing Experimental Design for Comparing Models of Brain Function
Daunizeau, Jean; Preuschoff, Kerstin; Friston, Karl; Stephan, Klaas
2011-01-01
This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work. PMID:22125485
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Exercise and the brain: something to chew on
van Praag, Henriette
2009-01-01
Evidence is accumulating that exercise has profound benefits for brain function. Physical activity improves learning and memory in humans and animals. Moreover, an active lifestyle might prevent or delay loss of cognitive function with aging or neurodegenerative disease. Recent research indicates that the effects of exercise on the brain can be enhanced by concurrent consumption of natural products such as omega fatty acids or plant polyphenols. The potential synergy between diet and exercise could involve common cellular pathways important for neurogenesis, cell survival, synaptic plasticity and vascular function. Optimal maintenance of brain health might depend on exercise and intake of natural products. PMID:19349082
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NASA Astrophysics Data System (ADS)
Krestyannikov, E.; Tohka, J.; Ruotsalainen, U.
2008-06-01
This paper presents a novel statistical approach for joint estimation of regions-of-interest (ROIs) and the corresponding time-activity curves (TACs) from dynamic positron emission tomography (PET) brain projection data. It is based on optimizing the joint objective function that consists of a data log-likelihood term and two penalty terms reflecting the available a priori information about the human brain anatomy. The developed local optimization strategy iteratively updates both the ROI and TAC parameters and is guaranteed to monotonically increase the objective function. The quantitative evaluation of the algorithm is performed with numerically and Monte Carlo-simulated dynamic PET brain data of the 11C-Raclopride and 18F-FDG tracers. The results demonstrate that the method outperforms the existing sequential ROI quantification approaches in terms of accuracy, and can noticeably reduce the errors in TACs arising due to the finite spatial resolution and ROI delineation.
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Fox, Michael D.; Buckner, Randy L.; Liu, Hesheng; Chakravarty, M. Mallar; Lozano, Andres M.; Pascual-Leone, Alvaro
2014-01-01
Brain stimulation, a therapy increasingly used for neurological and psychiatric disease, traditionally is divided into invasive approaches, such as deep brain stimulation (DBS), and noninvasive approaches, such as transcranial magnetic stimulation. The relationship between these approaches is unknown, therapeutic mechanisms remain unclear, and the ideal stimulation site for a given technique is often ambiguous, limiting optimization of the stimulation and its application in further disorders. In this article, we identify diseases treated with both types of stimulation, list the stimulation sites thought to be most effective in each disease, and test the hypothesis that these sites are different nodes within the same brain network as defined by resting-state functional-connectivity MRI. Sites where DBS was effective were functionally connected to sites where noninvasive brain stimulation was effective across diseases including depression, Parkinson's disease, obsessive-compulsive disorder, essential tremor, addiction, pain, minimally conscious states, and Alzheimer’s disease. A lack of functional connectivity identified sites where stimulation was ineffective, and the sign of the correlation related to whether excitatory or inhibitory noninvasive stimulation was found clinically effective. These results suggest that resting-state functional connectivity may be useful for translating therapy between stimulation modalities, optimizing treatment, and identifying new stimulation targets. More broadly, this work supports a network perspective toward understanding and treating neuropsychiatric disease, highlighting the therapeutic potential of targeted brain network modulation. PMID:25267639
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Patrick, Rhonda P; Ames, Bruce N
2015-06-01
Serotonin regulates a wide variety of brain functions and behaviors. Here, we synthesize previous findings that serotonin regulates executive function, sensory gating, and social behavior and that attention deficit hyperactivity disorder, bipolar disorder, schizophrenia, and impulsive behavior all share in common defects in these functions. It has remained unclear why supplementation with omega-3 fatty acids and vitamin D improve cognitive function and behavior in these brain disorders. Here, we propose mechanisms by which serotonin synthesis, release, and function in the brain are modulated by vitamin D and the 2 marine omega-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Brain serotonin is synthesized from tryptophan by tryptophan hydroxylase 2, which is transcriptionally activated by vitamin D hormone. Inadequate levels of vitamin D (∼70% of the population) and omega-3 fatty acids are common, suggesting that brain serotonin synthesis is not optimal. We propose mechanisms by which EPA increases serotonin release from presynaptic neurons by reducing E2 series prostaglandins and DHA influences serotonin receptor action by increasing cell membrane fluidity in postsynaptic neurons. We propose a model whereby insufficient levels of vitamin D, EPA, or DHA, in combination with genetic factors and at key periods during development, would lead to dysfunctional serotonin activation and function and may be one underlying mechanism that contributes to neuropsychiatric disorders and depression. This model suggests that optimizing vitamin D and marine omega-3 fatty acid intake may help prevent and modulate the severity of brain dysfunction. © FASEB.
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Stochastic resonance in attention control
NASA Astrophysics Data System (ADS)
Kitajo, K.; Yamanaka, K.; Ward, L. M.; Yamamoto, Y.
2006-12-01
We investigated the beneficial role of noise in a human higher brain function, namely visual attention control. We asked subjects to detect a weak gray-level target inside a marker box either in the left or the right visual field. Signal detection performance was optimized by presenting a low level of randomly flickering gray-level noise between and outside the two possible target locations. Further, we found that an increase in eye movement (saccade) rate helped to compensate for the usual deterioration in detection performance at higher noise levels. To our knowledge, this is the first experimental evidence that noise can optimize a higher brain function which involves distinct brain regions above the level of primary sensory systems -- switching behavior between multi-stable attention states -- via the mechanism of stochastic resonance.
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Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration
Cole, Michael W.
2016-01-01
The human brain is able to exceed modern computers on multiple computational demands (e.g., language, planning) using a small fraction of the energy. The mystery of how the brain can be so efficient is compounded by recent evidence that all brain regions are constantly active as they interact in so-called resting-state networks (RSNs). To investigate the brain's ability to process complex cognitive demands efficiently, we compared functional connectivity (FC) during rest and multiple highly distinct tasks. We found previously that RSNs are present during a wide variety of tasks and that tasks only minimally modify FC patterns throughout the brain. Here, we tested the hypothesis that, although subtle, these task-evoked FC updates from rest nonetheless contribute strongly to behavioral performance. One might expect that larger changes in FC reflect optimization of networks for the task at hand, improving behavioral performance. Alternatively, smaller changes in FC could reflect optimization for efficient (i.e., small) network updates, reducing processing demands to improve behavioral performance. We found across three task domains that high-performing individuals exhibited more efficient brain connectivity updates in the form of smaller changes in functional network architecture between rest and task. These smaller changes suggest that individuals with an optimized intrinsic network configuration for domain-general task performance experience more efficient network updates generally. Confirming this, network update efficiency correlated with general intelligence. The brain's reconfiguration efficiency therefore appears to be a key feature contributing to both its network dynamics and general cognitive ability. SIGNIFICANCE STATEMENT The brain's network configuration varies based on current task demands. For example, functional brain connections are organized in one way when one is resting quietly but in another way if one is asked to make a decision. We found that the efficiency of these updates in brain network organization is positively related to general intelligence, the ability to perform a wide variety of cognitively challenging tasks well. Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals. This suggests that the ability to modify network connectivity efficiently when task demands change is a hallmark of high intelligence. PMID:27535904
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Guseva, M V; Kamenskii, A A; Gusev, V B
2013-06-01
Choline diet promotes improvement of the brain cognitive functions in rats with moderate-to-severe traumatic brain injury. In previous studies, the rats received choline being standard (0.2%) or choline-supplemented (2%) diet for 2 weeks prior to and 2 weeks after experimental brain injury. To the end of the experiments (in 4 weeks), the post-traumatic disturbances in the cognitive functions were observed in both groups, although they were less pronounced than in the rats kept on the choline-supplemented diet. Based on original mathematical model, this paper proposes a method to calculate the most efficient use of choline to correct the brain cognitive functions. In addition to evaluating the cognitive functions, the study assessed expression of α7 nicotinic acetylcholine receptors, the amount of consumed food and water, and the dynamics of body weight.
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An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI
Churchill, Nathan W.; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C.
2015-01-01
BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets. PMID:26161667
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Lifelong Brain Health is a Lifelong Challenge: From Evolutionary Principles to Empirical Evidence
Mattson, Mark P.
2015-01-01
Although the human brain is exceptional in size and information processing capabilities, it is similar to other mammals with regards to the factors that promote its optimal performance. Three such factors are the challenges of physical exercise, food deprivation/fasting, and social/intellectual engagement. Because it evolved, in part, for success in seeking and acquiring food, the brain functions best when the individual is hungry and physically active, as typified by the hungry lion stalking and chasing its prey. Indeed, studies of animal models and human subjects demonstrate robust beneficial effects of regular exercise and intermittent energy restriction/fasting on cognitive function and mood, particularly in the contexts of aging and associated neurodegenerative disorders. Unfortunately, the agricultural revolution and the invention of effort-sparing technologies have resulted in a dramatic reduction or elimination of vigorous exercise and fasting, leaving only intellectual challenges to bolster brain function. In addition to disengaging beneficial adaptive responses in the brain, sedentary overindulgent lifestyles promote obesity, diabetes and cardiovascular disease, all of which may increase the risk of cognitive impairment and Alzheimer’s disease. It is therefore important to embrace the reality of the requirements for exercise, intermittent fasting and critical thinking for optimal brain health throughout life, and to recognize the dire consequences for our aging population of failing to implement such brain-healthy lifestyles. PMID:25576651
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Lifelong brain health is a lifelong challenge: from evolutionary principles to empirical evidence.
Mattson, Mark P
2015-03-01
Although the human brain is exceptional in size and information processing capabilities, it is similar to other mammals with regard to the factors that promote its optimal performance. Three such factors are the challenges of physical exercise, food deprivation/fasting, and social/intellectual engagement. Because it evolved, in part, for success in seeking and acquiring food, the brain functions best when the individual is hungry and physically active, as typified by the hungry lion stalking and chasing its prey. Indeed, studies of animal models and human subjects demonstrate robust beneficial effects of regular exercise and intermittent energy restriction/fasting on cognitive function and mood, particularly in the contexts of aging and associated neurodegenerative disorders. Unfortunately, the agricultural revolution and the invention of effort-sparing technologies have resulted in a dramatic reduction or elimination of vigorous exercise and fasting, leaving only intellectual challenges to bolster brain function. In addition to disengaging beneficial adaptive responses in the brain, sedentary overindulgent lifestyles promote obesity, diabetes and cardiovascular disease, all of which may increase the risk of cognitive impairment and Alzheimer's disease. It is therefore important to embrace the reality of the requirements for exercise, intermittent fasting and critical thinking for optimal brain health throughout life, and to recognize the dire consequences for our aging population of failing to implement such brain-healthy lifestyles. Published by Elsevier B.V.
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2016-01-01
An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain. PMID:26982717
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Li, Guangye; Zhang, Dingguo
2016-01-01
An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Wu, Q; Snyder, K; Liu, C
Purpose: To develop an optimization algorithm to reduce normal brain dose by optimizing couch and collimator angles for single isocenter multiple targets treatment of stereotactic radiosurgery. Methods: Three metastatic brain lesions were retrospectively planned using single-isocenter volumetric modulated arc therapy (VMAT). Three matrices were developed to calculate the projection of each lesion on Beam’s Eye View (BEV) by the rotating couch, collimator and gantry respectively. The island blocking problem was addressed by computing the total area of open space between any two lesions with shared MLC leaf pairs. The couch and collimator angles resulting in the smallest open areas weremore » the optimized angles for each treatment arc. Two treatment plans with and without couch and collimator angle optimization were developed using the same objective functions and to achieve 99% of each target volume receiving full prescription dose of 18Gy. Plan quality was evaluated by calculating each target’s Conformity Index (CI), Gradient Index (GI), and Homogeneity index (HI), and absolute volume of normal brain V8Gy, V10Gy, V12Gy, and V14Gy. Results: Using the new couch/collimator optimization strategy, dose to normal brain tissue was reduced substantially. V8, V10, V12, and V14 decreased by 2.3%, 3.6%, 3.5%, and 6%, respectively. There were no significant differences in the conformity index, gradient index, and homogeneity index between two treatment plans with and without the new optimization algorithm. Conclusion: We have developed a solution to the island blocking problem in delivering radiation to multiple brain metastases with shared isocenter. Significant reduction in dose to normal brain was achieved by using optimal couch and collimator angles that minimize total area of open space between any of the two lesions with shared MLC leaf pairs. This technique has been integrated into Eclipse treatment system using scripting API.« less
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Zhao, Yu; Ge, Fangfei; Liu, Tianming
2018-07-01
fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.
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Myelination, oligodendrocytes, and serious mental illness.
Haroutunian, V; Katsel, P; Roussos, P; Davis, K L; Altshuler, L L; Bartzokis, G
2014-11-01
Historically, the human brain has been conceptually segregated from the periphery and further dichotomized into gray matter (GM) and white matter (WM) based on the whitish appearance of the exceptionally high lipid content of the myelin sheaths encasing neuronal axons. These simplistic dichotomies were unfortunately extended to conceptually segregate neurons from glia, cognition from behavior, and have been codified in the separation of clinical and scientific fields into medicine, psychiatry, neurology, pathology, etc. The discrete classifications have helped obscure the importance of continual dynamic communication between all brain cell types (neurons, astrocytes, microglia, oligodendrocytes, and precursor (NG2) cells) as well as between brain and periphery through multiple signaling systems. The signaling systems range from neurotransmitters to insulin, angiotensin, and multiple kinases such a glycogen synthase kinase 3 (GSK-3) that together help integrate metabolism, inflammation, and myelination processes and orchestrate the development, plasticity, maintenance, and repair that continually optimize function of neural networks. A more comprehensive, evolution-based, systems biology approach that integrates brain, body, and environmental interactions may ultimately prove more fruitful in elucidating the complexities of human brain function. The historic focus on neurons/GM is rebalanced herein by highlighting the importance of a systems-level understanding of the interdependent age-related shifts in both central and peripheral homeostatic mechanisms that can lead to remarkably prevalent and devastating neuropsychiatric diseases. Herein we highlight the role of glia, especially the most recently evolved oligodendrocytes and the myelin they produce, in achieving and maintaining optimal brain function. The human brain undergoes exceptionally protracted and pervasive myelination (even throughout its GM) and can thus achieve and maintain the rapid conduction and synchronous timing of neural networks on which optimal function depends. The continuum of increasing myelin vulnerability resulting from the human brain's protracted myelination underlies underappreciated communalities between different disease phenotypes ranging from developmental ones such as schizophrenia (SZ) and bipolar disorder (BD) to degenerative ones such as Alzheimer's disease (AD). These shared vulnerabilities also expose significant yet underexplored opportunities for novel treatment and prevention approaches that have the potential to considerably reduce the tremendous burden of neuropsychiatric disease. © 2014 Wiley Periodicals, Inc.
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Optimal design of a bank of spatio-temporal filters for EEG signal classification.
Higashi, Hiroshi; Tanaka, Toshihisa
2011-01-01
The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery.
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Uğurbil, Kamil; Xu, Junqian; Auerbach, Edward J.; Moeller, Steen; Vu, An; Duarte-Carvajalino, Julio M.; Lenglet, Christophe; Wu, Xiaoping; Schmitter, Sebastian; Van de Moortele, Pierre Francois; Strupp, John; Sapiro, Guillermo; De Martino, Federico; Wang, Dingxin; Harel, Noam; Garwood, Michael; Chen, Liyong; Feinberg, David A.; Smith, Stephen M.; Miller, Karla L.; Sotiropoulos, Stamatios N; Jbabdi, Saad; Andersson, Jesper L; Behrens, Timothy EJ; Glasser, Matthew F.; Van Essen, David; Yacoub, Essa
2013-01-01
The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure. PMID:23702417
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Exercise, cognition, and the adolescent brain.
Herting, Megan M; Chu, Xiaofang
2017-12-01
Few adolescents engage in the recommended levels of physical activity, and daily exercise levels tend to drastically decrease throughout adolescence. Beyond physical health benefits, regular exercise may also have important implications for the teenage brain and cognitive and academic capabilities. This narrative review examines how physical activity and aerobic exercise relate to school performance, cognition, and brain structure and function. A number of studies have found that habitual exercise and physical activity are associated with academic performance, cognitive function, brain structure, and brain activity in adolescents. We also discuss how additional intervention studies that examine a wide range of neurological and cognitive outcomes are necessary, as well as characterizing the type, frequency, and dose of exercise and identifying individual differences that contribute to how exercise may benefit the teen brain. Routine exercise relates to adolescent brain structure and function as well as cognitive performance. Together, these studies suggest that physical activity and aerobic exercise may be important factors for optimal adolescent brain development. © 2017 Wiley Periodicals, Inc.
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Using non-invasive brain stimulation to augment motor training-induced plasticity
Bolognini, Nadia; Pascual-Leone, Alvaro; Fregni, Felipe
2009-01-01
Therapies for motor recovery after stroke or traumatic brain injury are still not satisfactory. To date the best approach seems to be the intensive physical therapy. However the results are limited and functional gains are often minimal. The goal of motor training is to minimize functional disability and optimize functional motor recovery. This is thought to be achieved by modulation of plastic changes in the brain. Therefore, adjunct interventions that can augment the response of the motor system to the behavioural training might be useful to enhance the therapy-induced recovery in neurological populations. In this context, noninvasive brain stimulation appears to be an interesting option as an add-on intervention to standard physical therapies. Two non-invasive methods of inducing electrical currents into the brain have proved to be promising for inducing long-lasting plastic changes in motor systems: transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). These techniques represent powerful methods for priming cortical excitability for a subsequent motor task, demand, or stimulation. Thus, their mutual use can optimize the plastic changes induced by motor practice, leading to more remarkable and outlasting clinical gains in rehabilitation. In this review we discuss how these techniques can enhance the effects of a behavioural intervention and the clinical evidence to date. PMID:19292910
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Sun, Yu; Li, Junhua; Suckling, John; Feng, Lei
2017-01-01
Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging. PMID:29209197
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Ting, Jonathan T; Lee, Brian R; Chong, Peter; Soler-Llavina, Gilberto; Cobbs, Charles; Koch, Christof; Zeng, Hongkui; Lein, Ed
2018-02-26
This protocol is a practical guide to the N-methyl-D-glucamine (NMDG) protective recovery method of brain slice preparation. Numerous recent studies have validated the utility of this method for enhancing neuronal preservation and overall brain slice viability. The implementation of this technique by early adopters has facilitated detailed investigations into brain function using diverse experimental applications and spanning a wide range of animal ages, brain regions, and cell types. Steps are outlined for carrying out the protective recovery brain slice technique using an optimized NMDG artificial cerebrospinal fluid (aCSF) media formulation and enhanced procedure to reliably obtain healthy brain slices for patch clamp electrophysiology. With this updated approach, a substantial improvement is observed in the speed and reliability of gigaohm seal formation during targeted patch clamp recording experiments while maintaining excellent neuronal preservation, thereby facilitating challenging experimental applications. Representative results are provided from multi-neuron patch clamp recording experiments to assay synaptic connectivity in neocortical brain slices prepared from young adult transgenic mice and mature adult human neurosurgical specimens. Furthermore, the optimized NMDG protective recovery method of brain slicing is compatible with both juvenile and adult animals, thus resolving a limitation of the original methodology. In summary, a single media formulation and brain slicing procedure can be implemented across various species and ages to achieve excellent viability and tissue preservation.
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Preparation of Acute Brain Slices Using an Optimized N-Methyl-D-glucamine Protective Recovery Method
Chong, Peter; Soler-Llavina, Gilberto; Cobbs, Charles; Koch, Christof; Zeng, Hongkui; Lein, Ed
2018-01-01
This protocol is a practical guide to the N-methyl-D-glucamine (NMDG) protective recovery method of brain slice preparation. Numerous recent studies have validated the utility of this method for enhancing neuronal preservation and overall brain slice viability. The implementation of this technique by early adopters has facilitated detailed investigations into brain function using diverse experimental applications and spanning a wide range of animal ages, brain regions, and cell types. Steps are outlined for carrying out the protective recovery brain slice technique using an optimized NMDG artificial cerebrospinal fluid (aCSF) media formulation and enhanced procedure to reliably obtain healthy brain slices for patch clamp electrophysiology. With this updated approach, a substantial improvement is observed in the speed and reliability of gigaohm seal formation during targeted patch clamp recording experiments while maintaining excellent neuronal preservation, thereby facilitating challenging experimental applications. Representative results are provided from multi-neuron patch clamp recording experiments to assay synaptic connectivity in neocortical brain slices prepared from young adult transgenic mice and mature adult human neurosurgical specimens. Furthermore, the optimized NMDG protective recovery method of brain slicing is compatible with both juvenile and adult animals, thus resolving a limitation of the original methodology. In summary, a single media formulation and brain slicing procedure can be implemented across various species and ages to achieve excellent viability and tissue preservation. PMID:29553547
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Batalle, Dafnis; Muñoz-Moreno, Emma; Tornador, Cristian; Bargallo, Nuria; Deco, Gustavo; Eixarch, Elisenda; Gratacos, Eduard
2016-04-01
The feasibility to use functional MRI (fMRI) during natural sleep to assess low-frequency basal brain activity fluctuations in human neonates has been demonstrated, although its potential to characterise pathologies of prenatal origin has not yet been exploited. In the present study, we used intrauterine growth restriction (IUGR) as a model of altered neurodevelopment due to prenatal condition to show the suitability of brain networks to characterise functional brain organisation at neonatal age. Particularly, we analysed resting-state fMRI signal of 20 neonates with IUGR and 13 controls, obtaining whole-brain functional networks based on correlations of blood oxygen level-dependent (BOLD) signal in 90 grey matter regions of an anatomical atlas (AAL). Characterisation of the networks obtained with graph theoretical features showed increased network infrastructure and raw efficiencies but reduced efficiency after normalisation, demonstrating hyper-connected but sub-optimally organised IUGR functional brain networks. Significant association of network features with neurobehavioral scores was also found. Further assessment of spatiotemporal dynamics displayed alterations into features associated to frontal, cingulate and lingual cortices. These findings show the capacity of functional brain networks to characterise brain reorganisation from an early age, and their potential to develop biomarkers of altered neurodevelopment. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Brain plasticity and rehabilitation in stroke patients.
Hara, Yukihiro
2015-01-01
In recent years, our understanding of motor learning, neuroplasticity and functional recovery after the occurrence of brain lesion has grown significantly. Novel findings in basic neuroscience have provided an impetus for research in motor rehabilitation. The brain reveals a spectrum of intrinsic capacities to react as a highly dynamic system which can change the properties of its neural circuits. This brain plasticity can lead to an extreme degree of spontaneous recovery and rehabilitative training may modify and boost the neuronal plasticity processes. Animal studies have extended these findings, providing insight into a broad range of underlying molecular and physiological events. Neuroimaging studies in human patients have provided observations at the systems level that often parallel findings in animals. In general, the best recoveries are associated with the greatest return toward the normal state of brain functional organization. Reorganization of surviving central nervous system elements supports behavioral recovery, for example, through changes in interhemispheric lateralization, activity of association cortices linked to injured zones, and organization of cortical representational maps. Evidence from animal models suggests that both motor learning and cortical stimulation alter intracortical inhibitory circuits and can facilitate long-term potentiation and cortical remodeling. Current researches on the physiology and use of cortical stimulation animal models and in humans with stroke related hemiplegia are reviewed in this article. In particular, electromyography (EMG) -controlled electrical muscle stimulation improves the motor function of the hemiparetic arm and hand. A multi-channel near-infrared spectroscopy (NIRS) studies in which the hemoglobin levels in the brain were non-invasively and dynamically measured during functional activity found that the cerebral blood flow in the injured sensory-motor cortex area is greatest during an EMG-controlled FES session. Only a few idea is, however, known for the optimal timing of the different processes and therapeutic interventions and for their interactions in detail. Finding optimal rehabilitation paradigms requires an optimal organization of the internal processes of neural plasticity and the therapeutic interventions in accordance with defined plastic time windows. In this review the mechanisms of spontaneous plasticity after stroke and experimental interventions to enhance plasticity are summarized, with an emphasis on functional electrical stimulation therapy.
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Neural Bases of Recovery after Brain Injury
ERIC Educational Resources Information Center
Nudo, Randolph J.
2011-01-01
Substantial data have accumulated over the past decade indicating that the adult brain is capable of substantial structural and functional reorganization after stroke. While some limited recovery is known to occur spontaneously, especially within the first month post-stroke, there is currently significant optimism that new interventions based on…
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Adaptive optical microscope for brain imaging in vivo
NASA Astrophysics Data System (ADS)
Wang, Kai
2017-04-01
The optical heterogeneity of biological tissue imposes a major limitation to acquire detailed structural and functional information deep in the biological specimens using conventional microscopes. To restore optimal imaging performance, we developed an adaptive optical microscope based on direct wavefront sensing technique. This microscope can reliably measure and correct biological samples induced aberration. We demonstrated its performance and application in structural and functional brain imaging in various animal models, including fruit fly, zebrafish and mouse.
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Rosales, Francisco J; Zeisel, Steven H
2008-06-01
This symposium examined current trends in neuroscience and developmental psychology as they apply to assessing the effects of nutrients on brain and behavioral development of 0-6-year-olds. Although the spectrum of nutrients with brain effects has not changed much in the last 25 years, there has been an explosion in new knowledge about the genetics, structure and function of the brain. This has helped to link the brain mechanistic pathway by which these nutrients act with cognitive functions. A clear example of this is linking of brain structural changes due to hypoglycemia versus hyperglycemia with cognitive functions by using magnetic resonance imaging (MRI) to assess changes in brain-region volumes in combination with cognitive test of intelligence, memory and processing speed. Another example is the use of event-related potential (ERP) studies to show that infants of diabetic mothers have impairments in memory from birth through 8 months of age that are consistent with alterations in mechanistic pathways of memory observed in animal models of perinatal iron deficiency. However, gaps remain in the understanding of how nutrients and neurotrophic factors interact with each other in optimizing brain development and function.
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Intermittent metabolic switching, neuroplasticity and brain health
Mattson, Mark P.; Moehl, Keelin; Ghena, Nathaniel; Schmaedick, Maggie; Cheng, Aiwu
2018-01-01
During evolution, individuals whose brains and bodies functioned well in a fasted state were successful in acquiring food, enabling their survival and reproduction. With fasting and extended exercise, liver glycogen stores are depleted and ketones are produced from adipose-cell-derived fatty acids. This metabolic switch in cellular fuel source is accompanied by cellular and molecular adaptations of neural networks in the brain that enhance their functionality and bolster their resistance to stress, injury and disease. Here, we consider how intermittent metabolic switching, repeating cycles of a metabolic challenge that induces ketosis (fasting and/or exercise) followed by a recovery period (eating, resting and sleeping), may optimize brain function and resilience throughout the lifespan, with a focus on the neuronal circuits involved in cognition and mood. Such metabolic switching impacts multiple signalling pathways that promote neuroplasticity and resistance of the brain to injury and disease. PMID:29321682
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Exercise, Energy Intake, Glucose Homeostasis, and the Brain
van Praag, Henriette; Fleshner, Monika; Schwartz, Michael W.
2014-01-01
Here we summarize topics covered in an SFN symposium that considered how and why exercise and energy intake affect neuroplasticity and, conversely, how the brain regulates peripheral energy metabolism. This article is not a comprehensive review of the subject, but rather a view of how the authors' findings fit into a broader context. Emerging findings elucidate cellular and molecular mechanisms by which exercise and energy intake modify the plasticity of neural circuits in ways that affect brain health. By enhancing neurogenesis, synaptic plasticity and neuronal stress robustness, exercise and intermittent energy restriction/fasting may optimize brain function and forestall metabolic and neurodegenerative diseases. Moreover, brain-centered glucoregulatory and immunomodulating systems that mediate peripheral health benefits of intermittent energetic challenges have recently been described. A better understanding of adaptive neural response pathways activated by energetic challenges will enable the development and optimization of interventions to reduce the burden of disease in our communities. PMID:25392482
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Targeted delivery of growth factors in ischemic stroke animal models.
Rhim, Taiyoun; Lee, Minhyung
2016-01-01
Ischemic stroke is caused by reduced blood supply and leads to loss of brain function. The reduced oxygen and nutrient supply stimulates various physiological responses, including induction of growth factors. Growth factors prevent neuronal cell death, promote neovascularization, and induce cell growth. However, the concentration of growth factors is not sufficient to recover brain function after the ischemic damage, suggesting that delivery of growth factors into the ischemic brain may be a useful treatment for ischemic stroke. In this review, various approaches for the delivery of growth factors to ischemic brain tissue are discussed, including local and targeting delivery systems. To develop growth factor therapy for ischemic stroke, important considerations should be taken into account. First, growth factors may have possible side effects. Thus, concentration of growth factors should be restricted to the ischemic tissues by local administration or targeted delivery. Second, the duration of growth factor therapy should be optimized. Growth factor proteins may be degraded too fast to have a high enough therapeutic effect. Therefore, delivery systems for controlled release or gene delivery may be useful. Third, the delivery systems to the brain should be optimized according to the delivery route.
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Salas-Gonzalez, D; Górriz, J M; Ramírez, J; Padilla, P; Illán, I A
2013-01-01
A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.
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The Sodium-Activated Potassium Channel Slack Is Required for Optimal Cognitive Flexibility in Mice
ERIC Educational Resources Information Center
Bausch, Anne E.; Dieter, Rebekka; Nann, Yvette; Hausmann, Mario; Meyerdierks, Nora; Kaczmarek, Leonard K.; Ruth, Peter; Lukowski, Robert
2015-01-01
"Kcnt1" encoded sodium-activated potassium channels (Slack channels) are highly expressed throughout the brain where they modulate the firing patterns and general excitability of many types of neurons. Increasing evidence suggests that Slack channels may be important for higher brain functions such as cognition and normal intellectual…
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Wei, Gao-Xia; Dong, Hao-Ming; Yang, Zhi; Luo, Jing; Zuo, Xi-Nian
2014-01-01
Whether Tai Chi Chuan (TCC) can influence the intrinsic functional architecture of the human brain remains unclear. To examine TCC-associated changes in functional connectomes, resting-state functional magnetic resonance images were acquired from 40 older individuals including 22 experienced TCC practitioners (experts) and 18 demographically matched TCC-naïve healthy controls, and their local functional homogeneities across the cortical mantle were compared. Compared to the controls, the TCC experts had significantly greater and more experience-dependent functional homogeneity in the right post-central gyrus (PosCG) and less functional homogeneity in the left anterior cingulate cortex (ACC) and the right dorsal lateral prefrontal cortex. Increased functional homogeneity in the PosCG was correlated with TCC experience. Intriguingly, decreases in functional homogeneity (improved functional specialization) in the left ACC and increases in functional homogeneity (improved functional integration) in the right PosCG both predicted performance gains on attention network behavior tests. These findings provide evidence for the functional plasticity of the brain’s intrinsic architecture toward optimizing locally functional organization, with great implications for understanding the effects of TCC on cognition, behavior and health in aging population. PMID:24860494
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The Role of Sleep in Emotional Brain Function
Goldstein, Andrea N.; Walker, Matthew P.
2014-01-01
Rapidly emerging evidence continues to describe an intimate and causal relationship between sleep and emotional brain function. These findings are mirrored by longstanding clinical observations demonstrating that nearly all mood and anxiety disorders co-occur with one or more sleep abnormalities. This review aims to (1) provide a synthesis of recent findings describing the emotional brain and behavioral benefits triggered by sleep, and conversely, the detrimental impairments following a lack of sleep, (2) outline a proposed framework in which sleep, and specifically rapid-eye movement (REM) sleep, supports a process of affective brain homeostasis, optimally preparing the organism for next-day social and emotional functioning, and (3) describe how this hypothesized framework can explain the prevalent relationships between sleep and psychiatric disorders, with a particular focus on post-traumatic stress disorder and major depression. PMID:24499013
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Long-Term Implanted cOFM Probe Causes Minimal Tissue Reaction in the Brain
Hochmeister, Sonja; Asslaber, Martin; Kroath, Thomas; Pieber, Thomas R.; Sinner, Frank
2014-01-01
This study investigated the histological tissue reaction to long-term implanted cerebral open flow microperfusion (cOFM) probes in the frontal lobe of the rat brain. Most probe-based cerebral fluid sampling techniques are limited in application time due to the formation of a glial scar that hinders substance exchange between brain tissue and the probe. A glial scar not only functions as a diffusion barrier but also alters metabolism and signaling in extracellular brain fluid. cOFM is a recently developed probe-based technique to continuously sample extracellular brain fluid with an intact blood-brain barrier. After probe implantation, a 2 week healing period is needed for blood-brain barrier reestablishment. Therefore, cOFM probes need to stay in place and functional for at least 15 days after implantation to ensure functionality. Probe design and probe materials are optimized to evoke minimal tissue reaction even after a long implantation period. Qualitative and quantitative histological tissue analysis revealed no continuous glial scar formation around the cOFM probe 30 days after implantation and only a minor tissue reaction regardless of perfusion of the probe. PMID:24621608
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Language comprehension and brain function in individuals with an optimal outcome from autism.
Eigsti, Inge-Marie; Stevens, Michael C; Schultz, Robert T; Barton, Marianne; Kelley, Elizabeth; Naigles, Letitia; Orinstein, Alyssa; Troyb, Eva; Fein, Deborah A
2016-01-01
Although Autism Spectrum Disorder (ASD) is generally a lifelong disability, a minority of individuals with ASD overcome their symptoms to such a degree that they are generally indistinguishable from their typically-developing peers. That is, they have achieved an Optimal Outcome (OO). The question addressed by the current study is whether this normalized behavior reflects normalized brain functioning, or alternatively, the action of compensatory systems. Either possibility is plausible, as most participants with OO received years of intensive therapy that could alter brain networks to align with typical function or work around ASD-related neural dysfunction. Individuals ages 8 to 21 years with high-functioning ASD (n = 23), OO (n = 16), or typical development (TD; n = 20) completed a functional MRI scan while performing a sentence comprehension task. Results indicated similar activations in frontal and temporal regions (left middle frontal, left supramarginal, and right superior temporal gyri) and posterior cingulate in OO and ASD groups, where both differed from the TD group. Furthermore, the OO group showed heightened "compensatory" activation in numerous left- and right-lateralized regions (left precentral/postcentral gyri, right precentral gyrus, left inferior parietal lobule, right supramarginal gyrus, left superior temporal/parahippocampal gyrus, left middle occipital gyrus) and cerebellum, relative to both ASD and TD groups. Behaviorally normalized language abilities in OO individuals appear to utilize atypical brain networks, with increased recruitment of language-specific as well as right homologue and other systems. Early intensive learning and experience may normalize behavioral language performance in OO, but some brain regions involved in language processing may continue to display characteristics that are more similar to ASD than typical development, while others show characteristics not like ASD or typical development.
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Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang
2017-01-01
Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897
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[Monitoring of brain function].
Doi, Matsuyuki
2012-01-01
Despite being the most important of organs, the brain is disproportionately unmonitored compared to other systems such as cardiorespiratory in anesthesia settings. In order to optimize level of anesthesia, it is important to quantify the brain activity suppressed by anesthetic agents. Adverse cerebral outcomes remain a continued problem in patients undergoing various surgical procedures. By providing information on a range of physiologic parameters, brain monitoring may contribute to improve perioperative outcomes. This article addresses the various brain monitoring equipments including bispectral index (BIS), auditory evoked potentials (AEP), near-infrared spectroscopy (NIRS), transcranial Doppler ultrasonography (TCD) and oxygen saturation of the jugular vein (Sjv(O2)).
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ERIC Educational Resources Information Center
Ratner, Faina Lazarevna; Efimova, Victoria Leonidovna; Efimov, Oleg Igorevich
2015-01-01
The article describes the results of application of the "inTime" neuroacoustic training by Advanced Brain Technologies (USA) when they were organizing assistance to children who had learning disabilities. This training optimizes the functional state of the brain by using sounds of various frequency and rhythm. The effectiveness of the…
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Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations
Horschig, Jörn M.; Zumer, Johanna M.; Bahramisharif, Ali
2014-01-01
Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works. PMID:25018706
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The default-mode, ego-functions and free-energy: a neurobiological account of Freudian ideas
Friston, K. J.
2010-01-01
This article explores the notion that Freudian constructs may have neurobiological substrates. Specifically, we propose that Freud’s descriptions of the primary and secondary processes are consistent with self-organized activity in hierarchical cortical systems and that his descriptions of the ego are consistent with the functions of the default-mode and its reciprocal exchanges with subordinate brain systems. This neurobiological account rests on a view of the brain as a hierarchical inference or Helmholtz machine. In this view, large-scale intrinsic networks occupy supraordinate levels of hierarchical brain systems that try to optimize their representation of the sensorium. This optimization has been formulated as minimizing a free-energy; a process that is formally similar to the treatment of energy in Freudian formulations. We substantiate this synthesis by showing that Freud’s descriptions of the primary process are consistent with the phenomenology and neurophysiology of rapid eye movement sleep, the early and acute psychotic state, the aura of temporal lobe epilepsy and hallucinogenic drug states. PMID:20194141
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Pallud, J; Mandonnet, E; Corns, R; Dezamis, E; Parraga, E; Zanello, M; Spena, G
2017-06-01
Intraoperative application of electrical current to the brain is a standard technique during brain surgery for inferring the function of the underlying brain. The purpose of intraoperative functional mapping is to reliably identify cortical areas and subcortical pathways involved in eloquent functions, especially motor, sensory, language and cognitive functions. The aim of this article is to review the rationale and the electrophysiological principles of the use of direct bipolar electrostimulation for cortical and subcortical mapping under awake conditions. Direct electrical stimulation is a window into the whole functional network that sustains a particular function. It is an accurate (spatial resolution of about 5mm) and a reproducible technique particularly adapted to clinical practice for brain resection in eloquent areas. If the procedure is rigorously applied, the sensitivity of direct electrical stimulation for the detection of cortical and subcortical eloquent areas is nearly 100%. The main disadvantage of this technique is its suboptimal specificity. Another limitation is the identification of eloquent areas during surgery, which, however, could have been functionally compensated postoperatively if removed surgically. Direct electrical stimulation is an easy, accurate, reliable and safe invasive technique for the intraoperative detection of both cortical and subcortical functional brain connectivity for clinical purpose. In our opinion, it is the optimal technique for minimizing the risk of neurological sequelae when resecting in eloquent brain areas. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
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Nan, J; Liu, J; Mu, J; Zhang, Y; Zhang, M; Tian, J; Liang, F; Zeng, F
2015-06-01
Previous studies summarized altered brain functional patterns in functional dyspepsia (FD) patients, but how the brain structural patterns are related to FD remains largely unclear. The objective of this study was to determine the brain structural characteristics in FD patients. Optimized voxel-based morphometry and tract-based spatial statistics were employed to investigate the changes in gray matter (GM) and white matter (WM) respectively in 34 FD patients with postprandial distress syndrome and 33 healthy controls based on T1-weighted and diffusion-weighted imaging. The Pearson's correlation evaluated the link among GM alterations, WM abnormalities, and clinical variables in FD patients. The optimal brain structural parameters for identifying FD were explored using the receiver operating characteristic curve. Compared to controls, FD patients exhibited a decrease in GM density (GMD) in the right posterior insula/temporal superior cortex (marked as pINS), right inferior frontal cortex (IFC), and left middle cingulate cortex, and an increase in fractional anisotropy (FA) in the posterior limb of the internal capsule, posterior thalamic radiation, and external capsule (EC). Interestingly, the GMD in the pINS was significantly associated with GMD in the IFC and FA in the EC. Moreover, the EC adjacent to the pINS provided the best performance for distinguishing FD patients from controls. Our results showed pINS-related structural abnormalities in FD patients, indicating that GM and WM parameters were not affected independently. These findings would lay the foundation for probing an efficient target in the brain for treating FD. © 2015 John Wiley & Sons Ltd.
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Self-transcending meditation is good for mental health: why this should be the case.
Hankey, Alex; Shetkar, Rashmi
2016-06-01
A simple theory of health has recently been proposed: while poor quality regulation corresponds to poor quality health so that improving regulation should improve health, optimal regulation optimizes function and optimizes health. Examining the term 'optimal regulation' in biological systems leads to a straightforward definition in terms of 'criticality' in complexity biology, a concept that seems to apply universally throughout biology. Criticality maximizes information processing and sensitivity of response to external stimuli, and for these reasons may be held to optimize regulation. In this way a definition of health has been given in terms of regulation, a scientific concept, which ties into detailed properties of complex systems, including brain cortices, and mental health. Models of experience and meditation built on complexity also point to criticality: it represents the condition making self-awareness possible, and is strengthened by meditation practices leading to the state of pure consciousness-the content-free state of mind in deep meditation. From this it follows that healthy function of the brain cortex, its sensitivity,y and consistency of response to external challenges should improve by practicing techniques leading to content-free awareness-transcending the original focus introduced during practice. Evidence for this is reviewed.
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Semi-automatic segmentation of brain tumors using population and individual information.
Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan
2013-08-01
Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.
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Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang
2017-05-01
Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l 1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
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2017-01-01
Brain-derived neurotrophic factor (BDNF) is a neurotrophin that is vital to the survival, growth, and maintenance of neurons in key brain circuits involved in emotional and cognitive function. Convergent evidence indicates that neuroplastic mechanisms involving BDNF are deleteriously altered in major depressive disorder (MDD) and animal models of stress. Herein, clinical and preclinical evidence provided that stress-induced depressive pathology contributes to altered BDNF level and function in persons with MDD and, thereby, disruptions in neuroplasticity at the regional and circuit level. Conversely, effective therapeutics that mitigate depressive-related symptoms (e.g., antidepressants and physical activity) optimize BDNF in key brain regions, promote neuronal health and recovery of function in MDD-related circuits, and enhance pharmacotherapeutic response. A greater knowledge of the interrelationship between BDNF, depression, therapeutic mechanisms of action, and neuroplasticity is important as it necessarily precedes the derivation and deployment of more efficacious treatments. PMID:28928987
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Phillips, Cristy
2017-01-01
Brain-derived neurotrophic factor (BDNF) is a neurotrophin that is vital to the survival, growth, and maintenance of neurons in key brain circuits involved in emotional and cognitive function. Convergent evidence indicates that neuroplastic mechanisms involving BDNF are deleteriously altered in major depressive disorder (MDD) and animal models of stress. Herein, clinical and preclinical evidence provided that stress-induced depressive pathology contributes to altered BDNF level and function in persons with MDD and, thereby, disruptions in neuroplasticity at the regional and circuit level. Conversely, effective therapeutics that mitigate depressive-related symptoms (e.g., antidepressants and physical activity) optimize BDNF in key brain regions, promote neuronal health and recovery of function in MDD-related circuits, and enhance pharmacotherapeutic response. A greater knowledge of the interrelationship between BDNF, depression, therapeutic mechanisms of action, and neuroplasticity is important as it necessarily precedes the derivation and deployment of more efficacious treatments.
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Early functional and morphological brain disturbances in late-onset intrauterine growth restriction.
Starčević, Mirta; Predojević, Maja; Butorac, Dražan; Tumbri, Jasna; Konjevoda, Paško; Kadić, Aida Salihagić
2016-02-01
To determine whether the brain disturbances develop in late-onset intrauterine growth restriction (IUGR) before blood flow redistribution towards the fetal brain (detected by Doppler measurements in the middle cerebral artery and umbilical artery). Further, to evaluate predictive values of Doppler arterial indices and umbilical cord blood gases and pH for early functional and/or morphological brain disturbances in late-onset IUGR. This cohort study included 60 singleton term pregnancies with placental insufficiency caused late-onset IUGR (IUGR occurring after 34 gestational weeks). Umbilical artery resistance index (URI), middle cerebral artery resistance index (CRI), and cerebroumbilical (C/U) ratio (CRI/URI) were monitored once weekly. Umbilical blood cord samples (arterial and venous) were collected for the analysis of pO2, pCO2 and pH. Morphological neurological outcome was evaluated by cranial ultrasound (cUS), whereas functional neurological outcome by Amiel-Tison Neurological Assessment at Term (ATNAT). 50 fetuses had C/U ratio>1, and 10 had C/U ratio≤1; among these 10 fetuses, 9 had abnormal neonatal cUS findings and all 10 had non-optimal ATNAT. However, the total number of abnormal neurological findings was much higher. 32 neonates had abnormal cUS (53.37%), and 42 (70.00%) had non-optimal ATNAT. Furthermore, Doppler indices had higher predictive validity for early brain disturbances than umbilical cord blood gases and pH. C/U ratio had the highest predictive validity with threshold for adverse neurological outcome at value 1.13 (ROC analysis), i.e., 1.18 (party machine learning algorithm). Adverse neurological outcome at average values of C/U ratios>1 confirmed that early functional and/or structural brain disturbances in late-onset IUGR develop even before activation of fetal cardiovascular compensatory mechanisms, i.e., before Doppler signs of blood flow redistribution between the fetal brain and the placenta. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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Embedded sparse representation of fMRI data via group-wise dictionary optimization
NASA Astrophysics Data System (ADS)
Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.
2016-03-01
Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.
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Exercise, energy intake, glucose homeostasis, and the brain.
van Praag, Henriette; Fleshner, Monika; Schwartz, Michael W; Mattson, Mark P
2014-11-12
Here we summarize topics covered in an SFN symposium that considered how and why exercise and energy intake affect neuroplasticity and, conversely, how the brain regulates peripheral energy metabolism. This article is not a comprehensive review of the subject, but rather a view of how the authors' findings fit into a broader context. Emerging findings elucidate cellular and molecular mechanisms by which exercise and energy intake modify the plasticity of neural circuits in ways that affect brain health. By enhancing neurogenesis, synaptic plasticity and neuronal stress robustness, exercise and intermittent energy restriction/fasting may optimize brain function and forestall metabolic and neurodegenerative diseases. Moreover, brain-centered glucoregulatory and immunomodulating systems that mediate peripheral health benefits of intermittent energetic challenges have recently been described. A better understanding of adaptive neural response pathways activated by energetic challenges will enable the development and optimization of interventions to reduce the burden of disease in our communities. Copyright © 2014 the authors 0270-6474/14/3415139-11$15.00/0.
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Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert
2018-05-01
Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.
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Chen, Yuhan; Wang, Shengjun
2017-01-01
The primate connectome, possessing a characteristic global topology and specific regional connectivity profiles, is well organized to support both segregated and integrated brain function. However, the organization mechanisms shaping the characteristic connectivity and its relationship to functional requirements remain unclear. The primate brain connectome is shaped by metabolic economy as well as functional values. Here, we explored the influence of two competing factors and additional advanced functional requirements on the primate connectome employing an optimal trade-off model between neural wiring cost and the representative functional requirement of processing efficiency. Moreover, we compared this model with a generative model combining spatial distance and topological similarity, with the objective of statistically reproducing multiple topological features of the network. The primate connectome indeed displays a cost-efficiency trade-off and that up to 67% of the connections were recovered by optimal combination of the two basic factors of wiring economy and processing efficiency, clearly higher than the proportion of connections (56%) explained by the generative model. While not explicitly aimed for, the trade-off model captured several key topological features of the real connectome as the generative model, yet better explained the connectivity of most regions. The majority of the remaining 33% of connections unexplained by the best trade-off model were long-distance links, which are concentrated on few cortical areas, termed long-distance connectors (LDCs). The LDCs are mainly non-hubs, but form a densely connected group overlapping on spatially segregated functional modalities. LDCs are crucial for both functional segregation and integration across different scales. These organization features revealed by the optimization analysis provide evidence that the demands of advanced functional segregation and integration among spatially distributed regions may play a significant role in shaping the cortical connectome, in addition to the basic cost-efficiency trade-off. These findings also shed light on inherent vulnerabilities of brain networks in diseases. PMID:28961235
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Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C; Zhou, Changsong
2017-09-01
The primate connectome, possessing a characteristic global topology and specific regional connectivity profiles, is well organized to support both segregated and integrated brain function. However, the organization mechanisms shaping the characteristic connectivity and its relationship to functional requirements remain unclear. The primate brain connectome is shaped by metabolic economy as well as functional values. Here, we explored the influence of two competing factors and additional advanced functional requirements on the primate connectome employing an optimal trade-off model between neural wiring cost and the representative functional requirement of processing efficiency. Moreover, we compared this model with a generative model combining spatial distance and topological similarity, with the objective of statistically reproducing multiple topological features of the network. The primate connectome indeed displays a cost-efficiency trade-off and that up to 67% of the connections were recovered by optimal combination of the two basic factors of wiring economy and processing efficiency, clearly higher than the proportion of connections (56%) explained by the generative model. While not explicitly aimed for, the trade-off model captured several key topological features of the real connectome as the generative model, yet better explained the connectivity of most regions. The majority of the remaining 33% of connections unexplained by the best trade-off model were long-distance links, which are concentrated on few cortical areas, termed long-distance connectors (LDCs). The LDCs are mainly non-hubs, but form a densely connected group overlapping on spatially segregated functional modalities. LDCs are crucial for both functional segregation and integration across different scales. These organization features revealed by the optimization analysis provide evidence that the demands of advanced functional segregation and integration among spatially distributed regions may play a significant role in shaping the cortical connectome, in addition to the basic cost-efficiency trade-off. These findings also shed light on inherent vulnerabilities of brain networks in diseases.
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Intraoperative virtual brain counseling
NASA Astrophysics Data System (ADS)
Jiang, Zhaowei; Grosky, William I.; Zamorano, Lucia J.; Muzik, Otto; Diaz, Fernando
1997-06-01
Our objective is to offer online real-tim e intelligent guidance to the neurosurgeon. Different from traditional image-guidance technologies that offer intra-operative visualization of medical images or atlas images, virtual brain counseling goes one step further. It can distinguish related brain structures and provide information about them intra-operatively. Virtual brain counseling is the foundation for surgical planing optimization and on-line surgical reference. It can provide a warning system that alerts the neurosurgeon if the chosen trajectory will pass through eloquent brain areas. In order to fulfill this objective, tracking techniques are involved for intra- operativity. Most importantly, a 3D virtual brian environment, different from traditional 3D digitized atlases, is an object-oriented model of the brain that stores information about different brain structures together with their elated information. An object-oriented hierarchical hyper-voxel space (HHVS) is introduced to integrate anatomical and functional structures. Spatial queries based on position of interest, line segment of interest, and volume of interest are introduced in this paper. The virtual brain environment is integrated with existing surgical pre-planning and intra-operative tracking systems to provide information for planning optimization and on-line surgical guidance. The neurosurgeon is alerted automatically if the planned treatment affects any critical structures. Architectures such as HHVS and algorithms, such as spatial querying, normalizing, and warping are presented in the paper. A prototype has shown that the virtual brain is intuitive in its hierarchical 3D appearance. It also showed that HHVS, as the key structure for virtual brain counseling, efficiently integrates multi-scale brain structures based on their spatial relationships.This is a promising development for optimization of treatment plans and online surgical intelligent guidance.
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What has fMRI told us about the Development of Cognitive Control through Adolescence?
Luna, Beatriz; Padmanabhan, Aarthi; O’Hearn, Kirsten
2009-01-01
Cognitive control, the ability to voluntarily guide our behavior, continues to improve throughout adolescence. Below we review the literature on age-related changes in brain function related to response inhibition and working memory, which support cognitive control. Findings from studies using functional magnetic imaging (fMRI) indicate that processing errors, sustaining a cognitive control state, and reaching adult levels of precision, persist through adolescence. Developmental changes in patterns of brain function suggest that core regions of the circuitry underlying cognitive control are on-line early in development. However, age-related changes in localized processes across the brain and in establishing long range connections that support top-down modulation of behavior may support more effective neural processing for optimal mature executive function. While great progress has been made in understanding the age-related changes in brain processes underlying cognitive development, there are still important challenges in developmental neuroimaging methods and the interpretation of data that need to be addressed. PMID:19765880
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Autoregulation in the Neuro ICU.
Wang, Anson; Ortega-Gutierrez, Santiago; Petersen, Nils H
2018-05-17
The purpose of this review is to briefly describe the concept of cerebral autoregulation, to detail several bedside techniques for measuring and assessing autoregulation, and to outline the impact of impaired autoregulation on clinical and functional outcomes in acute brain injury. Furthermore, we will review several autoregulation studies in select forms of acute brain injuries, discuss the potential for its use in patient management in the ICU, and suggest further avenues for research. Cerebral autoregulation plays a critical role in regulating cerebral blood flow, and impaired autoregulation has been associated with worse functional and clinical outcomes in various acute brain injuries. There exists a multitude of methods to assess the autoregulatory state in patients using both invasive and non-invasive modalities. Continuous monitoring of patients in the ICU has yielded autoregulatory-derived optimal perfusion pressures that may prevent secondary injury and improve outcomes. Measuring autoregulation continuously at the bedside is now a feasible option for clinicians working in the ICU, although there exists a great need to standardize autoregulatory measurement. While the clinical benefits await prospective and randomized trials, autoregulation-derived parameters show enormous potential for creating an optimal physiological environment for the injured brain.
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Graph theoretical analysis of complex networks in the brain
Stam, Cornelis J; Reijneveld, Jaap C
2007-01-01
Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern. PMID:17908336
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Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan
2013-02-01
In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.
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2010-09-01
working with equally experienced partners who can, cumulatively, help each other make sense of chaotic situations. “Human brains collect, organize...but a process reinforced by years of Fire Department training. No matter what we do, even an optimally functioning human brain will prepare for...trick or reorganize the brain of those who will be first responding incident commanders to an edge-of-chaos event into creatively making sense of
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Regional anatomy of the pedunculopontine nucleus: relevance for deep brain stimulation.
Fournier-Gosselin, Marie-Pierre; Lipsman, Nir; Saint-Cyr, Jean A; Hamani, Clement; Lozano, Andres M
2013-09-01
The pedunculopontine nucleus (PPN) is currently being investigated as a potential deep brain stimulation target to improve gait and posture in Parkinson's disease. This review examines the complex anatomy of the PPN region and suggests a functional mapping of the surrounding nuclei and fiber tracts that may serve as a guide to a more accurate placement of electrodes while avoiding potentially adverse effects. The relationships of the PPN were examined in different human brain atlases. Schematic representations of those structures in the vicinity of the PPN were generated and correlated with their potential stimulation effects. By providing a functional map and representative schematics of the PPN region, we hope to optimize the placement of deep brain stimulation electrodes, thereby maximizing safety and clinical efficacy. © 2013 International Parkinson and Movement Disorder Society.
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Geng, Shujie; Liu, Xiangyu; Biswal, Bharat B; Niu, Haijing
2017-01-01
As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.
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Priceman, Saul J; Tilakawardane, Dileshni; Jeang, Brook; Aguilar, Brenda; Murad, John P; Park, Anthony K; Chang, Wen-Chung; Ostberg, Julie R; Neman, Josh; Jandial, Rahul; Portnow, Jana; Forman, Stephen J; Brown, Christine E
2018-01-01
Purpose: Metastasis to the brain from breast cancer remains a significant clinical challenge, and may be targeted with CAR-based immunotherapy. CAR design optimization for solid tumors is crucial due to the absence of truly restricted antigen expression and potential safety concerns with "on-target off-tumor" activity. Here, we have optimized HER2-CAR T cells for the treatment of breast to brain metastases, and determined optimal second-generation CAR design and route of administration for xenograft mouse models of breast metastatic brain tumors, including multifocal and leptomeningeal disease. Experimental Design: HER2-CAR constructs containing either CD28 or 4-1BB intracellular costimulatory signaling domains were compared for functional activity in vitro by measuring cytokine production, T-cell proliferation, and tumor killing capacity. We also evaluated HER2-CAR T cells delivered by intravenous, local intratumoral, or regional intraventricular routes of administration using in vivo human xenograft models of breast cancer that have metastasized to the brain. Results: Here, we have shown that HER2-CARs containing the 4-1BB costimulatory domain confer improved tumor targeting with reduced T-cell exhaustion phenotype and enhanced proliferative capacity compared with HER2-CARs containing the CD28 costimulatory domain. Local intracranial delivery of HER2-CARs showed potent in vivo antitumor activity in orthotopic xenograft models. Importantly, we demonstrated robust antitumor efficacy following regional intraventricular delivery of HER2-CAR T cells for the treatment of multifocal brain metastases and leptomeningeal disease. Conclusions: Our study shows the importance of CAR design in defining an optimized CAR T cell, and highlights intraventricular delivery of HER2-CAR T cells for treating multifocal brain metastases. Clin Cancer Res; 24(1); 95-105. ©2017 AACR . ©2017 American Association for Cancer Research.
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Kringelbach, Morten L.; Berridge, Kent C.
2017-01-01
Arguably, emotion is always valenced—either pleasant or unpleasant—and dependent on the pleasure system. This system serves adaptive evolutionary functions; relying on separable wanting, liking, and learning neural mechanisms mediated by mesocorticolimbic networks driving pleasure cycles with appetitive, consummatory, and satiation phases. Liking is generated in a small set of discrete hedonic hotspots and coldspots, while wanting is linked to dopamine and to larger distributed brain networks. Breakdown of the pleasure system can lead to anhedonia and other features of affective disorders. Eudaimonia and well-being are difficult to study empirically, yet whole-brain computational models could offer novel insights (e.g., routes to eudaimonia such as caregiving of infants or music) potentially linking eudaimonia to optimal metastability in the pleasure system. PMID:28943891
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aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data
Niedworok, Christian J.; Brown, Alexander P. Y.; Jorge Cardoso, M.; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W.
2016-01-01
The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127
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Optimal-mass-transfer-based estimation of glymphatic transport in living brain
NASA Astrophysics Data System (ADS)
Ratner, Vadim; Zhu, Liangjia; Kolesov, Ivan; Nedergaard, Maiken; Benveniste, Helene; Tannenbaum, Allen
2015-03-01
It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the `glymphatic pathway' plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs . It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data. Keywords: inverse problem, optimal mass transport, diffusion equation, cerebrospinal fluid flow in brain, optical flow, liquid flow modeling, Monge Kantorovich problem, diffusion tensor estimation
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Functional Differences between Statistical Learning with and without Explicit Training
ERIC Educational Resources Information Center
Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.
2015-01-01
Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…
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Rezakova, M V; Mazhirina, K G; Pokrovskiy, M A; Savelov, A A; Savelova, O A; Shtark, M B
2013-04-01
Using functional magnetic resonance imaging technique, we performed online brain mapping of gamers, practiced to voluntary (cognitively) control their heart rate, the parameter that operated a competitive virtual gameplay in the adaptive feedback loop. With the default start picture, the regions of interest during the formation of optimal cognitive strategy were as follows: Brodmann areas 19, 37, 39 and 40, i.e. cerebellar structures (vermis, amygdala, pyramids, clivus). "Localization" concept of the contribution of the cerebellum to cognitive processes is discussed.
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Highly adaptive tests for group differences in brain functional connectivity.
Kim, Junghi; Pan, Wei
2015-01-01
Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.
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Optimal hemodynamic response model for functional near-infrared spectroscopy
Kamran, Muhammad A.; Jeong, Myung Yung; Mannan, Malik M. N.
2015-01-01
Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650–950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > tcritical and p-value < 0.05). PMID:26136668
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Optimal hemodynamic response model for functional near-infrared spectroscopy.
Kamran, Muhammad A; Jeong, Myung Yung; Mannan, Malik M N
2015-01-01
Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > t critical and p-value < 0.05).
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Tonello, Lucio; Gashi, Bekim; Scuotto, Alessandro; Cappello, Glenda; Cocchi, Massimo; Gabrielli, Fabio; Tuszynski, Jack A
2018-01-01
Living organisms tend to find viable strategies under ambient conditions that optimize their search for, and utilization of, life-sustaining resources. For plants, a leading role in this process is performed by auxin, a plant hormone that drives morphological development, dynamics, and movement to optimize the absorption of light (through branches and leaves) and chemical "food" (through roots). Similarly to auxin in plants, serotonin seems to play an important role in higher animals, especially humans. Here, it is proposed that morphological and functional similarities between (i) plant leaves and the animal/human brain and (ii) plant roots and the animal/human gastro-intestinal tract have general features in common. Plants interact with light and use it for biological energy, whereas, neurons in the central nervous system seem to interact with bio-photons and use them for proper brain function. Further, as auxin drives roots "arborescence" within the soil, similarly serotonin seems to facilitate enteric nervous system connectivity within the human gastro-intestinal tract. This auxin/serotonin parallel suggests the root-branches axis in plants may be an evolutionary precursor to the gastro-intestinal-brain axis in humans. Finally, we hypothesize that light might be an important factor, both in gastro-intestinal dynamics and brain function. Such a comparison may indicate a key role for the interaction of light and serotonin in neuronal physiology (possibly in both the central nervous system and the enteric nervous system), and according to recent work, mind and consciousness.
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Gahm, Jin Kyu; Shi, Yonggang
2018-01-01
Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer’s disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. PMID:29574399
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Moran, Rosalyn J; Symmonds, Mkael; Dolan, Raymond J; Friston, Karl J
2014-01-01
The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses--including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects--from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.
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Mild traumatic brain injury: graph-model characterization of brain networks for episodic memory.
Tsirka, Vasso; Simos, Panagiotis G; Vakis, Antonios; Kanatsouli, Kassiani; Vourkas, Michael; Erimaki, Sofia; Pachou, Ellie; Stam, Cornelis Jan; Micheloyannis, Sifis
2011-02-01
Episodic memory is among the cognitive functions that can be affected in the acute phase following mild traumatic brain injury (MTBI). The present study used EEG recordings to evaluate global synchronization and network organization of rhythmic activity during the encoding and recognition phases of an episodic memory task varying in stimulus type (kaleidoscope images, pictures, words, and pseudowords). Synchronization of oscillatory activity was assessed using a linear and nonlinear connectivity estimator and network analyses were performed using algorithms derived from graph theory. Twenty five MTBI patients (tested within days post-injury) and healthy volunteers were closely matched on demographic variables, verbal ability, psychological status variables, as well as on overall task performance. Patients demonstrated sub-optimal network organization, as reflected by changes in graph parameters in the theta and alpha bands during both encoding and recognition. There were no group differences in spectral energy during task performance or on network parameters during a control condition (rest). Evidence of less optimally organized functional networks during memory tasks was more prominent for pictorial than for verbal stimuli. Copyright © 2010 Elsevier B.V. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Keeling, V; Hossain, S; Hildebrand, K
Purpose: To show improvements in dose conformity and normal brain tissue sparing using an optimal planning technique (OPT) against clinically acceptable planning technique (CAP) in the treatment of multiple brain metastases. Methods: A standardized international benchmark case with12 intracranial tumors was planned using two different VMAT optimization methods. Plans were split into four groups with 3, 6, 9, and 12 targets each planned with 3, 5, and 7 arcs using Eclipse TPS. The beam geometries were 1 full coplanar and half non-coplanar arcs. A prescription dose of 20Gy was used for all targets. The following optimization criteria was used (OPTmore » vs. CAP): (No upper limit vs.108% upper limit for target volume), (priority 140–150 vs. 75–85 for normal-brain-tissue), and (selection of automatic sparing Normal-Tissue-Objective (NTO) vs. Manual NTO). Both had priority 50 to critical structures such as brainstem and optic-chiasm, and both had an NTO priority 150. Normal-brain-tissue doses along with Paddick Conformity Index (PCI) were evaluated. Results: In all cases PCI was higher for OPT plans. The average PCI (OPT,CAP) for all targets was (0.81,0.64), (0.81,0.63), (0.79,0.57), and (0.72,0.55) for 3, 6, 9, and 12 target plans respectively. The percent decrease in normal brain tissue volume (OPT/CAP*100) achieved by OPT plans was (reported as follows: V4, V8, V12, V16, V20) (184, 343, 350, 294, 371%), (192, 417, 380, 299, 360%), and (235, 390, 299, 281, 502%) for the 3, 5, 7 arc 12 target plans, respectively. The maximum brainstem dose decreased for the OPT plan by 4.93, 4.89, and 5.30 Gy for 3, 5, 7 arc 12 target plans, respectively. Conclusion: Substantial increases in PCI, critical structure sparing, and decreases in normal brain tissue dose were achieved by eliminating upper limits from optimization, using automatic sparing of normal tissue function with high priority, and a high priority to normal brain tissue.« less
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Fissler, Patrick; Kolassa, Iris-Tatjana; Schrader, Claudia
2015-01-01
Educational games link the motivational nature of games with learning of knowledge and skills. Here, we go beyond effects on these learning outcomes. We review two lines of evidence which indicate the currently unexplored potential of educational games to promote brain health: First, gaming with specific neurocognitive demands (e.g., executive control), and second, educational learning experiences (e.g., studying foreign languages) improve brain health markers. These markers include cognitive ability, brain function, and brain structure. As educational games allow the combination of specific neurocognitive demands with educational learning experiences, they seem to be optimally suited for promoting brain health. We propose a neurocognitive approach to reveal this unexplored potential of educational games in future research.
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Fissler, Patrick; Kolassa, Iris-Tatjana; Schrader, Claudia
2015-01-01
Educational games link the motivational nature of games with learning of knowledge and skills. Here, we go beyond effects on these learning outcomes. We review two lines of evidence which indicate the currently unexplored potential of educational games to promote brain health: First, gaming with specific neurocognitive demands (e.g., executive control), and second, educational learning experiences (e.g., studying foreign languages) improve brain health markers. These markers include cognitive ability, brain function, and brain structure. As educational games allow the combination of specific neurocognitive demands with educational learning experiences, they seem to be optimally suited for promoting brain health. We propose a neurocognitive approach to reveal this unexplored potential of educational games in future research. PMID:26257697
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NASA Astrophysics Data System (ADS)
Liu, Yutong; Uberti, Mariano; Dou, Huanyu; Mosley, R. Lee; Gendelman, Howard E.; Boska, Michael D.
2009-02-01
Coregistration of in vivo magnetic resonance imaging (MRI) with histology provides validation of disease biomarker and pathobiology studies. Although thin-plate splines are widely used in such image registration, point landmark selection is error prone and often time-consuming. We present a technique to optimize landmark selection for thin-plate splines and demonstrate its usefulness in warping rodent brain MRI to histological sections. In this technique, contours are drawn on the corresponding MRI slices and images of histological sections. The landmarks are extracted from the contours by equal spacing then optimized by minimizing a cost function consisting of the landmark displacement and contour curvature. The technique was validated using simulation data and brain MRI-histology coregistration in a murine model of HIV-1 encephalitis. Registration error was quantified by calculating target registration error (TRE). The TRE of approximately 8 pixels for 20-80 landmarks without optimization was stable at different landmark numbers. The optimized results were more accurate at low landmark numbers (TRE of approximately 2 pixels for 50 landmarks), while the accuracy decreased (TRE approximately 8 pixels for larger numbers of landmarks (70- 80). The results demonstrated that registration accuracy decreases with the increasing landmark numbers offering more confidence in MRI-histology registration using thin-plate splines.
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Local sleep homeostasis in the avian brain: convergence of sleep function in mammals and birds?
Lesku, John A; Vyssotski, Alexei L; Martinez-Gonzalez, Dolores; Wilzeck, Christiane; Rattenborg, Niels C
2011-08-22
The function of the brain activity that defines slow wave sleep (SWS) and rapid eye movement (REM) sleep in mammals is unknown. During SWS, the level of electroencephalogram slow wave activity (SWA or 0.5-4.5 Hz power density) increases and decreases as a function of prior time spent awake and asleep, respectively. Such dynamics occur in response to waking brain use, as SWA increases locally in brain regions used more extensively during prior wakefulness. Thus, SWA is thought to reflect homeostatically regulated processes potentially tied to maintaining optimal brain functioning. Interestingly, birds also engage in SWS and REM sleep, a similarity that arose via convergent evolution, as sleeping reptiles and amphibians do not show similar brain activity. Although birds deprived of sleep show global increases in SWA during subsequent sleep, it is unclear whether avian sleep is likewise regulated locally. Here, we provide, to our knowledge, the first electrophysiological evidence for local sleep homeostasis in the avian brain. After staying awake watching David Attenborough's The Life of Birds with only one eye, SWA and the slope of slow waves (a purported marker of synaptic strength) increased only in the hyperpallium--a primary visual processing region--neurologically connected to the stimulated eye. Asymmetries were specific to the hyperpallium, as the non-visual mesopallium showed a symmetric increase in SWA and wave slope. Thus, hypotheses for the function of mammalian SWS that rely on local sleep homeostasis may apply also to birds.
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MRI segmentation using dialectical optimization.
dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E
2009-01-01
Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T(1)- and T(2)-weighted synthetic brain images of 181 slices with 1 mm, resolution of 1 mm(3), for a normal brain and a noiseless MR tomographic system without field inhomogeneities, amounting a total of 543 images, generated by the simulator BrainWeb [2]. Our principal target here is comparing our proposal to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.
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De Martin, Elena; Duran, Dunja; Ghielmetti, Francesco; Visani, Elisa; Aquino, Domenico; Marchetti, Marcello; Sebastiano, Davide Rossi; Cusumano, Davide; Bruzzone, Maria Grazia; Panzica, Ferruccio; Fariselli, Laura
2017-12-01
Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) provide noninvasive localization of eloquent brain areas for presurgical planning. The aim of this study is the integration of MEG and fMRI maps into a CyberKnife (CK) system to optimize dose planning. Four patients with brain metastases in the motor area underwent functional imaging study of the hand motor cortex before radiosurgery. MEG data were acquired during a visually cued hand motor task. Motor activations were identified also using an fMRI block-designed paradigm. MEG and fMRI maps were then integrated into a CK system and contoured as organs at risk for treatment planning optimization. The integration of fMRI data into the CK system was achieved for all patients by means of a standardized protocol. We also implemented an ad hoc pipeline to convert the MEG signal into a DICOM standard, to make sure that it was readable by our CK treatment planning system. Inclusion of the activation areas into the optimization plan allowed the creation of treatment plans that reduced the irradiation of the motor cortex yet not affecting the brain peripheral dose. The availability of advanced neuroimaging techniques is playing an increasingly important role in radiosurgical planning strategy. We successfully imported MEG and fMRI activations into a CK system. This additional information can improve dose sparing of eloquent areas, allowing a more comprehensive investigation of the related dose-volume constraints that in theory could translate into a gain in tumor local control, and a reduction of neurological complications. Copyright © 2017 Elsevier Inc. All rights reserved.
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Learning-dependent plasticity with and without training in the human brain.
Zhang, Jiaxiang; Kourtzi, Zoe
2010-07-27
Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.
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Critical Care Management Focused on Optimizing Brain Function After Cardiac Arrest.
Nakashima, Ryuta; Hifumi, Toru; Kawakita, Kenya; Okazaki, Tomoya; Egawa, Satoshi; Inoue, Akihiko; Seo, Ryutaro; Inagaki, Nobuhiro; Kuroda, Yasuhiro
2017-03-24
The discussion of neurocritical care management in post-cardiac arrest syndrome (PCAS) has generally focused on target values used for targeted temperature management (TTM). There has been less attention paid to target values for systemic and cerebral parameters to minimize secondary brain damage in PCAS. And the neurologic indications for TTM to produce a favorable neurologic outcome remain to be determined. Critical care management of PCAS patients is fundamental and essential for both cardiologists and general intensivists to improve neurologic outcome, because definitive therapy of PCAS includes both special management of the cause of cardiac arrest, such as coronary intervention to ischemic heart disease, and intensive management of the results of cardiac arrest, such as ventilation strategies to avoid brain ischemia. We reviewed the literature and the latest research about the following issues and propose practical care recommendations. Issues are (1) prediction of TTM candidate on admission, (2) cerebral blood flow and metabolism and target value of them, (3) seizure management using continuous electroencephalography, (4) target value of hemodynamic stabilization and its method, (5) management and analysis of respiration, (6) sedation and its monitoring, (7) shivering control and its monitoring, and (8) glucose management. We hope to establish standards of neurocritical care to optimize brain function and produce a favorable neurologic outcome.
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Brain death and marginal grafts in liver transplantation.
Jiménez-Castro, M B; Gracia-Sancho, J; Peralta, C
2015-06-04
It is well known that most organs for transplantation are currently procured from brain-dead donors; however, the presence of brain death is an important risk factor in liver transplantation. In addition, one of the mechanisms to avoid the shortage of liver grafts for transplant is the use of marginal livers, which may show higher risk of primary non-function or initial poor function. To our knowledge, very few reviews have focused in the field of liver transplantation using brain-dead donors; moreover, reviews that focused on both brain death and marginal grafts in liver transplantation, both being key risk factors in clinical practice, have not been published elsewhere. The present review aims to describe the recent findings and the state-of-the-art knowledge regarding the pathophysiological changes occurring during brain death, their effects on marginal liver grafts and summarize the more controversial topics of this pathology. We also review the therapeutic strategies designed to date to reduce the detrimental effects of brain death in both marginal and optimal livers, attempting to explain why such strategies have not solved the clinical problem of liver transplantation.
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B Vitamins and the Brain: Mechanisms, Dose and Efficacy—A Review
Kennedy, David O.
2016-01-01
The B-vitamins comprise a group of eight water soluble vitamins that perform essential, closely inter-related roles in cellular functioning, acting as co-enzymes in a vast array of catabolic and anabolic enzymatic reactions. Their collective effects are particularly prevalent to numerous aspects of brain function, including energy production, DNA/RNA synthesis/repair, genomic and non-genomic methylation, and the synthesis of numerous neurochemicals and signaling molecules. However, human epidemiological and controlled trial investigations, and the resultant scientific commentary, have focused almost exclusively on the small sub-set of vitamins (B9/B12/B6) that are the most prominent (but not the exclusive) B-vitamins involved in homocysteine metabolism. Scant regard has been paid to the other B vitamins. This review describes the closely inter-related functions of the eight B-vitamins and marshals evidence suggesting that adequate levels of all members of this group of micronutrients are essential for optimal physiological and neurological functioning. Furthermore, evidence from human research clearly shows both that a significant proportion of the populations of developed countries suffer from deficiencies or insufficiencies in one or more of this group of vitamins, and that, in the absence of an optimal diet, administration of the entire B-vitamin group, rather than a small sub-set, at doses greatly in excess of the current governmental recommendations, would be a rational approach for preserving brain health. PMID:26828517
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B Vitamins and the Brain: Mechanisms, Dose and Efficacy--A Review.
Kennedy, David O
2016-01-27
The B-vitamins comprise a group of eight water soluble vitamins that perform essential, closely inter-related roles in cellular functioning, acting as co-enzymes in a vast array of catabolic and anabolic enzymatic reactions. Their collective effects are particularly prevalent to numerous aspects of brain function, including energy production, DNA/RNA synthesis/repair, genomic and non-genomic methylation, and the synthesis of numerous neurochemicals and signaling molecules. However, human epidemiological and controlled trial investigations, and the resultant scientific commentary, have focused almost exclusively on the small sub-set of vitamins (B9/B12/B6) that are the most prominent (but not the exclusive) B-vitamins involved in homocysteine metabolism. Scant regard has been paid to the other B vitamins. This review describes the closely inter-related functions of the eight B-vitamins and marshals evidence suggesting that adequate levels of all members of this group of micronutrients are essential for optimal physiological and neurological functioning. Furthermore, evidence from human research clearly shows both that a significant proportion of the populations of developed countries suffer from deficiencies or insufficiencies in one or more of this group of vitamins, and that, in the absence of an optimal diet, administration of the entire B-vitamin group, rather than a small sub-set, at doses greatly in excess of the current governmental recommendations, would be a rational approach for preserving brain health.
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[Cognitive advantages of the third age: a neural network model of brain aging].
Karpenko, M P; Kachalova, L M; Budilova, E V; Terekhin, A T
2009-01-01
We consider a neural network model of age-related cognitive changes in aging brain based on Hopfield network with a sigmoid function of neuron activation. Age is included in the activation function as a parameter in the form of exponential rate denominator, which makes it possible to take into account the weakening of interneuronal links really observed in the aging brain. Analysis of properties of the Lyapunov function associated with the network shows that, with increasing parameter of age, its relief becomes smoother and the number of local minima (network attractors) decreases. As a result, the network gets less frequently stuck in the nearest local minima of the Lyapunov function and reaches a global minimum corresponding to the most effective solution of the cognitive task. It is reasonable to assume that similar changes really occur in the aging brain. Phenomenologically, these changes can be manifested as emergence in aged people of a cognitive quality such as wisdom i.e. ability to find optimal decisions in difficult controversial situations, to distract from secondary aspects and to see the problem as a whole.
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Testosterone levels and cognition in elderly men: a review.
Holland, J; Bandelow, S; Hogervorst, E
2011-08-01
Average testosterone levels and many cognitive functions show a decline with age. There is evidence to suggest that this association is not just age related. Results from cell culture and animal studies provide convincing evidence that testosterone could have protective effects on brain function. Alzheimer's disease (AD) is characterised by brain pathology affecting cognitive function and AD prevalence increases with age. Testosterone levels are lower in AD cases compared to controls, and some studies have suggested that low free testosterone (FT) may precede AD onset. Men with AD may show accelerated endocrinological ageing, characterised by an earlier lowering of thyroid stimulating hormone, an earlier increase in sex hormone binding globulin (SHBG), a subsequent earlier decrease in FT and an earlier increase in gonadotropin levels in response to this. Positive associations have been found between testosterone levels and global cognition, memory, executive functions and spatial performance in observational studies. However, non-significant associations were also reported. It may be that an optimal level of testosterone exists at which some cognitive functions are improved. This may be modified with an older age, with a shifting of the optimal testosterone curve to maintain cognition to the left and a lower optimal level thus needed to be beneficial for the brain. Genetic factors, such as APOE and CAG polymorphisms may further interact with testosterone levels in their effects on cognition. The roles of SHBG, gonadotropins, thyroid hormones and estrogens in maintaining cognitive function and preventing dementia in men are also not completely understood and should be investigated further. Hypogonadal men do not seem to benefit from testosterone supplementation but small scale, short term intervention studies in eugonadal men with and without cognitive impairments have shown promising results. Larger randomised, controlled trials are needed to further investigate testosterone treatment in protecting against cognitive decline and/or dementia. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
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Optimization of PET instrumentation for brain activation studies
DOE Office of Scientific and Technical Information (OSTI.GOV)
Dahlbom, M.; Cherry, S.R.; Hoffman, E.J.
By performing cerebral blood flow studies with positron emission tomography (PET), and comparing blood flow images of different states of activation, functional mapping of the brain is possible. The ability of current commercial instruments to perform such studies is investigated in this work, based on a comparison of noise equivalent count (NEC) rates. Differences in the NEC performance of the different scanners in conjunction with scanner design parameters, provide insights into the importance of block design (size, dead time, crystal thickness) and overall scanner design (sensitivity and scatter fraction) for optimizing data from activation studies. The newer scanners with removablemore » septa, operating with 3-D acquisition, have much higher sensitivity, but require new methodology for optimized operation. Only by administering multiple low doses (fractionation) of the flow tracer can the high sensitivity be utilized.« less
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2013-01-01
Background Approximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD. Methods/Design The international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR (open-label). They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants (18 to 65 years old) will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study (approximately 67 participants in each treatment arm) and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging (MRI) with three cognitive tasks (auditory oddball, a continuous performance task, and Go-NoGo) and two emotion tasks (unmasked conscious and masked non-conscious emotion processing tasks). After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half (n = 102), test the findings in the second half, and then extend the analyses to the total sample. Trial registration International Study to Predict Optimized Treatment - in Depression (iSPOT-D). ClinicalTrials.gov, NCT00693849. PMID:23866851
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Grieve, Stuart M; Korgaonkar, Mayuresh S; Etkin, Amit; Harris, Anthony; Koslow, Stephen H; Wisniewski, Stephen; Schatzberg, Alan F; Nemeroff, Charles B; Gordon, Evian; Williams, Leanne M
2013-07-18
Approximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD. The international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR (open-label). They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants (18 to 65 years old) will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study (approximately 67 participants in each treatment arm) and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging (MRI) with three cognitive tasks (auditory oddball, a continuous performance task, and Go-NoGo) and two emotion tasks (unmasked conscious and masked non-conscious emotion processing tasks). After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half (n=102), test the findings in the second half, and then extend the analyses to the total sample. International Study to Predict Optimized Treatment--in Depression (iSPOT-D). ClinicalTrials.gov, NCT00693849.
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Traumatic brain injury rehabilitation: case management and insurance-related issues.
Pressman, Helaine Tobey
2007-02-01
Traumatic brain injury (TBI) cases are medically complex, involving the physical, cognitive, behavioral, social, and emotional aspects of the survivor. Often catastrophic, these cases require substantial financial resources not only for the patient's survival but to achieve the optimal outcome of a functional life with return to family and work responsibilities for the long term. TBI cases involve the injured person, the family, medical professionals such as treating physicians, therapists, attorneys, the employer, community resources, and the funding source, usually an insurance company. Case management is required to facilitate achievement of an optimal result by collaborating with all parties involved, assessing priorities and options, coordinating services, and educating and communicating with all concerned.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Cabral, Joana; Department of Psychiatry, University of Oxford, Oxford OX3 7JX; Fernandes, Henrique M.
The neuropathology of schizophrenia remains unclear. Some insight has come from modern neuroimaging techniques, which offer an unparalleled opportunity to explore in vivo the structure and function of the brain. Using functional magnetic resonance imaging, it has been found that the large-scale resting-state functional connectivity (rsFC) in schizophrenia — measured as the temporal correlations of the blood-oxygen-level-dependent (BOLD) signal — exhibit altered network topology, with lower small-world index. The origin of these rsFC alterations and link with the underlying structural connectivity remain unclear. In this work, we used a computational model of spontaneous large-scale brain activity to explore the rolemore » of the structural connectivity in the large-scale dynamics of the brain in health and schizophrenia. The structural connectomes from 15 adolescent patients with early-onset schizophrenia and 15 age- and gender-matched controls were built from diffusion tensor imaging data to detect the white matter tracts between 90 brain areas. Brain areas, simulated using a reduced dynamic mean-field model, receive excitatory input from other areas in proportion to the number of fibre tracts between them. The simulated mean field activity was transformed into BOLD signal, and the properties of the simulated functional networks were analyzed. Our results suggest that the functional alterations observed in schizophrenia are not directly linked to alterations in the structural topology. Instead, subtly randomized and less small-world functional networks appear when the brain operates with lower global coupling, which shifts the dynamics from the optimal healthy regime.« less
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NASA Astrophysics Data System (ADS)
Cabral, Joana; Fernandes, Henrique M.; Van Hartevelt, Tim J.; James, Anthony C.; Kringelbach, Morten L.; Deco, Gustavo
2013-12-01
The neuropathology of schizophrenia remains unclear. Some insight has come from modern neuroimaging techniques, which offer an unparalleled opportunity to explore in vivo the structure and function of the brain. Using functional magnetic resonance imaging, it has been found that the large-scale resting-state functional connectivity (rsFC) in schizophrenia — measured as the temporal correlations of the blood-oxygen-level-dependent (BOLD) signal — exhibit altered network topology, with lower small-world index. The origin of these rsFC alterations and link with the underlying structural connectivity remain unclear. In this work, we used a computational model of spontaneous large-scale brain activity to explore the role of the structural connectivity in the large-scale dynamics of the brain in health and schizophrenia. The structural connectomes from 15 adolescent patients with early-onset schizophrenia and 15 age- and gender-matched controls were built from diffusion tensor imaging data to detect the white matter tracts between 90 brain areas. Brain areas, simulated using a reduced dynamic mean-field model, receive excitatory input from other areas in proportion to the number of fibre tracts between them. The simulated mean field activity was transformed into BOLD signal, and the properties of the simulated functional networks were analyzed. Our results suggest that the functional alterations observed in schizophrenia are not directly linked to alterations in the structural topology. Instead, subtly randomized and less small-world functional networks appear when the brain operates with lower global coupling, which shifts the dynamics from the optimal healthy regime.
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Ulloa, Alvaro; Jingyu Liu; Vergara, Victor; Jiayu Chen; Calhoun, Vince; Pattichis, Marios
2014-01-01
In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.
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Gahm, Jin Kyu; Shi, Yonggang
2018-05-01
Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. Copyright © 2018 Elsevier B.V. All rights reserved.
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Bayesian estimation inherent in a Mexican-hat-type neural network
NASA Astrophysics Data System (ADS)
Takiyama, Ken
2016-05-01
Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.
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Amicuzi, Ileana; Stortini, Massimo; Petrarca, Maurizio; Di Giulio, Paola; Di Rosa, Giuseppe; Fariello, Giuseppe; Longo, Daniela; Cannatà, Vittorio; Genovese, Elisabetta; Castelli, Enrico
2006-10-01
We report the case of a 4.6-year-old girl born pre-term with early bilateral occipital damage. It was revealed that the child had non-severely impaired basic visual abilities and ocular motility, a selective perceptual deficit of figure-ground segregation, impaired visual recognition and abnormal navigating through space. Even if the child's visual functioning was not optimal, this was the expression of adaptive anatomic and functional brain modifications that occurred following the early lesion. Anatomic brain structure was studied with anatomic MRI and Diffusor Tensor Imaging (DTI)-MRI. This behavioral study may provide an important contribution to understanding the impact of an early lesion of the visual system on the development of visual functions and on the immature brain's potential for reorganisation related to when the damage occurred.
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Zinc Signal in Brain Diseases.
Portbury, Stuart D; Adlard, Paul A
2017-11-23
The divalent cation zinc is an integral requirement for optimal cellular processes, whereby it contributes to the function of over 300 enzymes, regulates intracellular signal transduction, and contributes to efficient synaptic transmission in the central nervous system. Given the critical role of zinc in a breadth of cellular processes, its cellular distribution and local tissue level concentrations remain tightly regulated via a series of proteins, primarily including zinc transporter and zinc import proteins. A loss of function of these regulatory pathways, or dietary alterations that result in a change in zinc homeostasis in the brain, can all lead to a myriad of pathological conditions with both acute and chronic effects on function. This review aims to highlight the role of zinc signaling in the central nervous system, where it may precipitate or potentiate diverse issues such as age-related cognitive decline, depression, Alzheimer's disease or negative outcomes following brain injury.
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Optimization of Blocked Designs in fMRI Studies
ERIC Educational Resources Information Center
Maus, Barbel; van Breukelen, Gerard J. P.; Goebel, Rainer; Berger, Martijn P. F.
2010-01-01
Blocked designs in functional magnetic resonance imaging (fMRI) are useful to localize functional brain areas. A blocked design consists of different blocks of trials of the same stimulus type and is characterized by three factors: the length of blocks, i.e., number of trials per blocks, the ordering of task and rest blocks, and the time between…
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Communication in neuronal networks.
Laughlin, Simon B; Sejnowski, Terrence J
2003-09-26
Brains perform with remarkable efficiency, are capable of prodigious computation, and are marvels of communication. We are beginning to understand some of the geometric, biophysical, and energy constraints that have governed the evolution of cortical networks. To operate efficiently within these constraints, nature has optimized the structure and function of cortical networks with design principles similar to those used in electronic networks. The brain also exploits the adaptability of biological systems to reconfigure in response to changing needs.
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Brain Functional Connectivity in MS: An EEG-NIRS Study
2015-10-01
electrical (EEG) and blood volume and blood oxygen-based (NIRS and fMRI ) signals, and to use the results to help optimize blood oxygen level...dependent (BOLD) fMRI analyses of brain activity. Participants will be patients with MS (n=25) and healthy demographically matched controls (n=25) who will...undergo standardized evaluations and imaging using combined EEG-NIRS- fMRI . EEG-NIRS data will be used to construct maps of neurovascular coupling
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Christy, Jennifer B; Lobo, Michele A; Bjornson, Kristie; Dusing, Stacey C; Field-Fote, Edelle; Gannotti, Mary; Heathcock, Jill C; OʼNeil, Margaret E; Rimmer, James H
Advances in technology show promise as tools to optimize functional mobility, independence, and participation in infants and children with motor disability due to brain injury. Although technologies are often used in adult rehabilitation, these have not been widely applied to rehabilitation of infants and children. In October 2015, the Academy of Pediatric Physical Therapy sponsored Research Summit IV, "Innovations in Technology for Children With Brain Insults: Maximizing Outcomes." The summit included pediatric physical therapist researchers, experts from other scientific fields, funding agencies, and consumers. Participants identified challenges in implementing technology in pediatric rehabilitation including accessibility, affordability, managing large data sets, and identifying relevant data elements. Participants identified 4 key areas for technology development: to determine (1) thresholds for learning, (2) appropriate transfer to independence, (3) optimal measurement of subtle changes, and (4) how to adapt to growth and changing abilities.
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Investigation of goal change to optimize upper-extremity motor performance in a robotic environment.
Brewer, Bambi R; Klatzky, Roberta; Markham, Heather; Matsuoka, Yoky
2009-10-01
Robotic devices for therapy have the potential to enable intensive, fully customized home rehabilitation over extended periods for individuals with stroke and traumatic brain injury, thus empowering them to maximize their functional recovery. For robotic rehabilitation to be most effective, systems must have the capacity to assign performance goals to the user and to increment those goals to encourage performance improvement. Otherwise, individuals may plateau at an artificially low level of function. Frequent goal change is needed to motivate improvements in performance by individuals with brain injury; but because of entrenched habits, these individuals may avoid striving for goals that they perceive as becoming ever more difficult. For this reason, implicit, undetectable goal change (distortion) may be more effective than explicit goal change at optimizing the motor performance of some individuals with brain injury. This paper reviews a body of work that provides a basis for incorporating implicit goal change into a robotic rehabilitation paradigm. This work was conducted with individuals without disability to provide foundational knowledge for using goal change in a robotic environment. In addition, we compare motor performance with goal change to performance with no goal or with a static goal for individuals without brain injury. Our results show that goal change can improve motor performance when participants attend to visual feedback. Building on these preliminary results can lead to more effective robotic paradigms for the rehabilitation of individuals with brain injury, including individuals with cerebral palsy.
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WE-AB-209-09: Optimization of Rotational Arc Station Parameter Optimized Radiation Therapy
DOE Office of Scientific and Technical Information (OSTI.GOV)
Dong, P; Xing, L; Ungun, B
Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of improving VMAT in both plan quality and delivery efficiency. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based Proximal Operator Graph Solver (POGS) within seconds. Apertures with zero or low weight were thrown out. Tomore » avoid being trapped in a local minimum, a stochastic gradient descent method was employed which also greatly increased the convergence rate of the objective function. The above procedure repeated until the plan could not be improved any further. A weighting factor associated with the total plan MU also indirectly controlled the complexities of aperture shapes. The number of apertures for VMAT and SPORT was confined to 180. The SPORT allowed the coexistence of multiple apertures in a single SP. The optimization technique was assessed by using three clinical cases (prostate, H&N and brain). Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. Prostate case: the volume of the 50% prescription dose was decreased by 22% for the rectum. H&N case: SPORT improved the mean dose for the left and right parotids by 15% each. Brain case: the doses to the eyes, chiasm and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the H&N case. Conclusion: The superior dosimetric quality and delivery efficiency presented here indicates that SPORT is an intriguing alternative treatment modality.« less
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Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten
2015-03-01
Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.
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Dynamic Filtering Improves Attentional State Prediction with fNIRS
NASA Technical Reports Server (NTRS)
Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.
2016-01-01
Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).
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Positive psychology perspective on traumatic brain injury recovery and rehabilitation.
Rabinowitz, Amanda R; Arnett, Peter A
2018-01-01
Recovery from traumatic brain injury (TBI) is heterogeneous, with injury characteristics and neuropathological findings accounting for a relatively modest proportion of the variance in clinical outcome. Furthermore, premorbid personality traits and psychological characteristics may moderate psychosocial recovery. Constructs from the field of positive psychology have been examined in multiple illness populations and are increasingly gaining attention as factors that may influence recovery from TBI. Positive affect, hope, optimism, adaptive coping style, and resilience have all been examined in the context of TBI. These phenomena are of particular interest because they may inform treatment, either by reducing psychological distress and promoting better adjustment, or by augmenting existing therapies to improve engagement. In general, research suggests that higher levels of these factors predict better psychosocial functioning after injury. However, brain injury itself is associated with reduced levels of many of these positive traits, either relative to uninjured control samples or preinjury functioning. There have been proposals for targeting these positive traits in the context of TBI rehabilitation. Although more research is needed, the few controlled trials aimed at improving adaptive coping skills have shown promising results. Other positive psychological phenomena, such as grit, optimism, and positive affect are deserving of further study as potential intervention targets.
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Adhikari, Mohit H; Hacker, Carl D; Siegel, Josh S; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo; Corbetta, Maurizio
2017-04-01
While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to represent stimuli and task states, and that information capacity measured through whole brain models is a theory-driven measure of processing capacity that could be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Optimal-mass-transfer-based estimation of glymphatic transport in living brain.
Ratner, Vadim; Zhu, Liangjia; Kolesov, Ivan; Nedergaard, Maiken; Benveniste, Helene; Tannenbaum, Allen
2015-02-21
It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the 'glymphatic pathway' plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs 1,2 . It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach 3 to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data.
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A Report on Applying EEGnet to Discriminate Human State Effects on Task Performance
2018-01-01
whether we could identify what task the participant was performing from differences in the recorded brain time series . We modeled the relationship...between input data (brain time series ) and output labels (task A and task B) as an unknown function, and we found an optimal approximation of that...this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. Citation of
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Sparse network-based models for patient classification using fMRI
Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina
2015-01-01
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459
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Optimizing Functional Network Representation of Multivariate Time Series
NASA Astrophysics Data System (ADS)
Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco Del; Menasalvas, Ernestina; Boccaletti, Stefano
2012-09-01
By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.
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Optimizing Functional Network Representation of Multivariate Time Series
Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco del; Menasalvas, Ernestina; Boccaletti, Stefano
2012-01-01
By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks. PMID:22953051
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NASA Technical Reports Server (NTRS)
Mulavara, A. P.; Peters, B.; De Dios, Y. E.; Gadd, N. E.; Caldwell, E. E.; Batson, C. D.; Goel, R.; Oddsson, L.; Kreutzberg, G.; Zanello, S.;
2017-01-01
Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. These alterations may disrupt crewmembers' ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts are affected will improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual spaceflight, which crewmembers are likely to experience greater challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures. Our approach includes: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features, using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; and 3) assessment of genetic polymorphisms in the catechol-O-methyl transferase, dopamine receptor D2, and brain-derived neurotrophic factor genes and genetic polymorphisms of alpha2-adrenergic receptors that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate that these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration spaceflight and exposure to an analog bed rest environment. We will be conducting a retrospective study, leveraging data already collected from relevant ongoing or completed bed rest and spaceflight studies. This data will be combined with predictor metrics that will be collected prospectively (as described for behavioral, brain imaging and genomic measures) from these returning subjects to build models for predicting post spaceflight and bed rest adaptive capability. In this presentation we will discuss the optimized set of tests for predictive metrics to be used for evaluating post mission adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive ability, brain structure, brain function, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to mitigate the deleterious effects of spaceflight.
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Dimitriadis, Stavros I.; Zouridakis, George; Rezaie, Roozbeh; Babajani-Feremi, Abbas; Papanicolaou, Andrew C.
2015-01-01
Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI. PMID:26640764
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Caspers, Svenja; Moebus, Susanne; Lux, Silke; Pundt, Noreen; Schütz, Holger; Mühleisen, Thomas W; Gras, Vincent; Eickhoff, Simon B; Romanzetti, Sandro; Stöcker, Tony; Stirnberg, Rüdiger; Kirlangic, Mehmet E; Minnerop, Martina; Pieperhoff, Peter; Mödder, Ulrich; Das, Samir; Evans, Alan C; Jöckel, Karl-Heinz; Erbel, Raimund; Cichon, Sven; Nöthen, Markus M; Sturma, Dieter; Bauer, Andreas; Jon Shah, N; Zilles, Karl; Amunts, Katrin
2014-01-01
The ongoing 1000 brains study (1000BRAINS) is an epidemiological and neuroscientific investigation of structural and functional variability in the human brain during aging. The two recruitment sources are the 10-year follow-up cohort of the German Heinz Nixdorf Recall (HNR) Study, and the HNR MultiGeneration Study cohort, which comprises spouses and offspring of HNR subjects. The HNR is a longitudinal epidemiological investigation of cardiovascular risk factors, with a comprehensive collection of clinical, laboratory, socioeconomic, and environmental data from population-based subjects aged 45-75 years on inclusion. HNR subjects underwent detailed assessments in 2000, 2006, and 2011, and completed annual postal questionnaires on health status. 1000BRAINS accesses these HNR data and applies a separate protocol comprising: neuropsychological tests of attention, memory, executive functions and language; examination of motor skills; ratings of personality, life quality, mood and daily activities; analysis of laboratory and genetic data; and state-of-the-art magnetic resonance imaging (MRI, 3 Tesla) of the brain. The latter includes (i) 3D-T1- and 3D-T2-weighted scans for structural analyses and myelin mapping; (ii) three diffusion imaging sequences optimized for diffusion tensor imaging, high-angular resolution diffusion imaging for detailed fiber tracking and for diffusion kurtosis imaging; (iii) resting-state and task-based functional MRI; and (iv) fluid-attenuated inversion recovery and MR angiography for the detection of vascular lesions and the mapping of white matter lesions. The unique design of 1000BRAINS allows: (i) comprehensive investigation of various influences including genetics, environment and health status on variability in brain structure and function during aging; and (ii) identification of the impact of selected influencing factors on specific cognitive subsystems and their anatomical correlates.
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Parker, Jason G; Zalusky, Eric J; Kirbas, Cemil
2014-03-01
Accurate mapping of visual function and selective attention using fMRI is important in the study of human performance as well as in presurgical treatment planning of lesions in or near visual centers of the brain. Conjunctive visual search (CVS) is a useful tool for mapping visual function during fMRI because of its greater activation extent compared with high-capacity parallel search processes. The purpose of this work was to develop and evaluate a CVS that was capable of generating consistent activation in the basic and higher level visual areas of the brain by using a high number of distractors as well as an optimized contrast condition. Images from 10 healthy volunteers were analyzed and brain regions of greatest activation and deactivation were determined using a nonbiased decomposition of the results at the hemisphere, lobe, and gyrus levels. The results were quantified in terms of activation and deactivation extent and mean z-statistic. The proposed CVS was found to generate robust activation of the occipital lobe, as well as regions in the middle frontal gyrus associated with coordinating eye movements and in regions of the insula associated with task-level control and focal attention. As expected, the task demonstrated deactivation patterns commonly implicated in the default-mode network. Further deactivation was noted in the posterior region of the cerebellum, most likely associated with the formation of optimal search strategy. We believe the task will be useful in studies of visual and selective attention in the neuroscience community as well as in mapping visual function in clinical fMRI.
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Central Chemoreceptors: Locations and Functions
Nattie, Eugene; Li, Aihua
2016-01-01
Central chemoreception traditionally refers to a change in ventilation attributable to changes in CO2/H+ detected within the brain. Interest in central chemoreception has grown substantially since the previous Handbook of Physiology published in 1986. Initially, central chemoreception was localized to areas on the ventral medullary surface, a hypothesis complemented by the recent identification of neurons with specific phenotypes near one of these areas as putative chemoreceptor cells. However, there is substantial evidence that many sites participate in central chemoreception some located at a distance from the ventral medulla. Functionally, central chemoreception, via the sensing of brain interstitial fluid H+, serves to detect and integrate information on 1) alveolar ventilation (arterial PCO2), 2) brain blood flow and metabolism and 3) acid-base balance, and, in response, can affect breathing, airway resistance, blood pressure (sympathetic tone) and arousal. In addition, central chemoreception provides a tonic ‘drive’ (source of excitation) at the normal, baseline PCO2 level that maintains a degree of functional connectivity among brainstem respiratory neurons necessary to produce eupneic breathing. Central chemoreception responds to small variations in PCO2 to regulate normal gas exchange and to large changes in PCO2 to minimize acid-base changes. Central chemoreceptor sites vary in function with sex and with development. From an evolutionary perspective, central chemoreception grew out of the demands posed by air vs. water breathing, homeothermy, sleep, optimization of the work of breathing with the ‘ideal’ arterial PCO2, and the maintenance of the appropriate pH at 37°C for optimal protein structure and function. PMID:23728974
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Hunt, Kevin W; Cook, Adam W; Watts, Ryan J; Clark, Christopher T; Vigers, Guy; Smith, Darin; Metcalf, Andrew T; Gunawardana, Indrani W; Burkard, Michael; Cox, April A; Geck Do, Mary K; Dutcher, Darrin; Thomas, Allen A; Rana, Sumeet; Kallan, Nicholas C; DeLisle, Robert K; Rizzi, James P; Regal, Kelly; Sammond, Douglas; Groneberg, Robert; Siu, Michael; Purkey, Hans; Lyssikatos, Joseph P; Marlow, Allison; Liu, Xingrong; Tang, Tony P
2013-04-25
A hallmark of Alzheimer's disease is the brain deposition of amyloid beta (Aβ), a peptide of 36-43 amino acids that is likely a primary driver of neurodegeneration. Aβ is produced by the sequential cleavage of APP by BACE1 and γ-secretase; therefore, inhibition of BACE1 represents an attractive therapeutic target to slow or prevent Alzheimer's disease. Herein we describe BACE1 inhibitors with limited molecular flexibility and molecular weight that decrease CSF Aβ in vivo, despite efflux. Starting with spirocycle 1a, we explore structure-activity relationships of core changes, P3 moieties, and Asp binding functional groups in order to optimize BACE1 affinity, cathepsin D selectivity, and blood-brain barrier (BBB) penetration. Using wild type guinea pig and rat, we demonstrate a PK/PD relationship between free drug concentrations in the brain and CSF Aβ lowering. Optimization of brain exposure led to the discovery of (R)-50 which reduced CSF Aβ in rodents and in monkey.
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Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.
2013-01-01
Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398
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Computational Neuropsychology and Bayesian Inference.
Parr, Thomas; Rees, Geraint; Friston, Karl J
2018-01-01
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
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Computational Neuropsychology and Bayesian Inference
Parr, Thomas; Rees, Geraint; Friston, Karl J.
2018-01-01
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology. PMID:29527157
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NASA Astrophysics Data System (ADS)
Li, Yongming; Li, Fan; Wang, Pin; Zhu, Xueru; Liu, Shujun; Qiu, Mingguo; Zhang, Jingna; Zeng, Xiaoping
2016-10-01
Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer’s disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.
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Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes.
Whiteley, Louise; Sahani, Maneesh
2008-03-06
Perception is an "inverse problem," in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (Helmholtz, 1856; Marr, 1982) and corrupted by noise (Green & Swets, 1989), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (Berger, 1985; Cox, 1961). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (Knill & Pouget, 2004). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain.
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Functional parcellation using time courses of instantaneous connectivity.
van Oort, Erik S B; Mennes, Maarten; Navarro Schröder, Tobias; Kumar, Vinod J; Zaragoza Jimenez, Nestor I; Grodd, Wolfgang; Doeller, Christian F; Beckmann, Christian F
2018-04-15
Functional neuroimaging studies have led to understanding the brain as a collection of spatially segregated functional networks. It is thought that each of these networks is in turn composed of a set of distinct sub-regions that together support each network's function. Considering the sub-regions to be an essential part of the brain's functional architecture, several strategies have been put forward that aim at identifying the functional sub-units of the brain by means of functional parcellations. Current parcellation strategies typically employ a bottom-up strategy, creating a parcellation by clustering smaller units. We propose a novel top-down parcellation strategy, using time courses of instantaneous connectivity to subdivide an initial region of interest into sub-regions. We use split-half reproducibility to choose the optimal number of sub-regions. We apply our Instantaneous Connectivity Parcellation (ICP) strategy on high-quality resting-state FMRI data, and demonstrate the ability to generate parcellations for thalamus, entorhinal cortex, motor cortex, and subcortex including brainstem and striatum. We evaluate the subdivisions against available cytoarchitecture maps to show that our parcellation strategy recovers biologically valid subdivisions that adhere to known cytoarchitectural features. Copyright © 2017 Elsevier Inc. All rights reserved.
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Rabipour, Sheida; Davidson, Patrick S R
2015-12-15
"Brain training" (i.e., enhancing, rehabilitating, or simply maintaining cognitive function through deliberate cognitive exercise) is growing rapidly in popularity, yet remains highly controversial. Among the greatest problems in current research is the lack of a measure of participants' expectations, which can influence the degree to which they improve over training (i.e., the placebo effect). Here we created a questionnaire to measure the perceived effectiveness of brain-training software. Given the growth in advertising of these programmes, we sought to determine whether even a brief positive (or negative) message about brain training would increase (or decrease) the reported optimism of participants. We measured participants' expectations at baseline, and then following exposure to separate, brief messages that such programmes have either high or low effectiveness. Based on the knowledge they have gleaned from advertising and other real-world sources, people are relatively optimistic about brain training. However, brief messages can influence reported expectations about brain-training results: Reading a brief positive message can increase reported optimism, whereas reading a brief negative message can decrease it. Older adults appear more optimistic about brain training than young adults, especially when they report being knowledgeable about brain training and computers. These data indicate that perceptions of brain training are malleable to at least some extent, and may vary depending on age and other factors. Our questionnaire can serve as a simple, easily-incorporated tool to assess the face validity of brain training interventions and to create a covariate to account for expectations in statistical analyses. Copyright © 2015 Elsevier B.V. All rights reserved.
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2013-01-01
Background The dimensional approach to autism spectrum disorder (ASD) considers ASD as the extreme of a dimension traversing through the entire population. We explored the potential utility of electroencephalography (EEG) functional connectivity as a biomarker. We hypothesized that individual differences in autistic traits of typical subjects would involve a long-range connectivity diminution within the delta band. Methods Resting-state EEG functional connectivity was measured for 74 neurotypical subjects. All participants also provided a questionnaire (Social Responsiveness Scale, SRS) that was completed by an informant who knows the participant in social settings. We conducted multivariate regression between the SRS score and functional connectivity in all EEG frequency bands. We explored modulations of network graph metrics characterizing the optimality of a network using the SRS score. Results Our results show a decay in functional connectivity mainly within the delta and theta bands (the lower part of the EEG spectrum) associated with an increasing number of autistic traits. When inspecting the impact of autistic traits on the global organization of the functional network, we found that the optimal properties of the network are inversely related to the number of autistic traits, suggesting that the autistic dimension, throughout the entire population, modulates the efficiency of functional brain networks. Conclusions EEG functional connectivity at low frequencies and its associated network properties may be associated with some autistic traits in the general population. PMID:23806204
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Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity.
Hillary, Frank G; Grafman, Jordan H
2017-05-01
A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Nomi, Jason S; Bolt, Taylor S; Ezie, C E Chiemeka; Uddin, Lucina Q; Heller, Aaron S
2017-05-31
Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6-85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation. SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan. Copyright © 2017 the authors 0270-6474/17/375539-10$15.00/0.
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The kinematic architecture of the Active Headframe: A new head support for awake brain surgery.
Malosio, Matteo; Negri, Simone Pio; Pedrocchi, Nicola; Vicentini, Federico; Cardinale, Francesco; Tosatti, Lorenzo Molinari
2012-01-01
This paper presents the novel hybrid kinematic structure of the Active Headframe, a robotic head support to be employed in brain surgery operations for an active and dynamic control of the patient's head position and orientation, particularly addressing awake surgery requirements. The topology has been conceived in order to satisfy all the installation, functional and dynamic requirements. A kinetostatic optimization has been performed to obtain the actual geometric dimensions of the prototype currently being developed.
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A preliminary study of the effects of working memory training on brain function.
Stevens, Michael C; Gaynor, Alexandra; Bessette, Katie L; Pearlson, Godfrey D
2016-06-01
Working memory (WM) training improves WM ability in Attention-Deficit/Hyperactivity Disorder (ADHD), but its efficacy for non-cognitive ADHD impairments ADHD has been sharply debated. The purpose of this preliminary study was to characterize WM training-related changes in ADHD brain function and see if they were linked to clinical improvement. We examined 18 adolescents diagnosed with DSM-IV Combined-subtype ADHD before and after 25 sessions of WM training using a frequently employed approach (Cogmed™) using a nonverbal Sternberg WM fMRI task, neuropsychological tests, and participant- and parent-reports of ADHD symptom severity and associated functional impairment. Whole brain SPM8 analyses identified ADHD activation deficits compared to 18 non-ADHD control participants, then tested whether impaired ADHD frontoparietal brain activation would increase following WM training. Post hoc tests examined the relationships between neural changes and neurocognitive or clinical improvements. As predicted, WM training increased WM performance, ADHD clinical functioning, and WM-related ADHD brain activity in several frontal, parietal and temporal lobe regions. Increased left inferior frontal sulcus region activity was seen in all Encoding, Maintenance, and Retrieval Sternberg task phases. ADHD symptom severity improvements were most often positively correlated with activation gains in brain regions known to be engaged for WM-related executive processing; improvement of different symptom types had different neural correlates. The responsiveness of both amodal WM frontoparietal circuits and executive process-specific WM brain regions was altered by WM training. The latter might represent a promising, relatively unexplored treatment target for researchers seeking to optimize clinical response in ongoing ADHD WM training development efforts.
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Executive dysfunction, brain aging, and political leadership.
Fisher, Mark; Franklin, David L; Post, Jerrold M
2014-01-01
Decision-making is an essential component of executive function, and a critical skill of political leadership. Neuroanatomic localization studies have established the prefrontal cortex as the critical brain site for executive function. In addition to the prefrontal cortex, white matter tracts as well as subcortical brain structures are crucial for optimal executive function. Executive function shows a significant decline beginning at age 60, and this is associated with age-related atrophy of prefrontal cortex, cerebral white matter disease, and cerebral microbleeds. Notably, age-related decline in executive function appears to be a relatively selective cognitive deterioration, generally sparing language and memory function. While an individual may appear to be functioning normally with regard to relatively obvious cognitive functions such as language and memory, that same individual may lack the capacity to integrate these cognitive functions to achieve normal decision-making. From a historical perspective, global decline in cognitive function of political leaders has been alternatively described as a catastrophic event, a slowly progressive deterioration, or a relatively episodic phenomenon. Selective loss of executive function in political leaders is less appreciated, but increased utilization of highly sensitive brain imaging techniques will likely bring greater appreciation to this phenomenon. Former Israeli Prime Minister Ariel Sharon was an example of a political leader with a well-described neurodegenerative condition (cerebral amyloid angiopathy) that creates a neuropathological substrate for executive dysfunction. Based on the known neuroanatomical and neuropathological changes that occur with aging, we should probably assume that a significant proportion of political leaders over the age of 65 have impairment of executive function.
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Dynamic filtering improves attentional state prediction with fNIRS
Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.
2016-01-01
Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise – thereby increasing such state prediction accuracy – remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%). PMID:27231602
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Energy Intake and Exercise as Determinants of Brain Health and Vulnerability to Injury and Disease
Mattson, Mark P.
2012-01-01
Evolution favored individuals with superior cognitive and physical abilities under conditions of limited food sources, and brain function can therefore be optimized by intermittent dietary energy restriction (ER) and exercise. Such energetic challenges engage adaptive cellular stress response signaling pathways in neurons involving neurotrophic factors, protein chaperones, DNA repair proteins, autophagy and mitochondrial biogenesis. By suppressing adaptive cellular stress responses, overeating and a sedentary lifestyle may increase the risk of Alzheimer’s and Parkinson’s diseases, stroke, and depression. Intense concerted efforts of governments, families, schools and physicians will be required to successfully implement brain-healthy lifestyles that incorporate ER and exercise. PMID:23168220
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WINCS Harmoni: Closed-loop dynamic neurochemical control of therapeutic interventions
NASA Astrophysics Data System (ADS)
Lee, Kendall H.; Lujan, J. Luis; Trevathan, James K.; Ross, Erika K.; Bartoletta, John J.; Park, Hyung Ook; Paek, Seungleal Brian; Nicolai, Evan N.; Lee, Jannifer H.; Min, Hoon-Ki; Kimble, Christopher J.; Blaha, Charles D.; Bennet, Kevin E.
2017-04-01
There has been significant progress in understanding the role of neurotransmitters in normal and pathologic brain function. However, preclinical trials aimed at improving therapeutic interventions do not take advantage of real-time in vivo neurochemical changes in dynamic brain processes such as disease progression and response to pharmacologic, cognitive, behavioral, and neuromodulation therapies. This is due in part to a lack of flexible research tools that allow in vivo measurement of the dynamic changes in brain chemistry. Here, we present a research platform, WINCS Harmoni, which can measure in vivo neurochemical activity simultaneously across multiple anatomical targets to study normal and pathologic brain function. In addition, WINCS Harmoni can provide real-time neurochemical feedback for closed-loop control of neurochemical levels via its synchronized stimulation and neurochemical sensing capabilities. We demonstrate these and other key features of this platform in non-human primate, swine, and rodent models of deep brain stimulation (DBS). Ultimately, systems like the one described here will improve our understanding of the dynamics of brain physiology in the context of neurologic disease and therapeutic interventions, which may lead to the development of precision medicine and personalized therapies for optimal therapeutic efficacy.
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The maternal brain and its plasticity in humans
Kim, Pilyoung; Strathearn, Lane; Swain, James E.
2015-01-01
Early mother-infant relationships play important roles in infants’ optimal development. New mothers undergo neurobiological changes that support developing mother-infant relationships regardless of great individual differences in those relationships. In this article, we review the neural plasticity in human mothers’ brains based on functional magnetic resonance imaging (fMRI) studies. First, we review the neural circuits that are involved in establishing and maintaining mother-infant relationships. Second, we discuss early postpartum factors (e.g., birth and feeding methods, hormones, and parental sensitivity) that are associated with individual differences in maternal brain neuroplasticity. Third, we discuss abnormal changes in the maternal brain related to psychopathology (i.e., postpartum depression, posttraumatic stress disorder, substance abuse) and potential brain remodeling associated with interventions. Last, we highlight potentially important future research directions to better understand normative changes in the maternal brain and risks for abnormal changes that may disrupt early mother-infant relationships. PMID:26268151
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Intersubject synchronization of cortical activity during natural vision.
Hasson, Uri; Nir, Yuval; Levy, Ifat; Fuhrmann, Galit; Malach, Rafael
2004-03-12
To what extent do all brains work alike during natural conditions? We explored this question by letting five subjects freely view half an hour of a popular movie while undergoing functional brain imaging. Applying an unbiased analysis in which spatiotemporal activity patterns in one brain were used to "model" activity in another brain, we found a striking level of voxel-by-voxel synchronization between individuals, not only in primary and secondary visual and auditory areas but also in association cortices. The results reveal a surprising tendency of individual brains to "tick collectively" during natural vision. The intersubject synchronization consisted of a widespread cortical activation pattern correlated with emotionally arousing scenes and regionally selective components. The characteristics of these activations were revealed with the use of an open-ended "reverse-correlation" approach, which inverts the conventional analysis by letting the brain signals themselves "pick up" the optimal stimuli for each specialized cortical area.
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Structural bases for neurophysiological investigations of amygdaloid complex of the brain
NASA Astrophysics Data System (ADS)
Kalimullina, Liliya B.; Kalkamanov, Kh. A.; Akhmadeev, Azat V.; Zakharov, Vadim P.; Sharafullin, Ildus F.
2015-11-01
Amygdala (Am) as a part of limbic system of the brain defines such important functions as adaptive behavior of animals, formation of emotions and memory, regulation of endocrine and visceral functions. We worked out, with the help of mathematic modelling of the pattern recognition theory, principles for organization of neurophysiological and neuromorphological studies of Am nuclei, which take into account the existing heterogeneity of its formations and optimize, to a great extent, the protocol for carrying out of such investigations. The given scheme of studies of Am’s structural-functional organization at its highly-informative sections can be used as a guide for precise placement of electrodes’, cannulae’s and microsensors into particular Am nucleus in the brain with the registration not only the nucleus itself, but also its extensions. This information is also important for defining the number of slices covering specific Am nuclei which must be investigated to reveal the physiological role of a particular part of amygdaloid complex.
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Optimal-mass-transfer-based estimation of glymphatic transport in living brain
Zhu, Liangjia; Kolesov, Ivan; Nedergaard, Maiken; Benveniste, Helene; Tannenbaum, Allen
2016-01-01
It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the ‘glymphatic pathway’ plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs1,2. It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach3 to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data. PMID:26877579
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Connectomics and neuroticism: an altered functional network organization.
Servaas, Michelle N; Geerligs, Linda; Renken, Remco J; Marsman, Jan-Bernard C; Ormel, Johan; Riese, Harriëtte; Aleman, André
2015-01-01
The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organization in neuroticism. To this end, we applied graph theory on resting-state functional magnetic resonance imaging (fMRI) data in 120 women selected based on their neuroticism score. Binary and weighted brain-wide graphs were constructed to examine changes in the functional network structure and functional connectivity strength. Furthermore, graphs were partitioned into modules to specifically investigate connectivity within and between functional subnetworks related to emotion processing and cognitive control. Subsequently, complex network measures (ie, efficiency and modularity) were calculated on the brain-wide graphs and modules, and correlated with neuroticism scores. Compared with low neurotic individuals, high neurotic individuals exhibited a whole-brain network structure resembling more that of a random network and had overall weaker functional connections. Furthermore, in these high neurotic individuals, functional subnetworks could be delineated less clearly and the majority of these subnetworks showed lower efficiency, while the affective subnetwork showed higher efficiency. In addition, the cingulo-operculum subnetwork demonstrated more ties with other functional subnetworks in association with neuroticism. In conclusion, the 'neurotic brain' has a less than optimal functional network organization and shows signs of functional disconnectivity. Moreover, in high compared with low neurotic individuals, emotion and salience subnetworks have a more prominent role in the information exchange, while sensory(-motor) and cognitive control subnetworks have a less prominent role.
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Perinatal programming of emotional brain circuits: an integrative view from systems to molecules
Bock, Jörg; Rether, Kathy; Gröger, Nicole; Xie, Lan; Braun, Katharina
2014-01-01
Environmental influences such as perinatal stress have been shown to program the developing organism to adapt brain and behavioral functions to cope with daily life challenges. Evidence is now accumulating that the specific and individual effects of early life adversity on the functional development of brain and behavior emerge as a function of the type, intensity, timing and the duration of the adverse environment, and that early life stress (ELS) is a major risk factor for developing behavioral dysfunctions and mental disorders. Results from clinical as well as experimental studies in animal models support the hypothesis that ELS can induce functional “scars” in prefrontal and limbic brain areas, regions that are essential for emotional control, learning and memory functions. On the other hand, the concept of “stress inoculation” is emerging from more recent research, which revealed positive functional adaptations in response to ELS resulting in resilience against stress and other adversities later in life. Moreover, recent studies indicate that early life experiences and the resulting behavioral consequences can be transmitted to the next generation, leading to a transgenerational cycle of adverse or positive adaptations of brain function and behavior. In this review we propose a unifying view of stress vulnerability and resilience by connecting genetic predisposition and programming sensitivity to the context of experience-expectancy and transgenerational epigenetic traits. The adaptive maturation of stress responsive neural and endocrine systems requires environmental challenges to optimize their functions. Repeated environmental challenges can be viewed within the framework of the match/mismatch hypothesis, the outcome, psychopathology or resilience, depends on the respective predisposition and on the context later in life. PMID:24550772
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Cai, Lin; Dong, Qi; Niu, Haijing
2018-04-01
Early childhood (7-8 years old) and early adolescence (11-12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological changes in network organization during development from early childhood and early adolescence to adulthood. Our results showed that the properties of small-worldness and modularity were not significantly different across development, demonstrating the developmental maturity of important functional brain organization in early childhood. Intriguingly, young children had a significantly lower global efficiency than early adolescents and adults, which revealed that the integration of the distributed networks strengthens across the developmental stages underlying cognitive development. Moreover, local efficiency of young children and adolescents was significantly lower than that of adults, while there was no difference between these two younger groups. This finding demonstrated that functional segregation remained relatively steady from early childhood to early adolescence, and the brain in these developmental periods possesses no optimal network configuration. Furthermore, we found heterogeneous developmental patterns in the regional nodal properties in various brain regions, such as linear increased nodal properties in the frontal cortex, indicating increasing cognitive capacity over development. Collectively, our results demonstrated that significant topological changes in functional network organization occurred during these two critical developmental stages, and provided a novel insight into elucidating subtle changes in brain functional networks across development. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Czuba, Ewelina; Steliga, Aleksandra; Lietzau, Grażyna; Kowiański, Przemysław
2017-08-01
The brain, demanding constant level of cholesterol, precisely controls its synthesis and homeostasis. The brain cholesterol pool is almost completely separated from the rest of the body by the functional blood-brain barrier (BBB). Only a part of cholesterol pool can be exchanged with the blood circulation in the form of the oxysterol metabolites such, as 27-hydroxycholesterol (27-OHC) and 24S-hydroxycholesterol (24S-OHC). Not only neurons but also blood vessels and neuroglia, constituting neurovascular unit (NVU), are crucial for the brain cholesterol metabolism and undergo precise regulation by numerous modulators, metabolites and signal molecules. In physiological conditions maintaining the optimal cholesterol concentration is important for the energetic metabolism, composition of cell membranes and myelination. However, a growing body of evidence indicates the consequences of the cholesterol homeostasis dysregulation in several pathophysiological processes. There is a causal relationship between hypercholesterolemia and 1) development of type 2 diabetes due to long-term high-fat diet consumption, 2) significance of the oxidative stress consequences for cerebral amyloid angiopathy and neurodegenerative diseases, 3) insulin resistance on progression of the neurodegenerative brain diseases. In this review, we summarize the current state of knowledge concerning the cholesterol influence upon functioning of the NVU under physiological and pathological conditions.
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Supervised dictionary learning for inferring concurrent brain networks.
Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming
2015-10-01
Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.
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Deatrick, Janet A; Thibodeaux, Annaka G; Mooney, Kim; Schmus, Cynthia; Pollack, Rosanna; Davey, Barbara Hieb
2006-01-01
Qualitative studies of families with children who have cancer or other serious illnesses have found that families often come to view their child and their lives as normal. They manage illness-related demands using family management styles that sustain usual patterns of family and child functioning. Few studies have addressed the family management styles of families who express less satisfaction with family and child functioning or who are identified by health care professionals as having difficulty with family functioning. Such families are likely to be overrepresented among those whose children are being treated for brain tumors that entail extremely burdensome treatments as well as a range of unfavorable prognoses and long-term sequelae. In fact, little is known about how these families manage on a day-to-day basis and how the interdisciplinary team can best provide supportive care to optimize their functioning. The purpose of this article is to present the Family Management Styles Framework as a tool that is useful in both clinical practice and research for assessing families who have children with cancer, including those with brain tumors.
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Dai, Shengfa; Wei, Qingguo
2017-01-01
Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. A population of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.
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Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming
2017-10-01
Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.
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Parker, Jason G; Zalusky, Eric J; Kirbas, Cemil
2014-01-01
Background Accurate mapping of visual function and selective attention using fMRI is important in the study of human performance as well as in presurgical treatment planning of lesions in or near visual centers of the brain. Conjunctive visual search (CVS) is a useful tool for mapping visual function during fMRI because of its greater activation extent compared with high-capacity parallel search processes. Aims The purpose of this work was to develop and evaluate a CVS that was capable of generating consistent activation in the basic and higher level visual areas of the brain by using a high number of distractors as well as an optimized contrast condition. Materials and methods Images from 10 healthy volunteers were analyzed and brain regions of greatest activation and deactivation were determined using a nonbiased decomposition of the results at the hemisphere, lobe, and gyrus levels. The results were quantified in terms of activation and deactivation extent and mean z-statistic. Results The proposed CVS was found to generate robust activation of the occipital lobe, as well as regions in the middle frontal gyrus associated with coordinating eye movements and in regions of the insula associated with task-level control and focal attention. As expected, the task demonstrated deactivation patterns commonly implicated in the default-mode network. Further deactivation was noted in the posterior region of the cerebellum, most likely associated with the formation of optimal search strategy. Conclusion We believe the task will be useful in studies of visual and selective attention in the neuroscience community as well as in mapping visual function in clinical fMRI. PMID:24683515
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Yang, Zhongqin; Hu, Bihe; Zhang, Yuhui; Luo, Qingming; Gong, Hui
2013-01-01
Fluorescent proteins serve as important biomarkers for visualizing both subcellular organelles in living cells and structural and functional details in large-volume tissues or organs. However, current techniques for plastic embedding are limited in their ability to preserve fluorescence while remaining suitable for micro-optical sectioning tomography of large-volume samples. In this study, we quantitatively evaluated the fluorescence preservation and penetration time of several commonly used resins in a Thy1-eYFP-H transgenic whole mouse brain, including glycol methacrylate (GMA), LR White, hydroxypropyl methacrylate (HPMA) and Unicryl. We found that HMPA embedding doubled the eYFP fluorescence intensity but required long durations of incubation for whole brain penetration. GMA, Unicryl and LR White each penetrated the brain rapidly but also led to variable quenching of eYFP fluorescence. Among the fast-penetrating resins, GMA preserved fluorescence better than LR White and Unicryl. We found that we could optimize the GMA formulation by reducing the polymerization temperature, removing 4-methoxyphenol and adjusting the pH of the resin solution to be alkaline. By optimizing the GMA formulation, we increased percentage of eYFP fluorescence preservation in GMA-embedded brains nearly two-fold. These results suggest that modified GMA is suitable for embedding large-volume tissues such as whole mouse brain and provide a novel approach for visualizing brain-wide networks. PMID:23577174
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Duquette, Lucien L; Mattiace, Frank; Blum, Kenneth; Waite, Roger L; Boland, Teresa; McLaughlin, Thomas; Dushaj, Kristina; Febo, Marcelo; Badgaiyan, Rajendra D
2016-01-01
With neurogenetic and epigenetic tools utilized in research and neuroimaging, we are unraveling the mysteries of brain function, especially as it relates to Reward Deficiency (RDS). We encourage the development of pharmaceuticals or nutraceuticals that promote a reduction in dopamine resistance and balance brain neurochemistry, leading to dopamine homeostasis. We disclose self-assessment of a highly functional professional under work-related stress following KB220Z use, a liquid (aqua) nano glutaminergic-dopaminergic optimization complex (GDOC). Subject took GDOC for one month. Subject self-administered GDOC using one-half-ounce twice a day. During first three days, unique brain activation occurred; resembling white noise after 30 minutes and sensation was strong for 45 minutes and then dissipated. He described effect as if his eyesight improved slightly and pointed out that his sense of smell and sleep greatly improved. Subject experienced a calming effect similar to meditation that could be linked to dopamine release. He also reported control of going over the edge after a hard day's work, which was coupled with a slight increase in energy, increased motivation to work, increased focus and multi-tasking, with clearer purpose of task at hand. Subject felt less inhibited in a social setting and suggested Syndrome that GDOC increased his Behavior Activating System (reward), while having a decrease in the Behavior Inhibition System (caution). These results and other related studies reveal an improved mood, work-related focus, and sleep. These effects as a subjective feeling of brain activation maybe due to direct or indirect dopaminergic interaction. While this case is encouraging, we must await more research in a larger randomized placebo-controlled study to map the role of GDOC, especially in a nano-sized product, to determine the possible effects on circuit inhibitory control and memory banks and the induction of dopamine homeostasis independent of either hypo- or hyper-dopaminergic traits/states.
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Robert's Rules for Optimal Learning: Model Development, Field Testing, Implications!
ERIC Educational Resources Information Center
McGinty, Robert L.
The value of accelerated learning techniques developed by the national organization for Suggestive Accelerated Learning Techniques (SALT) was tested in a study using Administrative Policy students taking the capstone course in the Eastern Washington University School of Business. Educators have linked the brain and how it functions to various…
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USDA-ARS?s Scientific Manuscript database
Brain function depends on a continuous supply of nutrients, including micronutrients and fatty acids. Pregnancy and postpartum (pp) are periods of increased nutrient demands, during which optimal maternal cognition is important to prepare for a healthy birth and care for a young infant. However, few...
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Gallos, Lazaros K; Makse, Hernán A; Sigman, Mariano
2012-02-21
The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are "large-world" self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the "strength of weak ties" crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain.
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Gallos, Lazaros K.; Makse, Hernán A.; Sigman, Mariano
2012-01-01
The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are “large-world” self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the “strength of weak ties” crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain. PMID:22308319
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Sensorimotor Training in Virtual Reality: A Review
Adamovich, Sergei V.; Fluet, Gerard G.; Tunik, Eugene; Merians, Alma S.
2010-01-01
Recent experimental evidence suggests that rapid advancement of virtual reality (VR) technologies has great potential for the development of novel strategies for sensorimotor training in neurorehabilitation. We discuss what the adaptive and engaging virtual environments can provide for massive and intensive sensorimotor stimulation needed to induce brain reorganization. Second, discrepancies between the veridical and virtual feedback can be introduced in VR to facilitate activation of targeted brain networks, which in turn can potentially speed up the recovery process. Here we review the existing experimental evidence regarding the beneficial effects of training in virtual environments on the recovery of function in the areas of gait, upper extremity function and balance, in various patient populations. We also discuss possible mechanisms underlying these effects. We feel that future research in the area of virtual rehabilitation should follow several important paths. Imaging studies to evaluate the effects of sensory manipulation on brain activation patterns and the effect of various training parameters on long term changes in brain function are needed to guide future clinical inquiry. Larger clinical studies are also needed to establish the efficacy of sensorimotor rehabilitation using VR approaches in various clinical populations and most importantly, to identify VR training parameters that are associated with optimal transfer into real-world functional improvements. PMID:19713617
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Freund, Jane E; Stetts, Deborah M
2010-10-01
The purpose of this study is to describe the effects of trunk stabilization training and locomotor training (LT) using body-weight support on a treadmill (BWST) and overground walking on balance, gait, self-reported function, and trunk muscle performance in an adult with severe ataxia secondary to brain injury. There are no studies on the effectiveness of these combined interventions in persons with ataxia. The subject was a 23-year-old male who had a traumatic brain injury 13 months prior. An A-B-A withdrawal single-system design was used. Outcome measures were Berg Balance Test (BBT), timed unsupported stance, Functional Ambulation Category (FAC), 10-meter walk test (10-MWT), Outpatient Physical Therapy Improvement in Movement Assessment Log (OPTIMAL), transverse abdominis (TrA) thickness, and isometric trunk endurance tests. Performance on the BBT, timed unsupported stance, FAC, 10-MWT, and OPTIMAL each improved after 10 weeks of intervention. In additions, TrA symmetry at rest improved as did right side-bridge endurance time. LT, using BWST and overground walking, and trunk stabilization training may be effective in improving balance, gait, function, and trunk performance in individuals with severe ataxia. Further research with additional subjects is indicated.
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Structured illumination diffuse optical tomography for noninvasive functional neuroimaging in mice.
Reisman, Matthew D; Markow, Zachary E; Bauer, Adam Q; Culver, Joseph P
2017-04-01
Optical intrinsic signal (OIS) imaging has been a powerful tool for capturing functional brain hemodynamics in rodents. Recent wide field-of-view implementations of OIS have provided efficient maps of functional connectivity from spontaneous brain activity in mice. However, OIS requires scalp retraction and is limited to superficial cortical tissues. Diffuse optical tomography (DOT) techniques provide noninvasive imaging, but previous DOT systems for rodent neuroimaging have been limited either by sparse spatial sampling or by slow speed. Here, we develop a DOT system with asymmetric source-detector sampling that combines the high-density spatial sampling (0.4 mm) detection of a scientific complementary metal-oxide-semiconductor camera with the rapid (2 Hz) imaging of a few ([Formula: see text]) structured illumination (SI) patterns. Analysis techniques are developed to take advantage of the system's flexibility and optimize trade-offs among spatial sampling, imaging speed, and signal-to-noise ratio. An effective source-detector separation for the SI patterns was developed and compared with light intensity for a quantitative assessment of data quality. The light fall-off versus effective distance was also used for in situ empirical optimization of our light model. We demonstrated the feasibility of this technique by noninvasively mapping the functional response in the somatosensory cortex of the mouse following electrical stimulation of the forepaw.
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EEG functional connectivity, axon delays and white matter disease.
Nunez, Paul L; Srinivasan, Ramesh; Fields, R Douglas
2015-01-01
Both structural and functional brain connectivities are closely linked to white matter disease. We discuss several such links of potential interest to neurologists, neurosurgeons, radiologists, and non-clinical neuroscientists. Treatment of brains as genuine complex systems suggests major emphasis on the multi-scale nature of brain connectivity and dynamic behavior. Cross-scale interactions of local, regional, and global networks are apparently responsible for much of EEG's oscillatory behaviors. Finite axon propagation speed, often assumed to be infinite in local network models, is central to our conceptual framework. Myelin controls axon speed, and the synchrony of impulse traffic between distant cortical regions appears to be critical for optimal mental performance and learning. Several experiments suggest that axon conduction speed is plastic, thereby altering the regional and global white matter connections that facilitate binding of remote local networks. Combined EEG and high resolution EEG can provide distinct multi-scale estimates of functional connectivity in both healthy and diseased brains with measures like frequency and phase spectra, covariance, and coherence. White matter disease may profoundly disrupt normal EEG coherence patterns, but currently these kinds of studies are rare in scientific labs and essentially missing from clinical environments. Copyright © 2014 International Federation of Clinical Neurophysiology. All rights reserved.
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NASA Astrophysics Data System (ADS)
Maugeri, L.; Moraschi, M.; Summers, P.; Favilla, S.; Mascali, D.; Cedola, A.; Porro, C. A.; Giove, F.; Fratini, M.
2018-02-01
Functional Magnetic Resonance Imaging (fMRI) based on Blood Oxygenation Level Dependent (BOLD) contrast has become one of the most powerful tools in neuroscience research. On the other hand, fMRI approaches have seen limited use in the study of spinal cord and subcortical brain regions (such as the brainstem and portions of the diencephalon). Indeed obtaining good BOLD signal in these areas still represents a technical and scientific challenge, due to poor control of physiological noise and to a limited overall quality of the functional series. A solution can be found in the combination of optimized experimental procedures at acquisition stage, and well-adapted artifact mitigation procedures in the data processing. In this framework, we studied two different data processing strategies to reduce physiological noise in cortical and subcortical brain regions and in the spinal cord, based on the aCompCor and RETROICOR denoising tools respectively. The study, performed in healthy subjects, was carried out using an ad hoc isometric motor task. We observed an increased signal to noise ratio in the denoised functional time series in the spinal cord and in the subcortical brain region.
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High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image
NASA Astrophysics Data System (ADS)
Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore
2016-03-01
Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.
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ENDOCRINE DISRUPTORS AS A THREAT TO NEUROLOGICAL FUNCTION
Weiss, Bernard
2011-01-01
Endocrine disruption is a concept and principle whose origins can be traced to the beginnings of the environmental movement in the 1960s. It began with puzzlement about and the flaring of research on the decline of wildlife, particularly avian species. The proposed causes accented pesticides, especially persistent organochlorines such as DDT. Its scope gradually widened beyond pesticides, and, as endocrine disruption offered an explanation for the wildlife phenomena, it seemed to explain, as well, changes in fertility and disorders of male reproduction such as testicular cancer. Once disturbed gonadal hormone function became the most likely explanation, it provoked other questions. The most challenging arose because of how critical gonadal hormones are to brain function, especially as determinants of brain sexual differentiation. Pursuit of such connections has generated a robust literature embracing a broad swath of chemical classes. How endocrine disrupting chemicals influence the adult and aging brain is a question, so far mostly ignored because of the emphasis on early development, that warrants vigorous investigation. Gonadal hormones are crucial to optimal brain function during maturity and even senescence. They are pivotal to the processes of neurogenesis. They exert protective actions against neurodegenerative disorders such as dementia and support smoothly functioning cognitive activities. The limited research conducted so far on endocrine disruptors, aging, and neurogenesis argues that they should be overlooked no longer. PMID:21474148
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Stevens, Michael C.; Gaynor, Alexandra; Bessette, Katie L.; Pearlson, Godfrey D.
2015-01-01
Working memory (WM) training improves WM ability in Attention-Deficit/Hyperactivity Disorder (ADHD), but its efficacy for non-cognitive ADHD impairments ADHD has been sharply debated. The purpose of this preliminary study was to characterize WM training-related changes in ADHD brain function and see if they were linked to clinical improvement. We examined 18 adolescents diagnosed with DSM-IV Combined-subtype ADHD before and after 25 sessions of WM training using a frequently employed approach (CogmedTM) using a nonverbal Sternberg WM fMRI task, neuropsychological tests, and participant- and parent-reports of ADHD symptom severity and associated functional impairment. Whole brain SPM8 analyses identified ADHD activation deficits compared to 18 non-ADHD control participants, then tested whether impaired ADHD frontoparietal brain activation would increase following WM training. Post hoc tests examined the relationships between neural changes and neurocognitive or clinical improvements. As predicted, WM training increased WM performance, ADHD clinical functioning, and WM-related ADHD brain activity in several frontal, parietal and temporal lobe regions. Increased left inferior frontal sulcus region activity was seen in all Encoding, Maintenance, and Retrieval Sternberg task phases. ADHD symptom severity improvements were most often positively correlated with activation gains in brain regions known to be engaged for WM-related executive processing; improvement of different symptom types had different neural correlates. The responsiveness of both amodal WM frontoparietal circuits and executive process-specific WM brain regions was altered by WM training. The latter might represent a promising, relatively unexplored treatment target for researchers seeking to optimize clinical response in ongoing ADHD WM training development efforts. PMID:26138580
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Löscher, Wolfgang; Cole, Andrew J; McLean, Michael J
2009-04-01
Physical approaches for the treatment of epilepsy currently under study or development include electrical or magnetic brain stimulators and cooling devices, each of which may be implanted or applied externally. Some devices may stimulate peripheral structures, whereas others may be implanted directly into the brain. Stimulation may be delivered chronically, intermittently, or in response to either manual activation or computer-based detection of events of interest. Physical approaches may therefore ultimately be appropriate for seizure prophylaxis by causing a modification of the underlying substrate, presumably with a reduction in the intrinsic excitability of cerebral structures, or for seizure termination, by interfering with the spontaneous discharge of pathological neuronal networks. Clinical trials of device-based therapies are difficult due to ethical issues surrounding device implantation, problems with blinding, potential carryover effects that may occur in crossover designs if substrate modification occurs, and subject heterogeneity. Unresolved issues in the development of physical treatments include optimization of stimulation parameters, identification of the optimal volume of brain to be stimulated, development of adequate power supplies to stimulate the necessary areas, and a determination that stimulation itself does not promote epileptogenesis or adverse long-term effects on normal brain function.
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Evidence for composite cost functions in arm movement planning: an inverse optimal control approach.
Berret, Bastien; Chiovetto, Enrico; Nori, Francesco; Pozzo, Thierry
2011-10-01
An important issue in motor control is understanding the basic principles underlying the accomplishment of natural movements. According to optimal control theory, the problem can be stated in these terms: what cost function do we optimize to coordinate the many more degrees of freedom than necessary to fulfill a specific motor goal? This question has not received a final answer yet, since what is optimized partly depends on the requirements of the task. Many cost functions were proposed in the past, and most of them were found to be in agreement with experimental data. Therefore, the actual principles on which the brain relies to achieve a certain motor behavior are still unclear. Existing results might suggest that movements are not the results of the minimization of single but rather of composite cost functions. In order to better clarify this last point, we consider an innovative experimental paradigm characterized by arm reaching with target redundancy. Within this framework, we make use of an inverse optimal control technique to automatically infer the (combination of) optimality criteria that best fit the experimental data. Results show that the subjects exhibited a consistent behavior during each experimental condition, even though the target point was not prescribed in advance. Inverse and direct optimal control together reveal that the average arm trajectories were best replicated when optimizing the combination of two cost functions, nominally a mix between the absolute work of torques and the integrated squared joint acceleration. Our results thus support the cost combination hypothesis and demonstrate that the recorded movements were closely linked to the combination of two complementary functions related to mechanical energy expenditure and joint-level smoothness.
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Patterson, Susan L
2015-09-01
Older individuals often experience declines in cognitive function after events (e.g. infection, or injury) that trigger activation of the immune system. This occurs at least in part because aging sensitizes the response of microglia (the brain's resident immune cells) to signals triggered by an immune challenge. In the aging brain, microglia respond to these signals by producing more pro-inflammatory cytokines (e.g. interleukin-1beta or IL-1β) and producing them for longer than microglia in younger brains. This exaggerated inflammatory response can compromise processes critical for optimal cognitive functioning. Interleukin-1β is central to the inflammatory response and is a key mediator and modulator of an array of associated biological functions; thus its production and release is usually very tightly regulated. This review will focus on the impact of dysregulated production of IL-1β on hippocampus dependent-memory systems and associated synaptic plasticity processes. The neurotrophin brain-derived neurotrophic factor (BNDF) helps to protect neurons from damage caused by infection or injury, and it plays a critical role in many of the same memory and hippocampal plasticity processes compromised by dysregulated production of IL-1β. This suggests that an exaggerated brain inflammatory response, arising from aging and a secondary immune challenge, may erode the capacity to provide the BDNF needed for memory-related plasticity processes at hippocampal synapses. This article is part of a Special Issue entitled 'Neuroimmunology and Synaptic Function'. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Moran, Richard; Smith, Joshua H; García, José J
2014-11-28
The mechanical properties of human brain tissue are the subject of interest because of their use in understanding brain trauma and in developing therapeutic treatments and procedures. To represent the behavior of the tissue, we have developed hyperelastic mechanical models whose parameters are fitted in accordance with experimental test results. However, most studies available in the literature have fitted parameters with data of a single type of loading, such as tension, compression, or shear. Recently, Jin et al. (Journal of Biomechanics 46:2795-2801, 2013) reported data from ex vivo tests of human brain tissue under tension, compression, and shear loading using four strain rates and four different brain regions. However, they do not report parameters of energy functions that can be readily used in finite element simulations. To represent the tissue behavior for the quasi-static loading conditions, we aimed to determine the best fit of the hyperelastic parameters of the hyperfoam, Ogden, and polynomial strain energy functions available in ABAQUS for the low strain rate data, while simultaneously considering all three loading modes. We used an optimization process conducted in MATLAB, calling iteratively three finite element models developed in ABAQUS that represent the three loadings. Results showed a relatively good fit to experimental data in all loading modes using two terms in the energy functions. Values for the shear modulus obtained in this analysis (897-1653Pa) are in the range of those presented in other studies. These energy-function parameters can be used in brain tissue simulations using finite element models. Copyright © 2014 Elsevier Ltd. All rights reserved.
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High-intensity interval exercise and cerebrovascular health: curiosity, cause, and consequence
Lucas, Samuel J E; Cotter, James D; Brassard, Patrice; Bailey, Damian M
2015-01-01
Exercise is a uniquely effective and pluripotent medicine against several noncommunicable diseases of westernised lifestyles, including protection against neurodegenerative disorders. High-intensity interval exercise training (HIT) is emerging as an effective alternative to current health-related exercise guidelines. Compared with traditional moderate-intensity continuous exercise training, HIT confers equivalent if not indeed superior metabolic, cardiac, and systemic vascular adaptation. Consequently, HIT is being promoted as a more time-efficient and practical approach to optimize health thereby reducing the burden of disease associated with physical inactivity. However, no studies to date have examined the impact of HIT on the cerebrovasculature and corresponding implications for cognitive function. This review critiques the implications of HIT for cerebrovascular function, with a focus on the mechanisms and translational impact for patient health and well-being. It also introduces similarly novel interventions currently under investigation as alternative means of accelerating exercise-induced cerebrovascular adaptation. We highlight a need for studies of the mechanisms and thereby also the optimal dose-response strategies to guide exercise prescription, and for studies to explore alternative approaches to optimize exercise outcomes in brain-related health and disease prevention. From a clinical perspective, interventions that selectively target the aging brain have the potential to prevent stroke and associated neurovascular diseases. PMID:25833341
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High-intensity interval exercise and cerebrovascular health: curiosity, cause, and consequence.
Lucas, Samuel J E; Cotter, James D; Brassard, Patrice; Bailey, Damian M
2015-06-01
Exercise is a uniquely effective and pluripotent medicine against several noncommunicable diseases of westernised lifestyles, including protection against neurodegenerative disorders. High-intensity interval exercise training (HIT) is emerging as an effective alternative to current health-related exercise guidelines. Compared with traditional moderate-intensity continuous exercise training, HIT confers equivalent if not indeed superior metabolic, cardiac, and systemic vascular adaptation. Consequently, HIT is being promoted as a more time-efficient and practical approach to optimize health thereby reducing the burden of disease associated with physical inactivity. However, no studies to date have examined the impact of HIT on the cerebrovasculature and corresponding implications for cognitive function. This review critiques the implications of HIT for cerebrovascular function, with a focus on the mechanisms and translational impact for patient health and well-being. It also introduces similarly novel interventions currently under investigation as alternative means of accelerating exercise-induced cerebrovascular adaptation. We highlight a need for studies of the mechanisms and thereby also the optimal dose-response strategies to guide exercise prescription, and for studies to explore alternative approaches to optimize exercise outcomes in brain-related health and disease prevention. From a clinical perspective, interventions that selectively target the aging brain have the potential to prevent stroke and associated neurovascular diseases.
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The Neuroprotective Aspects of Sleep.
Eugene, Andy R; Masiak, Jolanta
2015-03-01
Sleep is an important component of human life, yet many people do not understand the relationship between the brain and the process of sleeping. Sleep has been proven to improve memory recall, regulate metabolism, and reduce mental fatigue. A minimum of 7 hours of daily sleep seems to be necessary for proper cognitive and behavioral function. The emotional and mental handicaps associated with chronic sleep loss as well as the highly hazardous situations which can be contributed to the lack of sleep is a serious concern that people need to be aware of. When one sleeps, the brain reorganizes and recharges itself, and removes toxic waste byproducts which have accumulated throughout the day. This evidence demonstrates that sleeping can clear the brain and help maintain its normal functioning. Multiple studies have been done to determine the effects of total sleep deprivation; more recently some have been conducted to show the effects of sleep restriction, which is a much more common occurrence, have the same effects as total sleep deprivation. Each phase of the sleep cycle restores and rejuvenates the brain for optimal function. When sleep is deprived, the active process of the glymphatic system does not have time to perform that function, so toxins can build up, and the effects will become apparent in cognitive abilities, behavior, and judgment. As a background for this paper we have reviewed literature and research of sleep phases, effects of sleep deprivation, and the glymphatic system of the brain and its restorative effect during the sleep cycle.
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The Neuroprotective Aspects of Sleep
Eugene, Andy R.; Masiak, Jolanta
2015-01-01
Sleep is an important component of human life, yet many people do not understand the relationship between the brain and the process of sleeping. Sleep has been proven to improve memory recall, regulate metabolism, and reduce mental fatigue. A minimum of 7 hours of daily sleep seems to be necessary for proper cognitive and behavioral function. The emotional and mental handicaps associated with chronic sleep loss as well as the highly hazardous situations which can be contributed to the lack of sleep is a serious concern that people need to be aware of. When one sleeps, the brain reorganizes and recharges itself, and removes toxic waste byproducts which have accumulated throughout the day. This evidence demonstrates that sleeping can clear the brain and help maintain its normal functioning. Multiple studies have been done to determine the effects of total sleep deprivation; more recently some have been conducted to show the effects of sleep restriction, which is a much more common occurrence, have the same effects as total sleep deprivation. Each phase of the sleep cycle restores and rejuvenates the brain for optimal function. When sleep is deprived, the active process of the glymphatic system does not have time to perform that function, so toxins can build up, and the effects will become apparent in cognitive abilities, behavior, and judgment. As a background for this paper we have reviewed literature and research of sleep phases, effects of sleep deprivation, and the glymphatic system of the brain and its restorative effect during the sleep cycle. PMID:26594659
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Wiley, N C; Dinan, T G; Ross, R P; Stanton, C; Clarke, G; Cryan, J F
2017-07-01
The brain-gut-microbiota axis comprises an extensive communication network between the brain, the gut, and the microbiota residing there. Development of a diverse gut microbiota is vital for multiple features of behavior and physiology, as well as many fundamental aspects of brain structure and function. Appropriate early-life assembly of the gut microbiota is also believed to play a role in subsequent emotional and cognitive development. If the composition, diversity, or assembly of the gut microbiota is impaired, this impairment can have a negative impact on host health and lead to disorders such as obesity, diabetes, inflammatory diseases, and even potentially neuropsychiatric illnesses, including anxiety and depression. Therefore, much research effort in recent years has focused on understanding the potential of targeting the intestinal microbiota to prevent and treat such disorders. This review aims to explore the influence of the gut microbiota on host neural function and behavior, particularly those of relevance to stress-related disorders. The involvement of microbiota in diverse neural functions such as myelination, microglia function, neuronal morphology, and blood-brain barrier integrity across the life span, from early life to adolescence to old age, will also be discussed. Nurturing an optimal gut microbiome may also prove beneficial in animal science as a means to manage stressful situations and to increase productivity of farm animals. The implications of these observations are manifold, and researchers are hopeful that this promising body of preclinical work can be successfully translated to the clinic and beyond.
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Komanapalli, Esther S; Sherchan, Prativa; Rolland, William; Khatibi, Nikan; Martin, Robert D; Applegate, Richard L; Tang, Jiping; Zhang, John H
2016-01-01
Neurosurgical procedures can damage viable brain tissue unintentionally by a wide range of mechanisms. This surgically induced brain injury (SBI) can be a result of direct incision, electrocauterization, or tissue retraction. Plasmin, a serine protease that dissolves fibrin blood clots, has been shown to enhance cerebral edema and hemorrhage accumulation in the brain through disruption of the blood brain barrier. Epsilon aminocaproic acid (EAA), a recognized antifibrinolytic lysine analogue, can reduce the levels of active plasmin and, in doing so, potentially can preserve the neurovascular unit of the brain. We investigated the role of EAA as a pretreatment neuroprotective modality in a SBI rat model, hypothesizing that EAA therapy would protect brain tissue integrity, translating into preserved neurobehavioral function. Male Sprague-Dawley rats were randomly assigned to one of four groups: sham (n = 7), SBI (n = 7), SBI with low-dose EAA, 150 mg/kg (n = 7), and SBI with high-dose EAA, 450 mg/kg (n = 7). SBI was induced by partial right frontal lobe resection through a frontal craniotomy. Postoperative assessment at 24 h included neurobehavioral testing and measurement of brain water content. Results at 24 h showed both low- and high-dose EAA reduced brain water content and improved neurobehavioral function compared with the SBI groups. This suggests that EAA may be a useful pretherapeutic modality for SBI. Further studies are needed to clarify optimal therapeutic dosing and to identify mechanisms of neuroprotection in rat SBI models.
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Brain Imaging and Human Nutrition: Which Measures to Use in Intervention Studies?12
Sizonenko, Stéphane V.; Babiloni, Claudio; Sijben, John W.; Walhovd, Kristine B.
2013-01-01
Throughout the life span, the brain is a metabolically highly active organ that uses a large proportion of total nutrient and energy intake. Furthermore, the development and repair of neural tissue depend on the proper intake of essential structural nutrients, minerals, and vitamins. Therefore, what we eat, or refrain from eating, may have an important impact on our cognitive ability and mental performance. Two of the key areas in which diet is thought to play an important role are in optimizing neurodevelopment in children and in preventing neurodegeneration and cognitive decline during aging. From early development to aging, brain imaging can detect structural, functional, and metabolic changes in humans and modifications due to altered nutrition or to additional nutritional supplementation. Inclusion of imaging measures in clinical studies can increase understanding with regard to the modification of brain structure, metabolism, and functional endpoints and may provide early sensitive measures of long-term effects. In this symposium, the utility of existing brain imaging technologies to assess the effects of nutritional intervention in humans is described. Examples of current research showing the utility of these markers are reviewed. PMID:24038255
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Incorporating Movement with Fluency Instruction: A Motivation for Struggling Readers
ERIC Educational Resources Information Center
Peebles, Jodi L.
2007-01-01
This article discusses two activities--Readers Theatre and Rhythm Walks--that encourage students to "get moving" with fluency instruction. Movement can be a motivating factor for struggling students, as well as a kinesthetic tool for conceptualizing the rhythm and flow of fluent reading while triggering brain function for optimal learning. Also…
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Wang, J; Hao, Z; Wang, H
2018-01-01
The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI) data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC). The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.
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NASA Astrophysics Data System (ADS)
Chu, Yong; Chen, Ya-Fang; Su, Min-Ying; Nalcioglu, Orhan
2005-04-01
Image segmentation is an essential process for quantitative analysis. Segmentation of brain tissues in magnetic resonance (MR) images is very important for understanding the structural-functional relationship for various pathological conditions, such as dementia vs. normal brain aging. Different brain regions are responsible for certain functions and may have specific implication for diagnosis. Segmentation may facilitate the analysis of different brain regions to aid in early diagnosis. Region competition has been recently proposed as an effective method for image segmentation by minimizing a generalized Bayes/MDL criterion. However, it is sensitive to initial conditions - the "seeds", therefore an optimal choice of "seeds" is necessary for accurate segmentation. In this paper, we present a new skeleton-based region competition algorithm for automated gray and white matter segmentation. Skeletons can be considered as good "seed regions" since they provide the morphological a priori information, thus guarantee a correct initial condition. Intensity gradient information is also added to the global energy function to achieve a precise boundary localization. This algorithm was applied to perform gray and white matter segmentation using simulated MRI images from a realistic digital brain phantom. Nine different brain regions were manually outlined for evaluation of the performance in these separate regions. The results were compared to the gold-standard measure to calculate the true positive and true negative percentages. In general, this method worked well with a 96% accuracy, although the performance varied in different regions. We conclude that the skeleton-based region competition is an effective method for gray and white matter segmentation.
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Brenna, J Thomas; Carlson, Susan E
2014-12-01
Humans evolved a uniquely large brain among terrestrial mammals. Brain and nervous tissue is rich in the omega-3 polyunsaturated fatty acid (PUFA) docosahexaenoic acid (DHA). Docosahexaenoic acid is required for lower and high order functions in humans because of understood and emerging molecular mechanisms. Among brain components that depend on dietary components, DHA is limiting because its synthesis from terrestrial plant food precursors is low but its utilization when consumed in diet is very efficient. Negligible DHA is found in terrestrial plants, but in contrast, DHA is plentiful at the shoreline where it is made by single-celled organisms and plants, and in the seas supports development of very large marine mammal brains. Modern human brains accumulate DHA up to age 18, most aggressively from about half-way through gestation to about two years of age. Studies in modern humans and non-human primates show that modern infants consuming infant formulas that include only DHA precursors have lower DHA levels than for those with a source of preformed DHA. Functional measures show that infants consuming preformed DHA have improved visual and cognitive function. Dietary preformed DHA in the breast milk of modern mothers supports many-fold greater breast milk DHA than is found in the breast milk of vegans, a phenomenon linked to consumption of shore-based foods. Most current evidence suggests that the DHA-rich human brain required an ample and sustained source of dietary DHA to reach its full potential. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Bhagat, Nikunj A.; Venkatakrishnan, Anusha; Abibullaev, Berdakh; Artz, Edward J.; Yozbatiran, Nuray; Blank, Amy A.; French, James; Karmonik, Christof; Grossman, Robert G.; O'Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.
2016-01-01
This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration. PMID:27065787
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Mascheretti, S; De Luca, A; Trezzi, V; Peruzzo, D; Nordio, A; Marino, C; Arrigoni, F
2017-01-01
Developmental dyslexia (DD) is a complex neurodevelopmental deficit characterized by impaired reading acquisition, in spite of adequate neurological and sensorial conditions, educational opportunities and normal intelligence. Despite the successful characterization of DD-susceptibility genes, we are far from understanding the molecular etiological pathways underlying the development of reading (dis)ability. By focusing mainly on clinical phenotypes, the molecular genetics approach has yielded mixed results. More optimally reduced measures of functioning, that is, intermediate phenotypes (IPs), represent a target for researching disease-associated genetic variants and for elucidating the underlying mechanisms. Imaging data provide a viable IP for complex neurobehavioral disorders and have been extensively used to investigate both morphological, structural and functional brain abnormalities in DD. Performing joint genetic and neuroimaging studies in humans is an emerging strategy to link DD-candidate genes to the brain structure and function. A limited number of studies has already pursued the imaging–genetics integration in DD. However, the results are still not sufficient to unravel the complexity of the reading circuit due to heterogeneous study design and data processing. Here, we propose an interdisciplinary, multilevel, imaging–genetic approach to disentangle the pathways from genes to behavior. As the presence of putative functional genetic variants has been provided and as genetic associations with specific cognitive/sensorial mechanisms have been reported, new hypothesis-driven imaging–genetic studies must gain momentum. This approach would lead to the optimization of diagnostic criteria and to the early identification of ‘biologically at-risk’ children, supporting the definition of adequate and well-timed prevention strategies and the implementation of novel, specific remediation approach. PMID:28045463
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van Vliet, Danique; Bruinenberg, Vibeke M; Mazzola, Priscila N; van Faassen, Martijn Hjr; de Blaauw, Pim; Pascucci, Tiziana; Puglisi-Allegra, Stefano; Kema, Ido P; Heiner-Fokkema, M Rebecca; van der Zee, Eddy A; van Spronsen, Francjan J
2016-11-01
Phenylketonuria treatment consists mainly of a Phe-restricted diet, which leads to suboptimal neurocognitive and psychosocial outcomes. Supplementation of large neutral amino acids (LNAAs) has been suggested as an alternative dietary treatment strategy to optimize neurocognitive outcome in phenylketonuria and has been shown to influence 3 brain pathobiochemical mechanisms in phenylketonuria, but its optimal composition has not been established. In order to provide additional pathobiochemical insight and develop optimal LNAA treatment, several targeted LNAA supplements were investigated with respect to all 3 biochemical disturbances underlying brain dysfunction in phenylketonuria. Pah-enu2 (PKU) mice received 1 of 5 different LNAA-supplemented diets beginning at postnatal day 45. Control groups included phenylketonuria mice receiving an isonitrogenic and isocaloric high-protein diet or the AIN-93M diet, and wild-type mice receiving the AIN-93M diet. After 6 wk, brain and plasma amino acid profiles and brain monoaminergic neurotransmitter concentrations were measured. Brain Phe concentrations were most effectively reduced by supplementation of LNAAs, such as Leu and Ile, with a strong affinity for the LNAA transporter type 1. Brain non-Phe LNAAs could be restored on supplementation, but unbalanced LNAA supplementation further reduced brain concentrations of those LNAAs that were not (sufficiently) included in the LNAA supplement. To optimally ameliorate brain monoaminergic neurotransmitter concentrations, LNAA supplementation should include Tyr and Trp together with LNAAs that effectively reduce brain Phe concentrations. The requirement for Tyr supplementation is higher than it is for Trp, and the relative effect of brain Phe reduction is higher for serotonin than it is for dopamine and norepinephrine. The study shows that all 3 biochemical disturbances underlying brain dysfunction in phenylketonuria can be targeted by specific LNAA supplements. The study thus provides essential information for the development of optimal LNAA supplementation as an alternative dietary treatment strategy to optimize neurocognitive outcome in patients with phenylketonuria. © 2016 American Society for Nutrition.
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Eytan, Danny; Pang, Elizabeth W; Doesburg, Sam M; Nenadovic, Vera; Gavrilovic, Bojan; Laussen, Peter; Guerguerian, Anne-Marie
2016-01-01
Acute brain injury is a common cause of death and critical illness in children and young adults. Fundamental management focuses on early characterization of the extent of injury and optimizing recovery by preventing secondary damage during the days following the primary injury. Currently, bedside technology for measuring neurological function is mainly limited to using electroencephalography (EEG) for detection of seizures and encephalopathic features, and evoked potentials. We present a proof of concept study in patients with acute brain injury in the intensive care setting, featuring a bedside functional imaging set-up designed to map cortical brain activation patterns by combining high density EEG recordings, multi-modal sensory stimulation (auditory, visual, and somatosensory), and EEG source modeling. Use of source-modeling allows for examination of spatiotemporal activation patterns at the cortical region level as opposed to the traditional scalp potential maps. The application of this system in both healthy and brain-injured participants is demonstrated with modality-specific source-reconstructed cortical activation patterns. By combining stimulation obtained with different modalities, most of the cortical surface can be monitored for changes in functional activation without having to physically transport the subject to an imaging suite. The results in patients in an intensive care setting with anatomically well-defined brain lesions suggest a topographic association between their injuries and activation patterns. Moreover, we report the reproducible application of a protocol examining a higher-level cortical processing with an auditory oddball paradigm involving presentation of the patient's own name. This study reports the first successful application of a bedside functional brain mapping tool in the intensive care setting. This application has the potential to provide clinicians with an additional dimension of information to manage critically-ill children and adults, and potentially patients not suited for magnetic resonance imaging technologies.
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Phillips, Cristy
2017-01-01
The number of the elderly across the globe will approximate 2.1 billion by 2050. Juxtaposed against this burgeoning segment of the population is evidence that nonpathological aging is associated with an increased risk for cognitive decline in a variety of domains, changes that can cause mild disability even before the onset of dementia. Given that pharmacological treatments that mitigate dementia are still outstanding, alternative therapeutic options are being investigated increasingly. The results from translational studies have shown that modifiable lifestyle factors-including physical activity, cognitive engagement, and diet-are a key strategy for maintaining brain health during aging. Indeed, a multiplicity of studies has demonstrated relationships between lifestyle factors, brain structure and function, and cognitive function in aging adults. For example, physical activity and diet modulate common neuroplasticity substrates (neurotrophic signaling, neurogenesis, inflammation, stress response, and antioxidant defense) in the brain whereas cognitive engagement enhances brain and cognitive reserve. The aims of this review are to evaluate the relationship between modifiable lifestyle factors, neuroplasticity, and optimal brain health during aging; to identify putative mechanisms that contribute positive brain aging; and to highlight future directions for scientists and clinicians. Undoubtedly, the translation of cutting-edge knowledge derived from the field of cognitive neuroscience will advance our understanding and enhance clinical treatment interventions as we endeavor to promote brain health during aging.
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2017-01-01
The number of the elderly across the globe will approximate 2.1 billion by 2050. Juxtaposed against this burgeoning segment of the population is evidence that nonpathological aging is associated with an increased risk for cognitive decline in a variety of domains, changes that can cause mild disability even before the onset of dementia. Given that pharmacological treatments that mitigate dementia are still outstanding, alternative therapeutic options are being investigated increasingly. The results from translational studies have shown that modifiable lifestyle factors—including physical activity, cognitive engagement, and diet—are a key strategy for maintaining brain health during aging. Indeed, a multiplicity of studies has demonstrated relationships between lifestyle factors, brain structure and function, and cognitive function in aging adults. For example, physical activity and diet modulate common neuroplasticity substrates (neurotrophic signaling, neurogenesis, inflammation, stress response, and antioxidant defense) in the brain whereas cognitive engagement enhances brain and cognitive reserve. The aims of this review are to evaluate the relationship between modifiable lifestyle factors, neuroplasticity, and optimal brain health during aging; to identify putative mechanisms that contribute positive brain aging; and to highlight future directions for scientists and clinicians. Undoubtedly, the translation of cutting-edge knowledge derived from the field of cognitive neuroscience will advance our understanding and enhance clinical treatment interventions as we endeavor to promote brain health during aging. PMID:28695017
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Nutritional strategies to optimise cognitive function in the aging brain.
Wahl, Devin; Cogger, Victoria C; Solon-Biet, Samantha M; Waern, Rosilene V R; Gokarn, Rahul; Pulpitel, Tamara; Cabo, Rafael de; Mattson, Mark P; Raubenheimer, David; Simpson, Stephen J; Le Couteur, David G
2016-11-01
Old age is the greatest risk factor for most neurodegenerative diseases. During recent decades there have been major advances in understanding the biology of aging, and the development of nutritional interventions that delay aging including calorie restriction (CR) and intermittent fasting (IF), and chemicals that influence pathways linking nutrition and aging processes. CR influences brain aging in many animal models and recent findings suggest that dietary interventions can influence brain health and dementia in older humans. The role of individual macronutrients in brain aging also has been studied, with conflicting results about the effects of dietary protein and carbohydrates. A new approach known as the Geometric Framework (GF) has been used to unravel the complex interactions between macronutrients (protein, fat, and carbohydrate) and total energy on outcomes such as aging. These studies have shown that low-protein, high-carbohydrate (LPHC) diets are optimal for lifespan in ad libitum fed animals, while total calories have minimal effect once macronutrients are taken into account. One of the primary purposes of this review is to explore the notion that macronutrients may have a more translational potential than CR and IF in humans, and therefore there is a pressing need to use GF to study the impact of diet on brain aging. Furthermore, given the growing recognition of the role of aging biology in dementia, such studies might provide a new approach for dietary interventions for optimizing brain health and preventing dementia in older people. Copyright © 2016 Elsevier B.V. All rights reserved.
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Dual-slit confocal light sheet microscopy for in vivo whole-brain imaging of zebrafish
Yang, Zhe; Mei, Li; Xia, Fei; Luo, Qingming; Fu, Ling; Gong, Hui
2015-01-01
In vivo functional imaging at single-neuron resolution is an important approach to visualize biological processes in neuroscience. Light sheet microscopy (LSM) is a cutting edge in vivo imaging technique that provides micron-scale spatial resolution at high frame rate. Due to the scattering and absorption of tissue, however, conventional LSM is inadequate to resolve cells because of the attenuated signal to noise ratio (SNR). Using dual-beam illumination and confocal dual-slit detection, here a dual-slit confocal LSM is demonstrated to obtain the SNR enhanced images with frame rate twice as high as line confocal LSM method. Through theoretical calculations and experiments, the correlation between the slit’s width and SNR was determined to optimize the image quality. In vivo whole brain structural imaging stacks and the functional imaging sequences of single slice were obtained for analysis of calcium activities at single-cell resolution. A two-fold increase in imaging speed of conventional confocal LSM makes it possible to capture the sequence of the neurons’ activities and help reveal the potential functional connections in the whole zebrafish’s brain. PMID:26137381
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Katzman, Martin A; Habert, Jeffrey; McIntosh, Diane; MacQueen, Glenda M; Milev, Roumen V; McIntyre, Roger S; Blier, Pierre
2018-01-01
Abstract Major depressive disorder is an often chronic and recurring illness. Left untreated, major depressive disorder may result in progressive alterations in brain morphometry and circuit function. Recent findings, however, suggest that pharmacotherapy may halt and possibly reverse those effects. These findings, together with evidence that a delay in treatment is associated with poorer clinical outcomes, underscore the urgency of rapidly treating depression to full recovery. Early optimized treatment, using measurement-based care and customizing treatment to the individual patient, may afford the best possible outcomes for each patient. The aim of this article is to present recommendations for using a patient-centered approach to rapidly provide optimal pharmacological treatment to patients with major depressive disorder. Offering major depressive disorder treatment determined by individual patient characteristics (e.g., predominant symptoms, medical history, comorbidities), patient preferences and expectations, and, critically, their own definition of wellness provides the best opportunity for full functional recovery. PMID:29024974
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Oluboka, Oloruntoba J; Katzman, Martin A; Habert, Jeffrey; McIntosh, Diane; MacQueen, Glenda M; Milev, Roumen V; McIntyre, Roger S; Blier, Pierre
2018-02-01
Major depressive disorder is an often chronic and recurring illness. Left untreated, major depressive disorder may result in progressive alterations in brain morphometry and circuit function. Recent findings, however, suggest that pharmacotherapy may halt and possibly reverse those effects. These findings, together with evidence that a delay in treatment is associated with poorer clinical outcomes, underscore the urgency of rapidly treating depression to full recovery. Early optimized treatment, using measurement-based care and customizing treatment to the individual patient, may afford the best possible outcomes for each patient. The aim of this article is to present recommendations for using a patient-centered approach to rapidly provide optimal pharmacological treatment to patients with major depressive disorder. Offering major depressive disorder treatment determined by individual patient characteristics (e.g., predominant symptoms, medical history, comorbidities), patient preferences and expectations, and, critically, their own definition of wellness provides the best opportunity for full functional recovery. © The Author(s) 2017. Published by Oxford University Press on behalf of CINP.
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Flexible brain network reconfiguration supporting inhibitory control.
Spielberg, Jeffrey M; Miller, Gregory A; Heller, Wendy; Banich, Marie T
2015-08-11
The ability to inhibit distracting stimuli from interfering with goal-directed behavior is crucial for success in most spheres of life. Despite an abundance of studies examining regional brain activation, knowledge of the brain networks involved in inhibitory control remains quite limited. To address this critical gap, we applied graph theory tools to functional magnetic resonance imaging data collected while a large sample of adults (n = 101) performed a color-word Stroop task. Higher demand for inhibitory control was associated with restructuring of the global network into a configuration that was more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). In addition, there were regional changes with right inferior frontal sulcus and right anterior insula occupying more central positions as network hubs, and dorsal anterior cingulate cortex becoming more tightly coupled with its regional subnetwork. Given the crucial role of inhibitory control in goal-directed behavior, present findings identifying functional network organization supporting inhibitory control have the potential to provide additional insights into how inhibitory control may break down in a wide variety of individuals with neurological or psychiatric difficulties.
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Metal oxide resistive random access memory based synaptic devices for brain-inspired computing
NASA Astrophysics Data System (ADS)
Gao, Bin; Kang, Jinfeng; Zhou, Zheng; Chen, Zhe; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan
2016-04-01
The traditional Boolean computing paradigm based on the von Neumann architecture is facing great challenges for future information technology applications such as big data, the Internet of Things (IoT), and wearable devices, due to the limited processing capability issues such as binary data storage and computing, non-parallel data processing, and the buses requirement between memory units and logic units. The brain-inspired neuromorphic computing paradigm is believed to be one of the promising solutions for realizing more complex functions with a lower cost. To perform such brain-inspired computing with a low cost and low power consumption, novel devices for use as electronic synapses are needed. Metal oxide resistive random access memory (ReRAM) devices have emerged as the leading candidate for electronic synapses. This paper comprehensively addresses the recent work on the design and optimization of metal oxide ReRAM-based synaptic devices. A performance enhancement methodology and optimized operation scheme to achieve analog resistive switching and low-energy training behavior are provided. A three-dimensional vertical synapse network architecture is proposed for high-density integration and low-cost fabrication. The impacts of the ReRAM synaptic device features on the performances of neuromorphic systems are also discussed on the basis of a constructed neuromorphic visual system with a pattern recognition function. Possible solutions to achieve the high recognition accuracy and efficiency of neuromorphic systems are presented.
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Benevolent sexism alters executive brain responses.
Dardenne, Benoit; Dumont, Muriel; Sarlet, Marie; Phillips, Christophe; Balteau, Evelyne; Degueldre, Christian; Luxen, André; Salmon, Eric; Maquet, Pierre; Collette, Fabienne
2013-07-10
Benevolence is widespread in our societies. It is defined as considering a subordinate group nicely but condescendingly, that is, with charity. Deleterious consequences for the target have been reported in the literature. In this experiment, we used functional MRI (fMRI) to identify whether being the target of (sexist) benevolence induces changes in brain activity associated with a working memory task. Participants were confronted by benevolent, hostile, or neutral comments before and while performing a reading span test in an fMRI environment. fMRI data showed that brain regions associated previously with intrusive thought suppression (bilateral, dorsolateral, prefrontal, and anterior cingulate cortex) reacted specifically to benevolent sexism compared with hostile sexism and neutral conditions during the performance of the task. These findings indicate that, despite being subjectively positive, benevolence modifies task-related brain networks by recruiting supplementary areas likely to impede optimal cognitive performance.
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NASA Astrophysics Data System (ADS)
Shan, Bonan; Wang, Jiang; Deng, Bin; Zhang, Zhen; Wei, Xile
2017-03-01
Assessment of the effective connectivity among different brain regions during seizure is a crucial problem in neuroscience today. As a consequence, a new model inversion framework of brain function imaging is introduced in this manuscript. This framework is based on approximating brain networks using a multi-coupled neural mass model (NMM). NMM describes the excitatory and inhibitory neural interactions, capturing the mechanisms involved in seizure initiation, evolution and termination. Particle swarm optimization method is used to estimate the effective connectivity variation (the parameters of NMM) and the epileptiform dynamics (the states of NMM) that cannot be directly measured using electrophysiological measurement alone. The estimated effective connectivity includes both the local connectivity parameters within a single region NMM and the remote connectivity parameters between multi-coupled NMMs. When the epileptiform activities are estimated, a proportional-integral controller outputs control signal so that the epileptiform spikes can be inhibited immediately. Numerical simulations are carried out to illustrate the effectiveness of the proposed framework. The framework and the results have a profound impact on the way we detect and treat epilepsy.
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Three validation metrics for automated probabilistic image segmentation of brain tumours
Zou, Kelly H.; Wells, William M.; Kikinis, Ron; Warfield, Simon K.
2005-01-01
SUMMARY The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts’ manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered. PMID:15083482
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NASA Astrophysics Data System (ADS)
Guler, Seyhmus; Dannhauer, Moritz; Erem, Burak; Macleod, Rob; Tucker, Don; Turovets, Sergei; Luu, Phan; Erdogmus, Deniz; Brooks, Dana H.
2016-06-01
Objective. Transcranial direct current stimulation (tDCS) aims to alter brain function non-invasively via electrodes placed on the scalp. Conventional tDCS uses two relatively large patch electrodes to deliver electrical current to the brain region of interest (ROI). Recent studies have shown that using dense arrays containing up to 512 smaller electrodes may increase the precision of targeting ROIs. However, this creates a need for methods to determine effective and safe stimulus patterns as the number of degrees of freedom is much higher with such arrays. Several approaches to this problem have appeared in the literature. In this paper, we describe a new method for calculating optimal electrode stimulus patterns for targeted and directional modulation in dense array tDCS which differs in some important aspects with methods reported to date. Approach. We optimize stimulus pattern of dense arrays with fixed electrode placement to maximize the current density in a particular direction in the ROI. We impose a flexible set of safety constraints on the current power in the brain, individual electrode currents, and total injected current, to protect subject safety. The proposed optimization problem is convex and thus efficiently solved using existing optimization software to find unique and globally optimal electrode stimulus patterns. Main results. Solutions for four anatomical ROIs based on a realistic head model are shown as exemplary results. To illustrate the differences between our approach and previously introduced methods, we compare our method with two of the other leading methods in the literature. We also report on extensive simulations that show the effect of the values chosen for each proposed safety constraint bound on the optimized stimulus patterns. Significance. The proposed optimization approach employs volume based ROIs, easily adapts to different sets of safety constraints, and takes negligible time to compute. An in-depth comparison study gives insight into the relationship between different objective criteria and optimized stimulus patterns. In addition, the analysis of the interaction between optimized stimulus patterns and safety constraint bounds suggests that more precise current localization in the ROI, with improved safety criterion, may be achieved by careful selection of the constraint bounds.
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Bae, Sung Jin; Jang, Sung Ho; Seo, Jeong Pyo; Chang, Pyung Hun
2017-07-01
The optimal conditions inducing proper brain activation during performance of rehabilitation robots should be examined to enhance the efficiency of robot rehabilitation based on the concept of brain plasticity. In this study, we attempted to investigate differences in cortical activation according to the speeds of passive wrist movements performed by a rehabilitation robot for stroke patients. 9 stroke patients with right hemiparesis participated in this study. Passive movements of the affected wrist were performed by the rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity during the passive movements performed by a robot. Group-average activation map and the relative changes in oxy-hemoglobin (ΔOxyHb) in two regions of interest: the primary sensory-motor cortex (SM1); premotor area (PMA) and region of all channels were measured. In the result of group-averaged activation map, the contralateral SM1, PMA and somatosensory association cortex (SAC) showed the greatest significant activation according to the movements at 0.75 Hz, while there is no significantly activated area at 0.5 Hz. Regarding ΔOxyHb, no significant diiference was observed among three speeds regardless of region. In conclusion, the contralateral SM1, PMA and SAC showed the greatest activation by a fast speed (0.75 Hz) rather than slow (0.25 Hz) and moderate (0. 5 Hz) speed. Our results suggest an optimal speed for execution of the wrist rehabilitation robot. Therefore, we believe that our findings might point to several promising applications for future research regarding useful and empirically-based robot rehabilitation therapy.
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Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex.
Arslan, Salim; Ktena, Sofia Ira; Makropoulos, Antonios; Robinson, Emma C; Rueckert, Daniel; Parisot, Sarah
2018-04-15
The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously. Copyright © 2017 Elsevier Inc. All rights reserved.
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Hybrid reflecting objectives for functional multiphoton microscopy in turbid media
Vučinić, Dejan; Bartol, Thomas M.; Sejnowski, Terrence J.
2010-01-01
Most multiphoton imaging of biological specimens is performed using microscope objectives optimized for high image quality under wide-field illumination. We present a class of objectives designed de novo without regard for these traditional constraints, driven exclusively by the needs of fast multiphoton imaging in turbid media: the delivery of femtosecond pulses without dispersion and the efficient collection of fluorescence. We model the performance of one such design optimized for a typical brain-imaging setup and show that it can greatly outperform objectives commonly used for this task. PMID:16880851
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NASA Technical Reports Server (NTRS)
Mulavara, A. P.; DeDios, Y. E.; Gadd, N. E.; Caldwell, E. E.; Batson, C. D.; Goel, R.; Seidler, R. D.; Oddsson, L.; Zanello, S.; Clarke, T.;
2016-01-01
Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. These alterations may disrupt crewmembers' ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual spaceflight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures. Our approach includes: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features, using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; and 3) assessment of genotypic markers of genetic polymorphisms in the catechol-O-methyl transferase, dopamine receptor D2, and brain-derived neurotrophic factor genes and genetic polymorphisms of alpha2-adrenergic receptors that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate that these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration spaceflight and exposure to an analog bed rest environment. We will be conducting a retrospective study, leveraging data already collected from relevant ongoing or completed bed rest and spaceflight studies. These data will be combined with predictor metrics that will be collected prospectively (as described for behavioral, brain imaging and genomic measures) from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. To date we have completed a study on 15 normal subjects with all of the above measures. In this presentation we will discuss the optimized set of tests for predictive metrics to be used for evaluating post mission adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.
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Simultaneous real-time monitoring of multiple cortical systems.
Gupta, Disha; Jeremy Hill, N; Brunner, Peter; Gunduz, Aysegul; Ritaccio, Anthony L; Schalk, Gerwin
2014-10-01
Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance. We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time. This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic.
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Simultaneous Real-Time Monitoring of Multiple Cortical Systems
Gupta, Disha; Hill, N. Jeremy; Brunner, Peter; Gunduz, Aysegul; Ritaccio, Anthony L.; Schalk, Gerwin
2014-01-01
Objective Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor, or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance. Approach We study these questions using electrocorticographic (ECoG) signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (6 for offline parameter optimization, 6 for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main results Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelope. These decoders were trained separately and executed simultaneously in real time. Significance This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic. PMID:25080161
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Prolonged, brain-wide expression of nuclear-localized GCaMP3 for functional circuit mapping
Kim, Christina K.; Miri, Andrew; Leung, Louis C.; Berndt, Andre; Mourrain, Philippe; Tank, David W.; Burdine, Rebecca D.
2014-01-01
Larval zebrafish offer the potential for large-scale optical imaging of neural activity throughout the central nervous system; however, several barriers challenge their utility. First, ~panneuronal probe expression has to date only been demonstrated at early larval stages up to 7 days post-fertilization (dpf), precluding imaging at later time points when circuits are more mature. Second, nuclear exclusion of genetically-encoded calcium indicators (GECIs) limits the resolution of functional fluorescence signals collected during imaging. Here, we report the creation of transgenic zebrafish strains exhibiting robust, nuclearly targeted expression of GCaMP3 across the brain up to at least 14 dpf utilizing a previously described optimized Gal4-UAS system. We confirmed both nuclear targeting and functionality of the modified probe in vitro and measured its kinetics in response to action potentials (APs). We then demonstrated in vivo functionality of nuclear-localized GCaMP3 in transgenic zebrafish strains by identifying eye position-sensitive fluorescence fluctuations in caudal hindbrain neurons during spontaneous eye movements. Our methodological approach will facilitate studies of larval zebrafish circuitry by both improving resolution of functional Ca2+ signals and by allowing brain-wide expression of improved GECIs, or potentially any probe, further into development. PMID:25505384
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Functional heartburn: the stimulus, the pain, and the brain
Fass, R; Tougas, G
2002-01-01
Functional heartburn is a common disorder and appears to be composed of several distinct subgroups. Identifying the different subgroups based on clinical history only is not achievable at present. The mechanisms responsible for pain, clinical characteristics, and the optimal therapeutic approach remain poorly understood. Response to potent antireflux treatment is relatively limited. Current and future treatment strategies for functional heartburn patients who have failed standard dose proton pump inhibitors (PPIs) include increased PPI dose in some, as well as addition of pain modulators in others. PMID:12427796
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Personality and psychosocial function after brain injury.
Malia, K; Powell, G; Torode, S
1995-10-01
A total of 74 brain-injured patients and 46 non-neurologically matched controls consecutively admitted to a specialist medical rehabilitation unit, were administered the 'Headley Court psychosocial rating scale' and four questionnaires examining personality traits of 'locus of control', 'use of humour', 'optimism' and 'easy-going disposition'. Both pre- and post-injury personality ratings were obtained. The relatives of all participants were sent the same scales. Personality changes are reported in each of the four areas; however, time post-injury appears to be a significant factor in the type of change reported; in this cross-sectional study, at 6 and 12 months post-injury, changes are noted in all variables except locus of control, whereas at 18 months post-injury only 'easy-going disposition' showed significant change, at 24 months post-injury changes were noted in all variables except optimism, and at 30 months post-injury no changes were noted. In the present study, examining a period of 2.5 years post-injury, the personality changes remain static once they have occurred. Despite widespread reports in the literature on the importance of pre- and post-trauma personality to good psychosocial functioning, the present study found that it was only an 'easy-going disposition' post-trauma that was consistently related to good psychosocial functioning. Reasons for this are discussed.
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Abnormal small-world architecture of top–down control networks in obsessive–compulsive disorder
Zhang, Tijiang; Wang, Jinhui; Yang, Yanchun; Wu, Qizhu; Li, Bin; Chen, Long; Yue, Qiang; Tang, Hehan; Yan, Chaogan; Lui, Su; Huang, Xiaoqi; Chan, Raymond C.K.; Zang, Yufeng; He, Yong; Gong, Qiyong
2011-01-01
Background Obsessive–compulsive disorder (OCD) is a common neuropsychiatric disorder that is characterized by recurrent intrusive thoughts, ideas or images and repetitive ritualistic behaviours. Although focal structural and functional abnormalities in specific brain regions have been widely studied in populations with OCD, changes in the functional relations among them remain poorly understood. This study examined OCD–related alterations in functional connectivity patterns in the brain’s top–down control network. Methods We applied resting-state functional magnetic resonance imaging to investigate the correlation patterns of intrinsic or spontaneous blood oxygen level–dependent signal fluctuations in 18 patients with OCD and 16 healthy controls. The brain control networks were first constructed by thresholding temporal correlation matrices of 39 brain regions associated with top–down control and then analyzed using graph theory-based approaches. Results Compared with healthy controls, the patients with OCD showed decreased functional connectivity in the posterior temporal regions and increased connectivity in various control regions such as the cingulate, precuneus, thalamus and cerebellum. Furthermore, the brain’s control networks in the healthy controls showed small-world architecture (high clustering coefficients and short path lengths), suggesting an optimal balance between modularized and distributed information processing. In contrast, the patients with OCD showed significantly higher local clustering, implying abnormal functional organization in the control network. Further analysis revealed that the changes in network properties occurred in regions of increased functional connectivity strength in patients with OCD. Limitations The patient group in the present study was heterogeneous in terms of symptom clusters, and most of the patients with OCD were medicated. Conclusion Our preliminary results suggest that the organizational patterns of intrinsic brain activity in the control networks are altered in patients with OCD and thus provide empirical evidence for aberrant functional connectivity in the large-scale brain systems in people with this disorder. PMID:20964957
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Deogaonkar, Milind; Sharma, Mayur; Oluigbo, Chima; Nielson, Dylan M; Yang, Xiangyu; Vera-Portocarrero, Louis; Molnar, Gregory F; Abduljalil, Amir; Sederberg, Per B; Knopp, Michael; Rezai, Ali R
2016-02-01
The neurophysiological basis of pain relief due to spinal cord stimulation (SCS) and the related cortical processing of sensory information are not completely understood. The aim of this study was to use resting state functional magnetic resonance imaging (rs-fMRI) to detect changes in cortical networks and cortical processing related to the stimulator-induced pain relief. Ten patients with complex regional pain syndrome (CRPS) or neuropathic leg pain underwent thoracic epidural spinal cord stimulator implantation. Stimulation parameters associated with "optimal" pain reduction were evaluated prior to imaging studies. Rs-fMRI was obtained on a 3 Tesla, Philips Achieva MRI. Rs-fMRI was performed with stimulator off (300TRs) and stimulator at optimum (Opt, 300 TRs) pain relief settings. Seed-based analysis of the resting state functional connectivity was conducted using seeds in regions established as participating in pain networks or in the default mode network (DMN) in addition to the network analysis. NCUT (normalized cut) parcellation was used to generate 98 cortical and subcortical regions of interest in order to expand our analysis of changes in functional connections to the entire brain. We corrected for multiple comparisons by limiting the false discovery rate to 5%. Significant differences in resting state connectivity between SCS off and optimal state were seen between several regions related to pain perception, including the left frontal insula, right primary and secondary somatosensory cortices, as well as in regions involved in the DMN, such as the precuneus. In examining changes in connectivity across the entire brain, we found decreased connection strength between somatosensory and limbic areas and increased connection strength between somatosensory and DMN with optimal SCS resulting in pain relief. This suggests that pain relief from SCS may be reducing negative emotional processing associated with pain, allowing somatosensory areas to become more integrated into default mode activity. SCS reduces the affective component of pain resulting in optimal pain relief. Study shows a decreased connectivity between somatosensory and limbic areas associated with optimal pain relief due to SCS. © 2015 International Neuromodulation Society.
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A plastic corticostriatal circuit model of adaptation in perceptual decision making
Hsiao, Pao-Yueh; Lo, Chung-Chuan
2013-01-01
The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. PMID:24339814
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Model of brain activation predicts the neural collective influence map of the brain
Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.
2017-01-01
Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973
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Rehkämper, Gerd; Frahm, Heiko D; Cnotka, Julia
2008-01-01
Brain sizes and brain component sizes of five domesticated pigeon breeds including homing (racing) pigeons are compared with rock doves (Columba livia) based on an allometric approach to test the influence of domestication on brain and brain component size. Net brain volume, the volumes of cerebellum and telencephalon as a whole are significantly smaller in almost all domestic pigeons. Inside the telencephalon, mesopallium, nidopallium (+ entopallium + arcopallium) and septum are smaller as well. The hippocampus is significantly larger, particularly in homing pigeons. This finding is in contrast to the predictions of the 'regression hypothesis' of brain alteration under domestication. Among the domestic pigeons homing pigeons have significantly larger olfactory bulbs. These data are interpreted as representing a functional adaptation to homing that is based on spatial cognition and sensory integration. We argue that domestication as seen in domestic pigeons is not principally different from evolution in the wild, but represents a heuristic model to understand the evolutionary process in terms of adaptation and optimization. Copyright 2007 S. Karger AG, Basel.
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Optimal nonlinear information processing capacity in delay-based reservoir computers
NASA Astrophysics Data System (ADS)
Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo
2015-09-01
Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.
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Optimal nonlinear information processing capacity in delay-based reservoir computers.
Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo
2015-09-11
Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.
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Optimal nonlinear information processing capacity in delay-based reservoir computers
Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo
2015-01-01
Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature. PMID:26358528
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Semple, Bridgette D.; Lee, Sangmi; Sadjadi, Raha; Fritz, Nora; Carlson, Jaclyn; Griep, Carrie; Ho, Vanessa; Jang, Patrice; Lamb, Annick; Popolizio, Beth; Saini, Sonia; Bazarian, Jeffrey J.; Prins, Mayumi L.; Ferriero, Donna M.; Basso, D. Michele; Noble-Haeusslein, Linda J.
2015-01-01
Sports-related concussions are particularly common during adolescence, a time when even mild brain injuries may disrupt ongoing brain maturation and result in long-term complications. A recent focus on the consequences of repetitive concussions among professional athletes has prompted the development of several new experimental models in rodents, as well as the revision of guidelines for best management of sports concussions. Here, we consider the utility of rodent models to understand the functional consequences and pathobiology of concussions in the developing brain, identifying the unique behavioral and pathological signatures of concussive brain injuries. The impact of repetitive concussions on behavioral consequences and injury progression is also addressed. In particular, we focus on the epidemiological, clinical, and experimental evidence underlying current recommendations for physical and cognitive rest after concussion, and highlight key areas in which further research is needed. Lastly, we consider how best to promote recovery after injury, recognizing that optimally timed, activity-based rehabilitative strategies may hold promise for the adolescent athlete who has sustained single or repetitive concussions. The purpose of this review is to inform the clinical research community as it strives to develop and optimize evidence-based guidelines for the concussed adolescent, in terms of both acute and long-term management. PMID:25883586
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Litvak, Vladimir; Eusebio, Alexandre; Jha, Ashwani; Oostenveld, Robert; Barnes, Gareth R; Penny, William D; Zrinzo, Ludvic; Hariz, Marwan I; Limousin, Patricia; Friston, Karl J; Brown, Peter
2010-05-01
Insight into how brain structures interact is critical for understanding the principles of functional brain architectures and may lead to better diagnosis and therapy for neuropsychiatric disorders. We recorded, simultaneously, magnetoencephalographic (MEG) signals and subcortical local field potentials (LFP) in a Parkinson's disease (PD) patient with bilateral deep brain stimulation (DBS) electrodes in the subthalamic nucleus (STN). These recordings offer a unique opportunity to characterize interactions between the subcortical structures and the neocortex. However, high-amplitude artefacts appeared in the MEG. These artefacts originated from the percutaneous extension wire, rather than from the actual DBS electrode and were locked to the heart beat. In this work, we show that MEG beamforming is capable of suppressing these artefacts and quantify the optimal regularization required. We demonstrate how beamforming makes it possible to localize cortical regions whose activity is coherent with the STN-LFP, extract artefact-free virtual electrode time-series from regions of interest and localize cortical areas exhibiting specific task-related power changes. This furnishes results that are consistent with previously reported results using artefact-free MEG data. Our findings demonstrate that physiologically meaningful information can be extracted from heavily contaminated MEG signals and pave the way for further analysis of combined MEG-LFP recordings in DBS patients. 2009 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Bauer, Adam Q.; Kraft, Andrew; Baxter, Grant A.; Bruchas, Michael; Lee, Jin-Moo; Culver, Joseph P.
2017-02-01
Functional magnetic resonance imaging (fMRI) has transformed our understanding of the brain's functional organization. However, mapping subunits of a functional network using hemoglobin alone presents several disadvantages. Evoked and spontaneous hemodynamic fluctuations reflect ensemble activity from several populations of neurons making it difficult to discern excitatory vs inhibitory network activity. Still, blood-based methods of brain mapping remain powerful because hemoglobin provides endogenous contrast in all mammalian brains. To add greater specificity to hemoglobin assays, we integrated optical intrinsic signal(OIS) imaging with optogenetic stimulation to create an Opto-OIS mapping tool that combines the cell-specificity of optogenetics with label-free, hemoglobin imaging. Before mapping, titrated photostimuli determined which stimulus parameters elicited linear hemodynamic responses in the cortex. Optimized stimuli were then scanned over the left hemisphere to create a set of optogenetically-defined effective connectivity (Opto-EC) maps. For many sites investigated, Opto-EC maps exhibited higher spatial specificity than those determined using spontaneous hemodynamic fluctuations. For example, resting-state functional connectivity (RS-FC) patterns exhibited widespread ipsilateral connectivity while Opto-EC maps contained distinct short- and long-range constellations of ipsilateral connectivity. Further, RS-FC maps were usually symmetric about midline while Opto-EC maps displayed more heterogeneous contralateral homotopic connectivity. Both Opto-EC and RS-FC patterns were compared to mouse connectivity data from the Allen Institute. Unlike RS-FC maps, Thy1-based maps collected in awake, behaving mice closely recapitulated the connectivity structure derived using ex vivo anatomical tracer methods. Opto-OIS mapping could be a powerful tool for understanding cellular and molecular contributions to network dynamics and processing in the mouse brain.
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Grahn, Peter J.; Mallory, Grant W.; Berry, B. Michael; Hachmann, Jan T.; Lobel, Darlene A.; Lujan, J. Luis
2014-01-01
Movement is planned and coordinated by the brain and carried out by contracting muscles acting on specific joints. Motor commands initiated in the brain travel through descending pathways in the spinal cord to effector motor neurons before reaching target muscles. Damage to these pathways by spinal cord injury (SCI) can result in paralysis below the injury level. However, the planning and coordination centers of the brain, as well as peripheral nerves and the muscles that they act upon, remain functional. Neuroprosthetic devices can restore motor function following SCI by direct electrical stimulation of the neuromuscular system. Unfortunately, conventional neuroprosthetic techniques are limited by a myriad of factors that include, but are not limited to, a lack of characterization of non-linear input/output system dynamics, mechanical coupling, limited number of degrees of freedom, high power consumption, large device size, and rapid onset of muscle fatigue. Wireless multi-channel closed-loop neuroprostheses that integrate command signals from the brain with sensor-based feedback from the environment and the system's state offer the possibility of increasing device performance, ultimately improving quality of life for people with SCI. In this manuscript, we review neuroprosthetic technology for improving functional restoration following SCI and describe brain-machine interfaces suitable for control of neuroprosthetic systems with multiple degrees of freedom. Additionally, we discuss novel stimulation paradigms that can improve synergy with higher planning centers and improve fatigue-resistant activation of paralyzed muscles. In the near future, integration of these technologies will provide SCI survivors with versatile closed-loop neuroprosthetic systems for restoring function to paralyzed muscles. PMID:25278830
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Bolduc, Virginie; Thorin-Trescases, Nathalie; Thorin, Eric
2013-09-01
Cognitive performances are tightly associated with the maximal aerobic exercise capacity, both of which decline with age. The benefits on mental health of regular exercise, which slows the age-dependent decline in maximal aerobic exercise capacity, have been established for centuries. In addition, the maintenance of an optimal cerebrovascular endothelial function through regular exercise, part of a healthy lifestyle, emerges as one of the key and primary elements of successful brain aging. Physical exercise requires the activation of specific brain areas that trigger a local increase in cerebral blood flow to match neuronal metabolic needs. In this review, we propose three ways by which exercise could maintain the cerebrovascular endothelial function, a premise to a healthy cerebrovascular function and an optimal regulation of cerebral blood flow. First, exercise increases blood flow locally and increases shear stress temporarily, a known stimulus for endothelial cell maintenance of Akt-dependent expression of endothelial nitric oxide synthase, nitric oxide generation, and the expression of antioxidant defenses. Second, the rise in circulating catecholamines during exercise not only facilitates adequate blood and nutrient delivery by stimulating heart function and mobilizing energy supplies but also enhances endothelial repair mechanisms and angiogenesis. Third, in the long term, regular exercise sustains a low resting heart rate that reduces the mechanical stress imposed to the endothelium of cerebral arteries by the cardiac cycle. Any chronic variation from a healthy environment will perturb metabolism and thus hasten endothelial damage, favoring hypoperfusion and neuronal stress.
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Third World adversity: African infant precocity and the role of environment.
Saugstad, Letten F
2002-01-01
The war against illiteracy has not been won. The number of illiterates approaches a billion. Most reside in Third World countries--former colonies--where they are caught in a poverty trap of disease, low agricultural production and environmental adversity requiring technology beyond their means. I argue against the commonly held view that this is mainly attributable to the four hundred years of traffic in men. According to the late K.O. Dike, middle men along the African coast barred foreign merchants from the hinterland, and because of this the social, political structure and sovereignty of the African states remained fundamentally unchanged during the period 1400-1807, whereas a few decades after colonisation the socio-political system collapsed and was replaced by a small rich elite and many poor, while resources were taken out of Africa. Present poverty and underdevelopment represent as great a challenge as the trade in slaves. As did the African Middle-Men of that time, African leaders now must unite in an ambitious and confident Pan-African Union demonstrating strength. Western countries should focus on reducing poverty and improving nutrition. This also makes terrorism and legal and illegal migration less likely. Education is important, but the West should not limit its effort to fighting illiteracy but should also support the establishment of institutions for higher education. Africa possessed optimal conditions and an enriched environment for human evolution. African Infant Precocity is a persistent example. The human brain, like other brains, consists 60% of poly-unsaturated fatty acids (Marine-Fat), the rest being water. A sufficient amount is required to secure optimal brain growth. It normalizes brain function, and prevents sudden cardiac and infant death, which have been increasing in Western societies. Humans are unique in having a mismatch between the need for brain food--marine fat--and our common high protein diet. Nowhere is the neglect of the brain greater than in pregnancy when protein is the only major nutrient considered. Declining levels of polyunsaturated fatty acids have been observed in human milk. Deficient intake could, if not corrected, gradually impair brain function as has been seen in animal experiments.
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Chapman, Sandra B.; Mudar, Raksha A.
2014-01-01
Public awareness of cognitive health is fairly recent compared to physical health. Growing evidence suggests that cognitive training offers promise in augmenting cognitive brain performance in normal and clinical populations. Targeting higher-order cognitive functions, such as reasoning in particular, may promote generalized cognitive changes necessary for supporting the complexities of daily life. This data-driven perspective highlights cognitive and brain changes measured in randomized clinical trials that trained gist reasoning strategies in populations ranging from teenagers to healthy older adults, individuals with brain injury to those at-risk for Alzheimer's disease. The evidence presented across studies support the potential for Gist reasoning training to strengthen cognitive performance in trained and untrained domains and to engage more efficient communication across widespread neural networks that support higher-order cognition. The meaningful benefits of Gist training provide compelling motivation to examine optimal dose for sustained benefits as well as to explore additive benefits of meditation, physical exercise, and/or improved sleep in future studies. PMID:24808834
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Endurance Exercise as an “Endogenous” Neuro-enhancement Strategy to Facilitate Motor Learning
Taubert, Marco; Villringer, Arno; Lehmann, Nico
2015-01-01
Endurance exercise improves cardiovascular and musculoskeletal function and may also increase the information processing capacities of the brain. Animal and human research from the past decade demonstrated widespread exercise effects on brain structure and function at the systems-, cellular-, and molecular level of brain organization. These neurobiological mechanisms may explain the well-established positive influence of exercise on performance in various behavioral domains but also its contribution to improved skill learning and neuroplasticity. With respect to the latter, only few empirical and theoretical studies are available to date. The aim of this review is (i) to summarize the existing neurobiological and behavioral evidence arguing for endurance exercise-induced improvements in motor learning and (ii) to develop hypotheses about the mechanistic link between exercise and improved learning. We identify major knowledge gaps that need to be addressed by future research projects to advance our understanding of how exercise should be organized to optimize motor learning. PMID:26834602
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Gerritz, Samuel W; Zhai, Weixu; Shi, Shuhao; Zhu, Shirong; Toyn, Jeremy H; Meredith, Jere E; Iben, Lawrence G; Burton, Catherine R; Albright, Charles F; Good, Andrew C; Tebben, Andrew J; Muckelbauer, Jodi K; Camac, Daniel M; Metzler, William; Cook, Lynda S; Padmanabha, Ramesh; Lentz, Kimberley A; Sofia, Michael J; Poss, Michael A; Macor, John E; Thompson, Lorin A
2012-11-08
This report describes the discovery and optimization of a BACE-1 inhibitor series containing an unusual acyl guanidine chemotype that was originally synthesized as part of a 6041-membered solid-phase library. The synthesis of multiple follow-up solid- and solution-phase libraries facilitated the optimization of the original micromolar hit into a single-digit nanomolar BACE-1 inhibitor in both radioligand binding and cell-based functional assay formats. The X-ray structure of representative inhibitors bound to BACE-1 revealed a number of key ligand:protein interactions, including a hydrogen bond between the side chain amide of flap residue Gln73 and the acyl guanidine carbonyl group, and a cation-π interaction between Arg235 and the isothiazole 4-methoxyphenyl substituent. Following subcutaneous administration in rats, an acyl guanidine inhibitor with single-digit nanomolar activity in cells afforded good plasma exposures and a dose-dependent reduction in plasma Aβ levels, but poor brain exposure was observed (likely due to Pgp-mediated efflux), and significant reductions in brain Aβ levels were not obtained.
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Structure-Function Network Mapping and Its Assessment via Persistent Homology
2017-01-01
Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience. This work deals with the general method of structure-function mapping at the whole-brain level. We formulate the problem as a topological mapping of structure-function connectivity via matrix function, and find a stable solution by exploiting a regularization procedure to cope with large matrices. We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping, which enables a detailed comparison of network topological changes across all possible thresholds, rather than just at a single, arbitrary threshold that may not be optimal. We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity, and our network similarity measure outperforms other currently available methods. We systematically validate our approach with (1) a comparison of regularized vs. non-regularized procedures, (2) a null model of the degree-preserving random rewired structural matrix, (3) different network types (binary vs. weighted matrices), and (4) different brain parcellation schemes (low vs. high resolutions). Finally, we evaluate the scalability of our method with relatively large matrices (2514x2514) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest. Our results reveal a nonlinear structure-function relationship, suggesting that the resting-state functional connectivity depends on direct structural connections, as well as relatively parsimonious indirect connections via polysynaptic pathways. PMID:28046127
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Neural representations of kinematic laws of motion: evidence for action-perception coupling.
Dayan, Eran; Casile, Antonino; Levit-Binnun, Nava; Giese, Martin A; Hendler, Talma; Flash, Tamar
2007-12-18
Behavioral and modeling studies have established that curved and drawing human hand movements obey the 2/3 power law, which dictates a strong coupling between movement curvature and velocity. Human motion perception seems to reflect this constraint. The functional MRI study reported here demonstrates that the brain's response to this law of motion is much stronger and more widespread than to other types of motion. Compliance with this law is reflected in the activation of a large network of brain areas subserving motor production, visual motion processing, and action observation functions. Hence, these results strongly support the notion of similar neural coding for motion perception and production. These findings suggest that cortical motion representations are optimally tuned to the kinematic and geometrical invariants characterizing biological actions.
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Arbelaez, Ana Maria; Semenkovich, Katherine; Hershey, Tamara
2013-12-01
The adult brain accounts for a disproportionally large percentage of the body’s total energy consumption (1). However, during brain development,energy demand is even higher, reaching the adult rate by age 2 and increasing to nearly twice the adult rate by age 10, followed by gradual reduction toward adult levels in the next decade (1,2). The dramatic changes in brain metabolism occurring over the first two decades of life coincide with the initial proliferation and then pruning of synapses to adult levels.The brain derives its energy almost exclusively from glucose and is largely driven by neuronal signaling, biosynthesis, and neuroprotection (3–6).Glucose homeostasis in the body is tightly regulated by a series of hormones and physiologic responses. As a result, hypoglycemia and hyperglycemia are rare occurrences in normal individuals, but they occur commonly inpatients with type 1 diabetes mellitus (T1DM) due to a dysfunction of peripheral glucose-insulin-glucagon responses and non-physiologic doses of exogenous insulin, which imperfectly mimic normal physiology. These extremes can occur more frequently in children and adolescents with T1DM due to the inadequacies of insulin replacement therapy, events leading to the diagnosis [prolonged untreated hyperglycemia and diabetic ketoacidosis (DKA)], and to behavioral factors interfering with optimal treatment. When faced with fluctuations in glucose supply the metabolism of the body and brain change dramatically, largely to conserve resources and, at a cost to other organs, to preserve brain function (7). However,if the normal physiological mechanisms that prevent these severe glucose fluctuations and maintain homeostasis are impaired, neuronal function and potentially viability can be affected (8–11).
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Duquette, Lucien L; Mattiace, Frank; Blum, Kenneth; Waite, Roger L; Boland, Teresa; McLaughlin, Thomas; Dushaj, Kristina; Febo, Marcelo; Badgaiyan, Rajendra D
2017-01-01
Background With neurogenetic and epigenetic tools utilized in research and neuroimaging, we are unraveling the mysteries of brain function, especially as it relates to Reward Deficiency (RDS). We encourage the development of pharmaceuticals or nutraceuticals that promote a reduction in dopamine resistance and balance brain neurochemistry, leading to dopamine homeostasis. We disclose self-assessment of a highly functional professional under work-related stress following KB220Z use, a liquid (aqua) nano glutaminergic-dopaminergic optimization complex (GDOC). Case presentation Subject took GDOC for one month. Subject self-administered GDOC using one-half-ounce twice a day. During first three days, unique brain activation occurred; resembling white noise after 30 minutes and sensation was strong for 45 minutes and then dissipated. He described effect as if his eyesight improved slightly and pointed out that his sense of smell and sleep greatly improved. Subject experienced a calming effect similar to meditation that could be linked to dopamine release. He also reported control of going over the edge after a hard day’s work, which was coupled with a slight increase in energy, increased motivation to work, increased focus and multi-tasking, with clearer purpose of task at hand. Subject felt less inhibited in a social setting and suggested Syndrome that GDOC increased his Behavior Activating System (reward), while having a decrease in the Behavior Inhibition System (caution). Conclusion These results and other related studies reveal an improved mood, work-related focus, and sleep. These effects as a subjective feeling of brain activation maybe due to direct or indirect dopaminergic interaction. While this case is encouraging, we must await more research in a larger randomized placebo-controlled study to map the role of GDOC, especially in a nano-sized product, to determine the possible effects on circuit inhibitory control and memory banks and the induction of dopamine homeostasis independent of either hypo- or hyper-dopaminergic traits/states. PMID:29214221
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Perspective: Neuroregenerative Nutrition.
Steindler, Dennis A; Reynolds, Brent A
2017-07-01
Good health while aging depends upon optimal cellular and organ functioning that contribute to the regenerative ability of the body during the lifespan, especially when injuries and diseases occur. Although diet may help in the maintenance of cellular fitness during periods of stability or modest decline in the regenerative function of an organ, this approach is inadequate in an aged system, in which the ability to maintain homeostasis is further challenged by aging and the ensuing suboptimal functioning of the regenerative unit, tissue-specific stem cells. Focused nutritional approaches can be used as an intervention to reduce decline in the body's regenerative capacity. This article brings together nutrition-associated therapeutic approaches with the fields of aging, immunology, neurodegenerative disease, and cancer to propose ways in which diet and nutrition can work with standard-of-care and integrated medicine to help improve the brain's function as it ages. The field of regenerative medicine has exploded during the past 2 decades as a result of the discovery of stem cells in nearly every organ system of the body, including the brain, where neural stem cells persist in discrete areas throughout life. This fact, and the uncovering of the genetic basis of plasticity in somatic cells and cancer stem cells, open a door to a world where maintenance and regeneration of organ systems maintain health and extend life expectancy beyond its present limits. An area that has received little attention in regenerative medicine is the influence on regulatory mechanisms and therapeutic potential of nutrition. We propose that a strong relation exists between brain regenerative medicine and nutrition and that nutritional intervention at key times of life could be used to not only maintain optimal functioning of regenerative units as humans age but also play a primary role in therapeutic treatments to combat injury and diseases (in particular, those that occur in the latter one-third of the lifespan). © 2017 American Society for Nutrition.
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Blum, K; Febo, M; Badgaiyan, RD
2016-01-01
Dopamine along with other chemical messengers like serotonin, cannabinoids, endorphins and glutamine, play significant roles in brain reward processing. There is a devastating opiate/opioid epidemicin the United States. According to the Centers for Disease Control and Prevention (CDC), at least 127 people, young and old, are dying every day due to narcotic overdose and alarmingly heroin overdose is on the rise. The Food and Drug Administration (FDA) has approved some Medication-Assisted Treatments (MATs) for alcoholism, opiate and nicotine dependence, but nothing for psychostimulant and cannabis abuse. While these pharmaceuticals are essential for the short-term induction of “psychological extinction,” in the long-term caution is necessary because their use favors blocking dopaminergic function indispensable for achieving normal satisfaction in life. The two institutions devoted to alcoholism and drug dependence (NIAAA & NIDA) realize that MATs are not optimal and continue to seek better treatment options. We review, herein, the history of the development of a glutaminergic-dopaminergic optimization complex called KB220 to provide for the possible eventual balancing of the brain reward system and the induction of “dopamine homeostasis.” This complex may provide substantial clinical benefit to the victims of Reward Deficiency Syndrome (RDS) and assist in recovery from iatrogenically induced addiction to unwanted opiates/opioids and other addictive behaviors. PMID:27840857
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Zelinsky, Deborah; Feinberg, Corey
2017-01-01
Abstract. The brain is equipped with a complex system for processing sensory information, including retinal circuitry comprising part of the central nervous system. Retinal stimulation can influence brain function via customized eyeglasses at both subcortical and cortical levels. We investigated cortical effects from wearing therapeutic eyeglasses, hypothesizing that they can create measureable changes in electroencephalogram (EEG) tracings. A Z-BellSM test was performed on a participant to select optimal lenses. An EEG measurement was recorded before and after the participant wore the eyeglasses. Equivalent quantitative electroencephalography (QEEG) analyses (statistical analysis on raw EEG recordings) were performed and compared with baseline findings. With glasses on, the participant’s readings were found to be closer to the normed database. The original objective of our investigation was met, and additional findings were revealed. The Z-bellSM test identified lenses to influence neurotypical brain activity, supporting the paradigm that eyeglasses can be utilized as a therapeutic intervention. Also, EEG analysis demonstrated that encephalographic techniques can be used to identify channels through which neuro-optomertric treatments work. This case study’s preliminary exploration illustrates the potential role of QEEG analysis and EEG-derived brain imaging in neuro-optometric research endeavors to affect brain function. PMID:28386574
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Renewal Processes in the Critical Brain
NASA Astrophysics Data System (ADS)
Allegrini, Paolo; Paradisi, Paolo; Menicucci, Danilo; Gemignani, Angelo
We describe herein a multidisciplinary research, as it developes and applies concepts of the theory of complexity, in turn stemming from recent advancements of statistical physics, onto cognitive neuroscience. We discuss (define) complexity, and how the human brain is a paradigm of it. We discuss how the hypothesis of brain activity dynamically behaving as a critical system is taking momentum in literature, then we focus on a feature of critical systems (hence of the brain), which is the intermittent passage between metastable states, marked by events, locally resetting the memory, but giving rise to correlation functions with infinite correlation times. The events, extracted from multi-channel ElectroEncephaloGrams, mark (are interpreted as) a birth/death process of cooperation, namely of system elements being recruited into collective states. Finally we discuss a recently discovered form of control (in the form of a new Linear Response Theory), that allows an optimized information transmission between complex systems, named Complexity Matching.
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Surface chemistry governs cellular tropism of nanoparticles in the brain
NASA Astrophysics Data System (ADS)
Song, Eric; Gaudin, Alice; King, Amanda R.; Seo, Young-Eun; Suh, Hee-Won; Deng, Yang; Cui, Jiajia; Tietjen, Gregory T.; Huttner, Anita; Saltzman, W. Mark
2017-05-01
Nanoparticles are of long-standing interest for the treatment of neurological diseases such as glioblastoma. Most past work focused on methods to introduce nanoparticles into the brain, suggesting that reaching the brain interstitium will be sufficient to ensure therapeutic efficacy. However, optimized nanoparticle design for drug delivery to the central nervous system is limited by our understanding of their cellular deposition in the brain. Here, we investigated the cellular fate of poly(lactic acid) nanoparticles presenting different surface chemistries, after administration by convection-enhanced delivery. We demonstrate that nanoparticles with `stealth' properties mostly avoid internalization by all cell types, but internalization can be enhanced by functionalization with bio-adhesive end-groups. We also show that association rates measured in cultured cells predict the extent of internalization of nanoparticles in cell populations. Finally, evaluating therapeutic efficacy in an orthotopic model of glioblastoma highlights the need to balance significant uptake without inducing adverse toxicity.
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Dagar, Snigdha; Chowdhury, Shubhajit Roy; Bapi, Raju Surampudi; Dutta, Anirban; Roy, Dipanjan
2016-01-01
Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The poststroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS) techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG) and functional-near-infrared spectroscopy (fNIRS) can be leveraged for Brain State Dependent Electrotherapy (BSDE). In this hypothesis and theory article, we propose a computational approach based on excitation–inhibition (E–I) balance hypothesis to objectively quantify the poststroke individual brain state using online fNIRS–EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local E–I (that is the ratio of Glutamate/GABA), which may be targeted with NIBS using a computational pipeline that includes individual “forward models” to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons, which can be captured with E–I-based brain models. Furthermore, E–I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing, which can then be implicated in changes in function. We first review the evidence that shows how this local imbalance between E–I leading to functional dysfunction can be restored in targeted sites with NIBS (motor cortex and somatosensory cortex) resulting in large-scale plastic reorganization over the cortex, and probably facilitating recovery of functions. Second, we show evidence how BSDE based on E–I balance hypothesis may target a specific brain site or network as an adjuvant treatment. Hence, computational neural mass model-based integration of neurostimulation with online neuroimaging systems may provide less ambiguous, robust optimization of NIBS, and its application in neurological conditions and disorders across individual patients. PMID:27551273
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Visual analytics of brain networks.
Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming
2012-05-15
Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.
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Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang
2017-01-01
Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926
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Cysique, Lucette A; Brew, Bruce J
2014-07-01
The effect of HIV and aging on brain functions is an increasingly important topic of research: HIV-infected (HIV+) persons aged ≥50 represent a growing part of the HIV epidemic. Research is embracing this new axis, but there has been a lack of conceptualization of the factors that are at stake in both aging and HIV. To start to remedy this theoretical limitation, we are proposing a research framework in the hope that it will optimize how research questions and findings are formulated. Moreover, in the light of this proposed research framework, we review the last 3 years' research findings. Our review highlights that as HIV+ persons are aging, there is some signal for acceleration of normal aging processes and facilitated expression of age-associated diseases. Evidence for dramatic neurodegeneration in aging HIV+ persons remains limited and may be different in nature to typical neurodegenerative processes. Also, it should be kept in mind that most HIV+ persons are still below age 60. The vast majority of studies are still cross-sectional thereby underlining the critical importance of longitudinal studies to fully assess the effect of comorbidities. The complex effects of aging and nonaging comorbidities and key HIV effects (as opposed to only HIV status) need to be taken into account in future research by increasing sample size and selecting the most appropriate control group(s). Ideally, life-span studies should be established using neuropsychological and neuroimaging outcomes that have a proven track record in both HIV-related brain injury and brain aging. These would be similar to those that exist in non-HIV aging research and would optimally account for comorbidity effects and survivor bias.
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Cross-entropy optimization for neuromodulation.
Brar, Harleen K; Yunpeng Pan; Mahmoudi, Babak; Theodorou, Evangelos A
2016-08-01
This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the natural seizure suppression mechanism within the brain circuitry. Normal and pathological brain activity is simulated in this model by adjusting the feedback gain values of the internal controller. With insufficient gains, the internal controller cannot provide enough feedback to the brain dynamics causing an increase in correlation between different brain sites. This increase in synchronization results in the destabilization of the brain dynamics, which is representative of an epileptic seizure. To provide compensation for an insufficient internal controller an external controller is designed using proportional-integral feedback control strategy. A cross-entropy optimization algorithm is applied to the chaotic oscillator network model to learn the optimal feedback gains for the external controller instead of hand-tuning the gains to provide sufficient control to the pathological brain and prevent seizure generation. The correlation between the dynamics of neural activity within different brain sites is calculated for experimental data to show similar dynamics of epileptic neural activity as simulated by the network of chaotic oscillators.
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NASA Astrophysics Data System (ADS)
Wakamatsu, Hidetoshi; Gaohua, Lu
Various surface-cooling apparatus such as the cooling cap, muffler and blankets have been commonly used for the cooling of the brain to provide hypothermic neuro-protection for patients of hypoxic-ischemic encephalopathy. The present paper is aimed at the brain temperature regulation from the viewpoint of automatic system control, in order to help clinicians decide an optimal temperature of the cooling fluid provided for these three types of apparatus. At first, a biothermal model characterized by dynamic ambient temperatures is constructed for adult patient, especially on account of the clinical practice of hypothermia and anesthesia in the brain hypothermia treatment. Secondly, the model is represented by the state equation as a lumped parameter linear dynamic system. The biothermal model is justified from their various responses corresponding to clinical phenomena and treatment. Finally, the optimal regulator is tentatively designed to give clinicians some suggestions on the optimal temperature regulation of the patient’s brain. It suggests the patient’s brain temperature could be optimally controlled to follow-up the temperature process prescribed by the clinicians. This study benefits us a great clinical possibility for the automatic hypothermia treatment.
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Mapping brain development during childhood, adolescence and young adulthood
NASA Astrophysics Data System (ADS)
Guo, Xiaojuan; Jin, Zhen; Chen, Kewei; Peng, Danling; Li, Yao
2009-02-01
Using optimized voxel-based morphometry (VBM), this study systematically investigated the differences and similarities of brain structural changes during the early three developmental periods of human lives: childhood, adolescence and young adulthood. These brain changes were discussed in relationship to the corresponding cognitive function development during these three periods. Magnetic Resonance Imaging (MRI) data from 158 Chinese healthy children, adolescents and young adults, aged 7.26 to 22.80 years old, were included in this study. Using the customized brain template together with the gray matter/white matter/cerebrospinal fluid prior probability maps, we found that there were more age-related positive changes in the frontal lobe, less in hippocampus and amygdala during childhood, but more in bilateral hippocampus and amygdala and left fusiform gyrus during adolescence and young adulthood. There were more age-related negative changes near to central sulcus during childhood, but these changes extended to the frontal and parietal lobes, mainly in the parietal lobe, during adolescence and young adulthood, and more in the prefrontal lobe during young adulthood. So gray matter volume in the parietal lobe significantly decreased from childhood and continued to decrease till young adulthood. These findings may aid in understanding the age-related differences in cognitive function.
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Understanding and enhancing motor recovery after stroke using transcranial magnetic stimulation
Hoyer, Erik H.; Celnik, Pablo A.
2013-01-01
Stroke is the leading cause of long-term disability. Understanding how people recover from stroke and other brain lesions remain one of the biggest conundrums in neuroscience. As a result, concerted efforts in recent years have focused on investigating the neurophysiological changes that occur in the brain after stroke, and in developing novel strategies to enhance motor recovery. In particular, transcranial magnetic stimulation (TMS) is a non-invasive tool that has been used to investigate the brain plasticity changes resulting from stroke and as a therapeutic modality to safely improve motor function. In this review, we discuss the contributions of TMS to understand how different motor areas, such as the ipsilesional hemisphere, secondary motor areas, and contralesional hemisphere are involved in motor recovery. We also consider recent studies using repetitive TMS (rTMS) in stroke patients to enhance upper extremity function. Although further studies are needed, these investigations provide an important starting point to understand the stimulation parameters and patient characteristics that may influence the optimal response to non-invasive brain stimulation. Future directions of rTMS are discussed in the context of post-stroke motor recovery. PMID:22124033
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Yang, Tianzhi; Martin, Paige; Fogarty, Brittany; Brown, Alison; Schurman, Kayla; Phipps, Roger; Yin, Viravuth P; Lockman, Paul; Bai, Shuhua
2015-06-01
The blood-brain barrier (BBB) essentially restricts therapeutic drugs from entering into the brain. This study tests the hypothesis that brain endothelial cell derived exosomes can deliver anticancer drug across the BBB for the treatment of brain cancer in a zebrafish (Danio rerio) model. Four types of exosomes were isolated from brain cell culture media and characterized by particle size, morphology, total protein, and transmembrane protein markers. Transport mechanism, cell uptake, and cytotoxicity of optimized exosome delivery system were tested. Brain distribution of exosome delivered anticancer drugs was evaluated using transgenic zebrafish TG (fli1: GFP) embryos and efficacies of optimized formations were examined in a xenotransplanted zebrafish model of brain cancer model. Four exosomes in 30-100 diameters showed different morphologies and exosomes derived from brain endothelial cells expressed more CD63 tetraspanins transmembrane proteins. Optimized exosomes increased the uptake of fluorescent marker via receptor mediated endocytosis and cytotoxicity of anticancer drugs in cancer cells. Images of the zebrafish showed exosome delivered anticancer drugs crossed the BBB and entered into the brain. In the brain cancer model, exosome delivered anticancer drugs significantly decreased fluorescent intensity of xenotransplanted cancer cells and tumor growth marker. Brain endothelial cell derived exosomes could be potentially used as a carrier for brain delivery of anticancer drug for the treatment of brain cancer.
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Uncovering the mechanism(s) of deep brain stimulation
NASA Astrophysics Data System (ADS)
Gang, Li; Chao, Yu; Ling, Lin; C-Y Lu, Stephen
2005-01-01
Deep brain stimulators, often called `pacemakers for the brain', are implantable devices which continuously deliver impulse stimulation to specific targeted nuclei of deep brain structure, namely deep brain stimulation (DBS). To date, deep brain stimulation (DBS) is the most effective clinical technique for the treatment of several medically refractory movement disorders (e.g., Parkinson's disease, essential tremor, and dystonia). In addition, new clinical applications of DBS for other neurologic and psychiatric disorders (e.g., epilepsy and obsessive-compulsive disorder) have been put forward. Although DBS has been effective in the treatment of movement disorders and is rapidly being explored for the treatment of other neurologic disorders, the scientific understanding of its mechanisms of action remains unclear and continues to be debated in the scientific community. Optimization of DBS technology for present and future therapeutic applications will depend on identification of the therapeutic mechanism(s) of action. The goal of this review is to address our present knowledge of the effects of high-frequency stimulation within the central nervous system and comment on the functional implications of this knowledge for uncovering the mechanism(s) of DBS.
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Wu, Qixue; Snyder, Karen Chin; Liu, Chang; Huang, Yimei; Zhao, Bo; Chetty, Indrin J; Wen, Ning
2016-09-30
Treatment of patients with multiple brain metastases using a single-isocenter volumetric modulated arc therapy (VMAT) has been shown to decrease treatment time with the tradeoff of larger low dose to the normal brain tissue. We have developed an efficient Projection Summing Optimization Algorithm to optimize the treatment geometry in order to reduce dose to normal brain tissue for radiosurgery of multiple metastases with single-isocenter VMAT. The algorithm: (a) measures coordinates of outer boundary points of each lesion to be treated using the Eclipse Scripting Application Programming Interface, (b) determines the rotations of couch, collimator, and gantry using three matrices about the cardinal axes, (c) projects the outer boundary points of the lesion on to Beam Eye View projection plane, (d) optimizes couch and collimator angles by selecting the least total unblocked area for each specific treatment arc, and (e) generates a treatment plan with the optimized angles. The results showed significant reduction in the mean dose and low dose volume to normal brain, while maintaining the similar treatment plan qualities on the thirteen patients treated previously. The algorithm has the flexibility with regard to the beam arrangements and can be integrated in the treatment planning system for clinical application directly.
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Li, Weikai; Wang, Zhengxia; Zhang, Limei; Qiao, Lishan; Shen, Dinggang
2017-01-01
Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L 1 -norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods.
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Computationally optimized ECoG stimulation with local safety constraints.
Guler, Seyhmus; Dannhauer, Moritz; Roig-Solvas, Biel; Gkogkidis, Alexis; Macleod, Rob; Ball, Tonio; Ojemann, Jeffrey G; Brooks, Dana H
2018-06-01
Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.
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Detecting recurrence domains of dynamical systems by symbolic dynamics.
beim Graben, Peter; Hutt, Axel
2013-04-12
We propose an algorithm for the detection of recurrence domains of complex dynamical systems from time series. Our approach exploits the characteristic checkerboard texture of recurrence domains exhibited in recurrence plots. In phase space, recurrence plots yield intersecting balls around sampling points that could be merged into cells of a phase space partition. We construct this partition by a rewriting grammar applied to the symbolic dynamics of time indices. A maximum entropy principle defines the optimal size of intersecting balls. The final application to high-dimensional brain signals yields an optimal symbolic recurrence plot revealing functional components of the signal.
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Erdoğan, Sinem B; Tong, Yunjie; Hocke, Lia M; Lindsey, Kimberly P; deB Frederick, Blaise
2016-01-01
Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, "dynamic global signal regression" (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional "static" global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.
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Optimized temporal pattern of brain stimulation designed by computational evolution
Brocker, David T.; Swan, Brandon D.; So, Rosa Q.; Turner, Dennis A.; Gross, Robert E.; Grill, Warren M.
2017-01-01
Brain stimulation is a promising therapy for several neurological disorders, including Parkinson’s disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We used the temporal pattern of stimulation as a novel parameter of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson’s disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in the parkinsonian rat and in patients. Both optimized and standard stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution to design temporal pattern of stimulation to increase the efficiency of brain stimulation in Parkinson’s disease, thereby requiring substantially less energy than traditional brain stimulation. PMID:28053151
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NASA Astrophysics Data System (ADS)
Tang, Evelyn; Giusti, Chad; Baum, Graham; Gu, Shi; Pollock, Eli; Kahn, Ari; Roalf, David; Moore, Tyler; Ruparel, Kosha; Gur, Ruben; Gur, Raquel; Satterthwaite, Theodore; Bassett, Danielle
Motivated by a recent demonstration that the network architecture of white matter supports emerging control of diverse neural dynamics as children mature into adults, we seek to investigate structural mechanisms that support these changes. Beginning from a network representation of diffusion imaging data, we simulate network evolution with a set of simple growth rules built on principles of network control. Notably, the optimal evolutionary trajectory displays a striking correspondence to the progression of child to adult brain, suggesting that network control is a driver of development. More generally, and in comparison to the complete set of available models, we demonstrate that all brain networks from child to adult are structured in a manner highly optimized for the control of diverse neural dynamics. Within this near-optimality, we observe differences in the predicted control mechanisms of the child and adult brains, suggesting that the white matter architecture in children has a greater potential to increasingly support brain state transitions, potentially underlying cognitive switching.
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Functional differences between statistical learning with and without explicit training
Reber, Paul J.; Paller, Ken A.
2015-01-01
Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly. PMID:26472644
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2002-12-01
sections of formalin-fixed guinea pig brains using different MAP-2 monoclonal antibodies. Brain sections were boiled in sodium citrate, citric acid...citric acid solution at pH 6.0 is the optimal microwave-assisted AR method for immunolabeling MAP-2 in formalin-fixed, paraffin-processed guinea pig brain...studies on archival guinea pig brain paraffin blocks, ultimately relaxing the use of additional animals to evaluate changes in MAP-2 expression between chemical warfare nerve agent-treated and control samples.
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Music Listening modulates Functional Connectivity and Information Flow in the Human Brain.
Karmonik, Christof; Brandt, Anthony; Anderson, Jeff; Brooks, Forrest; Lytle, Julie; Silverman, Elliott; Frazier, Jeff T
2016-07-27
Listening to familiar music has recently been reported to be beneficial during recovery from stroke. A better understanding of changes in functional connectivity and information flow is warranted in order to further optimize and target this approach through music therapy. Twelve healthy volunteers listened to seven different auditory samples during an fMRI scanning session: a musical piece chosen by the volunteer that evokes a strong emotional response (referred to as: "self-selected emotional"), two unfamiliar music pieces (Invention #1 by J. S. Bach* and Gagaku - Japanese classical opera, referred to as "unfamiliar"), the Bach piece repeated with visual guidance (DML: Directed Music Listening) and three spoken language pieces (unfamiliar African click language, an excerpt of emotionally charged language, and an unemotional reading of a news bulletin). Functional connectivity and betweenness (BTW) maps, a measure for information flow, were created with a graph-theoretical approach. Distinct variation in functional connectivity was found for different music pieces consistently for all subjects. Largest brain areas were recruited for processing self-selected music with emotional attachment or culturally unfamiliar music. Maps of information flow correlated significantly with fMRI BOLD activation maps (p<0.05). Observed differences in BOLD activation and functional connectivity may help explain previously observed beneficial effects in stroke recovery, as increased blood flow to damaged brain areas stimulated by active engagement through music listening may have supported a state more conducive to therapy.
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Confidence as Bayesian Probability: From Neural Origins to Behavior.
Meyniel, Florent; Sigman, Mariano; Mainen, Zachary F
2015-10-07
Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability distributions, and summary confidence, pertaining to scalar summaries of those distributions. Summary confidence is, normatively, derived or "read out" from distributional confidence. Neural implementations of readout will trade off optimality versus flexibility of routing across brain systems, allowing confidence to serve diverse cognitive functions. Copyright © 2015 Elsevier Inc. All rights reserved.
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Skull removal in MR images using a modified artificial bee colony optimization algorithm.
Taherdangkoo, Mohammad
2014-01-01
Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications.
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Multimodal Registration of White Matter Brain Data via Optimal Mass Transport.
Rehman, Tauseefur; Haber, Eldad; Pohl, Kilian M; Haker, Steven; Halle, Mike; Talos, Florin; Wald, Lawrence L; Kikinis, Ron; Tannenbaum, Allen
2008-09-01
The elastic registration of medical scans from different acquisition sequences is becoming an important topic for many research labs that would like to continue the post-processing of medical scans acquired via the new generation of high-field-strength scanners. In this note, we present a parameter-free registration algorithm that is well suited for this scenario as it requires no tuning to specific acquisition sequences. The algorithm encompasses a new numerical scheme for computing elastic registration maps based on the minimizing flow approach to optimal mass transport. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A . Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. We apply the algorithm to register the white matter folds of two different scans and use the results to parcellate the cortex of the target image. To the best of our knowledge, this is the first time that the optimal mass transport function has been applied to register large 3D multimodal data sets.
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Multimodal Registration of White Matter Brain Data via Optimal Mass Transport
Rehman, Tauseefur; Haber, Eldad; Pohl, Kilian M.; Haker, Steven; Halle, Mike; Talos, Florin; Wald, Lawrence L.; Kikinis, Ron; Tannenbaum, Allen
2017-01-01
The elastic registration of medical scans from different acquisition sequences is becoming an important topic for many research labs that would like to continue the post-processing of medical scans acquired via the new generation of high-field-strength scanners. In this note, we present a parameter-free registration algorithm that is well suited for this scenario as it requires no tuning to specific acquisition sequences. The algorithm encompasses a new numerical scheme for computing elastic registration maps based on the minimizing flow approach to optimal mass transport. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. We apply the algorithm to register the white matter folds of two different scans and use the results to parcellate the cortex of the target image. To the best of our knowledge, this is the first time that the optimal mass transport function has been applied to register large 3D multimodal data sets. PMID:28626844
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Contemplating the GANE model using an extreme case paradigm.
Geva, Ronny
2016-01-01
Early experiences play a crucial role in programming brain function, affecting selective attention, learning, and memory. Infancy literature suggests an extension of the GANE (glutamate amplifies noradrenergic effects) model to conditions with minimal priority-map inputs, yet suggests qualifications by noting that its efficacy is increased when tonic levels of arousal are maintained in an optimal range, in manners that are age and exposure dependent.
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Scheef, Lukas; Nordmeyer-Massner, Jurek A; Smith-Collins, Adam Pr; Müller, Nicole; Stegmann-Woessner, Gaby; Jankowski, Jacob; Gieseke, Jürgen; Born, Mark; Seitz, Hermann; Bartmann, Peter; Schild, Hans H; Pruessmann, Klaas P; Heep, Axel; Boecker, Henning
2017-01-01
Functional magnetic resonance imaging (fMRI) in neonates has been introduced as a non-invasive method for studying sensorimotor processing in the developing brain. However, previous neonatal studies have delivered conflicting results regarding localization, lateralization, and directionality of blood oxygenation level dependent (BOLD) responses in sensorimotor cortex (SMC). Amongst the confounding factors in interpreting neonatal fMRI studies include the use of standard adult MR-coils providing insufficient signal to noise, and liberal statistical thresholds, compromising clinical interpretation at the single subject level. Here, we employed a custom-designed neonatal MR-coil adapted and optimized to the head size of a newborn in order to improve robustness, reliability and validity of neonatal sensorimotor fMRI. Thirteen preterm infants with a median gestational age of 26 weeks were scanned at term-corrected age using a prototype 8-channel neonatal head coil at 3T (Achieva, Philips, Best, NL). Sensorimotor stimulation was elicited by passive extension/flexion of the elbow at 1 Hz in a block design. Analysis of temporal signal to noise ratio (tSNR) was performed on the whole brain and the SMC, and was compared to data acquired with an 'adult' 8 channel head coil published previously. Task-evoked activation was determined by single-subject SPM8 analyses, thresholded at p < 0.05, whole-brain FWE-corrected. Using a custom-designed neonatal MR-coil, we found significant positive BOLD responses in contralateral SMC after unilateral passive sensorimotor stimulation in all neonates (analyses restricted to artifact-free data sets = 8/13). Improved imaging characteristics of the neonatal MR-coil were evidenced by additional phantom and in vivo tSNR measurements: phantom studies revealed a 240% global increase in tSNR; in vivo studies revealed a 73% global and a 55% local (SMC) increase in tSNR, as compared to the 'adult' MR-coil. Our findings strengthen the importance of using optimized coil settings for neonatal fMRI, yielding robust and reproducible SMC activation at the single subject level. We conclude that functional lateralization of SMC activation, as found in children and adults, is already present in the newborn period.
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Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction
Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.
2018-01-01
Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870
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Molina, Eric Suero; Stummer, Walter
2017-12-29
Spinal cord and brain stem lesions require a judicious approach with an optimized trajectory due to a clustering of functions on their surfaces. Intraoperative mapping helps locate function. To confidently locate such lesions, neuronavigation alone lacks the desired accuracy and is of limited use in the spinal cord. To evaluate the clinical value of fluoresceins for initial delineation of such critically located lesions. We evaluated fluorescein guidance in the surgical resection of lesions with blood-brain barrier disruption demonstrating contrast enhancement in magnet resonance imaging in the spinal cord and in the brain stem in 3 different patients. Two patients harbored a diffuse cervical and thoracic spinal cord lesion, respectively. Another patient suffered metastatic lesions in the brain stem and at the floor of the fourth ventricle. Low-dose fluorescein (4 mg/kg body weight) was applied after anesthesia induction and visualized using the Zeiss Pentero 900 Yellow560 filter (Carl Zeiss, Oberkochen, Germany). Fluorescein was helpful for locating lesions and for defining the best possible trajectory. During resection, however, we found unspecific propagation of fluorescein within the brain stem up to 6 mm within 3 h after application. As these lesions were otherwise distinguishable from surrounding tissue, monitoring resection was not an issue. Fluorescein guidance is a feasible tool for defining surgical entry zones when aiming for surgical removal of spinal cord and brain stem lesions. Unselective fluorescein extravasation cautions against using such methodology for monitoring completeness of resection. Providing the right timing, a window of pseudoselectivity could increase fluoresceins' clinical value in these cases. © Congress of Neurological Surgeons 2017.
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Using connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd
2017-01-01
Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017
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Sun, Yu; Lim, Julian; Dai, Zhongxiang; Wong, KianFoong; Taya, Fumihiko; Chen, Yu; Li, Junhua; Thakor, Nitish; Bezerianos, Anastasios
2017-05-15
Although rest breaks are commonly administered as a countermeasure to reduce mental fatigue and boost cognitive performance, the effects of taking a break on behavior are not consistent. Moreover, our understanding of the underlying neural mechanisms of rest breaks and how they modulate mental fatigue is still rudimentary. In this study, we investigated the effects of receiving a rest break on the topological properties of brain connectivity networks via a two-session experimental paradigm, in which one session comprised four successive blocks of a mentally demanding visual selective attention task (No-rest session), whereas the other contained a rest break between the second and third task blocks (Rest session). Functional brain networks were constructed using resting-state functional MRI data recorded from 20 healthy adults before and after the performance of the task blocks. Behaviorally, subjects displayed robust time-on-task (TOT) declines, as reflected by increasingly slower reaction time as the test progressed and lower post-task self-reported ratings of engagement. However, we did not find a significant effect on task performance due to administering a mid-task break. Compared to pre-task measurements, post-task functional brain networks demonstrated an overall decrease of optimal small-world properties together with lower global efficiency. Specifically, we found TOT-related reduced nodal efficiency in brain regions that mainly resided in the subcortical areas. More interestingly, a significant block-by-session interaction was revealed in local efficiency, attributing to a significant post-task decline in No-rest session and a preserved local efficiency when a mid-task break opportunity was introduced in the Rest session. Taken together, these findings augment our understanding of how the resting brain reorganizes following the accumulation of prolonged task, suggest dissociable processes between the neural mechanisms of fatigue and recovery, and provide some of the first quantitative insights into the cognitive neuroscience of work and rest. Copyright © 2017 Elsevier Inc. All rights reserved.
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The free-energy principle: a unified brain theory?
Friston, Karl
2010-02-01
A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.
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Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German
2017-01-01
We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897
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Information properties of morphologically complex words modulate brain activity during word reading.
Hakala, Tero; Hultén, Annika; Lehtonen, Minna; Lagus, Krista; Salmelin, Riitta
2018-06-01
Neuroimaging studies of the reading process point to functionally distinct stages in word recognition. Yet, current understanding of the operations linked to those various stages is mainly descriptive in nature. Approaches developed in the field of computational linguistics may offer a more quantitative approach for understanding brain dynamics. Our aim was to evaluate whether a statistical model of morphology, with well-defined computational principles, can capture the neural dynamics of reading, using the concept of surprisal from information theory as the common measure. The Morfessor model, created for unsupervised discovery of morphemes, is based on the minimum description length principle and attempts to find optimal units of representation for complex words. In a word recognition task, we correlated brain responses to word surprisal values derived from Morfessor and from other psycholinguistic variables that have been linked with various levels of linguistic abstraction. The magnetoencephalography data analysis focused on spatially, temporally and functionally distinct components of cortical activation observed in reading tasks. The early occipital and occipito-temporal responses were correlated with parameters relating to visual complexity and orthographic properties, whereas the later bilateral superior temporal activation was correlated with whole-word based and morphological models. The results show that the word processing costs estimated by the statistical Morfessor model are relevant for brain dynamics of reading during late processing stages. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Connecting the Brain to Itself through an Emulation
Serruya, Mijail D.
2017-01-01
Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions. PMID:28713235
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[Attentional impairment after traumatic brain injury: assessment and rehabilitation].
Ríos-Lago, M; Muñoz-Céspedes, J M; Paúl-Lapedriza, N
Attention disorders are a major problem after traumatic brain injury underlying deficits in other cognitive functions and in everyday activities, hindering the rehabilitation process and the possibility of return to work. Functional neuroimaging and neuropsychological assessment have depicted theoretical models considering attention as a complex and non-unitary process. Although there are conceptual difficulties, it seems possible to establish a theoretical background to better define attentional impairments and to guide the rehabilitation process. The aim of the present study is to review some of the most important pieces involved in the assessment and rehabilitation of attentional impairments. We also propose an appropriate model for the design of individualized rehabilitation programs. Lastly, different approaches for the rehabilitation are reviewed. Neuropsychological assessment should provide valuable strategies to better design the cognitive rehabilitation programs. It is necessary to establish a link between basic and applied neuropsychology, in order to optimize the treatments for traumatic brain injury patients. It is also emphasized that well-defined cognitive targets and skills are required, given that an unspecific stimulation of cognitive processes (pseudorehabilitation) has been shown to be unsuccessful.
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Selenium in the Therapy of Neurological Diseases. Where is it Going?
Dominiak, Agnieszka; Wilkaniec, Anna; Wroczyńsk, Piotr; Adamczyk, Agata
2016-01-01
Selenium (34Se), an antioxidant trace element, is an important regulator of brain function. These beneficial properties that Se possesses are attributed to its ability to be incorporated into selenoproteins as an amino acid. Several selenoproteins are expressed in the brain, in which some of them, e.g. glutathione peroxidases (GPxs), thioredoxin reductases (TrxRs) or selenoprotein P (SelP), are strongly involved in antioxidant defence and in maintaining intercellular reducing conditions. Since increased oxidative stress has been implicated in neurological disorders, including Parkinson’s disease, Alzheimer’s disease, stroke, epilepsy and others, a growing body of evidence suggests that Se depletion followed by decreased activity of Se-dependent enzymes may be important factors connected with those pathologies. Undoubtedly, the remarkable progress that has been made in understanding the biological function of Se in the brain has opened up new potential possibilities for the treatment of neurological diseases by using Se as a potential drug. However, further research in the search for optimal Se donors is necessary in order to achieve an effective and safe therapeutic income. PMID:26549649
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Brain mesenchymal stem cells: physiology and pathological implications.
Pombero, Ana; Garcia-Lopez, Raquel; Martinez, Salvador
2016-06-01
Mesenchymal stem cells (MSCs) are defined as progenitor cells that give rise to a number of unique, differentiated mesenchymal cell types. This concept has progressively evolved towards an all-encompassing concept including multipotent perivascular cells of almost any tissue. In central nervous system, pericytes are involved in blood-brain barrier, and angiogenesis and vascular tone regulation. They form the neurovascular unit (NVU) together with endothelial cells, astrocytes and neurons. This functional structure provides an optimal microenvironment for neural proliferation in the adult brain. Neurovascular niche include both diffusible signals and direct contact with endothelial and pericytes, which are a source of diffusible neurotrophic signals that affect neural precursors. Therefore, MSCs/pericyte properties such as differentiation capability, as well as immunoregulatory and paracrine effects make them a potential resource in regenerative medicine. © 2016 Japanese Society of Developmental Biologists.
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Post-traumatic stress disorder vs traumatic brain injury
Bryant, Richard
2011-01-01
Post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) often coexist because brain injuries are often sustained in traumatic experiences. This review outlines the significant overlap between PTSD and TBI by commencing with a critical outline of the overlapping symptoms and problems of differential diagnosis. The impact of TBI on PTSD is then described, with increasing evidence suggesting that mild TBI can increase risk for PTSD. Several explanations are offered for this enhanced risk. Recent evidence suggests that impairment secondary to mild TBI is largely attributable to stress reactions after TBI, which challenges the long-held belief that postconcussive symptoms are a function of neurological insult This recent evidence is pointing to new directions for treatment of postconcussive symptoms that acknowledge that treating stress factors following TBI may be the optimal means to manage the effects of many TBIs, PMID:22034252
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van Dokkum, L E H; Ward, T; Laffont, I
2015-02-01
The idea of using brain computer interfaces (BCI) for rehabilitation emerged relatively recently. Basically, BCI for neurorehabilitation involves the recording and decoding of local brain signals generated by the patient, as he/her tries to perform a particular task (even if imperfect), or during a mental imagery task. The main objective is to promote the recruitment of selected brain areas involved and to facilitate neural plasticity. The recorded signal can be used in several ways: (i) to objectify and strengthen motor imagery-based training, by providing the patient feedback on the imagined motor task, for example, in a virtual environment; (ii) to generate a desired motor task via functional electrical stimulation or rehabilitative robotic orthoses attached to the patient's limb – encouraging and optimizing task execution as well as "closing" the disrupted sensorimotor loop by giving the patient the appropriate sensory feedback; (iii) to understand cerebral reorganizations after lesion, in order to influence or even quantify plasticity-induced changes in brain networks. For example, applying cerebral stimulation to re-equilibrate inter-hemispheric imbalance as shown by functional recording of brain activity during movement may help recovery. Its potential usefulness for a patient population has been demonstrated on various levels and its diverseness in interface applications makes it adaptable to a large population. The position and status of these very new rehabilitation systems should now be considered with respect to our current and more or less validated traditional methods, as well as in the light of the wide range of possible brain damage. The heterogeneity in post-damage expression inevitably complicates the decoding of brain signals and thus their use in pathological conditions, asking for controlled clinical trials. Copyright © 2015. Published by Elsevier Masson SAS.
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Topological Isomorphisms of Human Brain and Financial Market Networks
Vértes, Petra E.; Nicol, Ruth M.; Chapman, Sandra C.; Watkins, Nicholas W.; Robertson, Duncan A.; Bullmore, Edward T.
2011-01-01
Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets. PMID:22007161
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Sakadzić, Sava; Roussakis, Emmanuel; Yaseen, Mohammad A; Mandeville, Emiri T; Srinivasan, Vivek J; Arai, Ken; Ruvinskaya, Svetlana; Devor, Anna; Lo, Eng H; Vinogradov, Sergei A; Boas, David A
2010-09-01
Measurements of oxygen partial pressure (pO(2)) with high temporal and spatial resolution in three dimensions is crucial for understanding oxygen delivery and consumption in normal and diseased brain. Among existing pO(2) measurement methods, phosphorescence quenching is optimally suited for the task. However, previous attempts to couple phosphorescence with two-photon laser scanning microscopy have faced substantial difficulties because of extremely low two-photon absorption cross-sections of conventional phosphorescent probes. Here we report to our knowledge the first practical in vivo two-photon high-resolution pO(2) measurements in small rodents' cortical microvasculature and tissue, made possible by combining an optimized imaging system with a two-photon-enhanced phosphorescent nanoprobe. The method features a measurement depth of up to 250 microm, sub-second temporal resolution and requires low probe concentration. The properties of the probe allowed for direct high-resolution measurement of cortical extravascular (tissue) pO(2), opening many possibilities for functional metabolic brain studies.
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Visual cortical areas of the mouse: comparison of parcellation and network structure with primates
Laramée, Marie-Eve; Boire, Denis
2015-01-01
Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals. PMID:25620914
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Visual cortical areas of the mouse: comparison of parcellation and network structure with primates.
Laramée, Marie-Eve; Boire, Denis
2014-01-01
Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals.
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Neuromonitoring after major neurosurgical procedures.
Messerer, M; Daniel, R T; Oddo, M
2012-07-01
Postoperative care of major neurosurgical procedures is aimed at the prevention, detection and treatment of secondary brain injury. This consists of a series of pathological events (i.e. brain edema and intracranial hypertension, cerebral hypoxia/ischemia, brain energy dysfunction, non-convulsive seizures) that occur early after the initial insult and surgical intervention and may add further burden to primary brain injury and thus impact functional recovery. Management of secondary brain injury requires specialized neuroscience intensive care units (ICU) and continuous advanced monitoring of brain physiology. Monitoring of intracranial pressure (ICP) is a mainstay of care and is recommended by international guidelines. However, ICP monitoring alone may be insufficient to detect all episodes of secondary brain insults. Additional invasive (i.e. brain tissue PO2, cerebral microdialysis, regional cerebral blood flow) and non-invasive (i.e. transcranial doppler, near-infrared spectroscopy, EEG) brain monitoring devices might complement ICP monitoring and help clinicians to target therapeutic interventions (e.g. management of cerebral perfusion pressure, blood transfusion, glucose control) to patient-specific pathophysiology. Several independent studies demonstrate such multimodal approach may optimize patient care after major neurosurgical procedures. The aim of this review is to evaluate some of the available monitoring systems and summarize recent important data showing the clinical utility of multimodal neuromonitoring for the management of main acute neurosurgical conditions, including traumatic brain injury, subarachnoid hemorrhage and stroke.
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Li, Mingfen; Liu, Ye; Wu, Yi; Liu, Sirao; Jia, Jie; Zhang, Liqing
2014-06-01
We investigated the efficacy of motor imagery-based Brain Computer Interface (MI-based BCI) training for eight stroke patients with severe upper extremity paralysis using longitudinal clinical assessments. The results were compared with those of a control group (n = 7) that only received FES (Functional Electrical Stimulation) treatment besides conventional therapies. During rehabilitation training, changes in the motor function of the upper extremity and in the neurophysiologic electroencephalographic (EEG) were observed for two groups. After 8 weeks of training, a significant improvement in the motor function of the upper extremity for the BCI group was confirmed (p < 0.05 for ARAT), simultaneously with the activation of bilateral cerebral hemispheres. Additionally, event-related desynchronization (ERD) of the affected sensorimotor cortexes (SMCs) was significantly enhanced when compared to the pretraining course, which was only observed in the BCI group (p < 0.05). Furthermore, the activation of affected SMC and parietal lobe were determined to contribute to motor function recovery (p < 0.05). In brief, our findings demonstrate that MI-based BCI training can enhance the motor function of the upper extremity for stroke patients by inducing the optimal cerebral motor functional reorganization.
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Functional Medicine Approach to Traumatic Brain Injury.
Richer, Alice C
2017-08-01
Background: The U.S. military has seen dramatic increases in traumatic brain injuries (TBIs) among military personnel due to the nature of modern-day conflicts. Conventional TBI treatment for secondary brain injuries has suboptimal success rates, and patients, families, and healthcare professionals are increasingly turning to alternative medicine treatments. Objective: Effective treatments for the secondary injury cascades that occur after an initial brain trauma are unclear at this time. The goal of successful treatment options for secondary TBI injuries is to reduce oxidative stress, excitotoxicity, and inflammation while supporting mitochondrial functions and repair of membranes, synapses, and axons. Intervention: A new paradigm of medical care, known as functional medicine, is increasing in popularity and acceptance. Functional medicine combines conventional treatment methods with complementary, genetic, holistic, and nutritional therapies. The approach is to assess the patient as a whole person, taking into account the interconnectedness of the body and its unique reaction to disease, injury, and illness while working to restore balance and optimal health. Functional medicine treatment recommendations often include the use of acupuncture, Ayurveda, chiropractic manipulation, detoxification programs, herbal and homeopathic supplements, specialized diets, massage, meditation and mindfulness practices, neurobiofeedback, nutritional supplements, t'ai chi , and yoga. At present, some of these alternative treatments appear to be beneficial, but more research is needed to validate reported outcomes. Conclusions: Few clinical studies validate the effectiveness of alternative therapies for TBIs. However, further clinical trials and empirical studies warrant further investigation based on some reported positive results from research studies, case histories, anecdotal evidence, and widespread popularity of some approaches. To date, only nutritional therapies and hyperbaric oxygen therapy have shown the most promise and potential for improved outcomes for the treatment of secondary TBI injuries.
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Functional Medicine Approach to Traumatic Brain Injury
2017-01-01
Abstract Background: The U.S. military has seen dramatic increases in traumatic brain injuries (TBIs) among military personnel due to the nature of modern-day conflicts. Conventional TBI treatment for secondary brain injuries has suboptimal success rates, and patients, families, and healthcare professionals are increasingly turning to alternative medicine treatments. Objective: Effective treatments for the secondary injury cascades that occur after an initial brain trauma are unclear at this time. The goal of successful treatment options for secondary TBI injuries is to reduce oxidative stress, excitotoxicity, and inflammation while supporting mitochondrial functions and repair of membranes, synapses, and axons. Intervention: A new paradigm of medical care, known as functional medicine, is increasing in popularity and acceptance. Functional medicine combines conventional treatment methods with complementary, genetic, holistic, and nutritional therapies. The approach is to assess the patient as a whole person, taking into account the interconnectedness of the body and its unique reaction to disease, injury, and illness while working to restore balance and optimal health. Functional medicine treatment recommendations often include the use of acupuncture, Ayurveda, chiropractic manipulation, detoxification programs, herbal and homeopathic supplements, specialized diets, massage, meditation and mindfulness practices, neurobiofeedback, nutritional supplements, t'ai chi, and yoga. At present, some of these alternative treatments appear to be beneficial, but more research is needed to validate reported outcomes. Conclusions: Few clinical studies validate the effectiveness of alternative therapies for TBIs. However, further clinical trials and empirical studies warrant further investigation based on some reported positive results from research studies, case histories, anecdotal evidence, and widespread popularity of some approaches. To date, only nutritional therapies and hyperbaric oxygen therapy have shown the most promise and potential for improved outcomes for the treatment of secondary TBI injuries. PMID:28874921
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Understanding neuromotor strategy during functional upper extremity tasks using symbolic dynamics.
Nathan, Dominic E; Guastello, Stephen J; Prost, Robert W; Jeutter, Dean C
2012-01-01
The ability to model and quantify brain activation patterns that pertain to natural neuromotor strategy of the upper extremities during functional task performance is critical to the development of therapeutic interventions such as neuroprosthetic devices. The mechanisms of information flow, activation sequence and patterns, and the interaction between anatomical regions of the brain that are specific to movement planning, intention and execution of voluntary upper extremity motor tasks were investigated here. This paper presents a novel method using symbolic dynamics (orbital decomposition) and nonlinear dynamic tools of entropy, self-organization and chaos to describe the underlying structure of activation shifts in regions of the brain that are involved with the cognitive aspects of functional upper extremity task performance. Several questions were addressed: (a) How is it possible to distinguish deterministic or causal patterns of activity in brain fMRI from those that are really random or non-contributory to the neuromotor control process? (b) Can the complexity of activation patterns over time be quantified? (c) What are the optimal ways of organizing fMRI data to preserve patterns of activation, activation levels, and extract meaningful temporal patterns as they evolve over time? Analysis was performed using data from a custom developed time resolved fMRI paradigm involving human subjects (N=18) who performed functional upper extremity motor tasks with varying time delays between the onset of intention and onset of actual movements. The results indicate that there is structure in the data that can be quantified through entropy and dimensional complexity metrics and statistical inference, and furthermore, orbital decomposition is sensitive in capturing the transition of states that correlate with the cognitive aspects of functional task performance.
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The collective therapeutic potential of cerebral ketone metabolism in traumatic brain injury
Prins, Mayumi L.; Matsumoto, Joyce H.
2014-01-01
The postinjury period of glucose metabolic depression is accompanied by adenosine triphosphate decreases, increased flux of glucose through the pentose phosphate pathway, free radical production, activation of poly-ADP ribose polymerase via DNA damage, and inhibition of glyceraldehyde dehydrogenase (a key glycolytic enzyme) via depletion of the cytosolic NAD pool. Under these post-brain injury conditions of impaired glycolytic metabolism, glucose becomes a less favorable energy substrate. Ketone bodies are the only known natural alternative substrate to glucose for cerebral energy metabolism. While it has been demonstrated that other fuels (pyruvate, lactate, and acetyl-L-carnitine) can be metabolized by the brain, ketones are the only endogenous fuel that can contribute significantly to cerebral metabolism. Preclinical studies employing both pre- and postinjury implementation of the ketogenic diet have demonstrated improved structural and functional outcome in traumatic brain injury (TBI) models, mild TBI/concussion models, and spinal cord injury. Further clinical studies are required to determine the optimal method to induce cerebral ketone metabolism in the postinjury brain, and to validate the neuroprotective benefits of ketogenic therapy in humans. PMID:24721741
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Determining Optimal Post-Stroke Exercise (DOSE)
2018-02-13
Cerebrovascular Accident; Stroke; Cerebral Infarction; Brain Infarction; Brain Ischemia; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases
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Granular computing with multiple granular layers for brain big data processing.
Wang, Guoyin; Xu, Ji
2014-12-01
Big data is the term for a collection of datasets so huge and complex that it becomes difficult to be processed using on-hand theoretical models and technique tools. Brain big data is one of the most typical, important big data collected using powerful equipments of functional magnetic resonance imaging, multichannel electroencephalography, magnetoencephalography, Positron emission tomography, near infrared spectroscopic imaging, as well as other various devices. Granular computing with multiple granular layers, referred to as multi-granular computing (MGrC) for short hereafter, is an emerging computing paradigm of information processing, which simulates the multi-granular intelligent thinking model of human brain. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of information and even knowledge from data. This paper analyzes three basic mechanisms of MGrC, namely granularity optimization, granularity conversion, and multi-granularity joint computation, and discusses the potential of introducing MGrC into intelligent processing of brain big data.
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Mapping Epileptic Activity: Sources or Networks for the Clinicians?
Pittau, Francesca; Mégevand, Pierre; Sheybani, Laurent; Abela, Eugenio; Grouiller, Frédéric; Spinelli, Laurent; Michel, Christoph M.; Seeck, Margitta; Vulliemoz, Serge
2014-01-01
Epileptic seizures of focal origin are classically considered to arise from a focal epileptogenic zone and then spread to other brain regions. This is a key concept for semiological electro-clinical correlations, localization of relevant structural lesions, and selection of patients for epilepsy surgery. Recent development in neuro-imaging and electro-physiology and combinations, thereof, have been validated as contributory tools for focus localization. In parallel, these techniques have revealed that widespread networks of brain regions, rather than a single epileptogenic region, are implicated in focal epileptic activity. Sophisticated multimodal imaging and analysis strategies of brain connectivity patterns have been developed to characterize the spatio-temporal relationships within these networks by combining the strength of both techniques to optimize spatial and temporal resolution with whole-brain coverage and directional connectivity. In this paper, we review the potential clinical contribution of these functional mapping techniques as well as invasive electrophysiology in human beings and animal models for characterizing network connectivity. PMID:25414692
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Rodrigues, Dario B; Maccarini, Paolo F; Salahi, Sara; Oliveira, Tiago R; Pereira, Pedro J S; Limao-Vieira, Paulo; Snow, Brent W; Reudink, Doug; Stauffer, Paul R
2014-07-01
We present the modeling efforts on antenna design and frequency selection to monitor brain temperature during prolonged surgery using noninvasive microwave radiometry. A tapered log-spiral antenna design is chosen for its wideband characteristics that allow higher power collection from deep brain. Parametric analysis with the software HFSS is used to optimize antenna performance for deep brain temperature sensing. Radiometric antenna efficiency (η) is evaluated in terms of the ratio of power collected from brain to total power received by the antenna. Anatomical information extracted from several adult computed tomography scans is used to establish design parameters for constructing an accurate layered 3-D tissue phantom. This head phantom includes separate brain and scalp regions, with tissue equivalent liquids circulating at independent temperatures on either side of an intact skull. The optimized frequency band is 1.1-1.6 GHz producing an average antenna efficiency of 50.3% from a two turn log-spiral antenna. The entire sensor package is contained in a lightweight and low-profile 2.8 cm diameter by 1.5 cm high assembly that can be held in place over the skin with an electromagnetic interference shielding adhesive patch. The calculated radiometric equivalent brain temperature tracks within 0.4 °C of the measured brain phantom temperature when the brain phantom is lowered 10 °C and then returned to the original temperature (37 °C) over a 4.6-h experiment. The numerical and experimental results demonstrate that the optimized 2.5-cm log-spiral antenna is well suited for the noninvasive radiometric sensing of deep brain temperature.
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Brain Network Analysis from High-Resolution EEG Signals
NASA Astrophysics Data System (ADS)
de Vico Fallani, Fabrizio; Babiloni, Fabio
Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an effective methodology improving the comprehension of the complex interactions in the brain.
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NASA Astrophysics Data System (ADS)
Li, Ting; Gong, Hui; Luo, Qingming
2011-04-01
Using the visible Chinese human data set, which faithfully represents human anatomy, we visualize the light propagation in the head in detail based on Monte Carlo simulation. The simulation is verified to agree with published experimental results in terms of a differential path-length factor. The spatial sensitivity profile turns out to seem like a fat tropical fish with strong distortion along the folding cerebral surface. The sensitive brain region covers the gray matter and extends to the superficial white matter, leading to a large penetration depth (>3 cm). Finally, the optimal source-detector separation is suggested to be narrowed down to 3-3.5 cm, while the sensitivity of the detected signal to brain activation reaches the peak of 8%. These results indicate that the cerebral cortex folding geometry actually has substantial effects on light propagation, which should be necessarily considered for applications of functional near-infrared spectroscopy.
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Levac, Danielle; Miller, Patricia; Missiuna, Cheryl
2012-05-01
Little is known about how therapists promote learning of functional motor skills for children with acquired brain injuries. This study explores physiotherapists' description of these interventions in comparison to virtual reality (VR) video game-based therapy. Six physiotherapists employed at a children's rehabilitation center participated in semi-structured interviews, which were transcribed and analyzed using thematic analysis. Physiotherapists describe using interventions that motivate children to challenge performance quality and optimize real-life functioning. Intervention strategies are influenced by characteristics of the child, parent availability to practice skills outside therapy, and therapist experience. VR use motivates children to participate, but can influence therapist use of verbal strategies and complicate interventions. Physiotherapists consider unique characteristics of this population when providing interventions that promote learning of motor skills. The VR technology has advantageous features but its use with this population can be challenging; further research is recommended.
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Brain Morphometry on Congenital Hand Deformities based on Teichmüller Space Theory.
Peng, Hao; Wang, Xu; Duan, Ye; Frey, Scott H; Gu, Xianfeng
2015-01-01
Congenital Hand Deformities (CHD) are usually occurred between fourth and eighth week after the embryo is formed. Failure of the transformation from arm bud cells to upper limb can lead to an abnormal appearing/functioning upper extremity which is presented at birth. Some causes are linked to genetics while others are affected by the environment, and the rest have remained unknown. CHD patients develop prehension through the use of their hands, which affect the brain as time passes. In recent years, CHD have gain increasing attention and researches have been conducted on CHD, both surgically and psychologically. However, the impacts of CHD on brain structure are not well-understood so far. Here, we propose a novel approach to apply Teichmüller space theory and conformal welding method to study brain morphometry in CHD patients. Conformal welding signature reflects the geometric relations among different functional areas on the cortex surface, which is intrinsic to the Riemannian metric, invariant under conformal deformation, and encodes complete information of the functional area boundaries. The computational algorithm is based on discrete surface Ricci flow, which has theoretic guarantees for the existence and uniqueness of the solutions. In practice, discrete Ricci flow is equivalent to a convex optimization problem, therefore has high numerically stability. In this paper, we compute the signatures of contours on general 3D surfaces with surface Ricci flow method, which encodes both global and local surface contour information. Then we evaluated the signatures of pre-central and post-central gyrus on healthy control and CHD subjects for analyzing brain cortical morphometry. Preliminary experimental results from 3D MRI data of CHD/control data demonstrate the effectiveness of our method. The statistical comparison between left and right brain gives us a better understanding on brain morphometry of subjects with Congenital Hand Deformities, in particular, missing the distal part of the upper limb.
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Blood-brain barrier structure and function and the challenges for CNS drug delivery.
Abbott, N Joan
2013-05-01
The neurons of the central nervous system (CNS) require precise control of their bathing microenvironment for optimal function, and an important element in this control is the blood-brain barrier (BBB). The BBB is formed by the endothelial cells lining the brain microvessels, under the inductive influence of neighbouring cell types within the 'neurovascular unit' (NVU) including astrocytes and pericytes. The endothelium forms the major interface between the blood and the CNS, and by a combination of low passive permeability and presence of specific transport systems, enzymes and receptors regulates molecular and cellular traffic across the barrier layer. A number of methods and models are available for examining BBB permeation in vivo and in vitro, and can give valuable information on the mechanisms by which therapeutic agents and constructs permeate, ways to optimize permeation, and implications for drug discovery, delivery and toxicity. For treating lysosomal storage diseases (LSDs), models can be included that mimic aspects of the disease, including genetically-modified animals, and in vitro models can be used to examine the effects of cells of the NVU on the BBB under pathological conditions. For testing CNS drug delivery, several in vitro models now provide reliable prediction of penetration of drugs including large molecules and artificial constructs with promising potential in treating LSDs. For many of these diseases it is still not clear how best to deliver appropriate drugs to the CNS, and a concerted approach using a variety of models and methods can give critical insights and indicate practical solutions.
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Li, Weikai; Wang, Zhengxia; Zhang, Limei; Qiao, Lishan; Shen, Dinggang
2017-01-01
Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods. PMID:28912708
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Avian reflex and electroencephalogram responses in different states of consciousness.
Sandercock, Dale A; Auckburally, Adam; Flaherty, Derek; Sandilands, Victoria; McKeegan, Dorothy E F
2014-06-22
Defining states of clinical consciousness in animals is important in veterinary anaesthesia and in studies of euthanasia and welfare assessment at slaughter. The aim of this study was to validate readily observable reflex responses in relation to different conscious states, as confirmed by EEG analysis, in two species of birds under laboratory conditions (35-week-old layer hens (n=12) and 11-week-old turkeys (n=10)). We evaluated clinical reflexes and characterised electroencephalograph (EEG) activity (as a measure of brain function) using spectral analyses in four different clinical states of consciousness: conscious (fully awake), semi-conscious (sedated), unconscious-optimal (general anaesthesia), unconscious-sub optimal (deep hypnotic state), as well as assessment immediately following euthanasia. Jaw or neck muscle tone was the most reliable reflex measure distinguishing between conscious and unconscious states. Pupillary reflex was consistently observed until respiratory arrest. Nictitating membrane reflex persisted for a short time (<1 min) after respiratory arrest and brain death (isoelectric EEG). The results confirm that the nictitating membrane reflex is a conservative measure of death in poultry. Using spectral analyses of the EEG waveforms it was possible to readily distinguish between the different states of clinical consciousness. In all cases, when birds progressed from a conscious to unconscious state; total spectral power (PTOT) significantly increased, whereas median (F50) and spectral edge (F95) frequencies significantly decreased. This study demonstrates that EEG analysis can differentiate between clinical states (and loss of brain function at death) in birds and provides a unique integration of reflex responses and EEG activity. Copyright © 2014 Elsevier Inc. All rights reserved.
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Minati, Ludovico; Cercignani, Mara; Chan, Dennis
2013-10-01
Graph theory-based analyses of brain network topology can be used to model the spatiotemporal correlations in neural activity detected through fMRI, and such approaches have wide-ranging potential, from detection of alterations in preclinical Alzheimer's disease through to command identification in brain-machine interfaces. However, due to prohibitive computational costs, graph-based analyses to date have principally focused on measuring connection density rather than mapping the topological architecture in full by exhaustive shortest-path determination. This paper outlines a solution to this problem through parallel implementation of Dijkstra's algorithm in programmable logic. The processor design is optimized for large, sparse graphs and provided in full as synthesizable VHDL code. An acceleration factor between 15 and 18 is obtained on a representative resting-state fMRI dataset, and maps of Euclidean path length reveal the anticipated heterogeneous cortical involvement in long-range integrative processing. These results enable high-resolution geodesic connectivity mapping for resting-state fMRI in patient populations and real-time geodesic mapping to support identification of imagined actions for fMRI-based brain-machine interfaces. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.
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The mental cost of cognitive enhancement.
Iuculano, Teresa; Cohen Kadosh, Roi
2013-03-06
Noninvasive brain stimulation provides a potential tool for affecting brain functions in the typical and atypical brain and offers in several cases an alternative to pharmaceutical intervention. Some studies have suggested that transcranial electrical stimulation (TES), a form of noninvasive brain stimulation, can also be used to enhance cognitive performance. Critically, research so far has primarily focused on optimizing protocols for effective stimulation, or assessing potential physical side effects of TES while neglecting the possibility of cognitive side effects. We assessed this possibility by targeting the high-level cognitive abilities of learning and automaticity in the mathematical domain. Notably, learning and automaticity represent critical abilities for potential cognitive enhancement in typical and atypical populations. Over 6 d, healthy human adults underwent cognitive training on a new numerical notation while receiving TES to the posterior parietal cortex or the dorsolateral prefrontal cortex. Stimulation to the the posterior parietal cortex facilitated numerical learning, whereas automaticity for the learned material was impaired. In contrast, stimulation to the dorsolateral prefrontal cortex impaired the learning process, whereas automaticity for the learned material was enhanced. The observed double dissociation indicates that cognitive enhancement through TES can occur at the expense of other cognitive functions. These findings have important implications for the future use of enhancement technologies for neurointervention and performance improvement in healthy populations.
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Shao, Fangjie; Jiang, Wenhong; Gao, Qingqing; Li, Baizhou; Sun, Chongran; Wang, Qiyuan; Chen, Qin; Sun, Bing; Shen, Hong; Zhu, Keqing; Zhang, Jianmin; Liu, Chong
2017-10-01
The availability of a comprehensive tissue library is essential for elucidating the function and pathology of human brains. Considering the irreplaceable status of the formalin-fixation-paraffin-embedding (FFPE) preparation in routine pathology and the advantage of ultra-low temperature to preserve nucleic acids and proteins for multi-omics studies, these methods have become major modalities for the construction of brain tissue libraries. Nevertheless, the use of FFPE and snap-frozen samples is limited in high-resolution histological analyses because the preparation destroys tissue integrity and/or many important cellular markers. To overcome these limitations, we detailed a protocol to prepare and analyze frozen human brain samples that is particularly suitable for high-resolution multiplex immunohistological studies. As an alternative, we offered an optimized procedure to rescue snap-frozen tissues for the same purpose. Importantly, we provided a guideline to construct libraries of frozen tissue with minimal effort, cost and space. Taking advantage of this new tissue preparation modality to nicely preserve the cellular information that was otherwise damaged using conventional methods and to effectively remove tissue autofluorescence, we described the high-resolution landscape of the cellular composition in both lower-grade gliomas and glioblastoma multiforme samples. Our work showcases the great value of fixed frozen tissue in understanding the cellular mechanisms of CNS functions and abnormalities.
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Recruitment of prefrontal-striatal circuit in response to skilled motor challenge.
Guo, Yumei; Wang, Zhuo; Prathap, Sandhya; Holschneider, Daniel P
2017-12-13
A variety of physical fitness regimens have been shown to improve cognition, including executive function, yet our understanding of which parameters of motor training are important in optimizing outcomes remains limited. We used functional brain mapping to compare the ability of two motor challenges to acutely recruit the prefrontal-striatal circuit. The two motor tasks - walking in a complex running wheel with irregularly spaced rungs or walking in a running wheel with a smooth internal surface - differed only in the extent of skill required for their execution. Cerebral perfusion was mapped in rats by intravenous injection of [C]-iodoantipyrine during walking in either a motorized complex wheel or in a simple wheel. Regional cerebral blood flow (rCBF) was quantified by whole-brain autoradiography and analyzed in three-dimensional reconstructed brains by statistical parametric mapping and seed-based functional connectivity. Skilled or simple walking compared with rest, increased rCBF in regions of the motor circuit, somatosensory and visual cortex, as well as the hippocampus. Significantly greater rCBF increases were noted during skilled walking than for simple walking. Skilled walking, unlike simple walking or the resting condition, was associated with a significant positive functional connectivity in the prefrontal-striatal circuit (prelimbic cortex-dorsomedial striatum) and greater negative functional connectivity in the prefrontal-hippocampal circuit. Our findings suggest that the level of skill of a motor training task determines the extent of functional recruitment of the prefrontal-corticostriatal circuit, with implications for a new approach in neurorehabilitation that uses circuit-specific neuroplasticity to improve motor and cognitive functions.
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Cordes, Dietmar; Nandy, Rajesh R.; Schafer, Scott; Wager, Tor D.
2014-01-01
It has recently been shown that both high-frequency and low-frequency cardiac and respiratory noise sources exist throughout the entire brain and can cause significant signal changes in fMRI data. It is also known that the brainstem, basal forebrain and spinal cord area are problematic for fMRI because of the magnitude of cardiac-induced pulsations at these locations. In this study, the physiological noise contributions in the lower brain areas (covering the brainstem and adjacent regions) are investigated and a novel method is presented for computing both low-frequency and high-frequency physiological regressors accurately for each subject. In particular, using a novel optimization algorithm that penalizes curvature (i.e. the second derivative) of the physiological hemodynamic response functions, the cardiac -and respiratory-related response functions are computed. The physiological noise variance is determined for each voxel and the frequency-aliasing property of the high-frequency cardiac waveform as a function of the repetition time (TR) is investigated. It is shown that for the brainstem and other brain areas associated with large pulsations of the cardiac rate, the temporal SNR associated with the low-frequency range of the BOLD response has maxima at subject-specific TRs. At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal. PMID:24355483
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Tian, Peifang; Devor, Anna; Sakadžić, Sava; Dale, Anders M.; Boas, David A.
2011-01-01
Absorption or fluorescence-based two-dimensional (2-D) optical imaging is widely employed in functional brain imaging. The image is a weighted sum of the real signal from the tissue at different depths. This weighting function is defined as “depth sensitivity.” Characterizing depth sensitivity and spatial resolution is important to better interpret the functional imaging data. However, due to light scattering and absorption in biological tissues, our knowledge of these is incomplete. We use Monte Carlo simulations to carry out a systematic study of spatial resolution and depth sensitivity for 2-D optical imaging methods with configurations typically encountered in functional brain imaging. We found the following: (i) the spatial resolution is <200 μm for NA ≤0.2 or focal plane depth ≤300 μm. (ii) More than 97% of the signal comes from the top 500 μm of the tissue. (iii) For activated columns with lateral size larger than spatial resolution, changing numerical aperature (NA) and focal plane depth does not affect depth sensitivity. (iv) For either smaller columns or large columns covered by surface vessels, increasing NA and∕or focal plane depth may improve depth sensitivity at deeper layers. Our results provide valuable guidance for the optimization of optical imaging systems and data interpretation. PMID:21280912
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Ozhinsky, Eugene; Vigneron, Daniel B; Nelson, Sarah J
2011-04-01
To develop a technique for optimizing coverage of brain 3D (1) H magnetic resonance spectroscopic imaging (MRSI) by automatic placement of outer-volume suppression (OVS) saturation bands (sat bands) and to compare the performance for point-resolved spectroscopic sequence (PRESS) MRSI protocols with manual and automatic placement of sat bands. The automated OVS procedure includes the acquisition of anatomic images from the head, obtaining brain and lipid tissue maps, calculating optimal sat band placement, and then using those optimized parameters during the MRSI acquisition. The data were analyzed to quantify brain coverage volume and data quality. 3D PRESS MRSI data were acquired from three healthy volunteers and 29 patients using protocols that included either manual or automatic sat band placement. On average, the automatic sat band placement allowed the acquisition of PRESS MRSI data from 2.7 times larger brain volumes than the conventional method while maintaining data quality. The technique developed helps solve two of the most significant problems with brain PRESS MRSI acquisitions: limited brain coverage and difficulty in prescription. This new method will facilitate routine clinical brain 3D MRSI exams and will be important for performing serial evaluation of response to therapy in patients with brain tumors and other neurological diseases. Copyright © 2011 Wiley-Liss, Inc.
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Brain metabolism in health, aging, and neurodegeneration.
Camandola, Simonetta; Mattson, Mark P
2017-06-01
Brain cells normally respond adaptively to bioenergetic challenges resulting from ongoing activity in neuronal circuits, and from environmental energetic stressors such as food deprivation and physical exertion. At the cellular level, such adaptive responses include the "strengthening" of existing synapses, the formation of new synapses, and the production of new neurons from stem cells. At the molecular level, bioenergetic challenges result in the activation of transcription factors that induce the expression of proteins that bolster the resistance of neurons to the kinds of metabolic, oxidative, excitotoxic, and proteotoxic stresses involved in the pathogenesis of brain disorders including stroke, and Alzheimer's and Parkinson's diseases. Emerging findings suggest that lifestyles that include intermittent bioenergetic challenges, most notably exercise and dietary energy restriction, can increase the likelihood that the brain will function optimally and in the absence of disease throughout life. Here, we provide an overview of cellular and molecular mechanisms that regulate brain energy metabolism, how such mechanisms are altered during aging and in neurodegenerative disorders, and the potential applications to brain health and disease of interventions that engage pathways involved in neuronal adaptations to metabolic stress. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.
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SU-E-T-436: Fluence-Based Trajectory Optimization for Non-Coplanar VMAT
DOE Office of Scientific and Technical Information (OSTI.GOV)
Smyth, G; Bamber, JC; Bedford, JL
2015-06-15
Purpose: To investigate a fluence-based trajectory optimization technique for non-coplanar VMAT for brain cancer. Methods: Single-arc non-coplanar VMAT trajectories were determined using a heuristic technique for five patients. Organ at risk (OAR) volume intersected during raytracing was minimized for two cases: absolute volume and the sum of relative volumes weighted by OAR importance. These trajectories and coplanar VMAT formed starting points for the fluence-based optimization method. Iterative least squares optimization was performed on control points 24° apart in gantry rotation. Optimization minimized the root-mean-square (RMS) deviation of PTV dose from the prescription (relative importance 100), maximum dose to the brainstemmore » (10), optic chiasm (5), globes (5) and optic nerves (5), plus mean dose to the lenses (5), hippocampi (3), temporal lobes (2), cochleae (1) and brain excluding other regions of interest (1). Control point couch rotations were varied in steps of up to 10° and accepted if the cost function improved. Final treatment plans were optimized with the same objectives in an in-house planning system and evaluated using a composite metric - the sum of optimization metrics weighted by importance. Results: The composite metric decreased with fluence-based optimization in 14 of the 15 plans. In the remaining case its overall value, and the PTV and OAR components, were unchanged but the balance of OAR sparing differed. PTV RMS deviation was improved in 13 cases and unchanged in two. The OAR component was reduced in 13 plans. In one case the OAR component increased but the composite metric decreased - a 4 Gy increase in OAR metrics was balanced by a reduction in PTV RMS deviation from 2.8% to 2.6%. Conclusion: Fluence-based trajectory optimization improved plan quality as defined by the composite metric. While dose differences were case specific, fluence-based optimization improved both PTV and OAR dosimetry in 80% of cases.« less
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Kalpinski, Ryan J.; Williamson, Meredith L. C.; Elliott, Timothy R.; Berry, Jack W.; Underhill, Andrea T.; Fine, Philip R.
2013-01-01
Identifying reliable predictors of positive adjustment following traumatic brain injury (TBI) remains an important area of inquiry. Unfortunately, much of available research examines direct relationships between predictor variables and outcomes without attending to the contextual relationships that can exist between predictor variables. Relying on theoretical models of well-being, we examined a theoretical model of adjustment in which the capacity to engage in intentional activities would be prospectively associated with greater participation, which in turn would predict subsequent life satisfaction and perceived health assessed at a later time. Structural equation modeling of data collected from 312 individuals (226 men, 86 women) with TBI revealed that two elements of participation—mobility and occupational activities—mediated the prospective influence of functional independence and injury severity to optimal adjustment 60 months following medical discharge for TBI. The model accounted for 21% of the variance in life satisfaction and 23% of the variance in self-rated health. Results indicate that the effects of functional independence and injury severity to optimal adjustment over time may be best understood in the context of participation in meaningful, productive activities. Implications for theoretical models of well-being and for clinical interventions that promote adjustmentafter TBI are discussed. PMID:24199186
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Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface.
Bakardjian, Hovagim; Tanaka, Toshihisa; Cichocki, Andrzej
2010-01-18
This study pursues the optimization of the brain responses to small reversing patterns in a Steady-State Visual Evoked Potentials (SSVEP) paradigm, which could be used to maximize the efficiency of applications such as Brain-Computer Interfaces (BCI). We investigated the SSVEP frequency response for 32 frequencies (5-84 Hz), and the time dynamics of the brain response at 8, 14 and 28 Hz, to aid the definition of the optimal neurophysiological parameters and to outline the onset-delay and other limitations of SSVEP stimuli in applications such as our previously described four-command BCI system. Our results showed that the 5.6-15.3 Hz pattern reversal stimulation evoked the strongest responses, peaking at 12 Hz, and exhibiting weaker local maxima at 28 and 42 Hz. After stimulation onset, the long-term SSVEP response was highly non-stationary and the dynamics, including the first peak, was frequency-dependent. The evaluation of the performance of a frequency-optimized eight-command BCI system with dynamic neurofeedback showed a mean success rate of 98%, and a time delay of 3.4s. Robust BCI performance was achieved by all subjects even when using numerous small patterns clustered very close to each other and moving rapidly in 2D space. These results emphasize the need for SSVEP applications to optimize not only the analysis algorithms but also the stimuli in order to maximize the brain responses they rely on. (c) 2009 Elsevier Ireland Ltd. All rights reserved.
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Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning.
Zu, Chen; Gao, Yue; Munsell, Brent; Kim, Minjeong; Peng, Ziwen; Cohen, Jessica R; Zhang, Daoqiang; Wu, Guorong
2018-06-14
The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hyperedge encodes a subnetwork with similar functional connectivity patterns between different subjects. Unlike previous learning-based methods, our approach is designed to jointly optimize the weights for all hyperedges such that the learned representation is in consensus with the distribution of phenotype data, i.e. clinical labels. In order to suppress the spurious subnetwork biomarkers, we further enforce a sparsity constraint on the hyperedge weights, where a larger hyperedge weight indicates the subnetwork with the capability of identifying the disorder condition. We apply our hypergraph learning-based method to identify subnetwork biomarkers in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A comprehensive quantitative and qualitative analysis is performed, and the results show that our approach can correctly classify ASD and ADHD subjects from normal controls with 87.65 and 65.08% accuracies, respectively.
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Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox
Sato, João R.; Basilio, Rodrigo; Paiva, Fernando F.; Garrido, Griselda J.; Bramati, Ivanei E.; Bado, Patricia; Tovar-Moll, Fernanda; Zahn, Roland; Moll, Jorge
2013-01-01
The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available. PMID:24312569
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Belova, L A
2012-01-01
We studied 209 patients with chronic brain ischemia due to arterial hypertension (hypertonic encephalopathy). 93 patients (44.5%) had clinical-anamnestic features of constitutional phlebopathy and 116 (55.5%) had not. Based on the conception of 5 functional-morphological levels of the vascular brain system, a complex ultrasound study was conducted. The control group included 30 people without cerebrovascular pathology. In hypertonic encephalopathy, pathological processes developing in the 1st and 2nd structural-functional levels (extra- and intracerebral arteries) correspond to remodeling, that is characteristic of arterial hypertension, and do not depend on the presence of the constitutional venous insufficiency. Changes in parameters of the blood flow in the 3rd, 4th and 5th structural-functional levels of the brain's blood supply (microcirculatory bed, head venous system, jugular and spine veins) form a dopplerographic pattern of the cerebral venous dyscirculation which is mostly pronounced in constitutional phlebopathy in patients with hypertonic encephalopathy. This pattern includes the reduction of linear blood flow velocity in nitroglycerine test, lower values of the resistance index and the increase in the linear blood flow velocity in the sinus transversus and Rosenthal vein, lack of ostial valves of the inner jugular veinas well as the decrease of linear and increase in the volume blood flow velocity along it. The methodology of the system approach based on using clinical and instrumental method in the study of cerebral hemodynamics is important for treatment optimization in patients with chronic brain ischemia.
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Li, Xiaojin; Hu, Xintao; Jin, Changfeng; Han, Junwei; Liu, Tianming; Guo, Lei; Hao, Wei; Li, Lingjiang
2013-01-01
Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.
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Segmentation of brain structures in presence of a space-occupying lesion.
Pollo, Claudio; Cuadra, Meritxell Bach; Cuisenaire, Olivier; Villemure, Jean-Guy; Thiran, Jean-Philippe
2005-02-15
Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.
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Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks
Hwang, Seong Jae; Adluru, Nagesh; Collins, Maxwell D.; Ravi, Sathya N.; Bendlin, Barbara B.; Johnson, Sterling C.; Singh, Vikas
2016-01-01
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points — quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer’s disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant’s brain connectivity into the future. PMID:27812274
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Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing
2016-01-01
Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371
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Mid-sagittal plane and mid-sagittal surface optimization in brain MRI using a local symmetry measure
NASA Astrophysics Data System (ADS)
Stegmann, Mikkel B.; Skoglund, Karl; Ryberg, Charlotte
2005-04-01
This paper describes methods for automatic localization of the mid-sagittal plane (MSP) and mid-sagittal surface (MSS). The data used is a subset of the Leukoaraiosis And DISability (LADIS) study consisting of three-dimensional magnetic resonance brain data from 62 elderly subjects (age 66 to 84 years). Traditionally, the mid-sagittal plane is localized by global measures. However, this approach fails when the partitioning plane between the brain hemispheres does not coincide with the symmetry plane of the head. We instead propose to use a sparse set of profiles in the plane normal direction and maximize the local symmetry around these using a general-purpose optimizer. The plane is parameterized by azimuth and elevation angles along with the distance to the origin in the normal direction. This approach leads to solutions confirmed as the optimal MSP in 98 percent of the subjects. Despite the name, the mid-sagittal plane is not always planar, but a curved surface resulting in poor partitioning of the brain hemispheres. To account for this, this paper also investigates an optimization strategy which fits a thin-plate spline surface to the brain data using a robust least median of squares estimator. Albeit computationally more expensive, mid-sagittal surface fitting demonstrated convincingly better partitioning of curved brains into cerebral hemispheres.
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Gu, Wenbo; O'Connor, Daniel; Nguyen, Dan; Yu, Victoria Y; Ruan, Dan; Dong, Lei; Sheng, Ke
2018-04-01
Intensity-Modulated Proton Therapy (IMPT) is the state-of-the-art method of delivering proton radiotherapy. Previous research has been mainly focused on optimization of scanning spots with manually selected beam angles. Due to the computational complexity, the potential benefit of simultaneously optimizing beam orientations and spot pattern could not be realized. In this study, we developed a novel integrated beam orientation optimization (BOO) and scanning-spot optimization algorithm for intensity-modulated proton therapy (IMPT). A brain chordoma and three unilateral head-and-neck patients with a maximal target size of 112.49 cm 3 were included in this study. A total number of 1162 noncoplanar candidate beams evenly distributed across 4π steradians were included in the optimization. For each candidate beam, the pencil-beam doses of all scanning spots covering the PTV and a margin were calculated. The beam angle selection and spot intensity optimization problem was formulated to include three terms: a dose fidelity term to penalize the deviation of PTV and OAR doses from ideal dose distribution; an L1-norm sparsity term to reduce the number of active spots and improve delivery efficiency; a group sparsity term to control the number of active beams between 2 and 4. For the group sparsity term, convex L2,1-norm and nonconvex L2,1/2-norm were tested. For the dose fidelity term, both quadratic function and linearized equivalent uniform dose (LEUD) cost function were implemented. The optimization problem was solved using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The IMPT BOO method was tested on three head-and-neck patients and one skull base chordoma patient. The results were compared with IMPT plans created using column generation selected beams or manually selected beams. The L2,1-norm plan selected spatially aggregated beams, indicating potential degeneracy using this norm. L2,1/2-norm was able to select spatially separated beams and achieve smaller deviation from the ideal dose. In the L2,1/2-norm plans, the [mean dose, maximum dose] of OAR were reduced by an average of [2.38%, 4.24%] and[2.32%, 3.76%] of the prescription dose for the quadratic and LEUD cost function, respectively, compared with the IMPT plan using manual beam selection while maintaining the same PTV coverage. The L2,1/2 group sparsity plans were dosimetrically superior to the column generation plans as well. Besides beam orientation selection, spot sparsification was observed. Generally, with the quadratic cost function, 30%~60% spots in the selected beams remained active. With the LEUD cost function, the percentages of active spots were in the range of 35%~85%.The BOO-IMPT run time was approximately 20 min. This work shows the first IMPT approach integrating noncoplanar BOO and scanning-spot optimization in a single mathematical framework. This method is computationally efficient, dosimetrically superior and produces delivery-friendly IMPT plans. © 2018 American Association of Physicists in Medicine.
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Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui
2014-09-01
Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. Copyright © 2014 Elsevier Inc. All rights reserved.
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Kumar, A; Kothari, M; Grigoriadis, A; Trulsson, M; Svensson, P
2018-04-01
Tooth loss, decreased mass and strength of the masticatory muscles leading to difficulty in chewing have been suggested as important determinants of eating and nutrition in the elderly. To compensate for the loss of teeth, in particular, a majority of the elderly rely on dental prosthesis for chewing. Chewing function is indeed an important aspect of oral health, and therefore, oral rehabilitation procedures should aim to restore or maintain adequate function. However, even if the possibilities to anatomically restore lost teeth and occlusion have never been better; conventional rehabilitation procedures may still fail to optimally restore oral functions. Perhaps this is due to the lack of focus on the importance of the brain in the rehabilitation procedures. Therefore, the aim of this narrative review was to discuss the importance of maintaining or restoring optimum chewing function in the superageing population and to summarise the emerging studies on oral motor task performance and measures of cortical neuroplasticity induced by systematic training paradigms in healthy participants. Further, brain imaging studies in patients undergoing or undergone oral rehabilitation procedures will be discussed. Overall, this information is believed to enhance the understanding and develop better rehabilitative strategies to exploit training-induced cortical neuroplasticity in individuals affected by impaired oral motor coordination and function. Training or relearning of oral motor tasks could be important to optimise masticatory performance in dental prosthesis users and may represent a much-needed paradigm shift in the approach to oral rehabilitation procedures. © 2018 John Wiley & Sons Ltd.
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Krüger, Marie T; Coenen, Volker A; Egger, Karl; Shah, Mukesch; Reinacher, Peter C
2018-06-13
In recent years, simulations based on phantom models have become increasingly popular in the medical field. In the field of functional and stereotactic neurosurgery, a cranial phantom would be useful to train operative techniques, such as stereo-electroencephalography (SEEG), to establish new methods as well as to develop and modify radiological techniques. In this study, we describe the construction of a cranial phantom and show examples for it in stereotactic and functional neurosurgery and its applicability with different radiological modalities. We prepared a plaster skull filled with agar. A complete operation for deep brain stimulation (DBS) was simulated using directional leads. Moreover, a complete SEEG operation including planning, implantation of the electrodes, and intraoperative and postoperative imaging was simulated. An optimally customized cranial phantom is filled with 10% agar. At 7°C, it can be stored for approximately 4 months. A DBS and an SEEG procedure could be realistically simulated. Lead artifacts can be studied in CT, X-ray, rotational fluoroscopy, and MRI. This cranial phantom is a simple and effective model to simulate functional and stereotactic neurosurgical operations. This might be useful for teaching and training of neurosurgeons, establishing operations in a new center and for optimization of radiological examinations. © 2018 S. Karger AG, Basel.
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Refined protocols of tamoxifen injection for inducible DNA recombination in mouse astroglia.
Jahn, Hannah M; Kasakow, Carmen V; Helfer, Andreas; Michely, Julian; Verkhratsky, Alexei; Maurer, Hans H; Scheller, Anja; Kirchhoff, Frank
2018-04-12
Inducible DNA recombination of floxed alleles in vivo by liver metabolites of tamoxifen (TAM) is an important tool to study gene functions. Here, we describe protocols for optimal DNA recombination in astrocytes, based on the GLAST-Cre ERT2 /loxP system. In addition, we demonstrate that quantification of genomic recombination allows to determine the proportion of cell types in various brain regions. We analyzed the presence and clearance of TAM and its metabolites (N-desmethyl-tamoxifen, 4-hydroxytamoxifen and endoxifen) in brain and serum of mice by liquid chromatographic-high resolution-tandem mass spectrometry (LC-HR-MS/MS) and assessed optimal injection protocols by quantitative RT-PCR of several floxed target genes (p2ry1, gria1, gabbr1 and Rosa26-tdTomato locus). Maximal recombination could be achieved in cortex and cerebellum by single daily injections for five and three consecutive days, respectively. Furthermore, quantifying the loss of floxed alleles predicted the percentage of GLAST-positive cells (astroglia) per brain region. We found that astrocytes contributed 20 to 30% of the total cell number in cortex, hippocampus, brainstem and optic nerve, while in the cerebellum Bergmann glia, velate astrocytes and white matter astrocytes accounted only for 8% of all cells.
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NASA Astrophysics Data System (ADS)
Yang, Qianli; Pitkow, Xaq
2015-03-01
Most interesting natural sensory stimuli are encoded in the brain in a form that can only be decoded nonlinearly. But despite being a core function of the brain, nonlinear population codes are rarely studied and poorly understood. Interestingly, the few existing models of nonlinear codes are inconsistent with known architectural features of the brain. In particular, these codes have information content that scales with the size of the cortical population, even if that violates the data processing inequality by exceeding the amount of information entering the sensory system. Here we provide a valid theory of nonlinear population codes by generalizing recent work on information-limiting correlations in linear population codes. Although these generalized, nonlinear information-limiting correlations bound the performance of any decoder, they also make decoding more robust to suboptimal computation, allowing many suboptimal decoders to achieve nearly the same efficiency as an optimal decoder. Although these correlations are extremely difficult to measure directly, particularly for nonlinear codes, we provide a simple, practical test by which one can use choice-related activity in small populations of neurons to determine whether decoding is suboptimal or optimal and limited by correlated noise. We conclude by describing an example computation in the vestibular system where this theory applies. QY and XP was supported by a grant from the McNair foundation.
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Nowak, Karl; Mix, Eilhard; Gimsa, Jan; Strauss, Ulf; Sriperumbudur, Kiran Kumar; Benecke, Reiner; Gimsa, Ulrike
2011-01-01
Deep brain stimulation (DBS) has become a treatment for a growing number of neurological and psychiatric disorders, especially for therapy-refractory Parkinson's disease (PD). However, not all of the symptoms of PD are sufficiently improved in all patients, and side effects may occur. Further progress depends on a deeper insight into the mechanisms of action of DBS in the context of disturbed brain circuits. For this, optimized animal models have to be developed. We review not only charge transfer mechanisms at the electrode/tissue interface and strategies to increase the stimulation's energy-efficiency but also the electrochemical, electrophysiological, biochemical and functional effects of DBS. We introduce a hemi-Parkinsonian rat model for long-term experiments with chronically instrumented rats carrying a backpack stimulator and implanted platinum/iridium electrodes. This model is suitable for (1) elucidating the electrochemical processes at the electrode/tissue interface, (2) analyzing the molecular, cellular and behavioral stimulation effects, (3) testing new target regions for DBS, (4) screening for potential neuroprotective DBS effects, and (5) improving the efficacy and safety of the method. An outlook is given on further developments of experimental DBS, including the use of transgenic animals and the testing of closed-loop systems for the direct on-demand application of electric stimulation. PMID:21603182
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[Stem Cells in the Brain of Mammals and Human: Fundamental and Applied Aspects].
Aleksandrova, M A; Marey, M V
2015-01-01
Brain stem cells represent an extremely intriguing phenomenon. The aim of our review is to present an integrity vision of their role in the brain of mammals and humans, and their clinical perspectives. Over last two decades, investigations of biology of the neural stem cells produced significant changes in general knowledge about the processes of development and functioning of the brain. Researches on the cellular and molecular mechanisms of NSC differentiation and behavior led to new understanding of their involvement in learning and memory. In the regenerative medicine, original therapeutic approaches to neurodegenerative brain diseases have been elaborated due to fundamental achievements in this field. They are based on specific regenerative potential of neural stem cells and progenitor cells, which possess the ability to replace dead cells and express crucially significant biologically active factors that are missing in the pathological brain. For the needs of cell substitution therapy in the neural diseases, adequate methods of maintaining stem cells in culture and their differentiation into different types of neurons and glial cells, have been developed currently. The success of modern cellular technologies has significantly expanded the range of cells used for cell therapy. The near future may bring new perspective and distinct progress in brain cell therapy due to optimizing the cells types most promising for medical needs.
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A Topological Criterion for Filtering Information in Complex Brain Networks
Latora, Vito; Chavez, Mario
2017-01-01
In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way. PMID:28076353
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Human connectome module pattern detection using a new multi-graph MinMax cut model.
De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng
2014-01-01
Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.
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Cossu, Giulia
2014-04-01
Traumatic brain injury is a leading cause of death and disability. Optimizing the recovery from coma is a priority in seeking to improve patients' functional outcomes. Standards of care have not been established: pharmacological interventions, right median nerve and sensory stimulation, dorsal column stimulation (DCS), deep brain stimulation, transcranial magnetic stimulation, hyperbaric oxygen therapy and cell transplantation have all been utilized with contrasting results. The aim of this review is to clarify the indications for the various techniques and to guide the clinical practice towards an earlier coma arousal. A systematic bibliographic search was undertaken using the principal search engines (Pubmed, Embase, Ovid and Cochrane databases) to locate the most pertinent studies. Traumatic injury is a highly individualized process, and subsequent impairments are dependent on multiple factors: this heterogeneity influences and determines therapeutic responses to the various interventions.
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Optimal compensation for neuron loss
Barrett, David GT; Denève, Sophie; Machens, Christian K
2016-01-01
The brain has an impressive ability to withstand neural damage. Diseases that kill neurons can go unnoticed for years, and incomplete brain lesions or silencing of neurons often fail to produce any behavioral effect. How does the brain compensate for such damage, and what are the limits of this compensation? We propose that neural circuits instantly compensate for neuron loss, thereby preserving their function as much as possible. We show that this compensation can explain changes in tuning curves induced by neuron silencing across a variety of systems, including the primary visual cortex. We find that compensatory mechanisms can be implemented through the dynamics of networks with a tight balance of excitation and inhibition, without requiring synaptic plasticity. The limits of this compensatory mechanism are reached when excitation and inhibition become unbalanced, thereby demarcating a recovery boundary, where signal representation fails and where diseases may become symptomatic. DOI: http://dx.doi.org/10.7554/eLife.12454.001 PMID:27935480
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Thompson, Brandon J; Sanchez-Covarrubias, Lucy; Slosky, Lauren M; Zhang, Yifeng; Laracuente, Mei-li; Ronaldson, Patrick T
2014-04-01
Cerebral hypoxia and subsequent reoxygenation stress (H/R) is a component of several diseases. One approach that may enable neural tissue rescue after H/R is central nervous system (CNS) delivery of drugs with brain protective effects such as 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (i.e., statins). Our present in vivo data show that atorvastatin, a commonly prescribed statin, attenuates poly (ADP-ribose) polymerase (PARP) cleavage in the brain after H/R, suggesting neuroprotective efficacy. However, atorvastatin use as a CNS therapeutic is limited by poor blood-brain barrier (BBB) penetration. Therefore, we examined regulation and functional expression of the known statin transporter organic anion transporting polypeptide 1a4 (Oatp1a4) at the BBB under H/R conditions. In rat brain microvessels, H/R (6% O2, 60 minutes followed by 21% O2, 10 minutes) increased Oatp1a4 expression. Brain uptake of taurocholate (i.e., Oap1a4 probe substrate) and atorvastatin were reduced by Oatp inhibitors (i.e., estrone-3-sulfate and fexofenadine), suggesting involvement of Oatp1a4 in brain drug delivery. Pharmacological inhibition of transforming growth factor-β (TGF-β)/activin receptor-like kinase 5 (ALK5) signaling with the selective inhibitor SB431542 increased Oatp1a4 functional expression, suggesting a role for TGF-β/ALK5 signaling in Oatp1a4 regulation. Taken together, our novel data show that targeting an endogenous BBB drug uptake transporter (i.e., Oatp1a4) may be a viable approach for optimizing CNS drug delivery for treatment of diseases with an H/R component.
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Brain structural plasticity in survivors of a major earthquake
Lui, Su; Chen, Long; Yao, Li; Xiao, Yuan; Wu, Qi-Zhu; Zhang, Jun-Ran; Huang, Xiao-Qi; Zhang, Wei; Wang, Yu-Qin; Chen, Hua-Fu; Chan, Raymond C.K.; Sweeney, John A.; Gong, Qi-Yong
2013-01-01
Background Stress responses have been studied extensively in animal models, but effects of major life stress on the human brain remain poorly understood. The aim of this study was to determine whether survivors of a major earthquake, who were presumed to have experienced extreme emotional stress during the disaster, demonstrate differences in brain anatomy relative to individuals who have not experienced such stressors. Methods Healthy survivors living in an area devastated by a major earthquake and matched healthy controls underwent 3-dimentional high-resolution magnetic resonance imaging (MRI). Survivors were scanned 13–25 days after the earthquake; controls had undergone MRI for other studies not long before the earthquake. We used optimized voxel-based morphometry analysis to identify regional differences of grey matter volume between the survivors and controls. Results We included 44 survivors (17 female, mean age 37 [standard deviation (SD) 10.6] yr) and 38 controls (14 female, mean age 35.3 [SD 11.2] yr) in our analysis. Compared with controls, the survivors showed significantly lower grey matter volume in the bilateral insula, hippocampus, left caudate and putamen, and greater grey matter volume in the bilateral orbitofrontal cortex and the parietal lobe (all p < 0.05, corrected for multiple comparison). Limitations Differences in the variance of survivor and control data could impact study findings. Conclusion Acute anatomic alterations could be observed in earthquake survivors in brain regions where functional alterations after stress have been described. Anatomic changes in the present study were observed earlier than previously reported and were seen in prefrontal–limbic, parietal and striatal brain systems. Together with the results of previous functional imaging studies, our observations suggest a complex pattern of human brain response to major life stress affecting brain systems that modulate and respond to heightened affective arousal. PMID:23710694
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NASA Technical Reports Server (NTRS)
Schmidt, M. A.; Goodwin, T. J.
2014-01-01
Brain derived neurotrophic factor (BDNF) is the main activity-dependent neurotrophin in the human nervous system. BDNF is implicated in production of new neurons from dentate gyrus stem cells (hippocampal neurogenesis), synapse formation, sprouting of new axons, growth of new axons, sprouting of new dendrites, and neuron survival. Alterations in the amount or activity of BDNF can produce significant detrimental changes to cortical function and synaptic transmission in the human brain. This can result in glial and neuronal dysfunction, which may contribute to a range of clinical conditions, spanning a number of learning, behavioral, and neurological disorders. There is an extensive body of work surrounding the BDNF molecular network, including BDNF gene polymorphisms, methylated BDNF gene promoters, multiple gene transcripts, varied BDNF functional proteins, and different BDNF receptors (whose activation differentially drive the neuron to neurogenesis or apoptosis). BDNF is also closely linked to mitochondrial biogenesis through PGC-1alpha, which can influence brain and muscle metabolic efficiency. BDNF AS A HUMAN SPACE FLIGHT COUNTERMEASURE TARGET Earth-based studies reveal that BDNF is negatively impacted by many of the conditions encountered in the space environment, including oxidative stress, radiation, psychological stressors, sleep deprivation, and many others. A growing body of work suggests that the BDNF network is responsive to a range of diet, nutrition, exercise, drug, and other types of influences. This section explores the BDNF network in the context of 1) protecting the brain and nervous system in the space environment, 2) optimizing neurobehavioral performance in space, and 3) reducing the residual effects of space flight on the nervous system on return to Earth
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ALL OUR SONS: THE DEVELOPMENTAL NEUROBIOLOGY AND NEUROENDOCRINOLOGY OF BOYS AT RISK.
Schore, Allan N
2017-01-01
Why are boys at risk? To address this question, I use the perspective of regulation theory to offer a model of the deeper psychoneurobiological mechanisms that underlie the vulnerability of the developing male. The central thesis of this work dictates that significant gender differences are seen between male and female social and emotional functions in the earliest stages of development, and that these result from not only differences in sex hormones and social experiences but also in rates of male and female brain maturation, specifically in the early developing right brain. I present interdisciplinary research which indicates that the stress-regulating circuits of the male brain mature more slowly than those of the female in the prenatal, perinatal, and postnatal critical periods, and that this differential structural maturation is reflected in normal gender differences in right-brain attachment functions. Due to this maturational delay, developing males also are more vulnerable over a longer period of time to stressors in the social environment (attachment trauma) and toxins in the physical environment (endocrine disruptors) that negatively impact right-brain development. In terms of differences in gender-related psychopathology, I describe the early developmental neuroendocrinological and neurobiological mechanisms that are involved in the increased vulnerability of males to autism, early onset schizophrenia, attention deficit hyperactivity disorder, and conduct disorders as well as the epigenetic mechanisms that can account for the recent widespread increase of these disorders in U.S. culture. I also offer a clinical formulation of early assessments of boys at risk, discuss the impact of early childcare on male psychopathogenesis, and end with a neurobiological model of optimal adult male socioemotional functions. © 2017 Michigan Association for Infant Mental Health.
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Power law scaling in synchronization of brain signals depends on cognitive load.
Tinker, Jesse; Velazquez, Jose Luis Perez
2014-01-01
As it has several features that optimize information processing, it has been proposed that criticality governs the dynamics of nervous system activity. Indications of such dynamics have been reported for a variety of in vitro and in vivo recordings, ranging from in vitro slice electrophysiology to human functional magnetic resonance imaging. However, there still remains considerable debate as to whether the brain actually operates close to criticality or in another governing state such as stochastic or oscillatory dynamics. A tool used to investigate the criticality of nervous system data is the inspection of power-law distributions. Although the findings are controversial, such power-law scaling has been found in different types of recordings. Here, we studied whether there is a power law scaling in the distribution of the phase synchronization derived from magnetoencephalographic recordings during executive function tasks performed by children with and without autism. Characterizing the brain dynamics that is different between autistic and non-autistic individuals is important in order to find differences that could either aid diagnosis or provide insights as to possible therapeutic interventions in autism. We report in this study that power law scaling in the distributions of a phase synchrony index is not very common and its frequency of occurrence is similar in the control and the autism group. In addition, power law scaling tends to diminish with increased cognitive load (difficulty or engagement in the task). There were indications of changes in the probability distribution functions for the phase synchrony that were associated with a transition from power law scaling to lack of power law (or vice versa), which suggests the presence of phenomenological bifurcations in brain dynamics associated with cognitive load. Hence, brain dynamics may fluctuate between criticality and other regimes depending upon context and behaviors.
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Rimes, Ridson Rosa; de Souza Moura, Antonio Marcos; Lamego, Murilo Khede; de Sá Filho, Alberto Souza; Manochio, João; Paes, Flávia; Carta, Mauro Giovanni; Mura, Gioia; Wegner, Mirko; Budde, Henning; Ferreira Rocha, Nuno Barbosa; Rocha, Joana; Tavares, João Manuel R S; Arias-Carrión, Oscar; Nardi, Antonio Egidio; Yuan, Ti-Fei; Machado, Sergio
2015-01-01
Exercise promotes several health benefits, such as cardiovascular, musculoskeletal and cardiorespiratory improvements. It is believed that the practice of exercise in individuals with psychiatric disorders, e.g. schizophrenia, can cause significant changes. Schizophrenic patients have problematic lifestyle habits compared with general population; this may cause a high mortality rate, mainly caused by cardiovascular and metabolic diseases. Thus, the aim of this study is to investigate changes in physical and mental health, cognitive and brain functioning due to the practice of exercise in patients with schizophrenia. Although still little is known about the benefits of exercise on mental health, cognitive and brain functioning of schizophrenic patients, exercise training has been shown to be a beneficial intervention in the control and reduction of disease severity. Type of training, form of execution, duration and intensity need to be better studied as the effects on physical and mental health, cognition and brain activity depend exclusively of interconnected factors, such as the combination of exercise and medication. However, one should understand that exercise is not only an effective nondrug alternative, but also acts as a supporting linking up interventions to promote improvements in process performance optimization. In general, the positive effects on mental health, cognition and brain activity as a result of an exercise program are quite evident. Few studies have been published correlating effects of exercise in patients with schizophrenia, but there is increasing evidence that positive and negative symptoms can be improved. Therefore, it is important that further studies be undertaken to expand the knowledge of physical exercise on mental health in people with schizophrenia, as well as its dose-response and the most effective type of exercise.
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Markovitz, Craig D.; Tang, Tien T.; Edge, David P.; Lim, Hubert H.
2012-01-01
The brain is a densely interconnected network that relies on populations of neurons within and across multiple nuclei to code for features leading to perception and action. However, the neurophysiology field is still dominated by the characterization of individual neurons, rather than simultaneous recordings across multiple regions, without consistent spatial reconstruction of their locations for comparisons across studies. There are sophisticated histological and imaging techniques for performing brain reconstructions. However, what is needed is a method that is relatively easy and inexpensive to implement in a typical neurophysiology lab and provides consistent identification of electrode locations to make it widely used for pooling data across studies and research groups. This paper presents our initial development of such an approach for reconstructing electrode tracks and site locations within the guinea pig inferior colliculus (IC) to identify its functional organization for frequency coding relevant for a new auditory midbrain implant (AMI). Encouragingly, the spatial error associated with different individuals reconstructing electrode tracks for the same midbrain was less than 65 μm, corresponding to an error of ~1.5% relative to the entire IC structure (~4–5 mm diameter sphere). Furthermore, the reconstructed frequency laminae of the IC were consistently aligned across three sampled midbrains, demonstrating the ability to use our method to combine location data across animals. Hopefully, through further improvements in our reconstruction method, it can be used as a standard protocol across neurophysiology labs to characterize neural data not only within the IC but also within other brain regions to help bridge the gap between cellular activity and network function. Clinically, correlating function with location within and across multiple brain regions can guide optimal placement of electrodes for the growing field of neural prosthetics. PMID:22754502
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Functional Brain Networks Develop from a “Local to Distributed” Organization
Power, Jonathan D.; Dosenbach, Nico U. F.; Church, Jessica A.; Miezin, Francis M.; Schlaggar, Bradley L.; Petersen, Steven E.
2009-01-01
The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways. PMID:19412534
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Functional brain networks develop from a "local to distributed" organization.
Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E
2009-05-01
The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.
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Optimizing the Usability of Brain-Computer Interfaces.
Zhang, Yin; Chase, Steve M
2018-05-01
Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.
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Optimized ratiometric calcium sensors for functional in vivo imaging of neurons and T lymphocytes.
Thestrup, Thomas; Litzlbauer, Julia; Bartholomäus, Ingo; Mues, Marsilius; Russo, Luigi; Dana, Hod; Kovalchuk, Yuri; Liang, Yajie; Kalamakis, Georgios; Laukat, Yvonne; Becker, Stefan; Witte, Gregor; Geiger, Anselm; Allen, Taylor; Rome, Lawrence C; Chen, Tsai-Wen; Kim, Douglas S; Garaschuk, Olga; Griesinger, Christian; Griesbeck, Oliver
2014-02-01
The quality of genetically encoded calcium indicators (GECIs) has improved dramatically in recent years, but high-performing ratiometric indicators are still rare. Here we describe a series of fluorescence resonance energy transfer (FRET)-based calcium biosensors with a reduced number of calcium binding sites per sensor. These 'Twitch' sensors are based on the C-terminal domain of Opsanus troponin C. Their FRET responses were optimized by a large-scale functional screen in bacterial colonies, refined by a secondary screen in rat hippocampal neuron cultures. We tested the in vivo performance of the most sensitive variants in the brain and lymph nodes of mice. The sensitivity of the Twitch sensors matched that of synthetic calcium dyes and allowed visualization of tonic action potential firing in neurons and high resolution functional tracking of T lymphocytes. Given their ratiometric readout, their brightness, large dynamic range and linear response properties, Twitch sensors represent versatile tools for neuroscience and immunology.
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Optimizing the learning rate for adaptive estimation of neural encoding models
2018-01-01
Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069
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Optimizing the learning rate for adaptive estimation of neural encoding models.
Hsieh, Han-Lin; Shanechi, Maryam M
2018-05-01
Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.
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Distinct roles of dopamine and subthalamic nucleus in learning and probabilistic decision making.
Coulthard, Elizabeth J; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K; Murphy, Gillian; Keeley, Sophie; Whone, Alan L
2012-12-01
Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making predict that learning individual stimulus-response associations and rapid combination of information from multiple stimuli are dependent on different components of basal ganglia circuitry. In particular, learning and retention of memory, required for optimal response choice, are significantly reliant on dopamine, whereas integrating information probabilistically is critically dependent upon functioning of the glutamatergic subthalamic nucleus (computing the 'normalization term' in Bayes' theorem). Here, we test these theories by investigating 22 patients with Parkinson's disease either treated with deep brain stimulation to the subthalamic nucleus and dopaminergic therapy or managed with dopaminergic therapy alone. We use computerized tasks that probe three cognitive functions-information acquisition (learning), memory over a delay and information integration when multiple pieces of sequentially presented information have to be combined. Patients performed the tasks ON or OFF deep brain stimulation and/or ON or OFF dopaminergic therapy. Consistent with the computational theories, we show that stopping dopaminergic therapy impairs memory for probabilistic information over a delay, whereas deep brain stimulation to the region of the subthalamic nucleus disrupts decision making when multiple pieces of acquired information must be combined. Furthermore, we found that when participants needed to update their decision on the basis of the last piece of information presented in the decision-making task, patients with deep brain stimulation of the subthalamic nucleus region did not slow down appropriately to revise their plan, a pattern of behaviour that mirrors the impulsivity described clinically in some patients with subthalamic nucleus deep brain stimulation. Thus, we demonstrate distinct mechanisms for two important facets of human decision making: first, a role for dopamine in memory consolidation, and second, the critical importance of the subthalamic nucleus in successful decision making when multiple pieces of information must be combined.
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Kelley, Daniel J; Johnson, Sterling C
2007-01-01
Background With rapid advances in functional imaging methods, human studies that feature functional neuroimaging techniques are increasing exponentially and have opened a vast arena of new possibilities for understanding brain function and improving the care of patients with cognitive disorders in the clinical setting. There is a growing need for medical centers to offer clinically relevant functional neuroimaging courses that emphasize the multifaceted and multidisciplinary nature of this field. In this paper, we describe the implementation of a functional neuroimaging course focusing on cognitive disorders that might serve as a model for other medical centers. We identify key components of an active learning course design that impact student learning gains in methods and issues pertaining to functional neuroimaging that deserve consideration when optimizing the medical neuroimaging curriculum. Methods Learning gains associated with the course were assessed using polychoric correlation analysis of responses to the SALG (Student Assessment of Learning Gains) instrument. Results Student gains in the functional neuroimaging of cognition as assessed by the SALG instrument were strongly associated with several aspects of the course design. Conclusion Our implementation of a multidisciplinary and active learning functional neuroimaging course produced positive learning outcomes. Inquiry-based learning activities and an online learning environment contributed positively to reported gains. This functional neuroimaging course design may serve as a useful model for other medical centers. PMID:17953758
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Auer, Tibor; Dewiputri, Wan Ilma; Frahm, Jens; Schweizer, Renate
2018-05-15
Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B; Dodd, Keith C; Young, Brittany M; Jacobson, Tyler; McMillan, Matthew; Thoma, Jaclyn; Advani, Hemali; Nair, Veena A; Kang, Theresa J; Caldera, Kristin; Edwards, Dorothy F; Williams, Justin C; Prabhakaran, Vivek
2018-01-01
Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.
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The tensor distribution function.
Leow, A D; Zhu, S; Zhan, L; McMahon, K; de Zubicaray, G I; Meredith, M; Wright, M J; Toga, A W; Thompson, P M
2009-01-01
Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues.
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2010-07-01
also will determine if ORL1 antagonists block downstream gene transcription subsequent to enzyme activation. BODY: Task 1a was to optimize blast...value between 60 and 80 psi at which defects in vestibulomotor function can be detected. These findings could potentially become a clinical...nociceptin-mediated desensitization of opioid receptor-like 1 receptor and mu opioid receptors involves protein kinase C: a molecular mechanism for
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Schulz, Geralyn M; Hosey, Lara A; Bradberry, Trent J; Stager, Sheila V; Lee, Li-Ching; Pawha, Rajesh; Lyons, Kelly E; Metman, Leo Verhagen; Braun, Allen R
2012-01-01
Deep brain stimulation (DBS) of the subthalamic nucleus improves the motor symptoms of Parkinson's disease, but may produce a worsening of speech and language performance at rates and amplitudes typically selected in clinical practice. The possibility that these dissociated effects might be modulated by selective stimulation of left and right STN has never been systematically investigated. To address this issue, we analyzed motor, speech and language functions of 12 patients implanted with bilateral stimulators configured for optimal motor responses. Behavioral responses were quantified under four stimulator conditions: bilateral DBS, right-only DBS, left-only DBS and no DBS. Under bilateral and left-only DBS conditions, our results exhibited a significant improvement in motor symptoms but worsening of speech and language. These findings contribute to the growing body of literature demonstrating that bilateral STN DBS compromises speech and language function and suggests that these negative effects may be principally due to left-sided stimulation. These findings may have practical clinical consequences, suggesting that clinicians might optimize motor, speech and language functions by carefully adjusting left- and right-sided stimulation parameters.
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Lorenz, Romy; Monti, Ricardo Pio; Violante, Inês R.; Anagnostopoulos, Christoforos; Faisal, Aldo A.; Montana, Giovanni; Leech, Robert
2016-01-01
Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals. PMID:26804778
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Lorenz, Romy; Monti, Ricardo Pio; Violante, Inês R; Anagnostopoulos, Christoforos; Faisal, Aldo A; Montana, Giovanni; Leech, Robert
2016-04-01
Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
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Goh, Joshua O S; Su, Yu-Shiang; Tang, Yong-Jheng; McCarrey, Anna C; Tereshchenko, Alexander; Elkins, Wendy; Resnick, Susan M
2016-12-07
Aging compromises the frontal, striatal, and medial temporal areas of the reward system, impeding accurate value representation and feedback processing critical for decision making. However, substantial variability characterizes age-related effects on the brain so that some older individuals evince clear neurocognitive declines whereas others are spared. Moreover, the functional correlates of normative individual differences in older-adult value-based decision making remain unclear. We performed a functional magnetic resonance imaging study in 173 human older adults during a lottery choice task in which costly to more desirable stakes were depicted using low to high expected values (EVs) of points. Across trials that varied in EVs, participants decided to accept or decline the offered stakes to maximize total accumulated points. We found that greater age was associated with less optimal decisions, accepting stakes when losses were likely and declining stakes when gains were likely, and was associated with increased frontal activity for costlier stakes. Critically, risk preferences varied substantially across older adults and neural sensitivity to EVs in the frontal, striatal, and medial temporal areas dissociated risk-aversive from risk-taking individuals. Specifically, risk-averters increased neural responses to increasing EVs as stakes became more desirable, whereas risk-takers increased neural responses with decreasing EV as stakes became more costly. Risk preference also modulated striatal responses during feedback with risk-takers showing more positive responses to gains compared with risk-averters. Our findings highlight the frontal, striatal, and medial temporal areas as key neural loci in which individual differences differentially affect value-based decision-making ability in older adults. Frontal, striatal, and medial temporal functions implicated in value-based decision processing of rewards and costs undergo substantial age-related changes. However, age effects on brain function and cognition differ across individuals. How this normative variation relates to older-adult value-based decision making is unclear. We found that although the ability make optimal decisions declines with age, there is still much individual variability in how this deterioration occurs. Critically, whereas risk-averters showed increased neural activity to increasingly valuable stakes in frontal, striatal, and medial temporal areas, risk-takers instead increased activity as stakes became more costly. Such distinct functional decision-making processing in these brain regions across normative older adults may reflect individual differences in susceptibility to age-related brain changes associated with incipient cognitive impairment. Copyright © 2016 the authors 0270-6474/16/3612498-12$15.00/0.
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Neuroeconomic approaches to mental disorders
Kishida, Kenneth T.; King-Casas, Brooks; Montague, P. Read
2010-01-01
The pervasiveness of decision-making in every area of human endeavor highlights the importance of understanding choice mechanisms and their detailed relationship to underlying neurobiological function. This review surveys the recent and productive application of game theoretic probes (economic games) to mental disorders. Such games typically possess concrete concepts of optimal play, thus providing quantitative ways to track when subjects’ choices match or deviate from optimal. This feature equips economic games with natural classes of control signals that should guide learning and choice in the agents that play them. These signals and their underlying physical correlates in the brain are now being used to generate objective biomarkers that may prove useful for exposing and understanding the neurogenetic basis of normal and pathological human cognition. Thus, game theoretic probes represent some of the first steps toward producing computationally principled, objective measures of cognitive function and dysfunction useful for the diagnosis, treatment and understanding of mental disorders. PMID:20797532
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Optimizing MR imaging-guided navigation for focused ultrasound interventions in the brain
NASA Astrophysics Data System (ADS)
Werner, B.; Martin, E.; Bauer, R.; O'Gorman, R.
2017-03-01
MR imaging during transcranial MR imaging-guided Focused Ultrasound surgery (tcMRIgFUS) is challenging due to the complex ultrasound transducer setup and the water bolus used for acoustic coupling. Achievable image quality in the tcMRIgFUS setup using the standard body coil is significantly inferior to current neuroradiologic standards. As a consequence, MR image guidance for precise navigation in functional neurosurgical interventions using tcMRIgFUS is basically limited to the acquisition of MR coordinates of salient landmarks such as the anterior and posterior commissure for aligning a stereotactic atlas. Here, we show how improved MR image quality provided by a custom built MR coil and optimized MR imaging sequences can support imaging-guided navigation for functional tcMRIgFUS neurosurgery by visualizing anatomical landmarks that can be integrated into the navigation process to accommodate for patient specific anatomy.
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Gene expression profiling of two distinct neuronal populations in the rodent spinal cord.
Ryge, Jesper; Westerdahl, Ann-Charlotte; Alstrøm, Preben; Kiehn, Ole
2008-01-01
In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50-250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord.
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Gene Expression Profiling of Two Distinct Neuronal Populations in the Rodent Spinal Cord
Alstrøm, Preben; Kiehn, Ole
2008-01-01
Background In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. Methodology/Principal Findings We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50–250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. Conclusions/Significance We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord. PMID:18923679
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3D nonrigid registration via optimal mass transport on the GPU.
Ur Rehman, Tauseef; Haber, Eldad; Pryor, Gallagher; Melonakos, John; Tannenbaum, Allen
2009-12-01
In this paper, we present a new computationally efficient numerical scheme for the minimizing flow approach for optimal mass transport (OMT) with applications to non-rigid 3D image registration. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. Our implementation also employs multigrid, and parallel methodologies on a consumer graphics processing unit (GPU) for fast computation. Although computing the optimal map has been shown to be computationally expensive in the past, we show that our approach is orders of magnitude faster then previous work and is capable of finding transport maps with optimality measures (mean curl) previously unattainable by other works (which directly influences the accuracy of registration). We give results where the algorithm was used to compute non-rigid registrations of 3D synthetic data as well as intra-patient pre-operative and post-operative 3D brain MRI datasets.
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Time course of clinical change following neurofeedback.
Rance, Mariela; Walsh, Christopher; Sukhodolsky, Denis G; Pittman, Brian; Qiu, Maolin; Kichuk, Stephen A; Wasylink, Suzanne; Koller, William N; Bloch, Michael; Gruner, Patricia; Scheinost, Dustin; Pittenger, Christopher; Hampson, Michelle
2018-05-02
Neurofeedback - learning to modulate brain function through real-time monitoring of current brain state - is both a powerful method to perturb and probe brain function and an exciting potential clinical tool. For neurofeedback effects to be useful clinically, they must persist. Here we examine the time course of symptom change following neurofeedback in two clinical populations, combining data from two ongoing neurofeedback studies. This analysis reveals a shared pattern of symptom change, in which symptoms continue to improve for weeks after neurofeedback. This time course has several implications for future neurofeedback studies. Most neurofeedback studies are not designed to test an intervention with this temporal pattern of response. We recommend that new studies incorporate regular follow-up of subjects for weeks or months after the intervention to ensure that the time point of greatest effect is sampled. Furthermore, this time course of continuing clinical change has implications for crossover designs, which may attribute long-term, ongoing effects of real neurofeedback to the control intervention that follows. Finally, interleaving neurofeedback sessions with assessments and examining when clinical improvement peaks may not be an appropriate approach to determine the optimal number of sessions for an application. Copyright © 2018 Elsevier Inc. All rights reserved.
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The role of the immune system in central nervous system plasticity after acute injury.
Peruzzotti-Jametti, Luca; Donegá, Matteo; Giusto, Elena; Mallucci, Giulia; Marchetti, Bianca; Pluchino, Stefano
2014-12-26
Acute brain injuries cause rapid cell death that activates bidirectional crosstalk between the injured brain and the immune system. In the acute phase, the damaged CNS activates resident and circulating immune cells via the local and systemic release of soluble mediators. This early immune activation is necessary to confine the injured tissue and foster the clearance of cellular debris, thus bringing the inflammatory reaction to a close. In the chronic phase, a sustained immune activation has been described in many CNS disorders, and the degree of this prolonged response has variable effects on spontaneous brain regenerative processes. The challenge for treating acute CNS damage is to understand how to optimally engage and modify these immune responses, thus providing new strategies that will compensate for tissue lost to injury. Herein we have reviewed the available information regarding the role and function of the innate and adaptive immune responses in influencing CNS plasticity during the acute and chronic phases of after injury. We have examined how CNS damage evolves along the activation of main cellular and molecular pathways that are associated with intrinsic repair, neuronal functional plasticity and facilitation of tissue reorganization. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.
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Zhang, Jianfeng; Huang, Zirui; Chen, Yali; Zhang, Jun; Ghinda, Diana; Nikolova, Yuliya; Wu, Jinsong; Xu, Jianghui; Bai, Wenjie; Mao, Ying; Yang, Zhong; Duncan, Niall; Qin, Pengmin; Wang, Hao; Chen, Bing; Weng, Xuchu; Northoff, Georg
2018-05-01
Which temporal features that can characterize different brain states (i.e., consciousness or unconsciousness) is a fundamental question in the neuroscience of consciousness. Using resting-state functional magnetic resonance imaging (rs-fMRI), we investigated the spatial patterns of two temporal features: the long-range temporal correlations (LRTCs), measured by power-law exponent (PLE), and temporal variability, measured by standard deviation (SD) during wakefulness and anesthetic-induced unconsciousness. We found that both PLE and SD showed global reductions across the whole brain during anesthetic state comparing to wakefulness. Importantly, the relationship between PLE and SD was altered in anesthetic state, in terms of a spatial "decoupling." This decoupling was mainly driven by a spatial pattern alteration of the PLE, rather than the SD, in the anesthetic state. Our results suggest differential physiological grounds of PLE and SD and highlight the functional importance of the topographical organization of LRTCs in maintaining an optimal spatiotemporal configuration of the neural dynamics during normal level of consciousness. The central role of the spatial distribution of LRTCs, reflecting temporo-spatial nestedness, may support the recently introduced temporo-spatial theory of consciousness (TTC). © 2018 Wiley Periodicals, Inc.
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Hirayama, Jun-ichiro; Hyvärinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito
2016-01-01
Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods. PMID:28002474
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Joshi, Shailendra; Singh-Moon, Rajinder; Wang, Mei; Bruce, Jeffrey N.; Bigio, Irving J.; Mayevsky, Avraham
2014-01-01
Disruption of blood brain barrier (BBB) is used to enhance chemotherapeutic drug delivery. The purpose of this study was to understand the time course of hemodynamic and metabolic response to intraarterial (IA) mannitol infusions in order to optimize the delivery of drugs for treating brain tumors. Principal results We compared hemodynamic response, EEG changes, and mitochondrial function as judged by relative changes in tissue NADH concentrations, after intracarotid (IC) infusion of equal volumes of normal saline and mannitol in our rabbit IC drug delivery model. We observed significantly greater, though transient, hyperemic response to IC infusion of mannitol compared to normal saline. Infusion of mannitol also resulted in a greater increase in tissue NADH concentrations relative to the baseline. These hemodynamic, and metabolic changes returned to baseline within 5 min of mannitol injection. Conclusion Significant, though transient, changes in blood flow and brain metabolism occur with IA mannitol infusion. The observed transient hyperemia would suggest that intravenous (IV) chemotherapy should be administered either just before, or concurrent with IA mannitol injections. On the other hand, IA chemotherapy should be delayed until the peak hyperemic response has subsided. PMID:24440631
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Fatouh, Ahmed M; Elshafeey, Ahmed H; Abdelbary, Ahmed
2017-01-01
Purpose Agomelatine is a novel antidepressant drug suffering from an extensive first-pass metabolism leading to a diminished absolute bioavailability. The aim of the study is: first to enhance its absolute bioavailability, and second to increase its brain delivery. Methods To achieve these aims, the nasal route was adopted to exploit first its avoidance of the hepatic first-pass metabolism to increase the absolute bioavailability, and second the direct nose-to-brain pathway to enhance the brain drug delivery. Solid lipid nanoparticles were selected as a drug delivery system to enhance agomelatine permeability across the blood–brain barrier and therefore its brain delivery. Results The optimum solid lipid nanoparticles have a particle size of 167.70 nm ±0.42, zeta potential of −17.90 mV ±2.70, polydispersity index of 0.12±0.10, entrapment efficiency % of 91.25%±1.70%, the percentage released after 1 h of 35.40%±1.13% and the percentage released after 8 h of 80.87%±5.16%. The pharmacokinetic study of the optimized solid lipid nanoparticles revealed a significant increase in each of the plasma peak concentration, the AUC(0–360 min) and the absolute bioavailability compared to that of the oral suspension of Valdoxan® with the values of 759.00 ng/mL, 7,805.69 ng⋅min/mL and 44.44%, respectively. The optimized solid lipid nanoparticles gave a drug-targeting efficiency of 190.02, which revealed more successful brain targeting by the intranasal route compared with the intravenous route. The optimized solid lipid nanoparticles had a direct transport percentage of 47.37, which indicates a significant contribution of the direct nose-to-brain pathway in the brain drug delivery. Conclusion The intranasal administration of agomelatine solid lipid nanoparticles has effectively enhanced both the absolute bioavailability and the brain delivery of agomelatine. PMID:28684900
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Two-photon microscope for multisite microphotolysis of caged neurotransmitters in acute brain slices
Losavio, Bradley E.; Iyer, Vijay; Saggau, Peter
2009-01-01
We developed a two-photon microscope optimized for physiologically manipulating single neurons through their postsynaptic receptors. The optical layout fulfills the stringent design criteria required for high-speed, high-resolution imaging in scattering brain tissue with minimal photodamage. We detail the practical compensation of spectral and temporal dispersion inherent in fast laser beam scanning with acousto-optic deflectors, as well as a set of biological protocols for visualizing nearly diffraction-limited structures and delivering physiological synaptic stimuli. The microscope clearly resolves dendritic spines and evokes electrophysiological transients in single neurons that are similar to endogenous responses. This system enables the study of multisynaptic integration and will assist our understanding of single neuron function and dendritic computation. PMID:20059271
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Sinn, Natalie; Milte, Catherine; Howe, Peter R C
2010-02-01
Around one in four people suffer from mental illness at some stage in their lifetime. There is increasing awareness of the importance of nutrition, particularly omega-3 polyunsaturated fatty acids (n-3 PUFA), for optimal brain development and function. Hence in recent decades, researchers have explored effects of n-3 PUFA on mental health problems over the lifespan, from developmental disorders in childhood, to depression, aggression, and schizophrenia in adulthood, and cognitive decline, dementia and Alzheimer's disease in late adulthood. This review provides an updated overview of the published and the registered clinical trials that investigate effects of n-3 PUFA supplementation on mental health and behavior, highlighting methodological differences and issues.
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Aphasia: Current Concepts in Theory and Practice
Tippett, Donna C.; Niparko, John K.; Hillis, Argye E.
2014-01-01
Recent advances in neuroimaging contribute to a new insights regarding brain-behavior relationships and expand understanding of the functional neuroanatomy of language. Modern concepts of the functional neuroanatomy of language invoke rich and complex models of language comprehension and expression, such as dual stream networks. Increasingly, aphasia is seen as a disruption of cognitive processes underlying language. Rehabilitation of aphasia incorporates evidence based and person-centered approaches. Novel techniques, such as methods of delivering cortical brain stimulation to modulate cortical excitability, such as repetitive transcranial magnetic stimulation and transcranial direct current stimulation, are just beginning to be explored. In this review, we discuss the historical context of the foundations of neuroscientific approaches to language. We sample the emergent theoretical models of the neural substrates of language and cognitive processes underlying aphasia that contribute to more refined and nuanced concepts of language. Current concepts of aphasia rehabilitation are reviewed, including the promising role of cortical stimulation as an adjunct to behavioral therapy and changes in therapeutic approaches based on principles of neuroplasticity and evidence-based/person-centered practice to optimize functional outcomes. PMID:24904925
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Dysregulated expression of neuregulin-1 by cortical pyramidal neurons disrupts synaptic plasticity.
Agarwal, Amit; Zhang, Mingyue; Trembak-Duff, Irina; Unterbarnscheidt, Tilmann; Radyushkin, Konstantin; Dibaj, Payam; Martins de Souza, Daniel; Boretius, Susann; Brzózka, Magdalena M; Steffens, Heinz; Berning, Sebastian; Teng, Zenghui; Gummert, Maike N; Tantra, Martesa; Guest, Peter C; Willig, Katrin I; Frahm, Jens; Hell, Stefan W; Bahn, Sabine; Rossner, Moritz J; Nave, Klaus-Armin; Ehrenreich, Hannelore; Zhang, Weiqi; Schwab, Markus H
2014-08-21
Neuregulin-1 (NRG1) gene variants are associated with increased genetic risk for schizophrenia. It is unclear whether risk haplotypes cause elevated or decreased expression of NRG1 in the brains of schizophrenia patients, given that both findings have been reported from autopsy studies. To study NRG1 functions in vivo, we generated mouse mutants with reduced and elevated NRG1 levels and analyzed the impact on cortical functions. Loss of NRG1 from cortical projection neurons resulted in increased inhibitory neurotransmission, reduced synaptic plasticity, and hypoactivity. Neuronal overexpression of cysteine-rich domain (CRD)-NRG1, the major brain isoform, caused unbalanced excitatory-inhibitory neurotransmission, reduced synaptic plasticity, abnormal spine growth, altered steady-state levels of synaptic plasticity-related proteins, and impaired sensorimotor gating. We conclude that an "optimal" level of NRG1 signaling balances excitatory and inhibitory neurotransmission in the cortex. Our data provide a potential pathomechanism for impaired synaptic plasticity and suggest that human NRG1 risk haplotypes exert a gain-of-function effect. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
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Colas, Jaron T; Pauli, Wolfgang M; Larsen, Tobias; Tyszka, J Michael; O'Doherty, John P
2017-10-01
Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.
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Pauli, Wolfgang M.; Larsen, Tobias; Tyszka, J. Michael; O’Doherty, John P.
2017-01-01
Prediction-error signals consistent with formal models of “reinforcement learning” (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models—namely, “actor/critic” models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning. PMID:29049406
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The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.
Howell, Brittany R; Styner, Martin A; Gao, Wei; Yap, Pew-Thian; Wang, Li; Baluyot, Kristine; Yacoub, Essa; Chen, Geng; Potts, Taylor; Salzwedel, Andrew; Li, Gang; Gilmore, John H; Piven, Joseph; Smith, J Keith; Shen, Dinggang; Ugurbil, Kamil; Zhu, Hongtu; Lin, Weili; Elison, Jed T
2018-03-22
The human brain undergoes extensive and dynamic growth during the first years of life. The UNC/UMN Baby Connectome Project (BCP), one of the Lifespan Connectome Projects funded by NIH, is an ongoing study jointly conducted by investigators at the University of North Carolina at Chapel Hill and the University of Minnesota. The primary objective of the BCP is to characterize brain and behavioral development in typically developing infants across the first 5 years of life. The ultimate goals are to chart emerging patterns of structural and functional connectivity during this period, map brain-behavior associations, and establish a foundation from which to further explore trajectories of health and disease. To accomplish these goals, we are combining state of the art MRI acquisition and analysis techniques, including high-resolution structural MRI (T1-and T2-weighted images), diffusion imaging (dMRI), and resting state functional connectivity MRI (rfMRI). While the overall design of the BCP largely is built on the protocol developed by the Lifespan Human Connectome Project (HCP), given the unique age range of the BCP cohort, additional optimization of imaging parameters and consideration of an age appropriate battery of behavioral assessments were needed. Here we provide the overall study protocol, including approaches for subject recruitment, strategies for imaging typically developing children 0-5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures. Combining HCP inspired neuroimaging data with well-established behavioral assessments during this time period will yield an invaluable resource for the scientific community. Copyright © 2018 Elsevier Inc. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Wang, Dongxu, E-mail: dongxu-wang@uiowa.edu; Dirksen, Blake; Hyer, Daniel E.
Purpose: To determine the plan quality of proton spot scanning (SS) radiosurgery as a function of spot size (in-air sigma) in comparison to x-ray radiosurgery for treating peripheral brain lesions. Methods: Single-field optimized (SFO) proton SS plans with sigma ranging from 1 to 8 mm, cone-based x-ray radiosurgery (Cone), and x-ray volumetric modulated arc therapy (VMAT) plans were generated for 11 patients. Plans were evaluated using secondary cancer risk and brain necrosis normal tissue complication probability (NTCP). Results: For all patients, secondary cancer is a negligible risk compared to brain necrosis NTCP. Secondary cancer risk was lower in proton SSmore » plans than in photon plans regardless of spot size (p = 0.001). Brain necrosis NTCP increased monotonically from an average of 2.34/100 (range 0.42/100–4.49/100) to 6.05/100 (range 1.38/100–11.6/100) as sigma increased from 1 to 8 mm, compared to the average of 6.01/100 (range 0.82/100–11.5/100) for Cone and 5.22/100 (range 1.37/100–8.00/100) for VMAT. An in-air sigma less than 4.3 mm was required for proton SS plans to reduce NTCP over photon techniques for the cohort of patients studied with statistical significance (p = 0.0186). Proton SS plans with in-air sigma larger than 7.1 mm had significantly greater brain necrosis NTCP than photon techniques (p = 0.0322). Conclusions: For treating peripheral brain lesions—where proton therapy would be expected to have the greatest depth-dose advantage over photon therapy—the lateral penumbra strongly impacts the SS plan quality relative to photon techniques: proton beamlet sigma at patient surface must be small (<7.1 mm for three-beam single-field optimized SS plans) in order to achieve comparable or smaller brain necrosis NTCP relative to photon radiosurgery techniques. Achieving such small in-air sigma values at low energy (<70 MeV) is a major technological challenge in commercially available proton therapy systems.« less
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Evaluation of Disease Lesions in the Developing Canine MPS IIIA Brain.
Winner, Leanne K; Marshall, Neil R; Jolly, Robert D; Trim, Paul J; Duplock, Stephen K; Snel, Marten F; Hemsley, Kim M
2018-06-20
Mucopolysaccharidosis IIIA (MPS IIIA) is an inherited neurodegenerative disease of childhood that results in early death. Post-mortem studies have been carried out on human MPS IIIA brain, but little is known about early disease development. Here, we utilised the Huntaway dog model of MPS IIIA to evaluate disease lesion development from 2 to 24 weeks of age. A significant elevation in primarily stored heparan sulphate was observed in all brain regions assessed in MPS IIIA pups ≤9.5 weeks of age. There was a significant elevation in secondarily stored ganglioside (GM3 36:1) in ≤9.5-week-old MPS IIIA pup cerebellum, and other brain regions also exhibited accumulation of this lipid with time. The number of neural stem cells and neuronal precursor cells was essentially unchanged in MPS IIIA dog brain (c.f. unaffected) over the time course assessed, a finding corroborated by neuron cell counts. We observed early neuroinflammatory changes in young MPS IIIA pup brain, with significantly increased numbers of activated microglia recorded in all but one brain region in MPS IIIA pups ≤9.5 weeks of age (c.f. age-matched unaffected pups). In conclusion, infant-paediatric-stage MPS IIIA canine brain exhibits substantial and progressive primary and secondary substrate accumulation, coupled with early and robust microgliosis. Whilst early initiation of treatment is likely to be required to maintain optimal neurological function, the brain's neurodevelopmental potential appears largely unaffected by the disease process; further investigations confirming this are warranted.
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Labeling and tracking exosomes within the brain using gold nanoparticles
NASA Astrophysics Data System (ADS)
Betzer, Oshra; Perets, Nisim; Barnoy, Eran; Offen, Daniel; Popovtzer, Rachela
2018-02-01
Cell-to-cell communication system involves Exosomes, small, membrane-enveloped nanovesicles. Exosomes are evolving as effective therapeutic tools for different pathologies. These extracellular vesicles can bypass biological barriers such as the blood-brain barrier, and can function as powerful nanocarriers for drugs, proteins and gene therapeutics. However, to promote exosomes' therapy development, especially for brain pathologies, a better understanding of their mechanism of action, trafficking, pharmacokinetics and bio-distribution is needed. In this research, we established a new method for non-invasive in-vivo neuroimaging of mesenchymal stem cell (MSC)-derived exosomes, based on computed tomography (CT) imaging with glucose-coated gold nanoparticle (GNP) labeling. We demonstrated that the exosomes were efficiently and directly labeled with GNPs, via an energy-dependent mechanism. Additionally, we found the optimal parameters for exosome labeling and neuroimaging, wherein 5 nm GNPs enhanced labeling, and intranasal administration produced superior brain accumulation. We applied our technique in a mouse model of focal ischemia. Imaging and tracking of intranasally-administered GNP-labeled exosomes revealed specific accumulation and prolonged presence at the lesion area, up to 24 hrs. We propose that this novel exosome labeling and in-vivo neuroimaging technique can serve as a general platform for brain theranostics.
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NASA Astrophysics Data System (ADS)
Lee, Mun Bae; Kwon, Oh-In
2018-04-01
Electrical brain stimulation (EBS) is an invasive electrotherapy and technique used in brain neurological disorders through direct or indirect stimulation using a small electric current. EBS has relied on computational modeling to achieve optimal stimulation effects and investigate the internal activations. Magnetic resonance diffusion weighted imaging (DWI) is commonly useful for diagnosis and investigation of tissue functions in various organs. The apparent diffusion coefficient (ADC) measures the intensity of water diffusion within biological tissues using DWI. By measuring trace ADC and magnetic flux density induced by the EBS, we propose a method to extract electrical properties including the effective extracellular ion-concentration (EEIC) and the apparent isotropic conductivity without any auxiliary additional current injection. First, the internal current density due to EBS is recovered using the measured one component of magnetic flux density. We update the EEIC by introducing a repetitive scheme called the diffusion weighting J-substitution algorithm using the recovered current density and the trace ADC. To verify the proposed method, we study an anesthetized canine brain to visualize electrical properties including electrical current density, effective extracellular ion-concentration, and effective isotropic conductivity by applying electrical stimulation of the brain.
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The collective therapeutic potential of cerebral ketone metabolism in traumatic brain injury.
Prins, Mayumi L; Matsumoto, Joyce H
2014-12-01
The postinjury period of glucose metabolic depression is accompanied by adenosine triphosphate decreases, increased flux of glucose through the pentose phosphate pathway, free radical production, activation of poly-ADP ribose polymerase via DNA damage, and inhibition of glyceraldehyde dehydrogenase (a key glycolytic enzyme) via depletion of the cytosolic NAD pool. Under these post-brain injury conditions of impaired glycolytic metabolism, glucose becomes a less favorable energy substrate. Ketone bodies are the only known natural alternative substrate to glucose for cerebral energy metabolism. While it has been demonstrated that other fuels (pyruvate, lactate, and acetyl-L-carnitine) can be metabolized by the brain, ketones are the only endogenous fuel that can contribute significantly to cerebral metabolism. Preclinical studies employing both pre- and postinjury implementation of the ketogenic diet have demonstrated improved structural and functional outcome in traumatic brain injury (TBI) models, mild TBI/concussion models, and spinal cord injury. Further clinical studies are required to determine the optimal method to induce cerebral ketone metabolism in the postinjury brain, and to validate the neuroprotective benefits of ketogenic therapy in humans. Copyright © 2014 by the American Society for Biochemistry and Molecular Biology, Inc.
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Thompson, Garth John; Pan, Wen-Ju; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella Dawn
2014-01-01
Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders. © 2013.
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Thompson, Garth John; Pan, Wen-Ju; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella Dawn
2013-01-01
Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders. PMID:24071524
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On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series
Fransson, Peter
2016-01-01
Abstract Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box–Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed. PMID:27784176
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On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.
Thompson, William Hedley; Fransson, Peter
2016-12-01
Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.
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Information properties of morphologically complex words modulate brain activity during word reading
Hultén, Annika; Lehtonen, Minna; Lagus, Krista; Salmelin, Riitta
2018-01-01
Abstract Neuroimaging studies of the reading process point to functionally distinct stages in word recognition. Yet, current understanding of the operations linked to those various stages is mainly descriptive in nature. Approaches developed in the field of computational linguistics may offer a more quantitative approach for understanding brain dynamics. Our aim was to evaluate whether a statistical model of morphology, with well‐defined computational principles, can capture the neural dynamics of reading, using the concept of surprisal from information theory as the common measure. The Morfessor model, created for unsupervised discovery of morphemes, is based on the minimum description length principle and attempts to find optimal units of representation for complex words. In a word recognition task, we correlated brain responses to word surprisal values derived from Morfessor and from other psycholinguistic variables that have been linked with various levels of linguistic abstraction. The magnetoencephalography data analysis focused on spatially, temporally and functionally distinct components of cortical activation observed in reading tasks. The early occipital and occipito‐temporal responses were correlated with parameters relating to visual complexity and orthographic properties, whereas the later bilateral superior temporal activation was correlated with whole‐word based and morphological models. The results show that the word processing costs estimated by the statistical Morfessor model are relevant for brain dynamics of reading during late processing stages. PMID:29524274
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Seeley, Helen; Pickard, John; Allanson, Judith; Hutchinson, Peter
2014-01-01
To examine the epidemiology of referrals to a specialist neurotrauma clinic and explore and highlight implications for clinical practice and service development for persons with head injury/traumatic brain injury (HI/TBI). A retrospective population-based cohort study of all referrals to a specialist neurotrauma clinic over a 9-year period. Data from a specialist head injury database (which included all persons presenting to hospital with traumatic brain injury) were analysed. In total, 1235 new patients of all ages, severities of injury, both admitted and non-admitted were referred. Referrals have increased due to successful integration with new service developments and resulting optimization of resources. Data gathered from the cohort gives increased understanding of the characteristics and numbers of patients requiring rehabilitation and adds to the evidence-base. Integration with new and complementary service developments has optimized the function/aims of the clinic and enhanced its role in terms of patient service and outcome and as a research resource. The model provides principles which may be applied to planning, organizing and providing follow-up/rehabilitation services for HI/TBI.
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Cell therapy attempted as a novel approach for chronic traumatic brain injury - a pilot study.
Sharma, Alok; Sane, Hemangi; Kulkarni, Pooja; Yadav, Jayanti; Gokulchandran, Nandini; Biju, Hema; Badhe, Prerna
2015-01-01
Traumatic brain injury is an injury to the brain parenchyma resulting from external factors such as vehicular accidents, falls, or sports injuries. Its outcome involves primary insult followed by a cascade of secondary insult, resulting in diffuse axonal injury further causing white matter damage. Surgical intervention targets the primary damage, whereas only few treatment alternatives are available to treat the secondary damage. Cellular therapy could be one of the prospective therapeutic options, as it has the potential to arrest the degeneration and promote regeneration of new cells in the brain. We conducted a pilot study on 14 cases who were administered with autologous bone marrow mononuclear cells, intrathecally. The follow up was done at 1 week, 3 months and 6 months after the intervention. The Functional Independence Measure scale, the SF-8 Health Survey Scoring and the disability rating scale were used as outcome measures. These scales showed a positive shift in scores at the end of 6 months. Improvements were observed in various symptoms, along with activities of daily living. Improvement in PET CT scan performed before and 6 months after the intervention in 3 patients corresponded to the clinical and functional improvements observed in these patients. The results of this study suggest that cell therapy may promote functional recovery leading to an improved quality of life in chronic TBI. Although the results are positive, the improvements after cell therapy are not optimal. Hence, additional multicenter, controlled studies are required to establish cell therapy as a standard therapeutic approach.
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IV. The cognitive implications of obesity and nutrition in childhood.
Khan, Naiman A; Raine, Lauren B; Donovan, Sharon M; Hillman, Charles H
2014-12-01
The prevalence of childhood obesity in the United States has tripled since the 1980s and is strongly linked to the early onset of several metabolic diseases. Recent studies indicate that lower cognitive function may be another complication of childhood obesity. This review considers the research to date on the role of obesity and nutrition on childhood cognition and brain health. Although a handful of studies point to a maladaptive relationship between obesity and aspects of cognitive control, remarkably little is known regarding the impact of fat mass on brain development and cognitive function. Further, missing from the literature is the role of nutrition in the obesity-cognition interaction. Nutrition may directly or indirectly influence cognitive performance via several pathways including provision of key substrates for optimal brain health, modulation of gut microbiota, and alterations in systemic energy balance. However, in the absence of malnutrition, the functional benefits of specific nutrient intake on particular cognitive domains are not well characterized. Here, we examine the literature linking childhood obesity and cognition while considering the effects of nutritional intake. Possible mechanisms for these relationships are discussed and suggestions are made for future study topics. Although childhood obesity prevalence rates in some developed countries have recently stabilized, significant disparities remain among groups based on sex and socioeconomic status. Given that the elevated prevalence of pediatric overweight and obesity may persist for the foreseeable future, it is crucial to develop a comprehensive understanding of the influence of obesity and nutrition on cognition and brain health in the pediatric population. © 2014 The Society for Research in Child Development, Inc.
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Breakfast staple types affect brain gray matter volume and cognitive function in healthy children.
Taki, Yasuyuki; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Asano, Michiko; Asano, Kohei; Kawashima, Ryuta
2010-12-08
Childhood diet is important for brain development. Furthermore, the quality of breakfast is thought to affect the cognitive functioning of well-nourished children. To analyze the relationship among breakfast staple type, gray matter volume, and intelligence quotient (IQ) in 290 healthy children, we used magnetic resonance images and applied voxel-based morphometry. We divided subjects into rice, bread, and both groups according to their breakfast staple. We showed that the rice group had a significantly larger gray matter ratio (gray matter volume percentage divided by intracranial volume) and significantly larger regional gray matter volumes of several regions, including the left superior temporal gyrus. The bread group had significantly larger regional gray and white matter volumes of several regions, including the right frontoparietal region. The perceptual organization index (POI; IQ subcomponent) of the rice group was significantly higher than that of the bread group. All analyses were adjusted for age, gender, intracranial volume, socioeconomic status, average weekly frequency of having breakfast, and number of side dishes eaten for breakfast. Although several factors may have affected the results, one possible mechanism underlying the difference between the bread and the rice groups may be the difference in the glycemic index (GI) of these two substances; foods with a low GI are associated with less blood-glucose fluctuation than are those with a high GI. Our study suggests that breakfast staple type affects brain gray and white matter volumes and cognitive function in healthy children; therefore, a diet of optimal nutrition is important for brain maturation during childhood and adolescence.
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Wogensen, Elise; Malá, Hana
2015-01-01
The objective of the present paper is to review the current status of exercise as a tool to promote cognitive rehabilitation after acquired brain injury (ABI) in animal model-based research. Searches were conducted on the PubMed, Scopus, and psycINFO databases in February 2014. Search strings used were: exercise (and) animal model (or) rodent (or) rat (and) traumatic brain injury (or) cerebral ischemia (or) brain irradiation. Studies were selected if they were (1) in English, (2) used adult animals subjected to acquired brain injury, (3) used exercise as an intervention tool after inflicted injury, (4) used exercise paradigms demanding movement of all extremities, (5) had exercise intervention effects that could be distinguished from other potential intervention effects, and (6) contained at least one measure of cognitive and/or emotional function. Out of 2308 hits, 22 publications fulfilled the criteria. The studies were examined relative to cognitive effects associated with three themes: exercise type (forced or voluntary), timing of exercise (early or late), and dose-related factors (intensity, duration, etc.). The studies indicate that exercise in many cases can promote cognitive recovery after brain injury. However, the optimal parameters to ensure cognitive rehabilitation efficacy still elude us, due to considerable methodological variations between studies. PMID:26509085
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Muscle coordination is habitual rather than optimal.
de Rugy, Aymar; Loeb, Gerald E; Carroll, Timothy J
2012-05-23
When sharing load among multiple muscles, humans appear to select an optimal pattern of activation that minimizes costs such as the effort or variability of movement. How the nervous system achieves this behavior, however, is unknown. Here we show that contrary to predictions from optimal control theory, habitual muscle activation patterns are surprisingly robust to changes in limb biomechanics. We first developed a method to simulate joint forces in real time from electromyographic recordings of the wrist muscles. When the model was altered to simulate the effects of paralyzing a muscle, the subjects simply increased the recruitment of all muscles to accomplish the task, rather than recruiting only the useful muscles. When the model was altered to make the force output of one muscle unusually noisy, the subjects again persisted in recruiting all muscles rather than eliminating the noisy one. Such habitual coordination patterns were also unaffected by real modifications of biomechanics produced by selectively damaging a muscle without affecting sensory feedback. Subjects naturally use different patterns of muscle contraction to produce the same forces in different pronation-supination postures, but when the simulation was based on a posture different from the actual posture, the recruitment patterns tended to agree with the actual rather than the simulated posture. The results appear inconsistent with computation of motor programs by an optimal controller in the brain. Rather, the brain may learn and recall command programs that result in muscle coordination patterns generated by lower sensorimotor circuitry that are functionally "good-enough."
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Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation.
Dmochowski, Jacek P; Koessler, Laurent; Norcia, Anthony M; Bikson, Marom; Parra, Lucas C
2017-08-15
To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4-7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
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Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation
Dmochowski, Jacek P.; Koessler, Laurent; Norcia, Anthony M.; Bikson, Marom; Parra, Lucas C.
2018-01-01
To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4–7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. PMID:28578130
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Rothman, Sarah M; Griffioen, Kathleen J; Wan, Ruiqian; Mattson, Mark P
2012-01-01
Overweight sedentary individuals are at increased risk for cardiovascular disease, diabetes, and some neurological disorders. Beneficial effects of dietary energy restriction (DER) and exercise on brain structural plasticity and behaviors have been demonstrated in animal models of aging and acute (stroke and trauma) and chronic (Alzheimer's and Parkinson's diseases) neurological disorders. The findings described later, and evolutionary considerations, suggest brain-derived neurotrophic factor (BDNF) plays a critical role in the integration and optimization of behavioral and metabolic responses to environments with limited energy resources and intense competition. In particular, BDNF signaling mediates adaptive responses of the central, autonomic, and peripheral nervous systems from exercise and DER. In the hypothalamus, BDNF inhibits food intake and increases energy expenditure. By promoting synaptic plasticity and neurogenesis in the hippocampus, BDNF mediates exercise- and DER-induced improvements in cognitive function and neuroprotection. DER improves cardiovascular stress adaptation by a mechanism involving enhancement of brainstem cholinergic activity. Collectively, findings reviewed in this paper provide a rationale for targeting BDNF signaling for novel therapeutic interventions in a range of metabolic and neurological disorders. PMID:22548651
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Blessy, S A Praylin Selva; Sulochana, C Helen
2015-01-01
Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Dong, P; Xing, L; Ma, L
Purpose: Radiosurgery of multiple (n>4) brain metastasis lesions requires 3–4 noncoplanar VMAT arcs with excessively high monitor units and long delivery time. We investigated whether an improved optimization technique would decrease the needed arc numbers and increase the delivery efficiency, while improving or maintaining the plan quality. Methods: The proposed 4pi arc space optimization algorithm consists of two steps: automatic couch angle selection followed by aperture generation for each arc with optimized control points distribution. We use a greedy algorithm to select the couch angles. Starting from a single coplanar arc plan we search through the candidate noncoplanar arcs tomore » pick a single noncoplanar arc that will bring the best plan quality when added into the existing treatment plan. Each time, only one additional noncoplanar arc is considered making the calculation time tractable. This process repeats itself until desired number of arc is reached. The technique is first evaluated in coplanar arc delivery scheme with testing cases and then applied to noncoplanar treatments of a case with 12 brain metastasis lesions. Results: Clinically acceptable plans are created within minutes. For the coplanar testing cases the algorithm yields singlearc plans with better dose distributions than that of two-arc VMAT, simultaneously with a 12–17% reduction in the delivery time and a 14–21% reduction in MUs. For the treatment of 12 brain mets while Paddick conformity indexes of the two plans were comparable the SCG-optimization with 2 arcs (1 noncoplanar and 1 coplanar) significantly improved the conventional VMAT with 3 arcs (2 noncoplanar and 1 coplanar). Specifically V16 V10 and V5 of the brain were reduced by 11%, 11% and 12% respectively. The beam delivery time was shortened by approximately 30%. Conclusion: The proposed 4pi arc space optimization technique promises to significantly reduce the brain toxicity while greatly improving the treatment efficiency.« less
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Cerebrovascular Pressure Reactivity in Children With Traumatic Brain Injury.
Lewis, Philip M; Czosnyka, Marek; Carter, Bradley G; Rosenfeld, Jeffrey V; Paul, Eldho; Singhal, Nitesh; Butt, Warwick
2015-10-01
Traumatic brain injury is a significant cause of morbidity and mortality in children. Cerebral autoregulation disturbance after traumatic brain injury is associated with worse outcome. Pressure reactivity is a fundamental component of cerebral autoregulation that can be estimated using the pressure-reactivity index, a correlation between slow arterial blood pressure, and intracranial pressure fluctuations. Pressure-reactivity index has shown prognostic value in adult traumatic brain injury, with one study confirming this in children. Pressure-reactivity index can identify a cerebral perfusion pressure range within which pressure reactivity is optimal. An increasing difference between optimal cerebral perfusion pressure and cerebral perfusion pressure is associated with worse outcome in adult traumatic brain injury; however, this has not been investigated in children. Our objective was to study pressure-reactivity index and optimal cerebral perfusion pressure in pediatric traumatic brain injury, including associations with outcome, age, and cerebral perfusion pressure. Prospective observational study. ICU, Royal Children's Hospital, Melbourne, Australia. Patients with traumatic brain injury who are 6 months to 16 years old, are admitted to the ICU, and require arterial blood pressure and intracranial pressure monitoring. None. Arterial blood pressure, intracranial pressure, and end-tidal CO2 were recorded electronically until ICU discharge or monitoring cessation. Pressure-reactivity index and optimal cerebral perfusion pressure were computed according to previously published methods. Clinical data were collected from electronic medical records. Outcome was assessed 6 months post discharge using the modified Glasgow Outcome Score. Thirty-six patients were monitored, with 30 available for follow-up. Pressure-reactivity index correlated with modified Glasgow Outcome Score (Spearman ρ = 0.42; p = 0.023) and was higher in patients with unfavorable outcome (0.23 vs -0.09; p = 0.0009). A plot of pressure-reactivity index averaged within 5 mm Hg cerebral perfusion pressure bins showed a U-shape, reaffirming the concept of cerebral perfusion pressure optimization in children. Optimal cerebral perfusion pressure increased with age (ρ = 0.40; p = 0.02). Both the duration and magnitude of negative deviations in the difference between cerebral perfusion pressure and optimal cerebral perfusion pressure were associated with unfavorable outcome. In pediatric patients with traumatic brain injury, pressure-reactivity index has prognostic value and can identify cerebral perfusion pressure targets that may differ from treatment protocols. Our results suggest but do not confirm that cerebral perfusion pressure targeting using pressure-reactivity index as a guide may positively impact on outcome. This question should be addressed by a prospective clinical study.
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Wang, Ying; Goh, Joshua O; Resnick, Susan M; Davatzikos, Christos
2013-01-01
In this study, we used high-dimensional pattern regression methods based on structural (gray and white matter; GM and WM) and functional (positron emission tomography of regional cerebral blood flow; PET) brain data to identify cross-sectional imaging biomarkers of cognitive performance in cognitively normal older adults from the Baltimore Longitudinal Study of Aging (BLSA). We focused on specific components of executive and memory domains known to decline with aging, including manipulation, semantic retrieval, long-term memory (LTM), and short-term memory (STM). For each imaging modality, brain regions associated with each cognitive domain were generated by adaptive regional clustering. A relevance vector machine was adopted to model the nonlinear continuous relationship between brain regions and cognitive performance, with cross-validation to select the most informative brain regions (using recursive feature elimination) as imaging biomarkers and optimize model parameters. Predicted cognitive scores using our regression algorithm based on the resulting brain regions correlated well with actual performance. Also, regression models obtained using combined GM, WM, and PET imaging modalities outperformed models based on single modalities. Imaging biomarkers related to memory performance included the orbito-frontal and medial temporal cortical regions with LTM showing stronger correlation with the temporal lobe than STM. Brain regions predicting executive performance included orbito-frontal, and occipito-temporal areas. The PET modality had higher contribution to most cognitive domains except manipulation, which had higher WM contribution from the superior longitudinal fasciculus and the genu of the corpus callosum. These findings based on machine-learning methods demonstrate the importance of combining structural and functional imaging data in understanding complex cognitive mechanisms and also their potential usage as biomarkers that predict cognitive status.
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Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging
Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.
2013-01-01
Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient. PMID:22945462
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MRI measurements of Blood-Brain Barrier function in dementia: A review of recent studies.
Raja, Rajikha; Rosenberg, Gary A; Caprihan, Arvind
2018-05-15
Blood-brain barrier (BBB) separates the systemic circulation and the brain, regulating transport of most molecules to protect the brain microenvironment. Multiple structural and functional components preserve the integrity of the BBB. Several imaging modalities are available to study disruption of the BBB. However, the subtle changes in BBB leakage that occurs in vascular cognitive impairment and Alzheimer's disease have been less well studied. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the most widely adopted non-invasive imaging technique for evaluating BBB breakdown. It is used as a significant marker for a wide variety of diseases with large permeability leaks, such as brain tumors and multiple sclerosis, to more subtle disruption in chronic vascular disease and dementia. DCE-MRI analysis of BBB includes both model-free parameters and quantitative parameters using pharmacokinetic modelling. We review MRI studies of BBB breakdown in dementia. The challenges in measuring subtle BBB changes and the state of the art techniques are initially examined. Subsequently, a systematic review comparing methodologies from recent in-vivo MRI studies is presented. Various factors related to subtle BBB permeability measurement such as DCE-MRI acquisition parameters, arterial input assessment, T 1 mapping and data analysis methods are reviewed with the focus on finding the optimal technique. Finally, the reported BBB permeability values in dementia are compared across different studies and across various brain regions. We conclude that reliable measurement of low-level BBB permeability across sites remains a difficult problem and a standardization of the methodology for both data acquisition and quantitative analysis is required. This article is part of the Special Issue entitled 'Cerebral Ischemia'. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Delivery of Chemotherapeutics Across the Blood–Brain Barrier: Challenges and Advances
Doolittle, Nancy D.; Muldoon, Leslie L.; Culp, Aliana Y.; Neuwelt, Edward A.
2017-01-01
The blood–brain barrier (BBB) limits drug delivery to brain tumors. We utilize intraarterial infusion of hyperosmotic mannitol to reversibly open the BBB by shrinking endothelial cells and opening tight junctions between the cells. This approach transiently increases the delivery of chemotherapy, antibodies, and nanoparticles to brain. Our preclinical studies have optimized the BBB disruption (BBBD) technique and clinical studies have shown its safety and efficacy. The delivery of methotrexate-based chemotherapy in conjunction with BBBD provides excellent outcomes in primary central nervous system lymphoma (PCNSL) including stable or improved cognitive function in survivors a median of 12 years (range 2–26 years) after diagnosis. The addition of rituximab to chemotherapy with BBBD for PCNSL can be safely accomplished with excellent overall survival. Our translational studies of thiol agents to protect against platinum-induced toxicities led to the development of a two-compartment model in brain tumor patients. We showed that delayed high-dose sodium thiosulfate protects against carboplatin-induced hearing loss, providing the framework for large cooperative group trials of hearing chemoprotection. Neuroimaging studies have identified that ferumoxytol, an iron oxide nanoparticle blood pool agent, appears to be a superior contrast agent to accurately assess therapy-induced changes in brain tumor vasculature, in brain tumor response to therapy, and in differentiating central nervous system lesions with inflammatory components. This chapter reviews the breakthroughs, challenges, and future directions for BBBD. PMID:25307218
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Neuroprosthetic Decoder Training as Imitation Learning.
Merel, Josh; Carlson, David; Paninski, Liam; Cunningham, John P
2016-05-01
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.
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Optimizing highly noncoplanar VMAT trajectories: the NoVo method.
Langhans, Marco; Unkelbach, Jan; Bortfeld, Thomas; Craft, David
2018-01-16
We introduce a new method called NoVo (Noncoplanar VMAT Optimization) to produce volumetric modulated arc therapy (VMAT) treatment plans with noncoplanar trajectories. While the use of noncoplanar beam arrangements for intensity modulated radiation therapy (IMRT), and in particular high fraction stereotactic radiosurgery (SRS), is common, noncoplanar beam trajectories for VMAT are less common as the availability of treatment machines handling these is limited. For both IMRT and VMAT, the beam angle selection problem is highly nonconvex in nature, which is why automated beam angle selection procedures have not entered mainstream clinical usage. NoVo determines a noncoplanar VMAT solution (i.e. the simultaneous trajectories of the gantry and the couch) by first computing a [Formula: see text] solution (beams from every possible direction, suitably discretized) and then eliminating beams by examing fluence contributions. Also all beam angles are scored via geometrical considerations only to find out the usefulness of the whole beam space in a very short time. A custom path finding algorithm is applied to find an optimized, continuous trajectory through the most promising beam angles using the calculated score of the beam space. Finally, using this trajectory a VMAT plan is optimized. For three clinical cases, a lung, brain, and liver case, we compare NoVo to the ideal [Formula: see text] solution, nine beam noncoplanar IMRT, coplanar VMAT, and a recently published noncoplanar VMAT algorithm. NoVo comes closest to the [Formula: see text] solution considering the lung case (brain and liver case: second), as well as improving the solution time by using geometrical considerations, followed by a time effective iterative process reducing the [Formula: see text] solution. Compared to a recently published noncoplanar VMAT algorithm, using NoVo the computation time is reduced by a factor of 2-3 (depending on the case). Compared to coplanar VMAT, NoVo reduces the objective function value by 24%, 49% and 6% for the lung, brain and liver cases, respectively.
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Optimizing highly noncoplanar VMAT trajectories: the NoVo method
NASA Astrophysics Data System (ADS)
Langhans, Marco; Unkelbach, Jan; Bortfeld, Thomas; Craft, David
2018-01-01
We introduce a new method called NoVo (Noncoplanar VMAT Optimization) to produce volumetric modulated arc therapy (VMAT) treatment plans with noncoplanar trajectories. While the use of noncoplanar beam arrangements for intensity modulated radiation therapy (IMRT), and in particular high fraction stereotactic radiosurgery (SRS), is common, noncoplanar beam trajectories for VMAT are less common as the availability of treatment machines handling these is limited. For both IMRT and VMAT, the beam angle selection problem is highly nonconvex in nature, which is why automated beam angle selection procedures have not entered mainstream clinical usage. NoVo determines a noncoplanar VMAT solution (i.e. the simultaneous trajectories of the gantry and the couch) by first computing a 4π solution (beams from every possible direction, suitably discretized) and then eliminating beams by examing fluence contributions. Also all beam angles are scored via geometrical considerations only to find out the usefulness of the whole beam space in a very short time. A custom path finding algorithm is applied to find an optimized, continuous trajectory through the most promising beam angles using the calculated score of the beam space. Finally, using this trajectory a VMAT plan is optimized. For three clinical cases, a lung, brain, and liver case, we compare NoVo to the ideal 4π solution, nine beam noncoplanar IMRT, coplanar VMAT, and a recently published noncoplanar VMAT algorithm. NoVo comes closest to the 4π solution considering the lung case (brain and liver case: second), as well as improving the solution time by using geometrical considerations, followed by a time effective iterative process reducing the 4π solution. Compared to a recently published noncoplanar VMAT algorithm, using NoVo the computation time is reduced by a factor of 2-3 (depending on the case). Compared to coplanar VMAT, NoVo reduces the objective function value by 24%, 49% and 6% for the lung, brain and liver cases, respectively.
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Lubrini, G; Martín-Montes, A; Díez-Ascaso, O; Díez-Tejedor, E
2018-04-01
Our conception of the mind-brain relationship has evolved from the traditional idea of dualism to current evidence that mental functions result from brain activity. This paradigm shift, combined with recent advances in neuroimaging, has led to a novel definition of brain functioning in terms of structural and functional connectivity. The purpose of this literature review is to describe the relationship between connectivity, brain lesions, cerebral plasticity, and functional recovery. Assuming that brain function results from the organisation of the entire brain in networks, brain dysfunction would be a consequence of altered brain network connectivity. According to this approach, cognitive and behavioural impairment following brain damage result from disrupted functional organisation of brain networks. However, the dynamic and versatile nature of these circuits makes recovering brain function possible. Cerebral plasticity allows for functional reorganisation leading to recovery, whether spontaneous or resulting from cognitive therapy, after brain disease. Current knowledge of brain connectivity and cerebral plasticity provides new insights into normal brain functioning, the mechanisms of brain damage, and functional recovery, which in turn serve as the foundations of cognitive therapy. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.
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Lee, Hae In; Lee, Sae-Won; Kim, Nam Gyun; Park, Kyoung-Jun; Choi, Byung Tae; Shin, Yong-Il; Shin, Hwa Kyoung
2017-12-01
We aimed to investigate the effects of low-level light emitting diode therapy (LED-T) on the long-term functional outcomes after cerebral ischemia, and the optimal timing of LED-T initiation for achieving suitable functional recovery. Focal cerebral ischemia was induced in mice via photothrombosis. These mice were assigned to a sham-operated (control), ischemic (vehicle), or LED-T group [initiation immediately (acute), 4 days (subacute) or 10 days (delayed) after ischemia, followed by once-daily treatment for 7 days]. Behavioral outcomes were assessed 21 and 28 days post-ischemia, and histopathological analysis was performed 28 days post-ischemia. The acute and subacute LED-T groups showed a significant improvement in motor function up to 28 days post-ischemia, although no brain atrophy recovery was noted. We observed proliferating cells (BrdU + ) in the ischemic brain, and significant increases in BrdU + /GFAP + , BrdU + /DCX + , BrdU + /NeuN + , and CD31 + cells in the subacute LED-T group. However, the BrdU + /Iba-1 + cell count was reduced in the subacute LED-T group. Furthermore, the brain-derived neurotrophic factor (BDNF) was significantly upregulated in the subacute LED-T group. We concluded that LED-T administered during the subacute stage had a positive impact on the long-term functional outcome, probably via neuron and astrocyte proliferation, blood vessel reconstruction, and increased BDNF expression. Picture: The rotarod test for motor coordination showed that acute and subacute LED-T improves long-term functional recovery after cerebral ischemia. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
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NASA Astrophysics Data System (ADS)
Chockanathan, Udaysankar; DSouza, Adora M.; Abidin, Anas Z.; Schifitto, Giovanni; Wismüller, Axel
2018-02-01
Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV- subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+/- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.
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Evans, Alan C; Janke, Andrew L; Collins, D Louis; Baillet, Sylvain
2012-08-15
The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution. Copyright © 2012 Elsevier Inc. All rights reserved.
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Val-Laillet, D; Aarts, E; Weber, B; Ferrari, M; Quaresima, V; Stoeckel, L E; Alonso-Alonso, M; Audette, M; Malbert, C H; Stice, E
2015-01-01
Functional, molecular and genetic neuroimaging has highlighted the existence of brain anomalies and neural vulnerability factors related to obesity and eating disorders such as binge eating or anorexia nervosa. In particular, decreased basal metabolism in the prefrontal cortex and striatum as well as dopaminergic alterations have been described in obese subjects, in parallel with increased activation of reward brain areas in response to palatable food cues. Elevated reward region responsivity may trigger food craving and predict future weight gain. This opens the way to prevention studies using functional and molecular neuroimaging to perform early diagnostics and to phenotype subjects at risk by exploring different neurobehavioral dimensions of the food choices and motivation processes. In the first part of this review, advantages and limitations of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), pharmacogenetic fMRI and functional near-infrared spectroscopy (fNIRS) will be discussed in the context of recent work dealing with eating behavior, with a particular focus on obesity. In the second part of the review, non-invasive strategies to modulate food-related brain processes and functions will be presented. At the leading edge of non-invasive brain-based technologies is real-time fMRI (rtfMRI) neurofeedback, which is a powerful tool to better understand the complexity of human brain-behavior relationships. rtfMRI, alone or when combined with other techniques and tools such as EEG and cognitive therapy, could be used to alter neural plasticity and learned behavior to optimize and/or restore healthy cognition and eating behavior. Other promising non-invasive neuromodulation approaches being explored are repetitive transcranial magnetic stimulation (rTMS) and transcranial direct-current stimulation (tDCS). Converging evidence points at the value of these non-invasive neuromodulation strategies to study basic mechanisms underlying eating behavior and to treat its disorders. Both of these approaches will be compared in light of recent work in this field, while addressing technical and practical questions. The third part of this review will be dedicated to invasive neuromodulation strategies, such as vagus nerve stimulation (VNS) and deep brain stimulation (DBS). In combination with neuroimaging approaches, these techniques are promising experimental tools to unravel the intricate relationships between homeostatic and hedonic brain circuits. Their potential as additional therapeutic tools to combat pharmacorefractory morbid obesity or acute eating disorders will be discussed, in terms of technical challenges, applicability and ethics. In a general discussion, we will put the brain at the core of fundamental research, prevention and therapy in the context of obesity and eating disorders. First, we will discuss the possibility to identify new biological markers of brain functions. Second, we will highlight the potential of neuroimaging and neuromodulation in individualized medicine. Third, we will introduce the ethical questions that are concomitant to the emergence of new neuromodulation therapies.
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The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review.
Wang, Tianlu; Mantini, Dante; Gillebert, Celine R
2017-09-18
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Busingye, Diana S; Turner, Renée J; Vink, Robert
2016-10-01
While a number of studies have shown that free magnesium (Mg) decline is a feature of traumatic brain injury (TBI), poor central penetration of Mg has potentially limited clinical translation. This study examines whether polyethylene glycol (PEG) facilitates central penetration of Mg after TBI, increasing neuroprotection while simultaneously reducing the dose requirements for Mg. Rats were exposed to diffuse TBI and administered intravenous MgCl2 either alone (254 μmol/kg or 25.4 μmol/kg) or in combination with PEG (1 g/kg PEG) at 30-min postinjury. Vehicle-treated (saline or PEG) and sham animals served as controls. All animals were subsequently assessed for blood-brain barrier permeability and edema at 5 h, and functional outcome for 1 week postinjury. Optimal dose (254 μmol/kg) MgCl2 or Mg PEG significantly improved all outcome parameters compared to vehicle or PEG controls. Intravenous administration of 10% MgCl2 alone (25.4 μmol/kg) had no beneficial effect on any of the outcome parameters, whereas 10% Mg in PEG had the same beneficial effects as optimal dose Mg administration. Polyethylene glycol facilitates central penetration of Mg following TBI, reducing the concentration of Mg required to confer neuroprotection while simultaneously reducing the risks associated with high peripheral Mg concentration. © 2016 John Wiley & Sons Ltd.
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Vilasboas, Tatiana; Herbet, Guillaume; Duffau, Hugues
2017-07-01
For many years, the right hemisphere (RH) was considered as nondominant, especially in right-handers. In neurosurgical practice, this dogma resulted in the selection of awake procedure with language mapping only for lesions of the left dominant hemisphere. Conversely, surgery under general anesthesia (possibly with motor mapping) was usually proposed for right lesions. However, when objective neuropsychological assessments were performed, they frequently showed cognitive and behavioral deficits after brain surgery, even in the RH. Therefore, to preserve an optimal quality of life, especially in patients with a long survival expectancy (as in low-grade gliomas), awake surgery with cortical and axonal electrostimulation mapping has recently been proposed for resection of right tumors. Here, we review new insights gained from intraoperative stimulation into the pivotal role of the RH in movement execution and control, visual processes and spatial cognition, language and nonverbal semantic processing, executive functions (e.g., attention), and social cognition (mentalizing and emotion recognition). These original findings, which break with the myth of a nondominant RH, may have important implications in cognitive neurosciences, by improving our knowledge of the functional connectivity of the RH, as well as for the clinical management of patients with a right lesion. In brain surgery, awake mapping should be considered more systematically in the RH. Moreover, neuropsychological examination must be achieved in a more systematic manner before and after surgery within the RH, to optimize care by predicting the likelihood of functional recovery and by elaborating specific programs of rehabilitation. Copyright © 2017 Elsevier Inc. All rights reserved.
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Whelan, Maxine E; Morgan, Paul S; Sherar, Lauren B; Orme, Mark W; Esliger, Dale W
2017-06-01
Unhealthy behaviors, including smoking, poor nutrition, excessive alcohol consumption, physical inactivity and sedentary lifestyles, are global risk factors for non-communicable diseases and premature death. Functional magnetic resonance imaging (fMRI) offers a unique approach to optimize health messages by examining how the brain responds to information relating to health. Our aim was to systematically review fMRI studies that have investigated variations in brain activation in response to health messages relating to (i) smoking; (ii) alcohol consumption; (iii) physical activity; (iv) diet; and (v) sedentary behavior. The electronic databases used were Medline/PubMed, Web of Science (Core Collection), PsychINFO, SPORTDiscuss, Cochrane Library and Open Grey. Studies were included if they investigated subjects aged ≥10years and were published before January 2017. Of the 13,836 studies identified in the database search, 18 studies (smoking k=15; diet k=2; physical activity/sedentary behavior k=1) were included in the review. The prefrontal cortex was activated in seven (47%) of the smoking-related studies and the physical activity study. Results suggest that activation of the ventromedial, dorsolateral and medial prefrontal cortex regions were predictive of subsequent behavior change following exposure to aversive anti-smoking stimuli. Studies investigating the neurological responses to anti-smoking material were most abundant. Of note, the prefrontal cortex and amygdala were most commonly activated in response to health messages across lifestyle behaviors. The review highlights an important disparity between research focusing on different lifestyle behaviors. Insights from smoking literature suggest fMRI may help to optimize health messaging in relation to other lifestyle behaviors. Copyright © 2017 Elsevier Inc. All rights reserved.
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Kanodia, JS; Gadkar, K; Bumbaca, D; Zhang, Y; Tong, RK; Luk, W; Hoyte, K; Lu, Y; Wildsmith, KR; Couch, JA; Watts, RJ; Dennis, MS; Ernst, JA; Scearce‐Levie, K; Atwal, JK; Joseph, S
2016-01-01
Anti‐transferrin receptor (TfR)‐based bispecific antibodies have shown promise for boosting antibody uptake in the brain. Nevertheless, there are limited data on the molecular properties, including affinity required for successful development of TfR‐based therapeutics. A complex nonmonotonic relationship exists between affinity of the anti‐TfR arm and brain uptake at therapeutically relevant doses. However, the quantitative nature of this relationship and its translatability to humans is heretofore unexplored. Therefore, we developed a mechanistic pharmacokinetic‐pharmacodynamic (PK‐PD) model for bispecific anti‐TfR/BACE1 antibodies that accounts for antibody‐TfR interactions at the blood‐brain barrier (BBB) as well as the pharmacodynamic (PD) effect of anti‐BACE1 arm. The calibrated model correctly predicted the optimal anti‐TfR affinity required to maximize brain exposure of therapeutic antibodies in the cynomolgus monkey and was scaled to predict the optimal affinity of anti‐TfR bispecifics in humans. Thus, this model provides a framework for testing critical translational predictions for anti‐TfR bispecific antibodies, including choice of candidate molecule for clinical development. PMID:27299941
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Bekar, Lane K; Wei, Helen S; Nedergaard, Maiken
2012-12-01
Given the brain's uniquely high cell density and tissue oxygen levels bordering on hypoxia, the ability to rapidly and precisely match blood flow to constantly changing patterns in neural activity is an essential feature of cerebrovascular regulation. Locus coeruleus-norepinephrine (LC-NE) projections innervate the cerebral vasculature and can mediate vasoconstriction. However, function of the LC-mediated constriction in blood-flow regulation has never been addressed. Here, using intrinsic optical imaging coupled with an anesthesia regimen that only minimally interferes with LC activity, we show that NE enhances spatial and temporal aspects of functional hyperemia in the mouse somatosensory cortex. Increasing NE levels in the cortex using an α(2)-adrenergic receptor antagonist paradoxically reduces the extent of functional hyperemia while enhancing the surround blood-flow reduction. However, the NE-mediated vasoconstriction optimizes spatial and temporal focusing of the hyperemic response resulting in a sixfold decrease in the disparity between blood volume and oxygen demand. In addition, NE-mediated vasoconstriction accelerated redistribution to subsequently active regions, enhancing temporal synchronization of blood delivery. These observations show an important role for NE in optimizing neurovascular coupling. As LC neuron loss is prominent in Alzheimer and Parkinson diseases, the diminished ability to couple blood volume to oxygen demand may contribute to their pathogenesis.
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Brain-computer interface analysis of a dynamic visuo-motor task.
Logar, Vito; Belič, Aleš
2011-01-01
The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface. For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used. The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable. The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface. Copyright © 2010 Elsevier B.V. All rights reserved.
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Chang, Herng-Hua; Chang, Yu-Ning
2017-04-01
Bilateral filters have been substantially exploited in numerous magnetic resonance (MR) image restoration applications for decades. Due to the deficiency of theoretical basis on the filter parameter setting, empirical manipulation with fixed values and noise variance-related adjustments has generally been employed. The outcome of these strategies is usually sensitive to the variation of the brain structures and not all the three parameter values are optimal. This article is in an attempt to investigate the optimal setting of the bilateral filter, from which an accelerated and automated restoration framework is developed. To reduce the computational burden of the bilateral filter, parallel computing with the graphics processing unit (GPU) architecture is first introduced. The NVIDIA Tesla K40c GPU with the compute unified device architecture (CUDA) functionality is specifically utilized to emphasize thread usages and memory resources. To correlate the filter parameters with image characteristics for automation, optimal image texture features are subsequently acquired based on the sequential forward floating selection (SFFS) scheme. Subsequently, the selected features are introduced into the back propagation network (BPN) model for filter parameter estimation. Finally, the k-fold cross validation method is adopted to evaluate the accuracy of the proposed filter parameter prediction framework. A wide variety of T1-weighted brain MR images with various scenarios of noise levels and anatomic structures were utilized to train and validate this new parameter decision system with CUDA-based bilateral filtering. For a common brain MR image volume of 256 × 256 × 256 pixels, the speed-up gain reached 284. Six optimal texture features were acquired and associated with the BPN to establish a "high accuracy" parameter prediction system, which achieved a mean absolute percentage error (MAPE) of 5.6%. Automatic restoration results on 2460 brain MR images received an average relative error in terms of peak signal-to-noise ratio (PSNR) less than 0.1%. In comparison with many state-of-the-art filters, the proposed automation framework with CUDA-based bilateral filtering provided more favorable results both quantitatively and qualitatively. Possessing unique characteristics and demonstrating exceptional performances, the proposed CUDA-based bilateral filter adequately removed random noise in multifarious brain MR images for further study in neurosciences and radiological sciences. It requires no prior knowledge of the noise variance and automatically restores MR images while preserving fine details. The strategy of exploiting the CUDA to accelerate the computation and incorporating texture features into the BPN to completely automate the bilateral filtering process is achievable and validated, from which the best performance is reached. © 2017 American Association of Physicists in Medicine.
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Live imaging of mitosis in the developing mouse embryonic cortex.
Pilaz, Louis-Jan; Silver, Debra L
2014-06-04
Although of short duration, mitosis is a complex and dynamic multi-step process fundamental for development of organs including the brain. In the developing cerebral cortex, abnormal mitosis of neural progenitors can cause defects in brain size and function. Hence, there is a critical need for tools to understand the mechanisms of neural progenitor mitosis. Cortical development in rodents is an outstanding model for studying this process. Neural progenitor mitosis is commonly examined in fixed brain sections. This protocol will describe in detail an approach for live imaging of mitosis in ex vivo embryonic brain slices. We will describe the critical steps for this procedure, which include: brain extraction, brain embedding, vibratome sectioning of brain slices, staining and culturing of slices, and time-lapse imaging. We will then demonstrate and describe in detail how to perform post-acquisition analysis of mitosis. We include representative results from this assay using the vital dye Syto11, transgenic mice (histone H2B-EGFP and centrin-EGFP), and in utero electroporation (mCherry-α-tubulin). We will discuss how this procedure can be best optimized and how it can be modified for study of genetic regulation of mitosis. Live imaging of mitosis in brain slices is a flexible approach to assess the impact of age, anatomy, and genetic perturbation in a controlled environment, and to generate a large amount of data with high temporal and spatial resolution. Hence this protocol will complement existing tools for analysis of neural progenitor mitosis.
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Brain Dominance & Self-Actualization.
ERIC Educational Resources Information Center
Bernhoft, Franklin O.
Numerous areas associated with brain dominance have been researched since Bogen and Sperry's work with split-brain patients in the 1960s, but only slight attention has been given to the connection between brain dominance and personality. No study appears in the literature seeking to understand optimal mental health as defined by Maslow's…
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Ulrich-Lai, Yvonne M.; Ryan, Karen K.
2014-01-01
Significant co-morbidities between obesity-related metabolic disease and stress-related psychological disorders suggest important functional interactions between energy balance and brain stress integration. Largely overlapping neural circuits control these systems, and this anatomical arrangement optimizes opportunities for mutual influence. Here we first review the current literature identifying effects of metabolic neuroendocrine signals on stress regulation, and vice versa. Next, the contributions of reward driven food intake to these metabolic and stress interactions are discussed. Lastly, we consider the inter-relationships among metabolism, stress and reward in light of their important implications in the development of therapies for metabolism- or stress-related disease. PMID:24630812
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Phase transitions in Pareto optimal complex networks
NASA Astrophysics Data System (ADS)
Seoane, Luís F.; Solé, Ricard
2015-09-01
The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.
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Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons.
Anton-Sanchez, Laura; Bielza, Concha; Benavides-Piccione, Ruth; DeFelipe, Javier; Larrañaga, Pedro
2016-10-01
The way in which a neuronal tree expands plays an important role in its functional and computational characteristics. We aimed to study the existence of an optimal neuronal design for different types of cortical GABAergic neurons. To do this, we hypothesized that both the axonal and dendritic trees of individual neurons optimize brain connectivity in terms of wiring length. We took the branching points of real three-dimensional neuronal reconstructions of the axonal and dendritic trees of different types of cortical interneurons and searched for the minimal wiring arborization structure that respects the branching points. We compared the minimal wiring arborization with real axonal and dendritic trees. We tested this optimization problem using a new approach based on graph theory and evolutionary computation techniques. We concluded that neuronal wiring is near-optimal in most of the tested neurons, although the wiring length of dendritic trees is generally nearer to the optimum. Therefore, wiring economy is related to the way in which neuronal arborizations grow irrespective of the marked differences in the morphology of the examined interneurons.
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NASA Astrophysics Data System (ADS)
Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.
2018-04-01
In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.
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Tsvetanov, Kamen A; Henson, Richard N A; Tyler, Lorraine K; Razi, Adeel; Geerligs, Linda; Ham, Timothy E; Rowe, James B
2016-03-16
The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18-88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. Copyright © 2016 Tsvetanov et al.
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Henson, Richard N.A.; Tyler, Lorraine K.; Razi, Adeel; Geerligs, Linda; Ham, Timothy E.; Rowe, James B.
2016-01-01
The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18–88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. PMID:26985024
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Wang, Jia; Fu, Kuang; Chen, Lei; Duan, Xujun; Guo, Xiaonan; Chen, Heng; Wu, Qiong; Xia, Wei; Wu, Lijie; Chen, Huafu
2017-01-01
Autism spectrum disorder (ASD) has been widely recognized as a complex neurodevelopmental disorder. A large number of neuroimaging studies suggest abnormalities in brain structure and function of patients with ASD, but there is still no consistent conclusion. We sought to investigate both of the structural and functional brain changes in 3-7-year-old children with ASD compared with typically developing controls (TDs), and to assess whether these alterations are associated with autistic behavioral symptoms. Firstly, we applied an optimized method of voxel-based morphometry (VBM) analysis on structural magnetic resonance imaging (sMRI) data to assess the differences of gray matter volume (GMV) between 31 autistic boys aged 3-7 and 31 age- and handness-matched male TDs. Secondly, we used clusters with between-group differences as seed regions to generate intrinsic functional connectivity maps based on resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) in order to evaluate the functional impairments induced by structural alterations. Brain-behavior correlations were assessed among GMV, functional connectivity and symptom severity in children with ASD. VBM analyses revealed increased GMV in left superior temporal gyrus (STG) and left postcentral gyrus (PCG) in ASD children, comparing with TDs. Using left PCG as a seed region, ASD children displayed significantly higher positive connectivity with right angular gyrus (AG) and greater negative connectivity with right superior parietal gyrus (SPG) and right superior occipital gyrus (SOG), which were associated with the severity of symptoms in social interaction, communication and self-care ability. We suggest that stronger functional connectivity between left PCG and right AG, SPG, and SOG detected in young boys with ASD may serve as important indicators of disease severity. Our study provided preliminary functional evidence that may underlie impaired higher-order multisensory integration in ASD children.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Naegle, John H.; Suppona, Roger A.; Aimone, James Bradley
In 2016, Lewis Rhodes Labs, (LRL), shipped the first commercially viable Neuromorphic Processing Unit, (NPU), branded as a Neuromorphic Data Microscope (NDM). This product leverages architectural mechanisms derived from the sensory cortex of the human brain to efficiently implement pattern matching. LRL and Sandia National Labs have optimized this product for streaming analytics, and demonstrated a 1,000x power per operation reduction in an FPGA format. When reduced to an ASIC, the efficiency will improve to 1,000,000x. Additionally, the neuromorphic nature of the device gives it powerful computational attributes that are counterintuitive to those schooled in traditional von Neumann architectures. Themore » Neuromorphic Data Microscope is the first of a broad class of brain-inspired, time domain processors that will profoundly alter the functionality and economics of data processing.« less
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Cognitive Reserve in Healthy Aging and Alzheimer's Disease: A Meta-Analysis of fMRI Studies.
Colangeli, Stefano; Boccia, Maddalena; Verde, Paola; Guariglia, Paola; Bianchini, Filippo; Piccardi, Laura
2016-08-01
Cognitive reserve (CR) has been defined as the ability to optimize or maximize performance through differential recruitment of brain networks. In the present study, we aimed at providing evidence for a consistent brain network underpinning CR in healthy and pathological aging. To pursue this aim, we performed a coordinate-based meta-analysis of 17 functional magnetic resonance imaging studies on CR proxies in healthy aging, Alzheimer's disease (AD), and mild cognitive impairment (MCI). We found that different brain areas were associated with CR proxies in healthy and pathological aging. A wide network of areas, including medial and lateral frontal areas, that is, anterior cingulate cortex and dorsolateral prefrontal cortex, as well as precuneus, was associated with proxies of CR in healthy elderly patients. The CR proxies in patients with AD and amnesic-MCI were associated with activation in the anterior cingulate cortex. These results were discussed hypothesizing the existence of possible compensatory mechanisms in healthy and pathological aging. © The Author(s) 2016.
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Folley, Bradley S.; Doop, Mikisha L.; Park, Sohee
2009-01-01
Recent evidence suggests that genetic and biochemical factors associated with psychoses may also provide an increased propensity to think creatively. The evolutionary theories linking brain growth and diet to the appearance of creative endeavors have been made recently, but they lack a direct link to research on the biological correlates of divergent and creative thought. Expanding upon Horrobin’s theory that changes in brain size and in neural microconnectivity came about as a result of changes in dietary fat and phospholipid incorporation of highly unsaturated fatty acids, we propose a theory relating phospholipase A2 (PLA2) activity to the neuromodulatory effects of the noradrenergic system. This theory offers probable links between attention, divergent thinking, and arousal through a mechanism that emphasizes optimal individual functioning of the PLA2 and NE systems as they interact with structural and biochemical states of the brain. We hope that this theory will stimulate new research in the neural basis of creativity and its connection to psychoses. PMID:14623501
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Task relevance modulates the behavioural and neural effects of sensory predictions
Friston, Karl J.; Nobre, Anna C.
2017-01-01
The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants’ brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling. PMID:29206225
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Murphy, Sarah
2012-01-01
Pediatric neurocritical care is an emerging multidisciplinary field of medicine and a new frontier in pediatric critical care and pediatric neurology. Central to pediatric neurocritical care is the goal of improving outcomes in critically ill pediatric patients with neurological illness or injury and limiting secondary brain injury through optimal critical care delivery and the support of brain function. There is a pressing need for evidence based guidelines in pediatric neurocritical care, notably in pediatric traumatic brain injury and pediatric stroke. These diseases have distinct clinical and pathophysiological features that distinguish them from their adult counterparts and prevent the direct translation of the adult experience to pediatric patients. Increased attention is also being paid to the broader application of neuromonitoring and neuroprotective strategies in the pediatric intensive care unit, in both primary neurological and primary non-neurological disease states. Although much can be learned from the adult experience, there are important differences in the critically ill pediatric population and in the circumstances that surround the emergence of neurocritical care in pediatrics.
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Unraveling the Links Between the Initiation of Ventilation and Brain Injury in Preterm Infants
Barton, Samantha K.; Tolcos, Mary; Miller, Suzie L.; Roehr, Charles C.; Schmölzer, Georg M.; Davis, Peter G.; Moss, Timothy J. M.; LaRosa, Domenic A.; Hooper, Stuart B.; Polglase, Graeme R.
2015-01-01
The initiation of ventilation in the delivery room is one of the most important but least controlled interventions a preterm infant will face. Tidal volumes (V T) used in the neonatal intensive care unit are carefully measured and adjusted. However, the V Ts that an infant receives during resuscitation are usually unmonitored and highly variable. Inappropriate V Ts delivered to preterm infants during respiratory support substantially increase the risk of injury and inflammation to the lungs and brain. These may cause cerebral blood flow instability and initiate a cerebral inflammatory cascade. The two pathways increase the risk of brain injury and potential life-long adverse neurodevelopmental outcomes. The employment of new technologies, including respiratory function monitors, can improve and guide the optimal delivery of V Ts and reduce confounders, such as leak. Better respiratory support in the delivery room has the potential to improve both respiratory and neurological outcomes in this vulnerable population. PMID:26618148
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van der Naalt, Joukje; Timmerman, Marieke E; de Koning, Myrthe E; van der Horn, Harm J; Scheenen, Myrthe E; Jacobs, Bram; Hageman, Gerard; Yilmaz, Tansel; Roks, Gerwin; Spikman, Jacoba M
2017-07-01
Mild traumatic brain injury (mTBI) accounts for most cases of TBI, and many patients show incomplete long-term functional recovery. We aimed to create a prognostic model for functional outcome by combining demographics, injury severity, and psychological factors to identify patients at risk for incomplete recovery at 6 months. In particular, we investigated additional indicators of emotional distress and coping style at 2 weeks above early predictors measured at the emergency department. The UPFRONT study was an observational cohort study done at the emergency departments of three level-1 trauma centres in the Netherlands, which included patients with mTBI, defined by a Glasgow Coma Scale score of 13-15 and either post-traumatic amnesia lasting less than 24 h or loss of consciousness for less than 30 min. Emergency department predictors were measured either on admission with mTBI-comprising injury severity (GCS score, post-traumatic amnesia, and CT abnormalities), demographics (age, gender, educational level, pre-injury mental health, and previous brain injury), and physical conditions (alcohol use on the day of injury, neck pain, headache, nausea, dizziness)-or at 2 weeks, when we obtained data on mood (Hospital Anxiety and Depression Scale), emotional distress (Impact of Event Scale), coping (Utrecht Coping List), and post-traumatic complaints. The functional outcome was recovery, assessed at 6 months after injury with the Glasgow Outcome Scale Extended (GOSE). We dichotomised recovery into complete (GOSE=8) and incomplete (GOSE≤7) recovery. We used logistic regression analyses to assess the predictive value of patient information collected at the time of admission to an emergency department (eg, demographics, injury severity) alone, and combined with predictors of outcome collected at 2 weeks after injury (eg, emotional distress and coping). Between Jan 25, 2013, and Jan 6, 2015, data from 910 patients with mTBI were collected 2 weeks after injury; the final date for 6-month follow-up was July 6, 2015. Of these patients, 764 (84%) had post-traumatic complaints and 414 (45%) showed emotional distress. At 6 months after injury, outcome data were available for 671 patients; complete recovery (GOSE=8) was observed in 373 (56%) patients and incomplete recovery (GOSE ≤7) in 298 (44%) patients. Logistic regression analyses identified several predictors for 6-month outcome, including education and age, with a clear surplus value of indicators of emotional distress and coping obtained at 2 weeks (area under the curve [AUC]=0·79, optimism 0·02; Nagelkerke R 2 =0·32, optimism 0·05) than only emergency department predictors at the time of admission (AUC=0·72, optimism 0·03; Nagelkerke R 2 =0·19, optimism 0·05). Psychological factors (ie, emotional distress and maladaptive coping experienced early after injury) in combination with pre-injury mental health problems, education, and age are important predictors for recovery at 6 months following mTBI. These findings provide targets for early interventions to improve outcome in a subgroup of patients at risk of incomplete recovery from mTBI, and warrant validation. Dutch Brain Foundation. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Encoder-Decoder Optimization for Brain-Computer Interfaces
Merel, Josh; Pianto, Donald M.; Cunningham, John P.; Paninski, Liam
2015-01-01
Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages. PMID:26029919
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Encoder-decoder optimization for brain-computer interfaces.
Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam
2015-06-01
Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.
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NASA Astrophysics Data System (ADS)
Favaro, Alberto; Lad, Akash; Formenti, Davide; Zani, Davide Danilo; De Momi, Elena
2017-03-01
In a translational neuroscience/neurosurgery perspective, sheep are considered good candidates to study because of the similarity between their brain and the human one. Automatic planning systems for safe keyhole neurosurgery maximize the probe/catheter distance from vessels and risky structures. This work consists in the development of a trajectories planner for straight catheters placement intended to be used for investigating the drug diffusivity mechanisms in sheep brain. Automatic brain segmentation of gray matter, white matter and cerebrospinal fluid is achieved using an online available sheep atlas. Ventricles, midbrain and cerebellum segmentation have been also carried out. The veterinary surgeon is asked to select a target point within the white matter to be reached by the probe and to define an entry area on the brain cortex. To mitigate the risk of hemorrhage during the insertion process, which can prevent the success of the insertion procedure, the trajectory planner performs a curvature analysis of the brain cortex and wipes out from the poll of possible entry points the sulci, as part of brain cortex where superficial blood vessels are naturally located. A limited set of trajectories is then computed and presented to the surgeon, satisfying an optimality criteria based on a cost function which considers the distance from critical brain areas and the whole trajectory length. The planner proved to be effective in defining rectilinear trajectories accounting for the safety constraints determined by the brain morphology. It also demonstrated a short computational time and good capability in segmenting gyri and sulci surfaces.
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Clinical implementation of stereotaxic brain implant optimization
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rosenow, U.F.; Wojcicka, J.B.
1991-03-01
This optimization method for stereotaxic brain implants is based on seed/strand configurations of the basic type developed for the National Cancer Institute (NCI) atlas of regular brain implants. Irregular target volume shapes are determined from delineation in a stack of contrast enhanced computed tomography scans. The neurosurgeon may then select up to ten directions, or entry points, of surgical approach of which the program finds the optimal one under the criterion of smallest target volume diameter. Target volume cross sections are then reconstructed in 5-mm-spaced planes perpendicular to the implantation direction defined by the entry point and the target volumemore » center. This information is used to define a closed line in an implant cross section along which peripheral seed strands are positioned and which has now an irregular shape. Optimization points are defined opposite peripheral seeds on the target volume surface to which the treatment dose rate is prescribed. Three different optimization algorithms are available: linear least-squares programming, quadratic programming with constraints, and a simplex method. The optimization routine is implemented into a commercial treatment planning system. It generates coordinate and source strength information of the optimized seed configurations for further dose rate distribution calculation with the treatment planning system, and also the coordinate settings for the stereotaxic Brown-Roberts-Wells (BRW) implantation device.« less
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Ruschin, Mark; Ma, Lijun; Verbakel, Wilko; Larson, David; Brown, Paul D.
2017-01-01
Abstract Over the past three decades several randomized trials have enabled evidence-based practice for patients presenting with limited brain metastases. These trials have focused on the role of surgery or stereotactic radiosurgery (SRS) with or without whole brain radiation therapy (WBRT). As a result, it is clear that local control should be optimized with surgery or SRS in patients with optimal prognostic factors presenting with up to 4 brain metastases. The routine use of adjuvant WBRT remains debatable, as although greater distant brain control rates are observed, there is no impact on survival, and modern outcomes suggest adverse effects from WBRT on patient cognition and quality of life. With dramatic technologic advances in radiation oncology facilitating the adoption of SRS into mainstream practice, the optimal management of patients with multiple brain metastases is now being put forward. Practice is evolving to SRS alone in these patients despite a lack of level 1 evidence to support a clinical departure from WBRT. The purpose of this review is to summarize the current state of the evidence for patients presenting with limited and multiple metastases, and to present an in-depth analysis of the technology and dosimetric issues specific to the treatment of multiple metastases. PMID:28380635
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Zidan, Ahmed S; Aldawsari, Hibah
2015-01-01
Delivering drugs to intracerebral regions can be accomplished by improving the capacity of transport through blood-brain barrier. Using sertraline as model drug for brain targeting, the current study aimed at modifying its liposomal vesicles with mannopyranoside. Box-Behnken design was employed to statistically optimize the ultrasound parameters, namely ultrasound amplitude, time, and temperature, for maximum mannosylation capacity, sertraline entrapment, and surface charge while minimizing vesicular size. Moreover, in vitro blood-brain barrier transport model was established to assess the transendothelial capacity of the optimized mannosylated vesicles. Results showed a dependence of vesicular size, mannosylation capacity, and sertraline entrapment on cavitation and bubble implosion events that were related to ultrasound power amplitude, temperature. However, short ultrasound duration was required to achieve >90% mannosylation with nanosized vesicles (<200 nm) of narrow size distribution. Optimized ultrasound parameters of 65°C, 27%, and 59 seconds for ultrasound temperature, amplitude, and time were elucidated to produce 81.1%, 46.6 nm, and 77.6% sertraline entrapment, vesicular size, and mannosylation capacity, respectively. Moreover, the transendothelial ability was significantly increased by 2.5-fold by mannosylation through binding with glucose transporters. Hence, mannosylated liposomes processed by ultrasound could be a promising approach for manufacturing and scale-up of brain-targeting liposomes.
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Dénes, Zoltán
2009-01-25
Recovery from brain injury is not only determined by the primary injury, but a very important element is the development of secondary complications which have a major role in determining the possibility of the achievement of available maximal functional abilities and the quality of life of the patients and their family after rehabilitation. This is why during medical treatment the prevention of secondary complications is at least as important as the prevention of primary injury. Determination of the most important secondary complications after severe brain injury, and observation of these effects on the rehabilitation process. Retrospective study in the Brain Injury Rehabilitation unit of the National Institute for Medical Rehabilitation in Hungary. 166 patients were treated with brain injury; the mean age of the patients was 33 (8-83) years in 2004. The majority of patients suffered traumatic brain injury in traffic accidents (125/166), while the rest of them through falls or acts of violence. Sixty-four patients were admitted directly from an intensive care unit, 18 from a second hospital ward (traumatology, neurosurgery or neurology) and the rest of the patients were treated in several different units before they were admitted for rehabilitation. The time that has elapsed between injury and rehabilitation admission was 50 days (21-177). At the time of admission 27 patients were in a vegetative state, 38 patients in a minimal conscious state, and 101 patients had already regained consciousness. 83 patients were hemiparetic, 54 presented tetraparesis, and 1 paraparesis, but 28 patients were not paretic. The most frequent complications in patients with severe brain injury at admission in our rehabilitation unit were: contractures (47%), pressure sores (35%), respiratory (14%) and urinary (11%) tract infections, malnutrition (20%). The functional outcome was worse in the cases arriving with secondary complications during the same rehabilitation period. The length of stay in the rehabilitation unit was much longer in these cases. We strongly suggest that actions to prevent secondary complications must be started at the acute care unit. After acute care, rehabilitation of patients with severe brain injury should be performed in specialised centers with multidisciplinary team for different functional deficits (physical, cognitive, communicative, psycho-social impairments). Early and direct admission from the neurologic intensive care unit to the rehabilitation centrum seems to be optimal for best patient outcome, because this lowers the chance for the development of secondary complications.
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Best Practices for Young Children's Music Education: Guidance from Brain Research
ERIC Educational Resources Information Center
Flohr, John W.
2010-01-01
This article reviews best practices for young children's music experiences in light of developments in brain research. The first section reviews research music and brain topics including neuromyths, effect of music on structural brain changes and general intelligence, plasticity, critical and optimal periods, and at-risk student populations. The…
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Intentionality and "free-will" from a neurodevelopmental perspective.
Leisman, Gerry; Machado, Calixto; Melillo, Robert; Mualem, Raed
2012-01-01
The nature of free-will as a subset of intentionality and probabilistic and deterministic function is explored with the indications being that human behavior is highly predictable which in turn, should compromise the notion of free-will. Data supports the notion that age relates to the ability to progressively effectively establish goals performed by fixed action patterns and that these FAPs produce outcomes that in turn modify choices (free-will) for which FAPs need to be employed. Early goals require behaviors that require greater automation in terms of FAPs that lead to goals being achieved or not; if not, then one can change behavior and that in turn is free-will. Goals change with age based on experience which is similar to the way in which movement functions. We hypothesize that human prefrontal cortex development was a natural expansion of the evolutionarily earlier developed areas of the frontal lobe and that goal-directed movements and behavior, including choice and free-will, provided for an expansion of those areas. The same regions of the human central nervous system that were already employed for better control, coordination, and timing of movements, expanded in parallel with the frontal cortex. The initial focus of the frontal lobes was the control of motor activity, but as the movements became more goal-directed, greater cognitive control over movement was necessitated leading to voluntary control of FAPs or free-will. The paper reviews the neurobiology, neurohistology, and electrophysiology of brain connectivities developmentally, along with the development of those brain functions linked to decision-making from a developmental viewpoint. The paper reviews the neurological development of the frontal lobes and inter-regional brain connectivities in the context of optimization of communication systems within the brain and nervous system and its relation to free-will.
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Pichora-Fuller, M. Kathleen; Mick, Paul; Reed, Marilyn
2015-01-01
Sensory input provides the signals used by the brain when listeners understand speech and participate in social activities with other people in a range of everyday situations. When sensory inputs are diminished, there can be short-term consequences to brain functioning, and long-term deprivation can affect brain neuroplasticity. Indeed, the association between hearing loss and cognitive declines in older adults is supported by experimental and epidemiologic evidence, although the causal mechanisms remain unknown. These interactions of auditory and cognitive aging play out in the challenges confronted by people with age-related hearing problems when understanding speech and engaging in social interactions. In the present article, we use the World Health Organization's International Classification of Functioning, Disability and Health and the Selective Optimization with Compensation models to highlight the importance of adopting a healthy aging perspective that focuses on facilitating active social participation by older adults. First, we examine epidemiologic evidence linking ARHL to cognitive declines and other health issues. Next, we examine how social factors influence and are influenced by auditory and cognitive aging and if they may provide a possible explanation for the association between ARHL and cognitive decline. Finally, we outline how audiologists could reposition hearing health care within the broader context of healthy aging. PMID:27516713
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Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan
2012-01-01
Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Awake Craniotomy for Tumor Resection: Further Optimizing Therapy of Brain Tumors.
Mehdorn, H Maximilian; Schwartz, Felix; Becker, Juliane
2017-01-01
In recent years more and more data have emerged linking the most radical resection to prolonged survival in patients harboring brain tumors. Since total tumor resection could increase postoperative morbidity, many methods have been suggested to reduce the risk of postoperative neurological deficits: awake craniotomy with the possibility of continuous patient-surgeon communication is one of the possibilities of finding out how radical a tumor resection can possibly be without causing permanent harm to the patient.In 1994 we started to perform awake craniotomy for glioma resection. In 2005 the use of intraoperative high-field magnetic resonance imaging (MRI) was included in the standard tumor therapy protocol. Here we review our experience in performing awake surgery for gliomas, gained in 219 patients.Patient selection by the operating surgeon and a neuropsychologist is of primary importance: the patient should feel as if they are part of the surgical team fighting against the tumor. The patient will undergo extensive neuropsychological testing, functional MRI, and fiber tractography in order to define the relationship between the tumor and the functionally relevant brain areas. Attention needs to be given at which particular time during surgery the intraoperative MRI is performed. Results from part of our series (without and with ioMRI scan) are presented.
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Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland
2018-05-30
Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.
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von Borries, Katinka; Volman, Inge; Bulten, Berend Hendrik; Cools, Roshan; Verkes, Robbert-Jan
2016-01-01
Criminal behaviour poses a big challenge for society. A thorough understanding of the neurobiological mechanisms underlying criminality could optimize its prevention and management. Specifically,elucidating the neural mechanisms underpinning reward expectation might be pivotal to understanding criminal behaviour. So far no study has assessed reward expectation and its mechanisms in a criminal sample. To fill this gap, we assessed reward expectation in incarcerated, psychopathic criminals. We compared this group to two groups of non-criminal individuals: one with high levels and another with low levels of impulsive/antisocial traits. Functional magnetic resonance imaging was used to quantify neural responses to reward expectancy. Psychophysiological interaction analyses were performed to examine differences in functional connectivity patterns of reward-related regions. The data suggest that overt criminality is characterized, not by abnormal reward expectation per se, but rather by enhanced communication between reward-related striatal regions and frontal brain regions. We establish that incarcerated psychopathic criminals can be dissociated from non-criminal individuals with comparable impulsive/antisocial personality tendencies based on the degree to which reward-related brain regions interact with brain regions that control behaviour. The present results help us understand why some people act according to their impulsive/antisocial personality while others are able to behave adaptively despite reward-related urges. PMID:27217111
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Waking up too early - the consequences of preterm birth on sleep development.
Bennet, Laura; Walker, David W; Horne, Rosemary S C
2018-04-24
Good quality sleep of sufficient duration is vital for optimal physiological function and our health. Sleep deprivation is associated with impaired neurocognitive function and emotional control, and increases the risk for cardiometabolic diseases, obesity and cancer. Sleep develops during fetal life with the emergence of a recognisable pattern of sleep states in the preterm fetus associated with the development, maturation, and connectivity within neural networks in the brain. Despite the physiological importance of sleep, surprisingly little is known about how sleep develops in individuals born preterm. Globally, an estimated 15 million babies are born preterm (<37 weeks gestation), and these babies are at significant risk of neural injury and impaired brain development. This review discusses how sleep develops during fetal and neonatal life, how preterm birth impacts on sleep development to adulthood, and the factors which may contribute to impaired brain and sleep development, leading to altered neurocognitive, behavioural and motor capabilities in the infant and child. Going forward, the challenge is to identify specific risk factors for impaired sleep development in preterm babies to allow for the design of interventions that will improve the quality and quantity of sleep throughout life. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
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Tong, Yunxia; Chen, Qiang; Nichols, Thomas E.; Rasetti, Roberta; Callicott, Joseph H.; Berman, Karen F.; Weinberger, Daniel R.; Mattay, Venkata S.
2016-01-01
A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others. PMID:26974435
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NASA Astrophysics Data System (ADS)
Taber, Larry A.
2018-05-01
The folded structure of the human brain is a hallmark of our intelligence — an optimized packing of neurons into a confined space. Similar wrinkling in brain-on-a-chip experiments provides a way of understanding the physics of how this occurs.
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Martí-Bonmatí, Luis; Lull, Juan José; García-Martí, Gracián; Aguilar, Eduardo J; Moratal-Pérez, David; Poyatos, Cecilio; Robles, Montserrat; Sanjuán, Julio
2007-08-01
To prospectively evaluate if functional magnetic resonance (MR) imaging abnormalities associated with auditory emotional stimuli coexist with focal brain reductions in schizophrenic patients with chronic auditory hallucinations. Institutional review board approval was obtained and all participants gave written informed consent. Twenty-one right-handed male patients with schizophrenia and persistent hallucinations (started to hear hallucinations at a mean age of 23 years +/- 10, with 15 years +/- 8 of mean illness duration) and 10 healthy paired participants (same ethnic group [white], age, and education level [secondary school]) were studied. Functional echo-planar T2*-weighted (after both emotional and neutral auditory stimulation) and morphometric three-dimensional gradient-recalled echo T1-weighted MR images were analyzed using Statistical Parametric Mapping (SPM2) software. Brain activation images were extracted by subtracting those with emotional from nonemotional words. Anatomic differences were explored by optimized voxel-based morphometry. The functional and morphometric MR images were overlaid to depict voxels statistically reported by both techniques. A coincidence map was generated by multiplying the emotional subtracted functional MR and volume decrement morphometric maps. Statistical analysis used the general linear model, Student t tests, random effects analyses, and analysis of covariance with a correction for multiple comparisons following the false discovery rate method. Large coinciding brain clusters (P < .005) were found in the left and right middle temporal and superior temporal gyri. Smaller coinciding clusters were found in the left posterior and right anterior cingular gyri, left inferior frontal gyrus, and middle occipital gyrus. The middle and superior temporal and the cingular gyri are closely related to the abnormal neural network involved in the auditory emotional dysfunction seen in schizophrenic patients.
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Linares, Gabriel R; Chiu, Chi-Tso; Scheuing, Lisa; Leng, Yan; Liao, Hsiao-Mei; Maric, Dragan; Chuang, De-Maw
2016-07-01
Huntington's disease (HD) is a fatal neurodegenerative disorder caused by CAG repeat expansions in the huntingtin gene. Although, stem cell-based therapy has emerged as a potential treatment for neurodegenerative diseases, limitations remain, including optimizing delivery to the brain and donor cell loss after transplantation. One strategy to boost cell survival and efficacy is to precondition cells before transplantation. Because the neuroprotective actions of the mood stabilizers lithium and valproic acid (VPA) induce multiple pro-survival signaling pathways, we hypothesized that preconditioning bone marrow-derived mesenchymal stem cells (MSCs) with lithium and VPA prior to intranasal delivery to the brain would enhance their therapeutic efficacy, and thereby facilitate functional recovery in N171-82Q HD transgenic mice. MSCs were treated in the presence or absence of combined lithium and VPA, and were then delivered by brain-targeted single intranasal administration to eight-week old HD mice. Histological analysis confirmed the presence of MSCs in the brain. Open-field test revealed that ambulatory distance and mean velocity were significantly improved in HD mice that received preconditioned MSCs, compared to HD vehicle-control and HD mice transplanted with non-preconditioned MSCs. Greater benefits on motor function were observed in HD mice given preconditioned MSCs, while HD mice treated with non-preconditioned MSCs showed no functional benefits. Moreover, preconditioned MSCs reduced striatal neuronal loss and huntingtin aggregates in HD mice. Gene expression profiling of preconditioned MSCs revealed a robust increase in expression of genes involved in trophic effects, antioxidant, anti-apoptosis, cytokine/chemokine receptor, migration, mitochondrial energy metabolism, and stress response signaling pathways. Consistent with this finding, preconditioned MSCs demonstrated increased survival after transplantation into the brain compared to non-preconditioned cells. Our results suggest that preconditioning stem cells with the mood stabilizers lithium and VPA before transplantation may serve as an effective strategy for enhancing the therapeutic efficacy of stem cell-based therapies. Copyright © 2016. Published by Elsevier Inc.
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Kuratko, Connye N.; Barrett, Erin Cernkovich; Nelson, Edward B.; Norman, Salem
2013-01-01
Childhood is a period of brain growth and maturation. The long chain omega-3 fatty acid, docosahexaenoic acid (DHA), is a major lipid in the brain recognized as essential for normal brain function. In animals, low brain DHA results in impaired learning and behavior. In infants, DHA is important for optimal visual and cognitive development. The usual intake of DHA among toddlers and children is low and some studies show improvements in cognition and behavior as the result of supplementation with polyunsaturated fatty acids including DHA. The purpose of this review was to identify and evaluate current knowledge regarding the relationship of DHA with measures of learning and behavior in healthy school-age children. A systematic search of the literature identified 15 relevant publications for review. The search found studies which were diverse in purpose and design and without consistent conclusions regarding the treatment effect of DHA intake or biomarker status on specific cognitive tests. However, studies of brain activity reported benefits of DHA supplementation and over half of the studies reported a favorable role for DHA or long chain omega-3 fatty acids in at least one area of cognition or behavior. Studies also suggested an important role for DHA in school performance. PMID:23877090
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Val-Laillet, D.; Aarts, E.; Weber, B.; Ferrari, M.; Quaresima, V.; Stoeckel, L.E.; Alonso-Alonso, M.; Audette, M.; Malbert, C.H.; Stice, E.
2015-01-01
Functional, molecular and genetic neuroimaging has highlighted the existence of brain anomalies and neural vulnerability factors related to obesity and eating disorders such as binge eating or anorexia nervosa. In particular, decreased basal metabolism in the prefrontal cortex and striatum as well as dopaminergic alterations have been described in obese subjects, in parallel with increased activation of reward brain areas in response to palatable food cues. Elevated reward region responsivity may trigger food craving and predict future weight gain. This opens the way to prevention studies using functional and molecular neuroimaging to perform early diagnostics and to phenotype subjects at risk by exploring different neurobehavioral dimensions of the food choices and motivation processes. In the first part of this review, advantages and limitations of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), pharmacogenetic fMRI and functional near-infrared spectroscopy (fNIRS) will be discussed in the context of recent work dealing with eating behavior, with a particular focus on obesity. In the second part of the review, non-invasive strategies to modulate food-related brain processes and functions will be presented. At the leading edge of non-invasive brain-based technologies is real-time fMRI (rtfMRI) neurofeedback, which is a powerful tool to better understand the complexity of human brain–behavior relationships. rtfMRI, alone or when combined with other techniques and tools such as EEG and cognitive therapy, could be used to alter neural plasticity and learned behavior to optimize and/or restore healthy cognition and eating behavior. Other promising non-invasive neuromodulation approaches being explored are repetitive transcranial magnetic stimulation (rTMS) and transcranial direct-current stimulation (tDCS). Converging evidence points at the value of these non-invasive neuromodulation strategies to study basic mechanisms underlying eating behavior and to treat its disorders. Both of these approaches will be compared in light of recent work in this field, while addressing technical and practical questions. The third part of this review will be dedicated to invasive neuromodulation strategies, such as vagus nerve stimulation (VNS) and deep brain stimulation (DBS). In combination with neuroimaging approaches, these techniques are promising experimental tools to unravel the intricate relationships between homeostatic and hedonic brain circuits. Their potential as additional therapeutic tools to combat pharmacorefractory morbid obesity or acute eating disorders will be discussed, in terms of technical challenges, applicability and ethics. In a general discussion, we will put the brain at the core of fundamental research, prevention and therapy in the context of obesity and eating disorders. First, we will discuss the possibility to identify new biological markers of brain functions. Second, we will highlight the potential of neuroimaging and neuromodulation in individualized medicine. Third, we will introduce the ethical questions that are concomitant to the emergence of new neuromodulation therapies. PMID:26110109
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Test-Retest Reliability of fMRI Brain Activity during Memory Encoding
Brandt, David J.; Sommer, Jens; Krach, Sören; Bedenbender, Johannes; Kircher, Tilo; Paulus, Frieder M.; Jansen, Andreas
2013-01-01
The mechanisms underlying hemispheric specialization of memory are not completely understood. Functional magnetic resonance imaging (fMRI) can be used to develop and test models of hemispheric specialization. In particular for memory tasks however, the interpretation of fMRI results is often hampered by the low reliability of the data. In the present study we therefore analyzed the test-retest reliability of fMRI brain activation related to an implicit memory encoding task, with a particular focus on brain activity of the medial temporal lobe (MTL). Fifteen healthy subjects were scanned with fMRI on two sessions (average retest interval 35 days) using a commonly applied novelty encoding paradigm contrasting known and unknown stimuli. To assess brain lateralization, we used three different stimuli classes that differed in their verbalizability (words, scenes, fractals). Test-retest reliability of fMRI brain activation was assessed by an intraclass-correlation coefficient (ICC), describing the stability of inter-individual differences in the brain activation magnitude over time. We found as expected a left-lateralized brain activation network for the words paradigm, a bilateral network for the scenes paradigm, and predominantly right-hemispheric brain activation for the fractals paradigm. Although these networks were consistently activated in both sessions on the group level, across-subject reliabilities were only poor to fair (ICCs ≤ 0.45). Overall, the highest ICC values were obtained for the scenes paradigm, but only in strongly activated brain regions. In particular the reliability of brain activity of the MTL was poor for all paradigms. In conclusion, for novelty encoding paradigms the interpretation of fMRI results on a single subject level is hampered by its low reliability. More studies are needed to optimize the retest reliability of fMRI activation for memory tasks. PMID:24367338
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Experimental missile wound to the brain.
Carey, M E; Sarna, G S; Farrell, J B; Happel, L T
1989-11-01
Among civilians in the United States, 33,000 gunshot wound deaths occur each year; probably half of these involve the head. In combat, head wounds account for approximately half of the immediate mortality when death can be attributed to a single wound. No significant reduction in the neurosurgical mortality associated with these wounds has occurred between World War II and the Vietnam conflict, and very little research into missile wounds of the brain has been undertaken. An experimental model has been developed in the anesthetized cat whereby a ballistic injury to the brain may be painlessly reproduced in order that the pathophysiological effects of brain wounding may be studied and better treatments may be designed to lower the mortality and morbidity rates associated with gunshot wounds. Prominent among physiological effects observed in this model was respiratory arrest even though the missile did not injure the brain stem directly. The incidence of prolonged respiratory arrest increased with increasing missile energy, but arrest was often reversible provided respiratory support was given. It is possible that humans who receive a brain wound die from missile-induced apnea instead of brain damage per se. The mortality rate in humans with brain wounding might be reduced by prompt respiratory support. Brain wounding was associated with persistently increased intracranial pressure and reduced cerebral perfusion pressure not entirely attributable to intracranial bleeding. The magnitude of these derangements appeared to be missile energy-dependent and approached dangerous levels in higher-energy wounds. All wounded cats exhibited postwounding increases in blood glucose concentrations consistent with a generalized stress reaction. A transient rise in hematocrit also occurred immediately after wounding. Both of these phenomena could prove deleterious to optimal brain function after injury.
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Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?
Lücke, Jörg
2012-01-01
Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing. PMID:22457610
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Feedforward inhibition and synaptic scaling--two sides of the same coin?
Keck, Christian; Savin, Cristina; Lücke, Jörg
2012-01-01
Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.
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Delivery of chemotherapeutics across the blood-brain barrier: challenges and advances.
Doolittle, Nancy D; Muldoon, Leslie L; Culp, Aliana Y; Neuwelt, Edward A
2014-01-01
The blood-brain barrier (BBB) limits drug delivery to brain tumors. We utilize intraarterial infusion of hyperosmotic mannitol to reversibly open the BBB by shrinking endothelial cells and opening tight junctions between the cells. This approach transiently increases the delivery of chemotherapy, antibodies, and nanoparticles to brain. Our preclinical studies have optimized the BBB disruption (BBBD) technique and clinical studies have shown its safety and efficacy. The delivery of methotrexate-based chemotherapy in conjunction with BBBD provides excellent outcomes in primary central nervous system lymphoma (PCNSL) including stable or improved cognitive function in survivors a median of 12 years (range 2-26 years) after diagnosis. The addition of rituximab to chemotherapy with BBBD for PCNSL can be safely accomplished with excellent overall survival. Our translational studies of thiol agents to protect against platinum-induced toxicities led to the development of a two-compartment model in brain tumor patients. We showed that delayed high-dose sodium thiosulfate protects against carboplatin-induced hearing loss, providing the framework for large cooperative group trials of hearing chemoprotection. Neuroimaging studies have identified that ferumoxytol, an iron oxide nanoparticle blood pool agent, appears to be a superior contrast agent to accurately assess therapy-induced changes in brain tumor vasculature, in brain tumor response to therapy, and in differentiating central nervous system lesions with inflammatory components. This chapter reviews the breakthroughs, challenges, and future directions for BBBD. © 2014 Elsevier Inc. All rights reserved.
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Rausch, Annika; Zhang, Wei; Haak, Koen V; Mennes, Maarten; Hermans, Erno J; van Oort, Erik; van Wingen, Guido; Beckmann, Christian F; Buitelaar, Jan K; Groen, Wouter B
2016-01-01
Amygdala dysfunction is hypothesized to underlie the social deficits observed in autism spectrum disorders (ASD). However, the neurobiological basis of this hypothesis is underspecified because it is unknown whether ASD relates to abnormalities of the amygdaloid input or output nuclei. Here, we investigated the functional connectivity of the amygdaloid social-perceptual input nuclei and emotion-regulation output nuclei in ASD versus controls. We collected resting state functional magnetic resonance imaging (fMRI) data, tailored to provide optimal sensitivity in the amygdala as well as the neocortex, in 20 adolescents and young adults with ASD and 25 matched controls. We performed a regular correlation analysis between the entire amygdala (EA) and the whole brain and used a partial correlation analysis to investigate whole-brain functional connectivity uniquely related to each of the amygdaloid subregions. Between-group comparison of regular EA correlations showed significantly reduced connectivity in visuospatial and superior parietal areas in ASD compared to controls. Partial correlation analysis revealed that this effect was driven by the left superficial and right laterobasal input subregions, but not the centromedial output nuclei. These results indicate reduced connectivity of specifically the amygdaloid sensory input channels in ASD, suggesting that abnormal amygdalo-cortical connectivity can be traced down to the socio-perceptual pathways.
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Graph theoretical analysis of EEG functional connectivity during music perception.
Wu, Junjie; Zhang, Junsong; Liu, Chu; Liu, Dongwei; Ding, Xiaojun; Zhou, Changle
2012-11-05
The present study evaluated the effect of music on large-scale structure of functional brain networks using graph theoretical concepts. While most studies on music perception used Western music as an acoustic stimulus, Guqin music, representative of Eastern music, was selected for this experiment to increase our knowledge of music perception. Electroencephalography (EEG) was recorded from non-musician volunteers in three conditions: Guqin music, noise and silence backgrounds. Phase coherence was calculated in the alpha band and between all pairs of EEG channels to construct correlation matrices. Each resulting matrix was converted into a weighted graph using a threshold, and two network measures: the clustering coefficient and characteristic path length were calculated. Music perception was found to display a higher level mean phase coherence. Over the whole range of thresholds, the clustering coefficient was larger while listening to music, whereas the path length was smaller. Networks in music background still had a shorter characteristic path length even after the correction for differences in mean synchronization level among background conditions. This topological change indicated a more optimal structure under music perception. Thus, prominent small-world properties are confirmed in functional brain networks. Furthermore, music perception shows an increase of functional connectivity and an enhancement of small-world network organizations. Copyright © 2012 Elsevier B.V. All rights reserved.
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Robert, O; Sabatier, J; Desoubzdanne, D; Lalande, J; Balayssac, S; Gilard, V; Martino, R; Malet-Martino, M
2011-01-01
The aim of this study was to define the optimal pH for (1)H nuclear magnetic resonance (NMR) spectroscopy analysis of perchloric acid or methanol-chloroform-water extracts from brain tumor cells and tissues. The systematic study of the proton chemical shift variations as a function of pH of 13 brain metabolites in model solutions demonstrated that recording (1)H NMR spectra at pH 10 allowed resolving resonances that are overlapped at pH 7, especially in the 3.2-3.3 ppm choline-containing-compounds region. (1)H NMR analysis of extracts at pH 7 or 10 showed that quantitative measurements of lactate, alanine, glutamate, glutamine (Gln), creatine + phosphocreatine and myo-inositol (m-Ino) can be readily performed at both pHs. The concentrations of glycerophosphocholine, phosphocholine and choline that are crucial metabolites for tumor brain malignancy grading were accurately measured at pH 10 only. Indeed, the resonances of their trimethylammonium moieties are cleared of any overlapping signal, especially those of taurine (Tau) and phosphoethanolamine. The four non-ionizable Tau protons resonating as a singlet in a non-congested spectral region permits an easier and more accurate quantitation of this apoptosis marker at pH 10 than at pH 7 where the triplet at 3.43 ppm can be overlapped with the signals of glucose or have an intensity too low to be measured. Glycine concentration was determined indirectly at both pHs after subtracting the contribution of the overlapped signals of m-Ino at pH 7 or Gln at pH 10.
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Neuroprosthetic Decoder Training as Imitation Learning
Merel, Josh; Paninski, Liam; Cunningham, John P.
2016-01-01
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user’s intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user’s intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector. PMID:27191387
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Bogoslovsky, Tanya; Bernstock, Joshua D; Bull, Greg; Gouty, Shawn; Cox, Brian M; Hallenbeck, John M; Maric, Dragan
2018-04-01
Traumatic brain injuries (TBIs) pose a massive burden of disease and continue to be a leading cause of morbidity and mortality throughout the world. A major obstacle in developing effective treatments is the lack of comprehensive understanding of the underlying mechanisms that mediate tissue damage and recovery after TBI. As such, our work aims to highlight the development of a novel experimental platform capable of fully characterizing the underlying pathobiology that unfolds after TBI. This platform encompasses an empirically optimized multiplex immunohistochemistry staining and imaging system customized to screen for a myriad of biomarkers required to comprehensively evaluate the extent of neuroinflammation, neural tissue damage, and repair in response to TBI. Herein, we demonstrate that our multiplex biomarker screening platform is capable of evaluating changes in both the topographical location and functional states of resident and infiltrating cell types that play a role in neuropathology after controlled cortical impact injury to the brain in male Sprague-Dawley rats. Our results demonstrate that our multiplex biomarker screening platform lays the groundwork for the comprehensive characterization of changes that occur within the brain after TBI. Such work may ultimately lead to the understanding of the governing pathobiology of TBI, thereby fostering the development of novel therapeutic interventions tailored to produce optimal tissue protection, repair, and/or regeneration with minimal side effects, and may ultimately find utility in a wide variety of other neurological injuries, diseases, and disorders that share components of TBI pathobiology. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.
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Mathematical modeling for control zika transmission
NASA Astrophysics Data System (ADS)
Nugraha, Edwin Setiawan; Naiborhu, Janson; Nuraini, Nuning; Ahmadin
2017-11-01
After 70 years since the zika was identified in Uganda, zika is now documented in 62 countries. In general, people infected with this disease do not experience severe conditions, but for pregnant women can cause serious problems because the zika can spread to the fetus. One result, zika can cause abnormalities in the fetal brain called microcephaly. Control and prevention are very important to reduce the spread of this disease. Here, we discussed the problem of optimal control in the model of zika transmission associated with the use of insecticide-treated nets (ITN) and indoor residual spraying (IRS). Using the approach of optimal control theory, we completed the objective function so that the infected population and its control cost are minimum. Numerically using the Forward-Backward Sweep Method, we obtained the control design of ITN and IRS as a function of time. The results show that the use of both simultaneously is more effective in reducing the population of infection than the use of ITN alone or the IRS alone.
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Regularized Filters for L1-Norm-Based Common Spatial Patterns.
Wang, Haixian; Li, Xiaomeng
2016-02-01
The l1 -norm-based common spatial patterns (CSP-L1) approach is a recently developed technique for optimizing spatial filters in the field of electroencephalogram (EEG)-based brain computer interfaces. The l1 -norm-based expression of dispersion in CSP-L1 alleviates the negative impact of outliers. In this paper, we further improve the robustness of CSP-L1 by taking into account noise which does not necessarily have as large a deviation as with outliers. The noise modelling is formulated by using the waveform length of the EEG time course. With the noise modelling, we then regularize the objective function of CSP-L1, in which the l1-norm is used in two folds: one is the dispersion and the other is the waveform length. An iterative algorithm is designed to resolve the optimization problem of the regularized objective function. A toy illustration and the experiments of classification on real EEG data sets show the effectiveness of the proposed method.
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Fazio, Leonardo; Logroscino, Giancarlo; Taurisano, Paolo; Amico, Graziella; Quarto, Tiziana; Antonucci, Linda Antonella; Barulli, Maria Rosaria; Mancini, Marina; Gelao, Barbara; Ferranti, Laura; Popolizio, Teresa; Bertolino, Alessandro; Blasi, Giuseppe
2016-01-01
Convergent evidence indicates that apathy affects cognitive behavior in different neurological and psychiatric conditions. Studies of clinical populations have also suggested the primary involvement of the prefrontal cortex and the basal ganglia in apathy. These brain regions are interconnected at both the structural and functional levels and are deeply involved in cognitive processes, such as working memory and attention. However, it is unclear how apathy modulates brain processing during cognition and whether such a modulation occurs in healthy young subjects. To address this issue, we investigated the link between apathy and prefrontal and basal ganglia function in healthy young individuals. We hypothesized that apathy may be related to sub-optimal activity and connectivity in these brain regions. Three hundred eleven healthy subjects completed an apathy assessment using the Starkstein's Apathy Scale and underwent fMRI during working memory and attentional performance tasks. Using an ROI approach, we investigated the association of apathy with activity and connectivity in the DLPFC and the basal ganglia. Apathy scores correlated positively with prefrontal activity and negatively with prefrontal-basal ganglia connectivity during both working memory and attention tasks. Furthermore, prefrontal activity was inversely related to attentional behavior. These results suggest that in healthy young subjects, apathy is a trait associated with inefficient cognitive-related prefrontal activity, i.e., it increases the need for prefrontal resources to process cognitive stimuli. Furthermore, apathy may alter the functional relationship between the prefrontal cortex and the basal ganglia during cognition.
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Komane, Patrick P; Kumar, Pradeep; Marimuthu, Thashree; Toit, Lisa C du; Kondiah, Pierre P D; Choonara, Yahya E; Pillay, Viness
2018-06-10
The complete synthesis, optimization, purification, functionalization and evaluation of vertically aligned multiwalled carbon nanotubes (VA-MWCNTs) was reported for potential application in dexamethasone delivery to the ischemic brain tissue. The conditions for high yield were optimized and carbon nanotubes functionalized and PEGylated prior to dexamethasone loading. Morphological changes were confirmed by SEM and TEM. Addition of functional groups to MWCNTs was demonstrated by FTIR. Thermal stability reduced following MWCNTs functionalization as demonstrated in TGA. The presence of carbon at 2θ of 25° and iron at 2θ of 45° in MWCNTs was illustrated by XRD. Polydispersive index and zeta potential were found to be 0.261 and −15.0 mV, respectively. Dexamethasone release increased by 55%, 65% and 95% in pH of 7.4, 6.5 and 5.5 respectively as evaluated by UV-VIS. The functionalized VA-MWCNTs were demonstrated to be less toxic in PC-12 cells in the concentration range from 20 to 20,000 µg/mL. These findings have demonstrated the potential of VA-MWCNTs in the enhancement of fast and prolonged release of dexamethasone which could lead to the effective treatment of ischemic stroke. More work is under way for targeting ischemic sites using atrial natriuretic peptide antibody in stroke rats.
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Fetal Therapy for Down Syndrome: Report of Three Cases and a Review of the Literature.
Baggot, Patrick James; Baggot, Rocel Medina
2017-01-01
Down syndrome (trisomy 21) is a well-known cause of mental retardation. It can be diagnosed in early pregnancy. Scientists have made great strides in outlining the pathophysiologic mechanisms of mental retardation in Down syndrome. Much less has been published on human therapy. To our knowledge, these are the first published cases of fetal therapy for Down syndrome. Reports of three cases. In all cases, treatment was both biochemical (e.g. nutritional) and educational. In all cases, treatment was both before and after birth. All children lacked the characteristic faces usually seen in the children with Down syndrome. This suggests a treatment effect before birth. All children had better than expected development. Enhancement of development is proposed as a new therapeutic principle. Developing neurons exchange neurotrophic factors during development when they give or receive stimulation from other neurons. Neurons which receive neurotrophic stimulation survive, and those, which do not, are lost to apoptosis. The developmental therapeutic principle seeks to optimize brain development. Biochemical inputs (neurotransmitters, drugs, hormones, nutrients) and functional stimulation are integrated to optimize the growth and survival of neurons individually; other cells; subcellular organelles; and the brain as a whole. Treatment may be before and after birth, both biochemical and functional. These principles may be applied to Down syndrome, other conditions, and normal fetuses or children. Baggot PJ and Baggot RM (2014). Fetal Therapy for Down Syndrome: Report of three cases and review of the literature. J Am Phys Surg 19(1):20-24.
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Optimized donor management and organ preservation before kidney transplantation.
Mundt, Heiko M; Yard, Benito A; Krämer, Bernhard K; Benck, Urs; Schnülle, Peter
2016-09-01
Kidney transplantation is a major medical improvement for patients with end-stage renal disease, but organ shortage limits its widespread use. As a consequence, the proportion of grafts procured from extended criteria donors (ECD) has increased considerably, but this comes along with increased rates of delayed graft function (DGF) and a higher incidence of immune-mediated rejection that limits organ and patient survival. Furthermore, most grafts are derived from brain dead organ donors, but the unphysiological state of brain death is associated with significant metabolic, hemodynamic, and pro-inflammatory changes, which further compromise patient and graft survival. Thus, donor interventions to preserve graft quality are fundamental to improve long-term transplantation outcome, but interventions must not harm other potentially transplantable grafts. Several donor pretreatment strategies have provided encouraging results in animal models, but evidence from human studies is sparse, as most clinical evidence is derived from single-center or nonrandomized trials. Furthermore, ethical matters have to be considered especially concerning consent from donors, donor families, and transplant recipients to research in the field of donor treatment. This review provides an overview of clinically proven and promising preclinical strategies of donor treatment to optimize long-term results after kidney transplantation. © 2015 Steunstichting ESOT.
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Representation of sweet and salty taste intensity in the brain.
Spetter, M S; Smeets, P A M; de Graaf, C; Viergever, M A
2010-11-01
The intensity of the taste of a food is affected mostly by the amount of sugars (mono- and disaccharides) or salt it contains. To season savory-tasting foods mainly table salt (NaCl) is used and to sweeten foods, sugars like sucrose are used. Foods with highly intense tastes are consumed in smaller amounts. The optimal taste intensity of a food is the intensity at which it is perceived as most pleasant. When taste intensity decreases or increases from optimal, the pleasantness of a food decreases. Here, we investigated the brain representation of sweet and salty taste intensity using functional magnetic resonance imaging. Fifteen subjects visited twice and tasted a range of 4 watery solutions (0-1 M) of either sucrose or NaCl in water. Middle insula activation increased with increasing concentration for both NaCl and sucrose. Despite similar subjective intensity ratings, anterior insula activation by NaCl increased more with concentration than that by sucrose. Amygdala activation increased with increasing NaCl concentration but not sucrose concentration. In conclusion, sweet and salty taste intensity are represented in the middle insula. Amygdala activation is only modulated by saltiness. Further research will need to extrapolate these results from simple solutions to real foods.
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Characterization of electroencephalography signals for estimating saliency features in videos.
Liang, Zhen; Hamada, Yasuyuki; Oba, Shigeyuki; Ishii, Shin
2018-05-12
Understanding the functions of the visual system has been one of the major targets in neuroscience formany years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model ( Tavakoli and Laaksonen, 2017). Subsequently, various unsupervised feature selection/extraction techniques were examined using different supervised regression models. The robustness of the presented model was fully verified by means of ten-fold or nested cross validation procedure, and promising results were achieved in the reconstruction of saliency features based on the selected EEG characteristics. Through the successful demonstration of using EEG characteristics to predict the real-time saliency distribution in natural videos, we suggest the feasibility of quantifying visual content through measuring brain activities (EEG signals) in real environments, which would facilitate the understanding of cortical involvement in the processing of natural visual stimuli and application developments motivated by human visual processing. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Wells, Elizabeth M; Goodkin, Howard P; Griesbach, Grace S
2016-01-01
Current consensus guidelines recommending physical and cognitive rest until a patient is asymptomatic after a sports concussion (ie, a mild traumatic brain injury) are being called into question, particularly for patients who are slower to recover and in light of preclinical and clinical research demonstrating that exercise aids neurorehabilitation. The pathophysiological response to mild traumatic brain injury includes a complex neurometabolic cascade of events resulting in a neurologic energy deficit. It has been proposed that this energy deficit leads to a period of vulnerability during which the brain is at risk for additional injury, explains why early postconcussive symptoms are exacerbated by cognitive and physical exertion, and is used to rationalize absolute rest until all symptoms have resolved. However, at some point, rest might no longer be beneficial and exercise might need to be introduced. At both extremes, excessive exertion and prolonged avoidance of exercise (physical and mental) have negative consequences. Individuals who have experienced a concussion need guidance for avoidance of triggers of severe symptoms and a plan for graduated exercise to promote recovery as well as optimal functioning (physical, educational, and social) during the postconcussion period. © The Author(s) 2015.
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Kim, Kyung Su; Wee, Chan Woo; Seok, Jin-Yong; Hong, Joo Wan; Chung, Jin-Beom; Eom, Keun-Yong; Kim, Jae-Sung; Kim, Chae-Yong; Park, Young Ho; Kim, Yu Jung; Kim, In Ah
2018-02-20
We hypothesized that hippocampal-sparing radiotherapy via volumetric modulated arc therapy (VMAT) could preserve the neurocognitive function (NCF) of patients with primary brain tumors treated with radiotherapy. We reviewed data from patients with primary brain tumors who underwent hippocampal-sparing brain radiotherapy via VMAT between February 2014 and December 2015. The optimization criteria for the contralateral hippocampus was a maximum dose (D max ) of less than 17 Gy. For NCF evaluations, the Seoul Verbal Learning Test for total recall, delayed recall, and recognition (SVLT-TR, DR, and Recognition) was performed at baseline and at seven months after radiotherapy. A total of 26 patients underwent NCF testing seven months after radiotherapy. Their median age was 49.5 years (range 26-77 years), and 14 (53.8%) had grade III/IV tumors. The median D max to the contralateral hippocampus was 16.4 Gy (range 3.5-63.4). The median mean dose to the contralateral hippocampus, expressed as equivalent to a 2-Gy dose (EQD 2/2 ), was 7.4 Gy 2 (0.7-13.1). The mean relative changes in SVLT-TR, SVLT-DR, and SVLT-Recognition at seven months compared to the baseline were - 7.7% (95% confidence interval [CI], - 19.6% to 4.2%), - 9.2% (95% CI, - 25.4% to 7.0%), and - 3.4% (- 12.7% to 5.8%), respectively. Two patients (7.7%) showed deteriorated NCF in the SVLT-TR and SVLT-DR, and three (11.5%) in the SVLT-Recognition. The mean dose of the left hippocampus and bilateral hippocampi were significantly higher in patients showing deterioration of the SVLT-TR and SVLT-Recognition than in those without deterioration. The contralateral hippocampus could be effectively spared in patients with primary brain tumor via VMAT to preserve the verbal memory function. Further investigation is needed to identify those patients who will most benefit from hippocampal-sparing radiotherapy of the primary brain tumor.
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Gates, Kathleen M.; Molenaar, Peter C. M.; Iyer, Swathi P.; Nigg, Joel T.; Fair, Damien A.
2014-01-01
Clinical investigations of many neuropsychiatric disorders rely on the assumption that diagnostic categories and typical control samples each have within-group homogeneity. However, research using human neuroimaging has revealed that much heterogeneity exists across individuals in both clinical and control samples. This reality necessitates that researchers identify and organize the potentially varied patterns of brain physiology. We introduce an analytical approach for arriving at subgroups of individuals based entirely on their brain physiology. The method begins with Group Iterative Multiple Model Estimation (GIMME) to assess individual directed functional connectivity maps. GIMME is one of the only methods to date that can recover both the direction and presence of directed functional connectivity maps in heterogeneous data, making it an ideal place to start since it addresses the problem of heterogeneity. Individuals are then grouped based on similarities in their connectivity patterns using a modularity approach for community detection. Monte Carlo simulations demonstrate that using GIMME in combination with the modularity algorithm works exceptionally well - on average over 97% of simulated individuals are placed in the accurate subgroup with no prior information on functional architecture or group identity. Having demonstrated reliability, we examine resting-state data of fronto-parietal regions drawn from a sample (N = 80) of typically developing and attention-deficit/hyperactivity disorder (ADHD) -diagnosed children. Here, we find 5 subgroups. Two subgroups were predominantly comprised of ADHD, suggesting that more than one biological marker exists that can be used to identify children with ADHD based from their brain physiology. Empirical evidence presented here supports notions that heterogeneity exists in brain physiology within ADHD and control samples. This type of information gained from the approach presented here can assist in better characterizing patients in terms of outcomes, optimal treatment strategies, potential gene-environment interactions, and the use of biological phenomenon to assist with mental health. PMID:24642753
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Shah, Brijesh; Khunt, Dignesh; Misra, Manju; Padh, Harish
2016-08-01
The objective of the present investigation was to optimize and develop quetiapine fumarate (QF) loaded chitosan nanoparticles (QF-NP) by ionic gelation method using Box-Behnken design. Three independent variables viz., X1-Concentration of chitosan, X2-Concentration of sodium tripolyphosphate and X3-Volume of sodium tripolyphosphate were taken to investigate their effect on dependent variables (Y1-Size, Y2-PDI and Y3-%EE). Optimized formula of QF-NP was selected from the design space which was further evaluated for physicochemical, morphological, solid state characterization, nasal diffusion and in-vivo distribution for brain targeting following non-invasive intranasal administration. The average particle size, PDI, %EE and nasal diffusion were found to be 131.08±7.45nm, 0.252±0.064, 89.93±3.85% and 65.24±5.26% respectively. Neither toxicity nor structural damage on nasal mucosa was observed upon histopathological examination. Significantly higher brain/blood ratio and 2 folds higher nasal bioavailability in brain with QF-NP in comparison to drug solution following intranasal administration revealed preferential nose to brain transport bypassing blood-brain barrier and prolonged retention of QF at site of action suggesting superiority of chitosan as permeability enhancer. Overall, the above finding shows promising results in the area of developing non-invasive intranasal route as an alternative to oral route for brain delivery. Copyright © 2016 Elsevier B.V. All rights reserved.
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Normal variation in early parental sensitivity predicts child structural brain development.
Kok, Rianne; Thijssen, Sandra; Bakermans-Kranenburg, Marian J; Jaddoe, Vincent W V; Verhulst, Frank C; White, Tonya; van IJzendoorn, Marinus H; Tiemeier, Henning
2015-10-01
Early caregiving can have an impact on brain structure and function in children. The influence of extreme caregiving experiences has been demonstrated, but studies on the influence of normal variation in parenting quality are scarce. Moreover, no studies to date have included the role of both maternal and paternal sensitivity in child brain maturation. This study examined the prospective relation between mothers' and fathers' sensitive caregiving in early childhood and brain structure later in childhood. Participants were enrolled in a population-based prenatal cohort. For 191 families, maternal and paternal sensitivity was repeatedly observed when the child was between 1 year and 4 years of age. Head circumference was assessed at 6 weeks, and brain structure was assessed using magnetic resonance imaging (MRI) measurements at 8 years of age. Higher levels of parental sensitivity in early childhood were associated with larger total brain volume (adjusted β = 0.15, p = .01) and gray matter volume (adjusted β = 0.16, p = .01) at 8 years, controlling for infant head size. Higher levels of maternal sensitivity in early childhood were associated with a larger gray matter volume (adjusted β = 0.13, p = .04) at 8 years, independent of infant head circumference. Associations with maternal versus paternal sensitivity were not significantly different. Normal variation in caregiving quality is related to markers of more optimal brain development in children. The results illustrate the important role of both mothers and fathers in child brain development. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.
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Cisler, Josh M.; Bush, Keith; James, G. Andrew; Smitherman, Sonet; Kilts, Clinton D.
2015-01-01
Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD. PMID:26241958