Nonlocal Intracranial Cavity Extraction

PubMed Central

Manjón, José V.; Eskildsen, Simon F.; Coupé, Pierrick; Romero, José E.; Collins, D. Louis; Robles, Montserrat

2014-01-01

Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden. PMID:25328511

Categorization for Faces and Tools—Two Classes of Objects Shaped by Different Experience—Differs in Processing Timing, Brain Areas Involved, and Repetition Effects

PubMed Central

Kozunov, Vladimir; Nikolaeva, Anastasia; Stroganova, Tatiana A.

2018-01-01

The brain mechanisms that integrate the separate features of sensory input into a meaningful percept depend upon the prior experience of interaction with the object and differ between categories of objects. Recent studies using representational similarity analysis (RSA) have characterized either the spatial patterns of brain activity for different categories of objects or described how category structure in neuronal representations emerges in time, but never simultaneously. Here we applied a novel, region-based, multivariate pattern classification approach in combination with RSA to magnetoencephalography data to extract activity associated with qualitatively distinct processing stages of visual perception. We asked participants to name what they see whilst viewing bitonal visual stimuli of two categories predominantly shaped by either value-dependent or sensorimotor experience, namely faces and tools, and meaningless images. We aimed to disambiguate the spatiotemporal patterns of brain activity between the meaningful categories and determine which differences in their processing were attributable to either perceptual categorization per se, or later-stage mentalizing-related processes. We have extracted three stages of cortical activity corresponding to low-level processing, category-specific feature binding, and supra-categorical processing. All face-specific spatiotemporal patterns were associated with bilateral activation of ventral occipito-temporal areas during the feature binding stage at 140–170 ms. The tool-specific activity was found both within the categorization stage and in a later period not thought to be associated with binding processes. The tool-specific binding-related activity was detected within a 210–220 ms window and was located to the intraparietal sulcus of the left hemisphere. Brain activity common for both meaningful categories started at 250 ms and included widely distributed assemblies within parietal, temporal, and prefrontal regions. Furthermore, we hypothesized and tested whether activity within face and tool-specific binding-related patterns would demonstrate oppositely acting effects following procedural perceptual learning. We found that activity in the ventral, face-specific network increased following the stimuli repetition. In contrast, tool processing in the dorsal network adapted by reducing its activity over the repetition period. Altogether, we have demonstrated that activity associated with visual processing of faces and tools during the categorization stage differ in processing timing, brain areas involved, and in their dynamics underlying stimuli learning. PMID:29379426

A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer.

PubMed

Kar, Subrata; Majumder, D Dutta

2017-08-01

Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%. The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.

Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

2016-10-01

Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

A novel tool for naturalistic assessment of behavioural dysregulation after traumatic brain injury: A pilot study.

PubMed

McKeon, Ashlee; Terhorst, Lauren; Skidmore, Elizabeth; Ding, Dan; Cooper, Rory; McCue, Michael

2017-01-01

This study aimed to develop a novel tool for measuring behavioural dysregulation in adults with traumatic brain injury (TBI) using objective data sources and real-world application and provide preliminary evidence for its psychometric properties. Fourteen adults with TBI receiving services at a local brain injury rehabilitation programme completed multiple assessments of behaviour and followed by a series of challenging problem-solving tasks while being video recorded. Trained clinicians completed post-hoc behavioural assessments using the behavioural dysregulation ratings scale, and behavioural event data were then extracted for comparison with self-report measures. Subject matter experts in neurorehabilitation were in 100% agreement that preliminarily, the new tool measured the construct of behavioural dysregulation. Construct validity was established through strong convergence with 'like' measures and weak correlation with 'unlike' measures. Substantial inter-rater reliability was established between two trained clinician raters. This study provides preliminary evidence supporting the use of a new precision measurement tool of behaviour in post-acute TBI that has the capability to be deployed naturalistically where deficits truly manifest. Future large-scaled confirmatory psychometric trials are warranted to further establish the utility of this new tool in rehabilitation research.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

PubMed Central

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A.; Zhang, Wenbo

2016-01-01

Objective Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Results Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions. PMID:27740473

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

PubMed

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A; Zhang, Wenbo; He, Bin

2016-12-01

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants.

PubMed

Sanchez, Justin C; Mahmoudi, Babak; DiGiovanna, Jack; Principe, Jose C

2009-04-01

The success of brain-machine interfaces (BMI) is enabled by the remarkable ability of the brain to incorporate the artificial neuroprosthetic 'tool' into its own cognitive space and use it as an extension of the user's body. Unlike other tools, neuroprosthetics create a shared space that seamlessly spans the user's internal goal representation of the world and the external physical environment enabling a much deeper human-tool symbiosis. A key factor in the transformation of 'simple tools' into 'intelligent tools' is the concept of co-adaptation where the tool becomes functionally involved in the extraction and definition of the user's goals. Recent advancements in the neuroscience and engineering of neuroprosthetics are providing a blueprint for how new co-adaptive designs based on reinforcement learning change the nature of a user's ability to accomplish tasks that were not possible using conventional methodologies. By designing adaptive controls and artificial intelligence into the neural interface, tools can become active assistants in goal-directed behavior and further enhance human performance in particular for the disabled population. This paper presents recent advances in computational and neural systems supporting the development of symbiotic neuroprosthetic assistants.

Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis.

PubMed

Popescu, V; Battaglini, M; Hoogstrate, W S; Verfaillie, S C J; Sluimer, I C; van Schijndel, R A; van Dijk, B W; Cover, K S; Knol, D L; Jenkinson, M; Barkhof, F; de Stefano, N; Vrenken, H

2012-07-16

Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5 T or 3T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the "f" parameter) and with either one of the main BET options ("B" - bias field correction and neck cleanup, "R" - robust brain center estimation, or "S" - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f=0.5, g=0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of "f" than the default value. Using native images, optimum BET performance was observed for f=0.2 with option "B", giving median DOC=0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f=0.1 with option "B", giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f=0.1 and option "B" did not differ statistically from NBV values obtained with gold-standard. Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option "B" with f=0.1 after removal of the neck slices seems to work best for all acquisition protocols. Copyright © 2012 Elsevier Inc. All rights reserved.

Metabolomics studies in brain tissue: A review.

PubMed

Gonzalez-Riano, Carolina; Garcia, Antonia; Barbas, Coral

2016-10-25

Brain is still an organ with a composition to be discovered but beyond that, mental disorders and especially all diseases that curse with dementia are devastating for the patient, the family and the society. Metabolomics can offer an alternative tool for unveiling new insights in the discovery of new treatments and biomarkers of mental disorders. Until now, most of metabolomic studies have been based on biofluids: serum/plasma or urine, because brain tissue accessibility is limited to animal models or post mortem studies, but even so it is crucial for understanding the pathological processes. Metabolomics studies of brain tissue imply several challenges due to sample extraction, along with brain heterogeneity, sample storage, and sample treatment for a wide coverage of metabolites with a wide range of concentrations of many lipophilic and some polar compounds. In this review, the current analytical practices for target and non-targeted metabolomics are described and discussed with emphasis on critical aspects: sample treatment (quenching, homogenization, filtration, centrifugation and extraction), analytical methods, as well as findings considering the used strategies. Besides that, the altered analytes in the different brain regions have been associated with their corresponding pathways to obtain a global overview of their dysregulation, trying to establish the link between altered biological pathways and pathophysiological conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Graph theory for feature extraction and classification: a migraine pathology case study.

PubMed

Jorge-Hernandez, Fernando; Garcia Chimeno, Yolanda; Garcia-Zapirain, Begonya; Cabrera Zubizarreta, Alberto; Gomez Beldarrain, Maria Angeles; Fernandez-Ruanova, Begonya

2014-01-01

Graph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used.

Reliable identification of deep sulcal pits: the effects of scan session, scanner, and surface extraction tool.

PubMed

Im, Kiho; Lee, Jong-Min; Jeon, Seun; Kim, Jong-Heon; Seo, Sang Won; Na, Duk L; Grant, P Ellen

2013-01-01

Sulcal pit analysis has been providing novel insights into brain function and development. The purpose of this study was to evaluate the reliability of sulcal pit extraction with respect to the effects of scan session, scanner, and surface extraction tool. Five subjects were scanned 4 times at 3 MRI centers and other 5 subjects were scanned 3 times at 2 MRI centers, including 1 test-retest session. Sulcal pits were extracted on the white matter surfaces reconstructed with both Montreal Neurological Institute and Freesurfer pipelines. We estimated similarity of the presence of sulcal pits having a maximum value of 1 and their spatial difference within the same subject. The tests showed high similarity of the sulcal pit presence and low spatial difference. The similarity was more than 0.90 and the spatial difference was less than 1.7 mm in most cases according to different scan sessions or scanners, and more than 0.85 and about 2.0 mm across surface extraction tools. The reliability of sulcal pit extraction was more affected by the image processing-related factors than the scan session or scanner factors. Moreover, the similarity of sulcal pit distribution appeared to be largely influenced by the presence or absence of the sulcal pits on the shallow and small folds. We suggest that our sulcal pit extraction from MRI is highly reliable and could be useful for clinical applications as an imaging biomarker.

The Development of Cortical Sensitivity to Visual Word Forms

ERIC Educational Resources Information Center

Ben-Shachar, Michal; Dougherty, Robert F.; Deutsch, Gayle K.; Wandell, Brian A.

2011-01-01

The ability to extract visual word forms quickly and efficiently is essential for using reading as a tool for learning. We describe the first longitudinal fMRI study to chart individual changes in cortical sensitivity to written words as reading develops. We conducted four annual measurements of brain function and reading skills in a heterogeneous…

Validated Automatic Brain Extraction of Head CT Images

PubMed Central

Muschelli, John; Ullman, Natalie L.; Mould, W. Andrew; Vespa, Paul; Hanley, Daniel F.; Crainiceanu, Ciprian M.

2015-01-01

Background X-ray Computed Tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction – the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0 – 100 Hounsfield units (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ = 1mm3) and re-thresholded to 0 – 100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomatedICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and and an intraclass correlation (ICC) was estimated using a one-way ANOVA. Results Smoothing images improves brain extraction results using BET for all measures except specificity (all p < 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p < 0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p < 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p < 0.001) and lower specificity (0.9981 vs. 0.9982; median difference: −0.0001, p < 0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains. Conclusion BET performs well at brain extraction on thresholded, 1mm3 smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided. PMID:25862260

Measurement and Finite Element Model Validation of Immature Porcine Brain-Skull Displacement during Rapid Sagittal Head Rotations.

PubMed

Pasquesi, Stephanie A; Margulies, Susan S

2018-01-01

Computational models are valuable tools for studying tissue-level mechanisms of traumatic brain injury, but to produce more accurate estimates of tissue deformation, these models must be validated against experimental data. In this study, we present in situ measurements of brain-skull displacement in the neonatal piglet head ( n  = 3) at the sagittal midline during six rapid non-impact rotations (two rotations per specimen) with peak angular velocities averaging 51.7 ± 1.4 rad/s. Marks on the sagittally cut brain and skull/rigid potting surfaces were tracked, and peak values of relative brain-skull displacement were extracted and found to be significantly less than values extracted from a previous axial plane model. In a finite element model of the sagittally transected neonatal porcine head, the brain-skull boundary condition was matched to the measured physical experiment data. Despite smaller sagittal plane displacements at the brain-skull boundary, the corresponding finite element boundary condition optimized for sagittal plane rotations is far less stiff than its axial counterpart, likely due to the prominent role of the boundary geometry in restricting interface movement. Finally, bridging veins were included in the finite element model. Varying the bridging vein mechanical behavior over a previously reported range had no influence on the brain-skull boundary displacements. This direction-specific sagittal plane boundary condition can be employed in finite element models of rapid sagittal head rotations.

Simultaneous and Noninvasive Imaging of Cerebral Oxygen Metabolic Rate, Blood Flow and Oxygen Extraction Fraction in Stroke Mice

PubMed Central

Zhu, Xiao-Hong; Chen, James; Tu, Tsang-Wei; Chen, Wei; Song, Sheng-Kwei

2012-01-01

Many brain diseases have been linked to abnormal oxygen metabolism and blood perfusion; nevertheless, there is still a lack of robust diagnostic tools for directly imaging cerebral metabolic rate of oxygen (CMRO2) and cerebral blood flow (CBF), as well as the oxygen extraction fraction (OEF) that reflects the balance between CMRO2 and CBF. This study employed the recently developed in vivo 17O MR spectroscopic imaging to simultaneously assess CMRO2, CBF and OEF in the brain using a preclinical middle cerebral arterial occlusion mouse model with a brief inhalation of 17O-labeled oxygen gas. The results demonstrated high sensitivity and reliability of the noninvasive 17O-MR approach for rapidly imaging CMRO2, CBF and OEF abnormalities in the ischemic cortex of the MCAO mouse brain. It was found that in the ischemic brain regions both CMRO2 and CBF were substantially lower than that of intact brain regions, even for the mildly damaged brain regions that were unable to be clearly identified by the conventional MRI. In contrast, OEF was higher in the MCAO affected brain regions. This study demonstrates a promising 17O MRI technique for imaging abnormal oxygen metabolism and perfusion in the diseased brain regions. This 17O MRI technique is advantageous because of its robustness, simplicity, noninvasiveness and reliability: features that are essential to potentially translate it to human patients for early diagnosis and monitoring of treatment efficacy. PMID:23000789

Simultaneous and noninvasive imaging of cerebral oxygen metabolic rate, blood flow and oxygen extraction fraction in stroke mice.

PubMed

Zhu, Xiao-Hong; Chen, James M; Tu, Tsang-Wei; Chen, Wei; Song, Sheng-Kwei

2013-01-01

Many brain diseases have been linked to abnormal oxygen metabolism and blood perfusion; nevertheless, there is still a lack of robust diagnostic tools for directly imaging cerebral metabolic rate of oxygen (CMRO(2)) and cerebral blood flow (CBF), as well as the oxygen extraction fraction (OEF) that reflects the balance between CMRO(2) and CBF. This study employed the recently developed in vivo (17)O MR spectroscopic imaging to simultaneously assess CMRO(2), CBF and OEF in the brain using a preclinical middle cerebral arterial occlusion mouse model with a brief inhalation of (17)O-labeled oxygen gas. The results demonstrated high sensitivity and reliability of the noninvasive (17)O-MR approach for rapidly imaging CMRO(2), CBF and OEF abnormalities in the ischemic cortex of the MCAO mouse brain. It was found that in the ischemic brain regions both CMRO(2) and CBF were substantially lower than that of intact brain regions, even for the mildly damaged brain regions that were unable to be clearly identified by the conventional MRI. In contrast, OEF was higher in the MCAO affected brain regions. This study demonstrates a promising (17)O MRI technique for imaging abnormal oxygen metabolism and perfusion in the diseased brain regions. This (17)O MRI technique is advantageous because of its robustness, simplicity, noninvasiveness and reliability: features that are essential to potentially translate it to human patients for early diagnosis and monitoring of treatment efficacy. Copyright © 2012 Elsevier Inc. All rights reserved.

Extracting morphologies from third harmonic generation images of structurally normal human brain tissue.

PubMed

Zhang, Zhiqing; Kuzmin, Nikolay V; Groot, Marie Louise; de Munck, Jan C

2017-06-01

The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging. We developed a salient edge-enhancing model of anisotropic diffusion for image filtering, based on higher order statistics. We split the intrinsic 3-phase segmentation problem into two 2-phase segmentation problems, each of which we solved with a dedicated model, active contour weighted by prior extreme. We applied the novel proposed algorithms to THG images of structurally normal ex-vivo human brain tissue, revealing key tissue components-brain cells, microvessels and neuropil, enabling statistical characterization of these components. Comprehensive comparison to manually delineated ground truth validated the proposed algorithms. Quantitative comparison to second harmonic generation/auto-fluorescence images, acquired simultaneously from the same tissue area, confirmed the correctness of the main THG features detected. The software and test datasets are available from the authors. z.zhang@vu.nl. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Expression of functional neurotransmitter receptors in Xenopus oocytes after injection of human brain membranes

NASA Astrophysics Data System (ADS)

Miledi, Ricardo; Eusebi, Fabrizio; Martínez-Torres, Ataúlfo; Palma, Eleonora; Trettel, Flavia

2002-10-01

The Xenopus oocyte is a very powerful tool for studies of the structure and function of membrane proteins, e.g., messenger RNA extracted from the brain and injected into oocytes leads to the synthesis and membrane incorporation of many types of functional receptors and ion channels, and membrane vesicles from Torpedo electroplaques injected into oocytes fuse with the oocyte membrane and cause the appearance of functional Torpedo acetylcholine receptors and Cl channels. This approach was developed further to transplant already assembled neurotransmitter receptors from human brain cells to the plasma membrane of Xenopus oocytes. Membranes isolated from the temporal neocortex of a patient, operated for intractable epilepsy, were injected into oocytes and, within a few hours, the oocyte membrane acquired functional neurotransmitter receptors to -aminobutyric acid, -amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, kainate, and glycine. These receptors were also expressed in the plasma membrane of oocytes injected with mRNA extracted from the temporal neocortex of the same patient. All of this makes the Xenopus oocyte a more useful model than it already is for studies of the structure and function of many human membrane proteins and opens the way to novel pathophysiological investigations of some human brain disorders.

The coevolution of innovation and technical intelligence in primates

PubMed Central

Street, Sally E.; Whalen, Andrew; Laland, Kevin N.

2016-01-01

In birds and primates, the frequency of behavioural innovation has been shown to covary with absolute and relative brain size, leading to the suggestion that large brains allow animals to innovate, and/or that selection for innovativeness, together with social learning, may have driven brain enlargement. We examined the relationship between primate brain size and both technical (i.e. tool using) and non-technical innovation, deploying a combination of phylogenetically informed regression and exploratory causal graph analyses. Regression analyses revealed that absolute and relative brain size correlated positively with technical innovation, and exhibited consistently weaker, but still positive, relationships with non-technical innovation. These findings mirror similar results in birds. Our exploratory causal graph analyses suggested that technical innovation shares strong direct relationships with brain size, body size, social learning rate and social group size, whereas non-technical innovation did not exhibit a direct relationship with brain size. Nonetheless, non-technical innovation was linked to brain size indirectly via diet and life-history variables. Our findings support ‘technical intelligence’ hypotheses in linking technical innovation to encephalization in the restricted set of primate lineages where technical innovation has been reported. Our findings also provide support for a broad co-evolving complex of brain, behaviour, life-history, social and dietary variables, providing secondary support for social and ecological intelligence hypotheses. The ability to gain access to difficult-to-extract, but potentially nutrient-rich, resources through tool use may have conferred on some primates adaptive advantages, leading to selection for brain circuitry that underlies technical proficiency. PMID:26926276

The coevolution of innovation and technical intelligence in primates.

PubMed

Navarrete, Ana F; Reader, Simon M; Street, Sally E; Whalen, Andrew; Laland, Kevin N

2016-03-19

In birds and primates, the frequency of behavioural innovation has been shown to covary with absolute and relative brain size, leading to the suggestion that large brains allow animals to innovate, and/or that selection for innovativeness, together with social learning, may have driven brain enlargement. We examined the relationship between primate brain size and both technical (i.e. tool using) and non-technical innovation, deploying a combination of phylogenetically informed regression and exploratory causal graph analyses. Regression analyses revealed that absolute and relative brain size correlated positively with technical innovation, and exhibited consistently weaker, but still positive, relationships with non-technical innovation. These findings mirror similar results in birds. Our exploratory causal graph analyses suggested that technical innovation shares strong direct relationships with brain size, body size, social learning rate and social group size, whereas non-technical innovation did not exhibit a direct relationship with brain size. Nonetheless, non-technical innovation was linked to brain size indirectly via diet and life-history variables. Our findings support 'technical intelligence' hypotheses in linking technical innovation to encephalization in the restricted set of primate lineages where technical innovation has been reported. Our findings also provide support for a broad co-evolving complex of brain, behaviour, life-history, social and dietary variables, providing secondary support for social and ecological intelligence hypotheses. The ability to gain access to difficult-to-extract, but potentially nutrient-rich, resources through tool use may have conferred on some primates adaptive advantages, leading to selection for brain circuitry that underlies technical proficiency. © 2016 The Author(s).

Live imaging of mitosis in the developing mouse embryonic cortex.

PubMed

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.

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images

PubMed Central

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections. PMID:29875639

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images.

PubMed

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

Listening to Brain Microcircuits for Interfacing With External World—Progress in Wireless Implantable Microelectronic Neuroengineering Devices

PubMed Central

Nurmikko, Arto V.; Donoghue, John P.; Hochberg, Leigh R.; Patterson, William R.; Song, Yoon-Kyu; Bull, Christopher W.; Borton, David A.; Laiwalla, Farah; Park, Sunmee; Ming, Yin; Aceros, Juan

2011-01-01

Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature’s amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic “brain-interfaces” within the body, a point of special emphasis of this paper. PMID:21654935

A hybrid brain-computer interface-based mail client.

PubMed

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method.

A Hybrid Brain-Computer Interface-Based Mail Client

PubMed Central

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method. PMID:23690880

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods.

PubMed

Serag, Ahmed; Blesa, Manuel; Moore, Emma J; Pataky, Rozalia; Sparrow, Sarah A; Wilkinson, A G; Macnaught, Gillian; Semple, Scott I; Boardman, James P

2016-03-24

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.

Listening to Brain Microcircuits for Interfacing With External World-Progress in Wireless Implantable Microelectronic Neuroengineering Devices: Experimental systems are described for electrical recording in the brain using multiple microelectrodes and short range implantable or wearable broadcasting units.

PubMed

Nurmikko, Arto V; Donoghue, John P; Hochberg, Leigh R; Patterson, William R; Song, Yoon-Kyu; Bull, Christopher W; Borton, David A; Laiwalla, Farah; Park, Sunmee; Ming, Yin; Aceros, Juan

2010-01-01

Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature's amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic "brain-interfaces" within the body, a point of special emphasis of this paper.

Enhanced subject-specific resting-state network detection and extraction with fast fMRI.

PubMed

Akin, Burak; Lee, Hsu-Lei; Hennig, Jürgen; LeVan, Pierre

2017-02-01

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Pediatric Brain Extraction Using Learning-based Meta-algorithm

PubMed Central

Shi, Feng; Wang, Li; Dai, Yakang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2012-01-01

Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2 months), infant (1–2 years), and child (5–18 years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range. PMID:22634859

A Learning-Based Wrapper Method to Correct Systematic Errors in Automatic Image Segmentation: Consistently Improved Performance in Hippocampus, Cortex and Brain Segmentation

PubMed Central

Wang, Hongzhi; Das, Sandhitsu R.; Suh, Jung Wook; Altinay, Murat; Pluta, John; Craige, Caryne; Avants, Brian; Yushkevich, Paul A.

2011-01-01

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively. PMID:21237273

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

NASA Astrophysics Data System (ADS)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

Neuroanatomy Predicts Individual Risk Attitudes

PubMed Central

Gilaie-Dotan, Sharon; Tymula, Agnieszka; Cooper, Nicole; Kable, Joseph W.; Glimcher, Paul W.

2014-01-01

Over the course of the last decade a multitude of studies have investigated the relationship between neural activations and individual human decision-making. Here we asked whether the anatomical features of individual human brains could be used to predict the fundamental preferences of human choosers. To that end, we quantified the risk attitudes of human decision-makers using standard economic tools and quantified the gray matter cortical volume in all brain areas using standard neurobiological tools. Our whole-brain analysis revealed that the gray matter volume of a region in the right posterior parietal cortex was significantly predictive of individual risk attitudes. Participants with higher gray matter volume in this region exhibited less risk aversion. To test the robustness of this finding we examined a second group of participants and used econometric tools to test the ex ante hypothesis that gray matter volume in this area predicts individual risk attitudes. Our finding was confirmed in this second group. Our results, while being silent about causal relationships, identify what might be considered the first stable biomarker for financial risk-attitude. If these results, gathered in a population of midlife northeast American adults, hold in the general population, they will provide constraints on the possible neural mechanisms underlying risk attitudes. The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers. PMID:25209279

The β-amyloid peptide compromises Reelin signaling in Alzheimer’s disease

PubMed Central

Cuchillo-Ibañez, Inmaculada; Mata-Balaguer, Trinidad; Balmaceda, Valeria; Arranz, Juan José; Nimpf, Johannes; Sáez-Valero, Javier

2016-01-01

Reelin is a signaling protein that plays a crucial role in synaptic function, which expression is influenced by β-amyloid (Aβ). We show that Reelin and Aβ oligomers co-immunoprecipitated in human brain extracts and were present in the same size-exclusion chromatography fractions. Aβ treatment of cells led to increase expression of Reelin, but secreted Reelin results trapped together with Aβ aggregates. In frontal cortex extracts an increase in Reelin mRNA, and in soluble and insoluble (guanidine-extractable) Reelin protein, was associated with late Braak stages of Alzheimer’s disease (AD), while expression of its receptor, ApoER2, did not change. However, Reelin-dependent induction of Dab1 phosphorylation appeared reduced in AD. In cells, Aβ reduced the capacity of Reelin to induce internalization of biotinylated ApoER2 and ApoER2 processing. Soluble proteolytic fragments of ApoER2 generated after Reelin binding can be detected in cerebrospinal fluid (CSF). Quantification of these soluble fragments in CSF could be a tool to evaluate the efficiency of Reelin signaling in the brain. These CSF-ApoER2 fragments correlated with Reelin levels only in control subjects, not in AD, where these fragments diminished. We conclude that while Reelin expression is enhanced in the Alzheimer’s brain, the interaction of Reelin with Aβ hinders its biological activity. PMID:27531658

A pediatric brain structure atlas from T1-weighted MR images

NASA Astrophysics Data System (ADS)

Shan, Zuyao Y.; Parra, Carlos; Ji, Qing; Ogg, Robert J.; Zhang, Yong; Laningham, Fred H.; Reddick, Wilburn E.

2006-03-01

In this paper, we have developed a digital atlas of the pediatric human brain. Human brain atlases, used to visualize spatially complex structures of the brain, are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1 weighted MR data set of a 9 year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. Higher curvature surfaces were simplified with more reference points; lower curvature surfaces, with fewer. We constructed a 3D triangular mesh model for each structure by triangulation of the structure's reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/BrainAtlas) can be used to plan treatment, to conduct knowledge and modeldriven segmentation, and to analyze the shapes of brain structures in pediatric patients.

Pomegranate extract protects against cerebral ischemia/reperfusion injury and preserves brain DNA integrity in rats.

PubMed

Ahmed, Maha A E; El Morsy, Engy M; Ahmed, Amany A E

2014-08-21

Interruption to blood flow causes ischemia and infarction of brain tissues with consequent neuronal damage and brain dysfunction. Pomegranate extract is well tolerated, and safely consumed all over the world. Interestingly, pomegranate extract has shown remarkable antioxidant and anti-inflammatory effects in experimental models. Many investigators consider natural extracts as novel therapies for neurodegenerative disorders. Therefore, this study was carried out to investigate the protective effects of standardized pomegranate extract against cerebral ischemia/reperfusion-induced brain injury in rats. Adult male albino rats were randomly divided into sham-operated control group, ischemia/reperfusion (I/R) group, and two other groups that received standardized pomegranate extract at two dose levels (250, 500 mg/kg) for 15 days prior to ischemia/reperfusion (PMG250+I/R, and PMG500+I/R groups). After I/R or sham operation, all rats were sacrificed and brains were harvested for subsequent biochemical analysis. Results showed reduction in brain contents of MDA (malondialdehyde), and NO (nitric oxide), in addition to enhancement of SOD (superoxide dismutase), GPX (glutathione peroxidase), and GRD (glutathione reductase) activities in rats treated with pomegranate extract prior to cerebral I/R. Moreover, pomegranate extract decreased brain levels of NF-κB p65 (nuclear factor kappa B p65), TNF-α (tumor necrosis factor-alpha), caspase-3 and increased brain levels of IL-10 (interleukin-10), and cerebral ATP (adenosine triphosphate) production. Comet assay showed less brain DNA (deoxyribonucleic acid) damage in rats protected with pomegranate extract. The present study showed, for the first time, that pre-administration of pomegranate extract to rats, can offer a significant dose-dependent neuroprotective activity against cerebral I/R brain injury and DNA damage via antioxidant, anti-inflammatory, anti-apoptotic and ATP-replenishing effects. Copyright © 2014 Elsevier Inc. All rights reserved.

Onion extract structural changes during in vitro digestion and its potential antioxidant effect on brain lipids obtained from low- and high-fat-fed mice.

PubMed

Hur, S J; Lee, S J; Kim, D H; Chun, S C; Lee, S K

2013-12-01

This study investigated the effects of onion (Allium cepa, L.) extract on the antioxidant activity of lipids in low-and high-fat-fed mouse brain lipids and its structural change during in vitro human digestion. The onion extracts were passed through an in vitro human digestion model that simulated the composition of the mouth, stomach, and small intestine juice. The brain lipids were collected from low- and high-fat-fed mouse brain and then incubated with the in vitro-digested onion extracts to determine the lipid oxidation. The results confirmed that the main phenolics of onion extract were kaempferol, myricetin, quercetin, and quercitrin. The quercetin content increased with digestion of the onion extract. Antioxidant activity was strongly influenced by in vitro human digestion of both onion extract and quercetin standard. After digestion by the small intestine, the antioxidant activity values were dramatically increased, whereas the antioxidant activity was less influenced by digestion in the stomach for both onion extract and quercetin standard. The inhibitory effect of lipid oxidation of onion extract in mouse brain lipids increased after digestion in the stomach. The inhibitory effect of lipid oxidation of onion extract was higher in the high-fat-fed mouse brain lipids than that in the low-fat-fed mouse brain lipids. The major study finding is that the antioxidative effect of onion extract may be higher in high-fat-fed mouse brain lipids than that in low-fat-fed mouse brain lipids. Thus, dietary onion may have important applications as a natural antioxidant agent in a high-fat diet.

Seizure classification in EEG signals utilizing Hilbert-Huang transform

PubMed Central

2011-01-01

Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool. PMID:21609459

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

PubMed

Oweis, Rami J; Abdulhay, Enas W

2011-05-24

Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

Radiomicrobiomics: Advancing Along the Gut-brain Axis Through Big Data Analysis.

PubMed

De Santis, Silvia; Moratal, David; Canals, Santiago

2017-12-10

The gut-brain axis communicates the brain with the gut microbiota, a bidirectional conduit that has received increasing attention in recent years thanks to its emerging role in brain development and function. Alterations in microbiota composition have been associated to neurological and psychiatric disorders, and several studies suggest that the immune system plays a fundamental role in the gut-brain interaction. Recent advances in brain imaging and in microbiome sequencing have generated a large amount of information, yet the data from both these sources need to be combined efficiently to extract biological meaning, and any diagnostic and/or prognostic benefit from these tools. In addition, the causal nature of the gut-brain interaction remains to be fully established, and preclinical findings translated to humans. In this "Perspective" article, we discuss recent efforts to combine data on the gut microbiota with the features that can be obtained from the conversion of brain images into mineable data. The subsequent analysis of these data for diagnostic and prognostic purposes is an approach we call radiomicrobiomics and it holds tremendous potential to enhance our understanding of this fascinating connection. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

NASA Astrophysics Data System (ADS)

Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2015-03-01

Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

Volumetric neuroimage analysis extensions for the MIPAV software package.

PubMed

Bazin, Pierre-Louis; Cuzzocreo, Jennifer L; Yassa, Michael A; Gandler, William; McAuliffe, Matthew J; Bassett, Susan S; Pham, Dzung L

2007-09-15

We describe a new collection of publicly available software tools for performing quantitative neuroimage analysis. The tools perform semi-automatic brain extraction, tissue classification, Talairach alignment, and atlas-based measurements within a user-friendly graphical environment. They are implemented as plug-ins for MIPAV, a freely available medical image processing software package from the National Institutes of Health. Because the plug-ins and MIPAV are implemented in Java, both can be utilized on nearly any operating system platform. In addition to the software plug-ins, we have also released a digital version of the Talairach atlas that can be used to perform regional volumetric analyses. Several studies are conducted applying the new tools to simulated and real neuroimaging data sets.

Text mining for neuroanatomy using WhiteText with an updated corpus and a new web application

PubMed Central

French, Leon; Liu, Po; Marais, Olivia; Koreman, Tianna; Tseng, Lucia; Lai, Artemis; Pavlidis, Paul

2015-01-01

We describe the WhiteText project, and its progress towards automatically extracting statements of neuroanatomical connectivity from text. We review progress to date on the three main steps of the project: recognition of brain region mentions, standardization of brain region mentions to neuroanatomical nomenclature, and connectivity statement extraction. We further describe a new version of our manually curated corpus that adds 2,111 connectivity statements from 1,828 additional abstracts. Cross-validation classification within the new corpus replicates results on our original corpus, recalling 67% of connectivity statements at 51% precision. The resulting merged corpus provides 5,208 connectivity statements that can be used to seed species-specific connectivity matrices and to better train automated techniques. Finally, we present a new web application that allows fast interactive browsing of the over 70,000 sentences indexed by the system, as a tool for accessing the data and assisting in further curation. Software and data are freely available at http://www.chibi.ubc.ca/WhiteText/. PMID:26052282

TRAFIC: fiber tract classification using deep learning

NASA Astrophysics Data System (ADS)

Ngattai Lam, Prince D.; Belhomme, Gaetan; Ferrall, Jessica; Patterson, Billie; Styner, Martin; Prieto, Juan C.

2018-03-01

We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.

Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment.

PubMed

Sirgo, Gonzalo; Esteban, Federico; Gómez, Josep; Moreno, Gerard; Rodríguez, Alejandro; Blanch, Lluis; Guardiola, Juan José; Gracia, Rafael; De Haro, Lluis; Bodí, María

2018-04-01

Big data analytics promise insights into healthcare processes and management, improving outcomes while reducing costs. However, data quality is a major challenge for reliable results. Business process discovery techniques and an associated data model were used to develop data management tool, ICU-DaMa, for extracting variables essential for overseeing the quality of care in the intensive care unit (ICU). To determine the feasibility of using ICU-DaMa to automatically extract variables for the minimum dataset and ICU quality indicators from the clinical information system (CIS). The Wilcoxon signed-rank test and Fisher's exact test were used to compare the values extracted from the CIS with ICU-DaMa for 25 variables from all patients attended in a polyvalent ICU during a two-month period against the gold standard of values manually extracted by two trained physicians. Discrepancies with the gold standard were classified into plausibility, conformance, and completeness errors. Data from 149 patients were included. Although there were no significant differences between the automatic method and the manual method, we detected differences in values for five variables, including one plausibility error and two conformance and completeness errors. Plausibility: 1) Sex, ICU-DaMa incorrectly classified one male patient as female (error generated by the Hospital's Admissions Department). Conformance: 2) Reason for isolation, ICU-DaMa failed to detect a human error in which a professional misclassified a patient's isolation. 3) Brain death, ICU-DaMa failed to detect another human error in which a professional likely entered two mutually exclusive values related to the death of the patient (brain death and controlled donation after circulatory death). Completeness: 4) Destination at ICU discharge, ICU-DaMa incorrectly classified two patients due to a professional failing to fill out the patient discharge form when thepatients died. 5) Length of continuous renal replacement therapy, data were missing for one patient because the CRRT device was not connected to the CIS. Automatic generation of minimum dataset and ICU quality indicators using ICU-DaMa is feasible. The discrepancies were identified and can be corrected by improving CIS ergonomics, training healthcare professionals in the culture of the quality of information, and using tools for detecting and correcting data errors. Copyright © 2018 Elsevier B.V. All rights reserved.

Removal of interfering nucleotides from brain extracts containing substance p. Effect of drugs on brain concentrations of substance p

PubMed Central

Laszlo, I.

1963-01-01

Several methods for removing interfering nucleotides, adenosine-5'-monophosphate and adenosine 5'-triphosphate from brain extracts have been studied. An enzymic method, using adenylic acid deaminase, has been found suitable. This deaminates adenosine monophosphate to 5'-inosinic acid, an inactive compound which does not influence the estimations of substance P. Owing to the adenosine triphosphatase content of the enzyme extract, adenosine triphosphate was also inactivated. For the estimation of adenosine monophosphate-deaminase activity, a simple colorimetric method is described which measures the ammonia liberated from adenosine monophosphate. Substance P in mouse brain extracts was estimated after treatment of the animals with various drugs, and after the enzymic removal of interfering nucleotides from the brain extracts. The drugs had no effect on the substance P content of mouse brain. The effect of drugs on the contractions of the guinea-pig ileum induced by substance P was also investigated, and the effect of drugs on the estimations of substance P in brain extracts is discussed. PMID:14066136

Comparison of continuously acquired resting state and extracted analogues from active tasks

PubMed Central

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S.; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried

2015-01-01

Abstract Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods for extraction of resting‐state data from task‐related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in‐between task blocks. Despite widespread application in current research, consensus on which method best resembles resting‐state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting‐state, two different task paradigms were assessed (emotion discrimination and right finger‐tapping) and five well‐described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting‐state (Dice, Intraclass correlation coefficient (ICC), R 2) showed that regression against task effects yields functional connectivity networks most alike to resting‐state. However, all methods exhibited significant differences when compared to continuous resting‐state and similarity metrics were lower than test‐retest of two resting‐state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting‐state when extracting signals from task designs, although functional connectivity computed from task‐specific data may indeed yield interesting information. Hum Brain Mapp 36:4053–4063, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:26178250

In vitro and in vivo studies of Allium sativum extract against deltamethrin-induced oxidative stress in rats brain and kidney.

PubMed

Ncir, Marwa; Saoudi, Mongi; Sellami, Hanen; Rahmouni, Fatma; Lahyani, Amina; Makni Ayadi, Fatma; El Feki, Abdelfattah; Allagui, Mohamed Salah

2017-09-18

The present study investigated the in vitro and the in vivo antioxidant capacities of Allium sativum (garlic) extract against deltamethrin-induced oxidative damage in rat's brain and kidney. The in vitro result showed that highest extraction yield was achieved with methanol (20.08%). Among the tested extracts, the methanol extract exhibited the highest total phenolic, flavonoids contents and antioxidant activity. The in vivo results showed that deltamethrin treatment caused an increase of the acetylcholinesterase level (AChE) in brain and plasma, the brain and kidney conjugated dienes and lipid peroxidation (LPO) levels as compared to control group. The antioxidant enzymes results showed that deltamethrin treatment induced a significantly decrease (p < 0.01) in brain and kidney antioxidant enzymes as catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx) to control group. The co-administration of garlic extract reduced the toxic effects in brain and kidney tissues induced by deltamethrin.

Augmentation-related brain plasticity

PubMed Central

Di Pino, Giovanni; Maravita, Angelo; Zollo, Loredana; Guglielmelli, Eugenio; Di Lazzaro, Vincenzo

2014-01-01

Today, the anthropomorphism of the tools and the development of neural interfaces require reconsidering the concept of human-tools interaction in the framework of human augmentation. This review analyses the plastic process that the brain undergoes when it comes into contact with augmenting artificial sensors and effectors and, on the other hand, the changes that the use of external augmenting devices produces in the brain. Hitherto, few studies investigated the neural correlates of augmentation, but clues on it can be borrowed from logically-related paradigms: sensorimotor training, cognitive enhancement, cross-modal plasticity, sensorimotor functional substitution, use and embodiment of tools. Augmentation modifies function and structure of a number of areas, i.e., primary sensory cortices shape their receptive fields to become sensitive to novel inputs. Motor areas adapt the neuroprosthesis representation firing-rate to refine kinematics. As for normal motor outputs, the learning process recruits motor and premotor cortices and the acquisition of proficiency decreases attentional recruitment, focuses the activity on sensorimotor areas and increases the basal ganglia drive on the cortex. Augmentation deeply relies on the frontoparietal network. In particular, premotor cortex is involved in learning the control of an external effector and owns the tool motor representation, while the intraparietal sulcus extracts its visual features. In these areas, multisensory integration neurons enlarge their receptive fields to embody supernumerary limbs. For operating an anthropomorphic neuroprosthesis, the mirror system is required to understand the meaning of the action, the cerebellum for the formation of its internal model and the insula for its interoception. In conclusion, anthropomorphic sensorized devices can provide the critical sensory afferences to evolve the exploitation of tools through their embodiment, reshaping the body representation and the sense of the self. PMID:24966816

ViSimpl: Multi-View Visual Analysis of Brain Simulation Data

PubMed Central

Galindo, Sergio E.; Toharia, Pablo; Robles, Oscar D.; Pastor, Luis

2016-01-01

After decades of independent morphological and functional brain research, a key point in neuroscience nowadays is to understand the combined relationships between the structure of the brain and its components and their dynamics on multiple scales, ranging from circuits of neurons at micro or mesoscale to brain regions at macroscale. With such a goal in mind, there is a vast amount of research focusing on modeling and simulating activity within neuronal structures, and these simulations generate large and complex datasets which have to be analyzed in order to gain the desired insight. In such context, this paper presents ViSimpl, which integrates a set of visualization and interaction tools that provide a semantic view of brain data with the aim of improving its analysis procedures. ViSimpl provides 3D particle-based rendering that allows visualizing simulation data with their associated spatial and temporal information, enhancing the knowledge extraction process. It also provides abstract representations of the time-varying magnitudes supporting different data aggregation and disaggregation operations and giving also focus and context clues. In addition, ViSimpl tools provide synchronized playback control of the simulation being analyzed. Finally, ViSimpl allows performing selection and filtering operations relying on an application called NeuroScheme. All these views are loosely coupled and can be used independently, but they can also work together as linked views, both in centralized and distributed computing environments, enhancing the data exploration and analysis procedures. PMID:27774062

ViSimpl: Multi-View Visual Analysis of Brain Simulation Data.

PubMed

Galindo, Sergio E; Toharia, Pablo; Robles, Oscar D; Pastor, Luis

2016-01-01

After decades of independent morphological and functional brain research, a key point in neuroscience nowadays is to understand the combined relationships between the structure of the brain and its components and their dynamics on multiple scales, ranging from circuits of neurons at micro or mesoscale to brain regions at macroscale. With such a goal in mind, there is a vast amount of research focusing on modeling and simulating activity within neuronal structures, and these simulations generate large and complex datasets which have to be analyzed in order to gain the desired insight. In such context, this paper presents ViSimpl, which integrates a set of visualization and interaction tools that provide a semantic view of brain data with the aim of improving its analysis procedures. ViSimpl provides 3D particle-based rendering that allows visualizing simulation data with their associated spatial and temporal information, enhancing the knowledge extraction process. It also provides abstract representations of the time-varying magnitudes supporting different data aggregation and disaggregation operations and giving also focus and context clues. In addition, ViSimpl tools provide synchronized playback control of the simulation being analyzed. Finally, ViSimpl allows performing selection and filtering operations relying on an application called NeuroScheme. All these views are loosely coupled and can be used independently, but they can also work together as linked views, both in centralized and distributed computing environments, enhancing the data exploration and analysis procedures.

Computerized Cognitive Rehabilitation of Attention and Executive Function in Acquired Brain Injury: A Systematic Review.

PubMed

Bogdanova, Yelena; Yee, Megan K; Ho, Vivian T; Cicerone, Keith D

Comprehensive review of the use of computerized treatment as a rehabilitation tool for attention and executive function in adults (aged 18 years or older) who suffered an acquired brain injury. Systematic review of empirical research. Two reviewers independently assessed articles using the methodological quality criteria of Cicerone et al. Data extracted included sample size, diagnosis, intervention information, treatment schedule, assessment methods, and outcome measures. A literature review (PubMed, EMBASE, Ovid, Cochrane, PsychINFO, CINAHL) generated a total of 4931 publications. Twenty-eight studies using computerized cognitive interventions targeting attention and executive functions were included in this review. In 23 studies, significant improvements in attention and executive function subsequent to training were reported; in the remaining 5, promising trends were observed. Preliminary evidence suggests improvements in cognitive function following computerized rehabilitation for acquired brain injury populations including traumatic brain injury and stroke. Further studies are needed to address methodological issues (eg, small sample size, inadequate control groups) and to inform development of guidelines and standardized protocols.

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.

PubMed

Han, Xu; Kwitt, Roland; Aylward, Stephen; Bakas, Spyridon; Menze, Bjoern; Asturias, Alexander; Vespa, Paul; Van Horn, John; Niethammer, Marc

2018-08-01

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images. Copyright © 2018 Elsevier Inc. All rights reserved.

High-throughput analysis of sulfatides in cerebrospinal fluid using automated extraction and UPLC-MS/MS.

PubMed

Blomqvist, Maria; Borén, Jan; Zetterberg, Henrik; Blennow, Kaj; Månsson, Jan-Eric; Ståhlman, Marcus

2017-07-01

Sulfatides (STs) are a group of glycosphingolipids that are highly expressed in brain. Due to their importance for normal brain function and their potential involvement in neurological diseases, development of accurate and sensitive methods for their determination is needed. Here we describe a high-throughput oriented and quantitative method for the determination of STs in cerebrospinal fluid (CSF). The STs were extracted using a fully automated liquid/liquid extraction method and quantified using ultra-performance liquid chromatography coupled to tandem mass spectrometry. With the high sensitivity of the developed method, quantification of 20 ST species from only 100 μl of CSF was performed. Validation of the method showed that the STs were extracted with high recovery (90%) and could be determined with low inter- and intra-day variation. Our method was applied to a patient cohort of subjects with an Alzheimer's disease biomarker profile. Although the total ST levels were unaltered compared with an age-matched control group, we show that the ratio of hydroxylated/nonhydroxylated STs was increased in the patient cohort. In conclusion, we believe that the fast, sensitive, and accurate method described in this study is a powerful new tool for the determination of STs in clinical as well as preclinical settings. Copyright © 2017 by the American Society for Biochemistry and Molecular Biology, Inc.

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates.

PubMed

Zhao, Gengyan; Liu, Fang; Oler, Jonathan A; Meyerand, Mary E; Kalin, Ned H; Birn, Rasmus M

2018-07-15

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10 -4 , two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases. Copyright © 2018 Elsevier Inc. All rights reserved.

Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

NASA Astrophysics Data System (ADS)

Devine, Jeremy; Sahgal, Arjun; Karam, Irene; Martel, Anne L.

2016-03-01

The accurate localization of brain metastases in magnetic resonance (MR) images is crucial for patients undergoing stereotactic radiosurgery (SRS) to ensure that all neoplastic foci are targeted. Computer automated tumor localization and analysis can improve both of these tasks by eliminating inter and intra-observer variations during the MR image reading process. Lesion localization is accomplished using adaptive thresholding to extract enhancing objects. Each enhancing object is represented as a vector of features which includes information on object size, symmetry, position, shape, and context. These vectors are then used to train a random forest classifier. We trained and tested the image analysis pipeline on 3D axial contrast-enhanced MR images with the intention of localizing the brain metastases. In our cross validation study and at the most effective algorithm operating point, we were able to identify 90% of the lesions at a precision rate of 60%.

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

PubMed

Chen, Zikuan; Calhoun, Vince D

2016-03-01

Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

Age- and brain region-dependent α-synuclein oligomerization is attributed to alterations in intrinsic enzymes regulating α-synuclein phosphorylation in aging monkey brains.

PubMed

Chen, Min; Yang, Weiwei; Li, Xin; Li, Xuran; Wang, Peng; Yue, Feng; Yang, Hui; Chan, Piu; Yu, Shun

2016-02-23

We previously reported that the levels of α-syn oligomers, which play pivotal pathogenic roles in age-related Parkinson's disease (PD) and dementia with Lewy bodies, increase heterogeneously in the aging brain. Here, we show that exogenous α-syn incubated with brain extracts from older cynomolgus monkeys and in Lewy body pathology (LBP)-susceptible brain regions (striatum and hippocampus) forms higher amounts of phosphorylated and oligomeric α-syn than that in extracts from younger monkeys and LBP-insusceptible brain regions (cerebellum and occipital cortex). The increased α-syn phosphorylation and oligomerization in the brain extracts from older monkeys and in LBP-susceptible brain regions were associated with higher levels of polo-like kinase 2 (PLK2), an enzyme promoting α-syn phosphorylation, and lower activity of protein phosphatase 2A (PP2A), an enzyme inhibiting α-syn phosphorylation, in these brain extracts. Further, the extent of the age- and brain-dependent increase in α-syn phosphorylation and oligomerization was reduced by inhibition of PLK2 and activation of PP2A. Inversely, phosphorylated α-syn oligomers reduced the activity of PP2A and showed potent cytotoxicity. In addition, the activity of GCase and the levels of ceramide, a product of GCase shown to activate PP2A, were lower in brain extracts from older monkeys and in LBP-susceptible brain regions. Our results suggest a role for altered intrinsic metabolic enzymes in age- and brain region-dependent α-syn oligomerization in aging brains.

PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation

PubMed Central

Portales-Casamar, Elodie; Kirov, Stefan; Lim, Jonathan; Lithwick, Stuart; Swanson, Magdalena I; Ticoll, Amy; Snoddy, Jay; Wasserman, Wyeth W

2007-01-01

PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction. Curated boutique data collections can be maintained and disseminated through the unified schema of the mall-like PAZAR repository. The Pleiades Promoter Project collection of brain-linked regulatory sequences is introduced to demonstrate the depth of annotation possible within PAZAR. PAZAR, located at , is open for business. PMID:17916232

PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation.

PubMed

Portales-Casamar, Elodie; Kirov, Stefan; Lim, Jonathan; Lithwick, Stuart; Swanson, Magdalena I; Ticoll, Amy; Snoddy, Jay; Wasserman, Wyeth W

2007-01-01

PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction. Curated boutique data collections can be maintained and disseminated through the unified schema of the mall-like PAZAR repository. The Pleiades Promoter Project collection of brain-linked regulatory sequences is introduced to demonstrate the depth of annotation possible within PAZAR. PAZAR, located at http://www.pazar.info, is open for business.

Stress does not increase blood–brain barrier permeability in mice

PubMed Central

Roszkowski, Martin

2016-01-01

Several studies have reported that exposure to acute psychophysiological stressors can lead to an increase in blood–brain barrier permeability, but these findings remain controversial and disputed. We thoroughly examined this issue by assessing the effect of several well-established paradigms of acute stress and chronic stress on blood–brain barrier permeability in several brain areas of adult mice. Using cerebral extraction ratio for the small molecule tracer sodium fluorescein (NaF, 376 Da) as a sensitive measure of blood–brain barrier permeability, we find that neither acute swim nor restraint stress lead to increased cerebral extraction ratio. Daily 6-h restraint stress for 21 days, a model for the severe detrimental impact of chronic stress on brain function, also does not alter cerebral extraction ratio. In contrast, we find that cold forced swim and cold restraint stress both lead to a transient, pronounced decrease of cerebral extraction ratio in hippocampus and cortex, suggesting that body temperature can be an important confounding factor in studies of blood–brain barrier permeability. To additionally assess if stress could change blood–brain barrier permeability for macromolecules, we measured cerebral extraction ratio for fluorescein isothiocyanate-dextran (70 kDa). We find that neither acute restraint nor cold swim stress affected blood–brain barrier permeability for macromolecules, thus corroborating our findings that various stressors do not increase blood–brain barrier permeability. PMID:27146513

Zingiber zerumbet L. (Smith) extract alleviates the ethanol-induced brain damage via its antioxidant activity.

PubMed

Hamid, Asmah; Ibrahim, Farah Wahida; Ming, Teoh Hooi; Nasrom, Mohd Nazir; Eusoff, Norelina; Husain, Khairana; Abdul Latif, Mazlyzam

2018-03-20

Zingiber zerumbet (L.) Smith belongs to the Zingiberaceae family that is widely distributed throughout the tropics, particularly in Southeast Asia. It is locally known as 'Lempoyang' and traditionally used to treat fever, constipation and to relieve pain. It is also known to possess antioxidant and anti-inflammatory activities. Based on these antioxidant and anti-inflammatory activities, this study was conducted to investigate the effects of ethyl-acetate extract of Z. zerumbet rhizomes against ethanol-induced brain damage in male Wistar rats. Twenty-four male Wistar rats were divided into four groups which consist of normal, 1.8 g/kg ethanol (40% v/v), 200 mg/kg Z. zerumbet extract plus ethanol and 400 mg/kg Z. zerumbet plus ethanol. The extract of Z. zerumbet was given once daily by oral gavage, 30 min prior to ethanol exposure via intraperitoneal route for 14 consecutive days. The rats were then sacrificed. Blood and brain homogenate were subjected to biochemical tests and part of the brain tissue was sectioned for histological analysis. Treatment with ethyl-acetate Z. zerumbet extract at 200 mg/kg and 400 mg/kg significantly reduced the level of malondialdehyde (MDA) and protein carbonyl (p < 0.05) in the brain homogenate. Both doses of extracts also significantly increased the level of serum superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) activities as well as glutathione (GSH) level (p < 0.05). However, administration of ethyl-acetate Z. zerumbet extract at 400 mg/kg showed better protective effects on the ethanol-induced brain damage as shown with higher levels of SOD, CAT, GPx and GSH in the brain homogenate as compared to 200 mg/kg dose. Histological observation of the cerebellum and cerebral cortex showed that the extract prevented the loss of Purkinje cells and retained the number and the shape of the cells. Ethyl-acetate extract of Z. zerumbet has protective effects against ethanol-induced brain damage and this is mediated through its antioxidant properties. Z. zerumbet extract protects against ethanol-induced brain damage via its antioxidant properties.

Automatic MRI 2D brain segmentation using graph searching technique.

PubMed

Pedoia, Valentina; Binaghi, Elisabetta

2013-09-01

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. Copyright © 2012 John Wiley & Sons, Ltd.

Early visual analysis tool using magnetoencephalography for treatment and recovery of neuronal dysfunction.

PubMed

Rasheed, Waqas; Neoh, Yee Yik; Bin Hamid, Nor Hisham; Reza, Faruque; Idris, Zamzuri; Tang, Tong Boon

2017-10-01

Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hawthorn extract reduces infarct volume and improves neurological score by reducing oxidative stress in rat brain following middle cerebral artery occlusion.

PubMed

Elango, Chinnasamy; Jayachandaran, Kasevan Sawaminathan; Niranjali Devaraj, S

2009-12-01

In our present investigation the neuroprotective effect of alcoholic extract of Hawthorn (Crataegus oxycantha) was evaluated against middle cerebral artery occlusion induced ischemia/reperfusion injury in rats. Male Sprague-Dawley rats were pretreated with 100 mg/kg body weight of the extract by oral gavage for 15 days. The middle cerebral artery was then occluded for 75 min followed by 24 h of reperfusion. The pretreated rats showed significantly improved neurological behavior with reduced brain infarct when compared to vehicle control rats. The glutathione level in brain was found to be significantly (p<0.05) low in vehicle control rats after 24 h of reperfusion when compared to sham operated animals. However, in Hawthorn extract pretreated rats the levels were found to be close to that of sham. Malondialdehyde levels in brain of sham and pretreated group were found to be significantly lower than the non-treated vehicle group (p<0.05). The nitric oxide levels in brain were measured and found to be significantly (p<0.05) higher in vehicle than in sham or extract treated rats. Our results suggest that Hawthorn extract which is a well known prophylactic for cardiac conditions may very well protect the brain against ischemia-reperfusion. The reduced brain damage and improved neurological behavior after 24 h of reperfusion in Hawthorn extract pretreated group may be attributed to its antioxidant property which restores glutathione levels, circumvents the increase in lipid peroxidation and nitric oxide levels thereby reducing peroxynitrite formation and free radical induced brain damage.

Development of representative magnetic resonance imaging-based atlases of the canine brain and evaluation of three methods for atlas-based segmentation.

PubMed

Milne, Marjorie E; Steward, Christopher; Firestone, Simon M; Long, Sam N; O'Brien, Terrence J; Moffat, Bradford A

2016-04-01

To develop representative MRI atlases of the canine brain and to evaluate 3 methods of atlas-based segmentation (ABS). 62 dogs without clinical signs of epilepsy and without MRI evidence of structural brain disease. The MRI scans from 44 dogs were used to develop 4 templates on the basis of brain shape (brachycephalic, mesaticephalic, dolichocephalic, and combined mesaticephalic and dolichocephalic). Atlas labels were generated by segmenting the brain, ventricular system, hippocampal formation, and caudate nuclei. The MRI scans from the remaining 18 dogs were used to evaluate 3 methods of ABS (manual brain extraction and application of a brain shape-specific template [A], automatic brain extraction and application of a brain shape-specific template [B], and manual brain extraction and application of a combined template [C]). The performance of each ABS method was compared by calculation of the Dice and Jaccard coefficients, with manual segmentation used as the gold standard. Method A had the highest mean Jaccard coefficient and was the most accurate ABS method assessed. Measures of overlap for ABS methods that used manual brain extraction (A and C) ranged from 0.75 to 0.95 and compared favorably with repeated measures of overlap for manual extraction, which ranged from 0.88 to 0.97. Atlas-based segmentation was an accurate and repeatable method for segmentation of canine brain structures. It could be performed more rapidly than manual segmentation, which should allow the application of computer-assisted volumetry to large data sets and clinical cases and facilitate neuroimaging research and disease diagnosis.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

PubMed

Magliaro, Chiara; Callara, Alejandro L; Vanello, Nicola; Ahluwalia, Arti

2017-01-01

To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual "gold-standard" generated for the competition.

A Brain-Machine-Brain Interface for Rewiring of Cortical Circuitry after Traumatic Brain Injury

DTIC Science & Technology

2012-09-01

Oral presentations (Dr. Nudo):  Invited Speaker, Neuroprosthetic tools for repair of the injured brain, American Society for Neurorehabilitation... Neuroprosthetic tools for repair of the injured brain, Neurobiology of Disease Course, University of Texas Health Science Center, Houston, Texas...Congress of NeuroRehabilitation, Melbourne, Australia, May 17, 2012.  Invited Speaker, Novel neuroprosthetic tools for repair of the injured brain

A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction.

PubMed

Chang, Huibin; Huang, Weimin; Wu, Chunlin; Huang, Su; Guan, Cuntai; Sekar, Sakthivel; Bhakoo, Kishore Kumar; Duan, Yuping

2017-03-01

Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L 0 regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

PubMed

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

Tool-use-associated sound in the evolution of language.

PubMed

Larsson, Matz

2015-09-01

Proponents of the motor theory of language evolution have primarily focused on the visual domain and communication through observation of movements. In the present paper, it is hypothesized that the production and perception of sound, particularly of incidental sound of locomotion (ISOL) and tool-use sound (TUS), also contributed. Human bipedalism resulted in rhythmic and more predictable ISOL. It has been proposed that this stimulated the evolution of musical abilities, auditory working memory, and abilities to produce complex vocalizations and to mimic natural sounds. Since the human brain proficiently extracts information about objects and events from the sounds they produce, TUS, and mimicry of TUS, might have achieved an iconic function. The prevalence of sound symbolism in many extant languages supports this idea. Self-produced TUS activates multimodal brain processing (motor neurons, hearing, proprioception, touch, vision), and TUS stimulates primate audiovisual mirror neurons, which is likely to stimulate the development of association chains. Tool use and auditory gestures involve motor processing of the forelimbs, which is associated with the evolution of vertebrate vocal communication. The production, perception, and mimicry of TUS may have resulted in a limited number of vocalizations or protowords that were associated with tool use. A new way to communicate about tools, especially when out of sight, would have had selective advantage. A gradual change in acoustic properties and/or meaning could have resulted in arbitrariness and an expanded repertoire of words. Humans have been increasingly exposed to TUS over millions of years, coinciding with the period during which spoken language evolved. ISOL and tool-use-related sound are worth further exploration.

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.

Manipulation complexity in primates coevolved with brain size and terrestriality

PubMed Central

Heldstab, Sandra A.; Kosonen, Zaida K.; Koski, Sonja E.; Burkart, Judith M.; van Schaik, Carel P.; Isler, Karin

2016-01-01

Humans occupy by far the most complex foraging niche of all mammals, built around sophisticated technology, and at the same time exhibit unusually large brains. To examine the evolutionary processes underlying these features, we investigated how manipulation complexity is related to brain size, cognitive test performance, terrestriality, and diet quality in a sample of 36 non-human primate species. We categorized manipulation bouts in food-related contexts into unimanual and bimanual actions, and asynchronous or synchronous hand and finger use, and established levels of manipulative complexity using Guttman scaling. Manipulation categories followed a cumulative ranking. They were particularly high in species that use cognitively challenging food acquisition techniques, such as extractive foraging and tool use. Manipulation complexity was also consistently positively correlated with brain size and cognitive test performance. Terrestriality had a positive effect on this relationship, but diet quality did not affect it. Unlike a previous study on carnivores, we found that, among primates, brain size and complex manipulations to acquire food underwent correlated evolution, which may have been influenced by terrestriality. Accordingly, our results support the idea of an evolutionary feedback loop between manipulation complexity and cognition in the human lineage, which may have been enhanced by increasingly terrestrial habits. PMID:27075921

The CONNECT project: Combining macro- and micro-structure.

PubMed

Assaf, Yaniv; Alexander, Daniel C; Jones, Derek K; Bizzi, Albero; Behrens, Tim E J; Clark, Chris A; Cohen, Yoram; Dyrby, Tim B; Huppi, Petra S; Knoesche, Thomas R; Lebihan, Denis; Parker, Geoff J M; Poupon, Cyril; Anaby, Debbie; Anwander, Alfred; Bar, Leah; Barazany, Daniel; Blumenfeld-Katzir, Tamar; De-Santis, Silvia; Duclap, Delphine; Figini, Matteo; Fischi, Elda; Guevara, Pamela; Hubbard, Penny; Hofstetter, Shir; Jbabdi, Saad; Kunz, Nicolas; Lazeyras, Francois; Lebois, Alice; Liptrot, Matthew G; Lundell, Henrik; Mangin, Jean-François; Dominguez, David Moreno; Morozov, Darya; Schreiber, Jan; Seunarine, Kiran; Nava, Simone; Poupon, Cyril; Riffert, Till; Sasson, Efrat; Schmitt, Benoit; Shemesh, Noam; Sotiropoulos, Stam N; Tavor, Ido; Zhang, Hui Gary; Zhou, Feng-Lei

2013-10-15

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome. Copyright © 2013 Elsevier Inc. All rights reserved.

Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Lindner, Dirk; Arlt, Felix; Ituna-Yudonago, Jean Fulbert; Chalopin, Claire

2018-03-01

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS. A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented. Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods. The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

Mapping brain activity in gradient-echo functional MRI using principal component analysis

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Development of a brain MRI-based hidden Markov model for dementia recognition.

PubMed

Chen, Ying; Pham, Tuan D

2013-01-01

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

NASA Astrophysics Data System (ADS)

Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo

2016-04-01

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Bharath, Halandur N; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Van Huffel, Sabine

2017-01-01

Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an initialization method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common initialization methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an initialization method, as it allows further enhancement of the sources during the NMF iterative procedure.

Effects of Treating Old Rats with an Aqueous Agaricus blazei Extract on Oxidative and Functional Parameters of the Brain Tissue and Brain Mitochondria

PubMed Central

de Sá-Nakanishi, Anacharis B.; Soares, Andréia A.; de Oliveira, Andrea Luiza; Fernando Comar, Jurandir; Peralta, Rosane M.; Bracht, Adelar

2014-01-01

Dysfunction of the mitochondrial respiratory chain and increased oxidative stress is a striking phenomenon in the brain of aged individuals. For this reason there has been a constant search for drugs and natural products able to prevent or at least to mitigate these problems. In the present study the effects of an aqueous extract of Agaricus blazei, a medicinal mushroom, on the oxidative state and on the functionality of mitochondria from the brain of old rats (21 months) were conducted. The extract was administered intragastrically during 21 days at doses of 200 mg/kg. The administration of the A. blazei extract was protective to the brain of old rats against oxidative stress by decreasing the lipid peroxidation levels and the reactive oxygen species content and by increasing the nonenzymic and enzymic antioxidant capacities. Administration of the A. blazei extract also increased the activity of several mitochondrial respiratory enzymes and, depending on the substrate, the mitochondrial coupled respiration. PMID:24876914

Effects of treating old rats with an aqueous Agaricus blazei extract on oxidative and functional parameters of the brain tissue and brain mitochondria.

PubMed

de Sá-Nakanishi, Anacharis B; Soares, Andréia A; de Oliveira, Andrea Luiza; Comar, Jurandir Fernando; Peralta, Rosane M; Bracht, Adelar

2014-01-01

Dysfunction of the mitochondrial respiratory chain and increased oxidative stress is a striking phenomenon in the brain of aged individuals. For this reason there has been a constant search for drugs and natural products able to prevent or at least to mitigate these problems. In the present study the effects of an aqueous extract of Agaricus blazei, a medicinal mushroom, on the oxidative state and on the functionality of mitochondria from the brain of old rats (21 months) were conducted. The extract was administered intragastrically during 21 days at doses of 200 mg/kg. The administration of the A. blazei extract was protective to the brain of old rats against oxidative stress by decreasing the lipid peroxidation levels and the reactive oxygen species content and by increasing the nonenzymic and enzymic antioxidant capacities. Administration of the A. blazei extract also increased the activity of several mitochondrial respiratory enzymes and, depending on the substrate, the mitochondrial coupled respiration.

Brain extraction in partial volumes T2*@7T by using a quasi-anatomic segmentation with bias field correction.

PubMed

Valente, João; Vieira, Pedro M; Couto, Carlos; Lima, Carlos S

2018-02-01

Poor brain extraction in Magnetic Resonance Imaging (MRI) has negative consequences in several types of brain post-extraction such as tissue segmentation and related statistical measures or pattern recognition algorithms. Current state of the art algorithms for brain extraction work on weighted T1 and T2, being not adequate for non-whole brain images such as the case of T2*FLASH@7T partial volumes. This paper proposes two new methods that work directly in T2*FLASH@7T partial volumes. The first is an improvement of the semi-automatic threshold-with-morphology approach adapted to incomplete volumes. The second method uses an improved version of a current implementation of the fuzzy c-means algorithm with bias correction for brain segmentation. Under high inhomogeneity conditions the performance of the first method degrades, requiring user intervention which is unacceptable. The second method performed well for all volumes, being entirely automatic. State of the art algorithms for brain extraction are mainly semi-automatic, requiring a correct initialization by the user and knowledge of the software. These methods can't deal with partial volumes and/or need information from atlas which is not available in T2*FLASH@7T. Also, combined volumes suffer from manipulations such as re-sampling which deteriorates significantly voxel intensity structures making segmentation tasks difficult. The proposed method can overcome all these difficulties, reaching good results for brain extraction using only T2*FLASH@7T volumes. The development of this work will lead to an improvement of automatic brain lesions segmentation in T2*FLASH@7T volumes, becoming more important when lesions such as cortical Multiple-Sclerosis need to be detected. Copyright © 2017 Elsevier B.V. All rights reserved.

Hyperforin modifies neuronal membrane properties in vivo.

PubMed

Eckert, Gunter P; Keller, Jan-Henning; Jourdan, Claudia; Karas, Michael; Volmer, Dietrich A; Schubert-Zsilavecz, Manfred; Müller, Walter E

2004-09-02

Hyperforin, the major active constituent of St. John Wort (SJW) extract, affects several neurotransmitter systems in the brain putatively by modulation of the physical state of neuronal membranes. Accordingly, we tested the effects of SJW extract and of hyperforin on the properties of murine brain membrane fluidity. Oral administration of SJW extract and of hyperforin sodium salt results in significant hyperforin brain levels. Treatment of mice with hyperforin leads to decreased annular- and bulk fluidity and increased acyl-chain flexibility of brain membranes. All hyperforin related changes of membrane properties were significantly correlated with the corresponding hyperforin brain levels. Our data emphasises a membrane interaction of hyperforin that possibly contributes to its pharmacological effects.

Indexing Anatomical Phrases in Neuro-Radiology Reports to the UMLS 2005AA

PubMed Central

Bashyam, Vijayaraghavan; Taira, Ricky K.

2005-01-01

This work describes a methodology to index anatomical phrases to the 2005AA release of the Unified Medical Language System (UMLS). A phrase chunking tool based on Natural Language Processing (NLP) was developed to identify semantically coherent phrases within medical reports. Using this phrase chunker, a set of 2,551 unique anatomical phrases was extracted from brain radiology reports. These phrases were mapped to the 2005AA release of the UMLS using a vector space model. Precision for the task of indexing unique phrases was 0.87. PMID:16778995

Detection and quantification of microRNA in cerebral microdialysate.

PubMed

Bache, Søren; Rasmussen, Rune; Rossing, Maria; Hammer, Niels Risør; Juhler, Marianne; Friis-Hansen, Lennart; Nielsen, Finn Cilius; Møller, Kirsten

2015-05-07

Secondary brain injury accounts for a major part of the morbidity and mortality in patients with spontaneous aneurysmal subarachnoid hemorrhage (SAH), but the pathogenesis and pathophysiology remain controversial. MicroRNAs (miRNAs) are important posttranscriptional regulators of complementary mRNA targets and have been implicated in the pathophysiology of other types of acute brain injury. Cerebral microdialysis is a promising tool to investigate these mechanisms. We hypothesized that miRNAs would be present in human cerebral microdialysate. RNA was extracted and miRNA profiles were established using high throughput real-time quantification PCR on the following material: 1) Microdialysate sampled in vitro from A) a solution of total RNA extracted from human brain, B) cerebrospinal fluid (CSF) from a neurologically healthy patient, and C) a patient with SAH; and 2) cerebral microdialysate and CSF sampled in vivo from two patients with SAH. MiRNAs were categorized according to their relative recovery (RR) and a pathway analysis was performed for miRNAs exhibiting a high RR in vivo. Seventy-one of the 160 miRNAs detected in CSF were also found in in vivo microdialysate from SAH patients. Furthermore specific miRNAs consistently exhibited either a high or low RR in both in vitro and in vivo microdialysate. Analysis of repeatability showed lower analytical variation in microdialysate than in CSF. MiRNAs are detectable in cerebral microdialysate; a large group of miRNAs consistently showed a high RR in cerebral microdialysate. Measurement of cerebral interstitial miRNA concentrations may aid in the investigation of secondary brain injury in neurocritical conditions.

A human scFv antibody that targets and neutralizes high molecular weight pathogenic amyloid-β oligomers.

PubMed

Sebollela, Adriano; Cline, Erika N; Popova, Izolda; Luo, Kevin; Sun, Xiaoxia; Ahn, Jay; Barcelos, Milena A; Bezerra, Vanessa N; Lyra E Silva, Natalia M; Patel, Jason; Pinheiro, Nathalia R; Qin, Lei A; Kamel, Josette M; Weng, Anthea; DiNunno, Nadia; Bebenek, Adrian M; Velasco, Pauline T; Viola, Kirsten L; Lacor, Pascale N; Ferreira, Sergio T; Klein, William L

2017-07-03

Brain accumulation of soluble oligomers of the amyloid-β peptide (AβOs) is increasingly considered a key early event in the pathogenesis of Alzheimer's disease (AD). A variety of AβO species have been identified, both in vitro and in vivo, ranging from dimers to 24mers and higher order oligomers. However, there is no consensus in the literature regarding which AβO species are most germane to AD pathogenesis. Antibodies capable of specifically recognizing defined subpopulations of AβOs would be a valuable asset in the identification, isolation, and characterization of AD-relevant AβO species. Here, we report the characterization of a human single chain antibody fragment (scFv) denoted NUsc1, one of a number of scFvs we have identified that stringently distinguish AβOs from both monomeric and fibrillar Aβ. NUsc1 readily detected AβOs previously bound to dendrites in cultured hippocampal neurons. In addition, NUsc1 blocked AβO binding and reduced AβO-induced neuronal oxidative stress and tau hyperphosphorylation in cultured neurons. NUsc1 further distinguished brain extracts from AD-transgenic mice from wild type (WT) mice, and detected endogenous AβOs in fixed AD brain tissue and AD brain extracts. Biochemical analyses indicated that NUsc1 targets a subpopulation of AβOs with apparent molecular mass greater than 50 kDa. Results indicate that NUsc1 targets a particular AβO species relevant to AD pathogenesis, and suggest that NUsc1 may constitute an effective tool for AD diagnostics and therapeutics. © 2017 International Society for Neurochemistry.

Learning-based meta-algorithm for MRI brain extraction.

PubMed

Shi, Feng; Wang, Li; Gilmore, John H; Lin, Weili; Shen, Dinggang

2011-01-01

Multiple-segmentation-and-fusion method has been widely used for brain extraction, tissue segmentation, and region of interest (ROI) localization. However, such studies are hindered in practice by their computational complexity, mainly coming from the steps of template selection and template-to-subject nonlinear registration. In this study, we address these two issues and propose a novel learning-based meta-algorithm for MRI brain extraction. Specifically, we first use exemplars to represent the entire template library, and assign the most similar exemplar to the test subject. Second, a meta-algorithm combining two existing brain extraction algorithms (BET and BSE) is proposed to conduct multiple extractions directly on test subject. Effective parameter settings for the meta-algorithm are learned from the training data and propagated to subject through exemplars. We further develop a level-set based fusion method to combine multiple candidate extractions together with a closed smooth surface, for obtaining the final result. Experimental results show that, with only a small portion of subjects for training, the proposed method is able to produce more accurate and robust brain extraction results, at Jaccard Index of 0.956 +/- 0.010 on total 340 subjects under 6-fold cross validation, compared to those by the BET and BSE even using their best parameter combinations.

Topodynamics of metastable brains

NASA Astrophysics Data System (ADS)

Tozzi, Arturo; Peters, James F.; Fingelkurts, Andrew A.; Fingelkurts, Alexander A.; Marijuán, Pedro C.

2017-07-01

The brain displays both the anatomical features of a vast amount of interconnected topological mappings as well as the functional features of a nonlinear, metastable system at the edge of chaos, equipped with a phase space where mental random walks tend towards lower energetic basins. Nevertheless, with the exception of some advanced neuro-anatomic descriptions and present-day connectomic research, very few studies have been addressing the topological path of a brain embedded or embodied in its external and internal environment. Herein, by using new formal tools derived from algebraic topology, we provide an account of the metastable brain, based on the neuro-scientific model of Operational Architectonics of brain-mind functioning. We introduce a ;topodynamic; description that shows how the relationships among the countless intertwined spatio-temporal levels of brain functioning can be assessed in terms of projections and mappings that take place on abstract structures, equipped with different dimensions, curvatures and energetic constraints. Such a topodynamical approach, apart from providing a biologically plausible model of brain function that can be operationalized, is also able to tackle the issue of a long-standing dichotomy: it throws indeed a bridge between the subjective, immediate datum of the naïve complex of sensations and mentations and the objective, quantitative, data extracted from experimental neuro-scientific procedures. Importantly, it opens the door to a series of new predictions and future directions of advancement for neuroscientific research.

Non-invasive imaging of oxygen extraction fraction in adults with sickle cell anaemia.

PubMed

Jordan, Lori C; Gindville, Melissa C; Scott, Allison O; Juttukonda, Meher R; Strother, Megan K; Kassim, Adetola A; Chen, Sheau-Chiann; Lu, Hanzhang; Pruthi, Sumit; Shyr, Yu; Donahue, Manus J

2016-03-01

Sickle cell anaemia is a monogenetic disorder with a high incidence of stroke. While stroke screening procedures exist for children with sickle cell anaemia, no accepted screening procedures exist for assessing stroke risk in adults. The purpose of this study is to use novel magnetic resonance imaging methods to evaluate physiological relationships between oxygen extraction fraction, cerebral blood flow, and clinical markers of cerebrovascular impairment in adults with sickle cell anaemia. The specific goal is to determine to what extent elevated oxygen extraction fraction may be uniquely present in patients with higher levels of clinical impairment and therefore may represent a candidate biomarker of stroke risk. Neurological evaluation, structural imaging, and the non-invasive T2-relaxation-under-spin-tagging magnetic resonance imaging method were applied in sickle cell anaemia (n = 34) and healthy race-matched control (n = 11) volunteers without sickle cell trait to assess whole-brain oxygen extraction fraction, cerebral blood flow, degree of vasculopathy, severity of anaemia, and presence of prior infarct; findings were interpreted in the context of physiological models. Cerebral blood flow and oxygen extraction fraction were elevated (P < 0.05) in participants with sickle cell anaemia (n = 27) not receiving monthly blood transfusions (interquartile range cerebral blood flow = 46.2-56.8 ml/100 g/min; oxygen extraction fraction = 0.39-0.50) relative to controls (interquartile range cerebral blood flow = 40.8-46.3 ml/100 g/min; oxygen extraction fraction = 0.33-0.38). Oxygen extraction fraction (P < 0.0001) but not cerebral blood flow was increased in participants with higher levels of clinical impairment. These data provide support for T2-relaxation-under-spin-tagging being able to quickly and non-invasively detect elevated oxygen extraction fraction in individuals with sickle cell anaemia with higher levels of clinical impairment. Our results support the premise that magnetic resonance imaging-based assessment of elevated oxygen extraction fraction might be a viable screening tool for evaluating stroke risk in adults with sickle cell anaemia. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Comparison of continuously acquired resting state and extracted analogues from active tasks.

PubMed

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

2015-10-01

Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R(2) ) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

HPLC-MS-MS Determination of ZCZ-011, A Novel Pharmacological Tool for Investigation of the Cannabinoid Receptor in Mouse Brain Using Clean Screen FASt™ Column Extraction.

PubMed

Poklis, Justin L; Clay, Deborah J; Ignatowska-Jankowska, Bogna M; Zanato, Chiara; Ross, Ruth A; Greig, Iain R; Abdullah, Rehab A; Mustafa, Mohammed A; Lichtman, Aron H; Poklis, Alphonse

2015-06-01

A high-performance liquid chromatography tandem mass spectrometry method was developed for the detection and quantification of 6-methyl-3-(2-nitro-1-(thiophen-2-yl)propyl)-2-phenyl-1H-indole (ZCZ-011) using 2-phenylindole as the internal standard (ISTD). ZCZ-011 was synthesized as a possible positive allosteric modulator with the CB1 cannabinoid receptor. The analytical method employs a rapid extraction technique using Clean Screen FASt™ columns with a Positive Pressure Manifold. FASt™ columns were originally developed for urine drug analysis but we have successfully adapted them to the extraction of brain tissue. Chromatographic separation was performed on a Restek Allure Biphenyl 5 µ, 100 × 3.2 mm column (Bellefonte, PA). The mobile phase consisted of 1:9 deionized water with 10 mmol ammonium acetate and 0.1% formic acid-methanol. The following transition ions (m/z) were monitored for ZCZ-011: 363 > 207 and 363 > 110 and for the ISTD: 194 > 165 and 194 > 89. The FASt™ columns lowered and stabilized the ion suppression over the linear range of the assay (40-4,000 ng/g). The method was evaluated for recovery, ion suppression, accuracy/bias, intraday and interday precision, bench-top stability, freeze-thaw and post-preparative stability. The method was successfully applied to brain tissue from C57BL/6J mice that received intraperitoneal (i.p.) injections with 40 mg/kg of ZCZ-011 or vehicle. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

PubMed Central

Höller, Yvonne; Bergmann, Jürgen; Thomschewski, Aljoscha; Kronbichler, Martin; Höller, Peter; Crone, Julia S.; Schmid, Elisabeth V.; Butz, Kevin; Nardone, Raffaele; Trinka, Eugen

2013-01-01

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results. PMID:24282545

Studies on brain biogenic amines in methanolic extract of Cuscuta reflexa Roxb. and Corchorus olitorius Linn. seed treated mice.

PubMed

Gupta, Malaya; Mazumder, Upal Kanti; Pal, Dilipkumar; Bhattacharya, Shiladitya; Chakrabarty, Sumit

2003-01-01

The methanolic extract of both Cuscuta reflexa stem and Corchorus olitorius seed showed marked protection against convulsion induced by chemoconvulsive agents in mice. The catecholamines contained were significantly increased in the processed extract treated mice. The amount of GABA, which is most likely to be involved in seizure activity, was increased significantly in mice brain after a six week treatment. Results of the present study revealed that both the processed extracts showed a significant anticonvulsive property by altering the level of catecholamines and brain amino acids in mice.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Statistical parametric mapping of stimuli-evoked changes in quantitative blood flow using extended-focus optical coherence microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marchand, Paul J.; Bouwens, Arno; Shamaei, Vincent; Nguyen, David; Extermann, Jerome; Bolmont, Tristan; Lasser, Theo

2016-03-01

Magnetic Resonance Imaging has revolutionised our understanding of brain function through its ability to image human cerebral structures non-invasively over the entire brain. By exploiting the different magnetic properties of oxygenated and deoxygenated blood, functional MRI can indirectly map areas undergoing neural activation. Alongside the development of fMRI, powerful statistical tools have been developed in an effort to shed light on the neural pathways involved in processing of sensory and cognitive information. In spite of the major improvements made in fMRI technology, the obtained spatial resolution of hundreds of microns prevents MRI in resolving and monitoring processes occurring at the cellular level. In this regard, Optical Coherence Microscopy is an ideal instrumentation as it can image at high spatio-temporal resolution. Moreover, by measuring the mean and the width of the Doppler spectra of light scattered by moving particles, OCM allows extracting the axial and lateral velocity components of red blood cells. The ability to assess quantitatively total blood velocity, as opposed to classical axial velocity Doppler OCM, is of paramount importance in brain imaging as a large proportion of cortical vascular is oriented perpendicularly to the optical axis. We combine here quantitative blood flow imaging with extended-focus Optical Coherence Microscopy and Statistical Parametric Mapping tools to generate maps of stimuli-evoked cortical hemodynamics at the capillary level.

Schaltenbrand-Wahren-Talairach-Tournoux brain atlas registration

NASA Astrophysics Data System (ADS)

Nowinski, Wieslaw L.; Fang, Anthony; Nguyen, Bonnie T.

1995-04-01

The CIeMed electronic brain atlas system contains electronic versions of multiple paper brain atlases with 3D extensions; some other 3D brain atlases are under development. Its primary goal is to provide automatic labeling and quantification of brains. The atlas data are digitized, enhanced, color coded, labeled, and organized into volumes. The atlas system provides several tools for registration, 3D display and real-time manipulation, object extraction/editing, quantification, image processing and analysis, reformatting, anatomical index operations, and file handling. The two main stereotactic atlases provided by the system are electronic and enhanced versions of Atlas of Stereotaxy of the Human Brain by Schaltenbrand and Wahren and Co-Planar Stereotactic Atlas of the Human Brain by Talairach and Tournoux. Each of these atlases has its own strengths and their combination has several advantages. First, a complementary information is merged and provided to the user. Second, the user can register data with a single atlas only, as the Schaltenbrand-Wahren-Talairach-Tournoux registration is data-independent. And last but not least, a direct registration of the Schaltenbrand-Wahren microseries with MRI data may not be feasible, since cerebral deep structures are usually not clearly discernible on MRI images. This paper addresses registration of the Schaltenbrand- Wahren and Talairach-Tournoux brain atlases. A modified proportional grid system transformation is introduced and suitable sets of landmarks identifiable in both atlases are defined. The accuracy of registration is discussed. A continuous navigation in the multi- atlas/patient data space is presented.

Reproducibility and discriminability of brain patterns of semantic categories enhanced by congruent audiovisual stimuli.

PubMed

Li, Yuanqing; Wang, Guangyi; Long, Jinyi; Yu, Zhuliang; Huang, Biao; Li, Xiaojian; Yu, Tianyou; Liang, Changhong; Li, Zheng; Sun, Pei

2011-01-01

One of the central questions in cognitive neuroscience is the precise neural representation, or brain pattern, associated with a semantic category. In this study, we explored the influence of audiovisual stimuli on the brain patterns of concepts or semantic categories through a functional magnetic resonance imaging (fMRI) experiment. We used a pattern search method to extract brain patterns corresponding to two semantic categories: "old people" and "young people." These brain patterns were elicited by semantically congruent audiovisual, semantically incongruent audiovisual, unimodal visual, and unimodal auditory stimuli belonging to the two semantic categories. We calculated the reproducibility index, which measures the similarity of the patterns within the same category. We also decoded the semantic categories from these brain patterns. The decoding accuracy reflects the discriminability of the brain patterns between two categories. The results showed that both the reproducibility index of brain patterns and the decoding accuracy were significantly higher for semantically congruent audiovisual stimuli than for unimodal visual and unimodal auditory stimuli, while the semantically incongruent stimuli did not elicit brain patterns with significantly higher reproducibility index or decoding accuracy. Thus, the semantically congruent audiovisual stimuli enhanced the within-class reproducibility of brain patterns and the between-class discriminability of brain patterns, and facilitate neural representations of semantic categories or concepts. Furthermore, we analyzed the brain activity in superior temporal sulcus and middle temporal gyrus (STS/MTG). The strength of the fMRI signal and the reproducibility index were enhanced by the semantically congruent audiovisual stimuli. Our results support the use of the reproducibility index as a potential tool to supplement the fMRI signal amplitude for evaluating multimodal integration.

Reproducibility and Discriminability of Brain Patterns of Semantic Categories Enhanced by Congruent Audiovisual Stimuli

PubMed Central

Long, Jinyi; Yu, Zhuliang; Huang, Biao; Li, Xiaojian; Yu, Tianyou; Liang, Changhong; Li, Zheng; Sun, Pei

2011-01-01

One of the central questions in cognitive neuroscience is the precise neural representation, or brain pattern, associated with a semantic category. In this study, we explored the influence of audiovisual stimuli on the brain patterns of concepts or semantic categories through a functional magnetic resonance imaging (fMRI) experiment. We used a pattern search method to extract brain patterns corresponding to two semantic categories: “old people” and “young people.” These brain patterns were elicited by semantically congruent audiovisual, semantically incongruent audiovisual, unimodal visual, and unimodal auditory stimuli belonging to the two semantic categories. We calculated the reproducibility index, which measures the similarity of the patterns within the same category. We also decoded the semantic categories from these brain patterns. The decoding accuracy reflects the discriminability of the brain patterns between two categories. The results showed that both the reproducibility index of brain patterns and the decoding accuracy were significantly higher for semantically congruent audiovisual stimuli than for unimodal visual and unimodal auditory stimuli, while the semantically incongruent stimuli did not elicit brain patterns with significantly higher reproducibility index or decoding accuracy. Thus, the semantically congruent audiovisual stimuli enhanced the within-class reproducibility of brain patterns and the between-class discriminability of brain patterns, and facilitate neural representations of semantic categories or concepts. Furthermore, we analyzed the brain activity in superior temporal sulcus and middle temporal gyrus (STS/MTG). The strength of the fMRI signal and the reproducibility index were enhanced by the semantically congruent audiovisual stimuli. Our results support the use of the reproducibility index as a potential tool to supplement the fMRI signal amplitude for evaluating multimodal integration. PMID:21750692

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

PubMed Central

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups. Methods The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods. PMID:29213908

Development of a versatile enrichment analysis tool reveals associations between the maternal brain and mental health disorders, including autism

PubMed Central

2013-01-01

Background A recent study of lateral septum (LS) suggested a large number of autism-related genes with altered expression in the postpartum state. However, formally testing the findings for enrichment of autism-associated genes proved to be problematic with existing software. Many gene-disease association databases have been curated which are not currently incorporated in popular, full-featured enrichment tools, and the use of custom gene lists in these programs can be difficult to perform and interpret. As a simple alternative, we have developed the Modular Single-set Enrichment Test (MSET), a minimal tool that enables one to easily evaluate expression data for enrichment of any conceivable gene list of interest. Results The MSET approach was validated by testing several publicly available expression data sets for expected enrichment in areas of autism, attention deficit hyperactivity disorder (ADHD), and arthritis. Using nine independent, unique autism gene lists extracted from association databases and two recent publications, a striking consensus of enrichment was detected within gene expression changes in LS of postpartum mice. A network of 160 autism-related genes was identified, representing developmental processes such as synaptic plasticity, neuronal morphogenesis, and differentiation. Additionally, maternal LS displayed enrichment for genes associated with bipolar disorder, schizophrenia, ADHD, and depression. Conclusions The transition to motherhood includes the most fundamental social bonding event in mammals and features naturally occurring changes in sociability. Some individuals with autism, schizophrenia, or other mental health disorders exhibit impaired social traits. Genes involved in these deficits may also contribute to elevated sociability in the maternal brain. To date, this is the first study to show a significant, quantitative link between the maternal brain and mental health disorders using large scale gene expression data. Thus, the postpartum brain may provide a novel and promising platform for understanding the complex genetics of improved sociability that may have direct relevance for multiple psychiatric illnesses. This study also provides an important new tool that fills a critical analysis gap and makes evaluation of enrichment using any database of interest possible with an emphasis on ease of use and methodological transparency. PMID:24245670

Development of a versatile enrichment analysis tool reveals associations between the maternal brain and mental health disorders, including autism.

PubMed

Eisinger, Brian E; Saul, Michael C; Driessen, Terri M; Gammie, Stephen C

2013-11-19

A recent study of lateral septum (LS) suggested a large number of autism-related genes with altered expression in the postpartum state. However, formally testing the findings for enrichment of autism-associated genes proved to be problematic with existing software. Many gene-disease association databases have been curated which are not currently incorporated in popular, full-featured enrichment tools, and the use of custom gene lists in these programs can be difficult to perform and interpret. As a simple alternative, we have developed the Modular Single-set Enrichment Test (MSET), a minimal tool that enables one to easily evaluate expression data for enrichment of any conceivable gene list of interest. The MSET approach was validated by testing several publicly available expression data sets for expected enrichment in areas of autism, attention deficit hyperactivity disorder (ADHD), and arthritis. Using nine independent, unique autism gene lists extracted from association databases and two recent publications, a striking consensus of enrichment was detected within gene expression changes in LS of postpartum mice. A network of 160 autism-related genes was identified, representing developmental processes such as synaptic plasticity, neuronal morphogenesis, and differentiation. Additionally, maternal LS displayed enrichment for genes associated with bipolar disorder, schizophrenia, ADHD, and depression. The transition to motherhood includes the most fundamental social bonding event in mammals and features naturally occurring changes in sociability. Some individuals with autism, schizophrenia, or other mental health disorders exhibit impaired social traits. Genes involved in these deficits may also contribute to elevated sociability in the maternal brain. To date, this is the first study to show a significant, quantitative link between the maternal brain and mental health disorders using large scale gene expression data. Thus, the postpartum brain may provide a novel and promising platform for understanding the complex genetics of improved sociability that may have direct relevance for multiple psychiatric illnesses. This study also provides an important new tool that fills a critical analysis gap and makes evaluation of enrichment using any database of interest possible with an emphasis on ease of use and methodological transparency.

Defining ischemic burden after traumatic brain injury using 15O PET imaging of cerebral physiology.

PubMed

Coles, Jonathan P; Fryer, Tim D; Smielewski, Peter; Rice, Kenneth; Clark, John C; Pickard, John D; Menon, David K

2004-02-01

Whereas postmortem ischemic damage is common in head injury, antemortem demonstration of ischemia has proven to be elusive. Although 15O positron emission tomography may be useful in this area, the technique has traditionally analyzed data within regions of interest (ROIs) to improve statistical accuracy. In head injury, such techniques are limited because of the lack of a priori knowledge regarding the location of ischemia, coexistence of hyperaemia, and difficulty in defining ischemic cerebral blood flow (CBF) and cerebral oxygen metabolism (CMRO2) levels. We report a novel method for defining disease pathophysiology following head injury. Voxel-based approaches are used to define the distribution of oxygen extraction fraction (OEF) across the entire brain; the standard deviation of this distribution provides a measure of the variability of OEF. These data are also used to integrate voxels above a threshold OEF value to produce an ROI based upon coherent physiology rather than spatial contiguity (the ischemic brain volume; IBV). However, such approaches may suffer from poor statistical accuracy, particularly in regions with low blood flow. The magnitude of these errors has been assessed in modeling experiments using the Hoffman brain phantom and modified control datasets. We conclude that this technique is a valid and useful tool for quantifying ischemic burden after traumatic brain injury.

Reversal of P-glycoprotein overexpression by Ginkgo biloba extract in the brains of pentylenetetrazole-kindled and phenytoin-treated mice.

PubMed

Zhang, Ce; Fan, Qing; Chen, Shu-Liang; Ma, Hui

2015-08-01

The purpose of this study was to investigate the combined effects of Ginkgo biloba extract and phenytoin (PHT) sodium as a dose regimen simulating the clinical treatment of patients with epilepsy, on P-glycoprotein (P-GP) overexpression in a pentylenetetrazole-kindled mouse model of epilepsy. Epilepsy was induced by intraperitoneal administration of pentylenetetrazole (40 mg/kg) for 7 days followed by intragastric administration of PHT (40 mg/kg) for 14 days. Thirty mice that developed seizures were randomly divided into three groups and administered PHT as well as the following treatments: saline (negative control); verapamil (20 mg/kg, positive control); and G. biloba (30 mg/kg). Seizure severity was recorded 30 minutes after treatment on Day 4 of drug administration, after which the mice were euthanized, and their brains isolated. Western blots and immunohistochemistry were performed to analyze the expression of P-GP and caspase-3, respectively, in the brain tissue. High-performance liquid chromatography was used to measure the concentrations of PHT in the brains of the treated mice. After 4 consecutive days of treatment, the seizure severity in the mice in the G. biloba extract group was more significantly reduced than the seizure severity in the saline control group, and a significant difference was observed between the G. biloba extract and verapamil control groups (p < 0.05). P-GP expression in the brain more significantly decreased in the mice treated with G. biloba extract and verapamil than it did in the saline-treated control group (p < 0.05). Compared with the saline-treated control group, the mice treated with G. biloba extract and verapamil showed significantly increased brain PHT concentrations (p < 0.05). Furthermore, caspase-3 expression in the brain tissue of the G. biloba extract group was significantly lower than that in the vehicle control group (p < 0.05); this finding demonstrated the neuroprotective effects of G. biloba. Therefore, this study showed that treatment with G. biloba extract in combination with PHT prevented the upregulation of P-GP expression in mice. Moreover, G. biloba extract decreased seizure severity in pentylenetetrazole-kindled/PHT-treated mice through a mechanism that might be related to the reduction of P-GP expression in the brain. Copyright © 2015. Published by Elsevier Taiwan.

Does the Golem Feel Pain? Moral Instincts and Ethical Dilemmas Concerning Suffering and the Brain.

PubMed

Devor, Marshall; Rappaport, Isabelle; Rappaport, Z Harry

2015-07-01

Pain has variously been used as a means of punishment, extracting information, or testing commitment, as a tool for education and social control, as a commodity for sacrifice, and as a draw for sport and entertainment. Attitudes concerning these uses have undergone major changes in the modern era. Normative convictions on what is right and wrong are generally attributed to religious tradition or to secular-humanist reasoning. Here, we elaborate the perspective that ethical choices concerning pain have much earlier roots that are based on instincts and brain-seated empathetic responses. They are fundamentally a function of brain circuitry shaped by processes of Darwinian evolution. Social convention and other environmental influences, with their idiosyncrasies, are a more recent, ever-changing overlay. We close with an example in which details on the neurobiology of pain processing, specifically the question of where in the brain the experience of pain is generated, affect decision making in end-of-life situations. By separating innate biological substrates from culturally imposed attitudes (memes), we may arrive at a more reasoned approach to a morality of pain prevention. © 2014 World Institute of Pain.

Quantitative assessment of brain tissue oxygenation in porcine models of cardiac arrest and cardiopulmonary resuscitation using hyperspectral near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Lotfabadi, Shahin S.; Toronov, Vladislav; Ramadeen, Andrew; Hu, Xudong; Kim, Siwook; Dorian, Paul; Hare, Gregory M. T.

2014-03-01

Near-infrared spectroscopy (NIRS) is a non-invasive tool to measure real-time tissue oxygenation in the brain. In an invasive animal experiment we were able to directly compare non-invasive NIRS measurements on the skull with invasive measurements directly on the brain dura matter. We used a broad-band, continuous-wave hyper-spectral approach to measure tissue oxygenation in the brain of pigs under the conditions of cardiac arrest, cardiopulmonary resuscitation (CPR), and defibrillation. An additional purpose of this research was to find a correlation between mortality due to cardiac arrest and inadequacy of the tissue perfusion during attempts at resuscitation. Using this technique we measured the changes in concentrations of oxy-hemoglobin [HbO2] and deoxy-hemoglobin [HHb] to quantify the tissue oxygenation in the brain. We also extracted cytochrome c oxidase changes Δ[Cyt-Ox] under the same conditions to determine increase or decrease in cerebral oxygen delivery. In this paper we proved that applying CPR, [HbO2] concentration and tissue oxygenation in the brain increase while [HHb] concentration decreases which was not possible using other measurement techniques. We also discovered a similar trend in changes of both [Cyt-Ox] concentration and tissue oxygen saturation (StO2). Both invasive and non-invasive measurements showed similar results.

Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

PubMed

Lohse, Christian; Bassett, Danielle S; Lim, Kelvin O; Carlson, Jean M

2014-10-01

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

PubMed Central

Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

2013-01-01

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

SCoT: a Python toolbox for EEG source connectivity.

PubMed

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

SCoT: a Python toolbox for EEG source connectivity

PubMed Central

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R.

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT. PMID:24653694

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

PubMed

Kyathanahally, Sreenath P; Mocioiu, Victor; Pedrosa de Barros, Nuno; Slotboom, Johannes; Wright, Alan J; Julià-Sapé, Margarida; Arús, Carles; Kreis, Roland

2018-05-01

To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system. Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Traumatic brain injury and co-occurring problems in prison populations: A systematic review.

PubMed

O'Rourke, Conall; Linden, Mark A; Lohan, Maria; Bates-Gaston, Jackie

2016-01-01

A growing body of epidemiological research suggests high rates of traumatic brain injury (TBI) in prisoners. The aim of this review is to systematically explore the literature surrounding the rates of TBI and their co-occurrences in a prison population. Six electronic databases were systematically searched for articles published between 1980-2014. Studies were screened for inclusion based on pre-determined criteria by two researchers who independently performed data extraction. Study quality was appraised based on a modified quality assessment tool. Twenty-six studies were included in this review. Quality assessment ranged from 20% (poor) to 80% (good), with an overall average of 60%. Twenty-four papers included TBI prevalence rates, which ranged from 5.69-88%. Seventeen studies explored co-occurring factors including rates of aggression (n = 7), substance abuse (n = 9), anxiety and depression (n = 5), neurocognitive deficits (n = 4) and psychiatric conditions (n = 3). The high degree of variation in TBI rates may be attributed to the inconsistent way in which TBI was measured, with only seven studies using valid and reliable screening tools. Additionally, gaps in the literature surrounding personality outcomes in prisoners with TBI, female prisoners with TBI and qualitative outcomes were found.

Analysis of intracranial pressure: past, present, and future.

PubMed

Di Ieva, Antonio; Schmitz, Erika M; Cusimano, Michael D

2013-12-01

The monitoring of intracranial pressure (ICP) is an important tool in medicine for its ability to portray the brain's compliance status. The bedside monitor displays the ICP waveform and intermittent mean values to guide physicians in the management of patients, particularly those having sustained a traumatic brain injury. Researchers in the fields of engineering and physics have investigated various mathematical analysis techniques applicable to the waveform in order to extract additional diagnostic and prognostic information, although they largely remain limited to research applications. The purpose of this review is to present the current techniques used to monitor and interpret ICP and explore the potential of using advanced mathematical techniques to provide information about system perturbations from states of homeostasis. We discuss the limits of each proposed technique and we propose that nonlinear analysis could be a reliable approach to describe ICP signals over time, with the fractal dimension as a potential predictive clinically meaningful biomarker. Our goal is to stimulate translational research that can move modern analysis of ICP using these techniques into widespread practical use, and to investigate to the clinical utility of a tool capable of simplifying multiple variables obtained from various sensors.

Transport of nanoparticles through the blood-brain barrier for imaging and therapeutic applications.

PubMed

Shilo, Malka; Motiei, Menachem; Hana, Panet; Popovtzer, Rachela

2014-02-21

A critical problem in the treatment of neurodegenerative disorders and diseases, such as Alzheimer's and Parkinson's, is the incapability to overcome the restrictive mechanism of the blood-brain barrier (BBB) and to deliver important therapeutic agents to the brain. During the last decade, nanoparticles have gained attention as promising drug delivery agents that can transport across the BBB and increase the uptake of appropriate drugs in the brain. In this study we have developed insulin-targeted gold nanoparticles (INS-GNPs) and investigated quantitatively the amount of INS-GNPs that cross the BBB by the receptor-mediated endocytosis process. For this purpose, INS-GNPs and control GNPs were injected into the tail vein of male BALB/c mice. Major organs were then extracted and a blood sample was taken from the mice, and thereafter analyzed for gold content by flame atomic absorption spectroscopy. Results show that two hours post-intravenous injection, the amount of INS-GNPs found in mouse brains is over 5 times greater than that of the control, untargeted GNPs. Results of further experimentation on a rat model show that INS-GNPs can also serve as CT contrast agents to highlight specific brain regions in which they accumulate. Due to the fact that they can overcome the restrictive mechanism of the BBB, this approach could be a potentially valuable tool, helping to confront the great challenge of delivering important imaging and therapeutic agents to the brain for detection and treatment of neurodegenerative disorders and diseases.

Transport of nanoparticles through the blood-brain barrier for imaging and therapeutic applications

NASA Astrophysics Data System (ADS)

Shilo, Malka; Motiei, Menachem; Hana, Panet; Popovtzer, Rachela

2014-01-01

A critical problem in the treatment of neurodegenerative disorders and diseases, such as Alzheimer's and Parkinson's, is the incapability to overcome the restrictive mechanism of the blood-brain barrier (BBB) and to deliver important therapeutic agents to the brain. During the last decade, nanoparticles have gained attention as promising drug delivery agents that can transport across the BBB and increase the uptake of appropriate drugs in the brain. In this study we have developed insulin-targeted gold nanoparticles (INS-GNPs) and investigated quantitatively the amount of INS-GNPs that cross the BBB by the receptor-mediated endocytosis process. For this purpose, INS-GNPs and control GNPs were injected into the tail vein of male BALB/c mice. Major organs were then extracted and a blood sample was taken from the mice, and thereafter analyzed for gold content by flame atomic absorption spectroscopy. Results show that two hours post-intravenous injection, the amount of INS-GNPs found in mouse brains is over 5 times greater than that of the control, untargeted GNPs. Results of further experimentation on a rat model show that INS-GNPs can also serve as CT contrast agents to highlight specific brain regions in which they accumulate. Due to the fact that they can overcome the restrictive mechanism of the BBB, this approach could be a potentially valuable tool, helping to confront the great challenge of delivering important imaging and therapeutic agents to the brain for detection and treatment of neurodegenerative disorders and diseases.

BEaST: brain extraction based on nonlocal segmentation technique.

PubMed

Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir; Manjón, José V; Leung, Kelvin K; Guizard, Nicolas; Wassef, Shafik N; Østergaard, Lasse Riis; Collins, D Louis

2012-02-01

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Copyright © 2011 Elsevier Inc. All rights reserved.

Voxels in the Brain: Neuroscience, Informatics and Changing Notions of Objectivity.

ERIC Educational Resources Information Center

Beaulieu, Anne

2001-01-01

Examines a subset of tools (atlases of the brain) developed in the Human Brain Project (HBP) in order to understand how the use of these tools changes the practice of science. Discusses the redefinition of what constitutes 'objective' neuroscientific knowledge according to both technological possibilities built into these tools and the constraints…

Business change process, creativity and the brain: a practitioner's reflective account with suggestions for future research.

PubMed

Yeats, Rowena M; Yeats, Martyn F

2007-11-01

Resolution of a critical organizational problem requires the use of carefully selected techniques. This is the work of a management consultant: facilitating a business change process in an organizational setting. Here, an account is provided of a practitioner's reflections on one such case study that demonstrates a structure for a business change process. The reflective account highlights certain affective states and social behaviors that were extracted from participants during the business change process. These affective states and social behaviors are mediated by specific neural networks in the brain that are activated during organizational intervention. By breaking down the process into the affective states and social behaviors highlighted, cognitive neuroscience can be a useful tool for investigating the neural substrates of such intervention. By applying a cognitive neuroscience approach to examine organizational change, it is possible to converge on a greater understanding of the neural substrates of everyday social behavior.

Depth discrimination in acousto-optic cerebral blood flow measurement simulation

NASA Astrophysics Data System (ADS)

Tsalach, A.; Schiffer, Z.; Ratner, E.; Breskin, I.; Zeitak, R.; Shechter, R.; Balberg, M.

2016-03-01

Monitoring cerebral blood flow (CBF) is crucial, as inadequate perfusion, even for relatively short periods of time, may lead to brain damage or even death. Thus, significant research efforts are directed at developing reliable monitoring tools that will enable continuous, bed side, simple and cost-effective monitoring of CBF. All existing non invasive bed side monitoring methods, which are mostly NIRS based, such as Laser Doppler or DCS, tend to underestimate CBF in adults, due to the indefinite effect of extra-cerebral tissues on the obtained signal. If those are to find place in day to day clinical practice, the contribution of extra-cerebral tissues must be eliminated and data from the depth (brain) should be extracted and discriminated. Recently, a novel technique, based on ultrasound modulation of light was developed for non-invasive, continuous CBF monitoring (termed ultrasound-tagged light (UTL or UT-NIRS)), and shown to correlate with readings of 133Xe SPECT and laser Doppler. We have assembled a comprehensive computerized simulation, modeling this acousto-optic technique in a highly scattering media. Using the combination of light and ultrasound, we show how depth information may be extracted, thus distinguishing between flow patterns taking place at different depths. Our algorithm, based on the analysis of light modulated by ultrasound, is presented and examined in a computerized simulation. Distinct depth discrimination ability is presented, suggesting that using such method one can effectively nullify the extra-cerebral tissues influence on the obtained signals, and specifically extract cerebral flow data.

Effect of pomegranate extracts on brain antioxidant markers and cholinesterase activity in high fat-high fructose diet induced obesity in rat model.

PubMed

Amri, Zahra; Ghorbel, Asma; Turki, Mouna; Akrout, Férièle Messadi; Ayadi, Fatma; Elfeki, Abdelfateh; Hammami, Mohamed

2017-06-27

To investigate beneficial effects of Pomegranate seeds oil (PSO), leaves (PL), juice (PJ) and (PP) on brain cholinesterase activity, brain oxidative stress and lipid profile in high-fat-high fructose diet (HFD) induced-obese rat. In vitro and in vivo cholinesterase activity, brain oxidative status, body and brain weight and plasma lipid profile were measured in control rats, HFD-fed rats and HFD-fed rats treated by PSO, PL, PJ and PP. In vitro study showed that PSO, PL, PP, PJ inhibited cholinesterase activity in dose dependant manner. PL extract displayed the highest inhibitory activity by IC50 of 151.85 mg/ml. For in vivo study, HFD regime induced a significant increase of cholinesterase activity in brain by 17.4% as compared to normal rats. However, the administration of PSO, PL, PJ and PP to HDF-rats decreased cholinesterase activity in brain respectively by 15.48%, 6.4%, 20% and 18.7% as compared to untreated HFD-rats. Moreover, HFD regime caused significant increase in brain stress, brain and body weight, and lipid profile disorders in blood. Furthermore, PSO, PL, PJ and PP modulated lipid profile in blood and prevented accumulation of lipid in brain and body evidenced by the decrease of their weights as compared to untreated HFD-rats. In addition administration of these extract protected brain from stress oxidant, evidenced by the decrease of malondialdehyde (MDA) and Protein carbonylation (PC) levels and the increase in superoxide dismutase (SOD) and glutathione peroxidase (GPx) levels. These findings highlight the neuroprotective effects of pomegranate extracts and one of mechanisms is the inhibition of cholinesterase and the stimulation of antioxidant capacity.

Decoding magnetoencephalographic rhythmic activity using spectrospatial information.

PubMed

Kauppi, Jukka-Pekka; Parkkonen, Lauri; Hari, Riitta; Hyvärinen, Aapo

2013-12-01

We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). The method allows investigation of changes in rhythmic neural activity as a result of different stimuli and tasks. The introduced classification model only assumes that each "brain state" can be characterized as a combination of neural sources, each of which shows rhythmic activity at one or several frequency bands. Furthermore, the model allows the oscillation frequencies to be different for each such state. We present decoding results from 9 subjects in a four-category classification problem defined by an experiment involving randomly alternating epochs of auditory, visual and tactile stimuli interspersed with rest periods. The performance of Spectral LDA was very competitive compared with four alternative classifiers based on different assumptions concerning the organization of rhythmic brain activity. In addition, the spectral and spatial patterns extracted automatically on the basis of trained classifiers showed that Spectral LDA offers a novel and interesting way of analyzing spectrospatial oscillatory neural activity across the brain. All the presented classification methods and visualization tools are freely available as a Matlab toolbox. © 2013.

Characterizing brain patterns in conversion from mild cognitive impairment (MCI) to Alzheimer's disease

NASA Astrophysics Data System (ADS)

Silva R., Santiago S.; Giraldo, Diana L.; Romero, Eduardo

2017-11-01

Structural Magnetic Resonance (MR) brain images should provide quantitative information about the stage and progression of Alzheimer's disease. However, the use of MRI is limited and practically reduced to corroborate a diagnosis already performed with neuropsychological tools. This paper presents an automated strategy for extraction of relevant anatomic patterns related with the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) using T1-weighted MR images. The process starts by representing each of the possible classes with models generated from a linear combination of volumes. The difference between models allows us to establish which are the regions where relevant patterns might be located. The approach searches patterns in a space of brain sulci, herein approximated by the most representative gradients found in regions of interest defined by the difference between the linear models. This hypothesis is assessed by training a conventional SVM model with the found relevant patterns under a leave-one-out scheme. The resultant AUC was 0.86 for the group of women and 0.61 for the group of men.

Real-time interactive tractography analysis for multimodal brain visualization tool: MultiXplore

NASA Astrophysics Data System (ADS)

Bakhshmand, Saeed M.; de Ribaupierre, Sandrine; Eagleson, Roy

2017-03-01

Most debilitating neurological disorders can have anatomical origins. Yet unlike other body organs, the anatomy alone cannot easily provide an understanding of brain functionality. In fact, addressing the challenge of linking structural and functional connectivity remains in the frontiers of neuroscience. Aggregating multimodal neuroimaging datasets may be critical for developing theories that span brain functionality, global neuroanatomy and internal microstructures. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are main such techniques that are employed to investigate the brain under normal and pathological conditions. FMRI records blood oxygenation level of the grey matter (GM), whereas DTI is able to reveal the underlying structure of the white matter (WM). Brain global activity is assumed to be an integration of GM functional hubs and WM neural pathways that serve to connect them. In this study we developed and evaluated a two-phase algorithm. This algorithm is employed in a 3D interactive connectivity visualization framework and helps to accelerate clustering of virtual neural pathways. In this paper, we will detail an algorithm that makes use of an index-based membership array formed for a whole brain tractography file and corresponding parcellated brain atlas. Next, we demonstrate efficiency of the algorithm by measuring required times for extracting a variety of fiber clusters, which are chosen in such a way to resemble all sizes probable output data files that algorithm will generate. The proposed algorithm facilitates real-time visual inspection of neuroimaging data to further the discovery in structure-function relationship of the brain networks.

Development of a brain MRI-based hidden Markov model for dementia recognition

PubMed Central

2013-01-01

Background Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Methods Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. Results The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. Conclusion The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia. PMID:24564961

ExAtlas: An interactive online tool for meta-analysis of gene expression data.

PubMed

Sharov, Alexei A; Schlessinger, David; Ko, Minoru S H

2015-12-01

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

Optimal design of a bank of spatio-temporal filters for EEG signal classification.

PubMed

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.

Ruta graveolens L. induces death of glioblastoma cells and neural progenitors, but not of neurons, via ERK 1/2 and AKT activation.

PubMed

Gentile, Maria Teresa; Ciniglia, Claudia; Reccia, Mafalda G; Volpicelli, Floriana; Gatti, Monica; Thellung, Stefano; Florio, Tullio; Melone, Mariarosa A B; Colucci-D'Amato, Luca

2015-01-01

Glioblastoma multiforme is a highly aggressive brain tumor whose prognosis is very poor. Due to early invasion of brain parenchyma, its complete surgical removal is nearly impossible, and even after aggressive combined treatment (association of surgery and chemo- and radio-therapy) five-year survival is only about 10%. Natural products are sources of novel compounds endowed with therapeutic properties in many human diseases, including cancer. Here, we report that the water extract of Ruta graveolens L., commonly known as rue, induces death in different glioblastoma cell lines (U87MG, C6 and U138) widely used to test novel drugs in preclinical studies. Ruta graveolens' effect was mediated by ERK1/2 and AKT activation, and the inhibition of these pathways, via PD98058 and wortmannin, reverted its antiproliferative activity. Rue extract also affects survival of neural precursor cells (A1) obtained from embryonic mouse CNS. As in the case of glioma cells, rue stimulates the activation of ERK1/2 and AKT in A1 cells, whereas their blockade by pharmacological inhibitors prevents cell death. Interestingly, upon induction of differentiation and cell cycle exit, A1 cells become resistant to rue's noxious effects but not to those of temozolomide and cisplatin, two alkylating agents widely used in glioblastoma therapy. Finally, rutin, a major component of the Ruta graveolens water extract, failed to cause cell death, suggesting that rutin by itself is not responsible for the observed effects. In conclusion, we report that rue extracts induce glioma cell death, discriminating between proliferating/undifferentiated and non-proliferating/differentiated neurons. Thus, it can be a promising tool to isolate novel drugs and also to discover targets for therapeutic intervention.

Protective effects of Petroselinum crispum (Mill) Nyman ex A. W. Hill leaf extract on D-galactose-induced oxidative stress in mouse brain.

PubMed

Vora, Shreya R; Patil, Rahul B; Pillai, Meena M

2009-05-01

With an aim to examine the effect of ethanolic extract of P. crispum (Parsley) leaves on the D-galactose-induced oxidative stress in the brain of mouse, the activities of antioxidant enzymes (superoxide dismutase, catalase and glutathione peroxidase) involved in oxygen radical (OR)-detoxification and antiperoxidative defense were measured in conjunction with an index of lipid peroxidation in mitochondrial fraction of various regions of the mouse brain. A significant decrease in superoxide dismutase and glutathione peroxidase activity was observed in D-galactose-stressed mice, while catalase activity was increased. Treatment of D-galactose-stressed mice with the ethanolic extract of P. crispum showed protection against the induced oxidative stress in brain regions. Concentration of thiobarbituric acid-reactive product was greatly elevated in D-galactose stress-induced mice and was significantly reduced in the brain regions of these mice upon treatment with P. crispum. It is postulated that parsley shows a protective effect against mitochondrial oxidative damage in the mouse brain.

The Effect of Flax Seed (Linum Usitatissimum) Hydroalcoholic Extract on Brain, Weight and Plasma Sexual Hormone Levels in Aged and Young Mice.

PubMed

Bahmanpour, Soghra; Kamali, Mahsa

2016-05-01

Flax is a food and fiber crop that is grown in some regions of the world. Its value will account for its great popularity as a food, medical and cosmetic applications. Flax fibers are taken from the stem of the plant and are two to three times as strong as cotton. In this study, we compared brain weight and plasma sex hormone levels in young and aged mice after the administration of Linum usitatissimum (flax seed) hydro alcoholic extract. In this study, 32 aged and 32 young mice were divided into 4 groups. Controls remained untreated and experimental groups were fed with flax seed hydroalcoholic extract by oral gavages during 3 weeks. After 3 weeks, the brain was removed and blood samples were collected to measure sex hormone levels by ELISA. Data analysis was done by statistical ANOVA test using SPSS version 18 (P<0.05). The results of this study shows that the brain weight of mice did not change significantly, but the sex hormone levels in the experimental groups in comparison with the control groups increased significantly (P<0.05). The hydroalcoholic extract of flax seed had no effect on the brain weight, but this extract improved the sexual hormone levels.

BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

PubMed

Griffanti, Ludovica; Zamboni, Giovanna; Khan, Aamira; Li, Linxin; Bonifacio, Guendalina; Sundaresan, Vaanathi; Schulz, Ursula G; Kuker, Wilhelm; Battaglini, Marco; Rothwell, Peter M; Jenkinson, Mark

2016-11-01

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a "predominantly neurodegenerative" and a "predominantly vascular" cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Fetal magnetic resonance imaging (MRI): a tool for a better understanding of normal and abnormal brain development.

PubMed

Saleem, Sahar N

2013-07-01

Knowledge of the anatomy of the developing fetal brain is essential to detect abnormalities and understand their pathogenesis. Capability of magnetic resonance imaging (MRI) to visualize the brain in utero and to differentiate between its various tissues makes fetal MRI a potential diagnostic and research tool for the developing brain. This article provides an approach to understand the normal and abnormal brain development through schematic interpretation of fetal brain MR images. MRI is a potential screening tool in the second trimester of pregnancies in fetuses at risk for brain anomalies and helps in describing new brain syndromes with in utero presentation. Accurate interpretation of fetal MRI can provide valuable information that helps genetic counseling, facilitates management decisions, and guides therapy. Fetal MRI can help in better understanding the pathogenesis of fetal brain malformations and can support research that could lead to disease-specific interventions.

Parallel workflow tools to facilitate human brain MRI post-processing

PubMed Central

Cui, Zaixu; Zhao, Chenxi; Gong, Gaolang

2015-01-01

Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues. PMID:26029043

Brain Volume Estimation Enhancement by Morphological Image Processing Tools.

PubMed

Zeinali, R; Keshtkar, A; Zamani, A; Gharehaghaji, N

2017-12-01

Volume estimation of brain is important for many neurological applications. It is necessary in measuring brain growth and changes in brain in normal/abnormal patients. Thus, accurate brain volume measurement is very important. Magnetic resonance imaging (MRI) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. Stereology method is a good method for estimating volume but it requires to segment enough MRI slices and have a good resolution. In this study, it is desired to enhance stereology method for volume estimation of brain using less MRI slices with less resolution. In this study, a program for calculating volume using stereology method has been introduced. After morphologic method, dilation was applied and the stereology method enhanced. For the evaluation of this method, we used T1-wighted MR images from digital phantom in BrainWeb which had ground truth. The volume of 20 normal brain extracted from BrainWeb, was calculated. The volumes of white matter, gray matter and cerebrospinal fluid with given dimension were estimated correctly. Volume calculation from Stereology method in different cases was made. In three cases, Root Mean Square Error (RMSE) was measured. Case I with T=5, d=5, Case II with T=10, D=10 and Case III with T=20, d=20 (T=slice thickness, d=resolution as stereology parameters). By comparing these results of two methods, it is obvious that RMSE values for our proposed method are smaller than Stereology method. Using morphological operation, dilation allows to enhance the estimation volume method, Stereology. In the case with less MRI slices and less test points, this method works much better compared to Stereology method.

Histological evaluation of brain damage caused by crude quinolizidine alkaloid extracts from lupines.

PubMed

Bañuelos Pineda, J; Nolasco Rodríguez, G; Monteon, J A; García López, P M; Ruiz Lopez, M A; García Estrada, J

2005-10-01

The effects of the intracerebroventricular (ICV) administration of crude extracts of lupin quinolizidine alkaloids (LQAs) were studied in adult rat brain tissue. Mature L. exaltatus and L. montanus seeds were collected in western Mexico, and the LQAs from these seeds were extracted and analyzed by capillary gas chromatography. This LQA extract was administered to the right lateral ventricle of adult rats through a stainless steel cannula on five consecutive days. While control animals received 10 microl of sesame oil daily (vehicle), the experimental rats (10 per group) received 20 ng of LQA from either L. exaltatus or from L. montanus. All the animals were sacrificed 40 h after receiving the last dose of alkaloids, and their brains were removed, fixed and coronal paraffin sections were stained with haematoxylin and eosin. Immediately after the administration of LQA the animals began grooming and suffered tachycardia, tachypnea, piloerection, tail erection, muscular contractions, loss of equilibrium, excitation, and unsteady walk. In the brains of the animals treated with LQA damaged neurons were identified. The most frequent abnormalities observed in this brain tissue were "red neurons" with shrunken eosinophilic cytoplasm, strongly stained pyknotic nuclei, neuronal swelling, spongiform neuropil, "ghost cells" (hypochromasia), and abundant neuronophagic figures in numerous brain areas. While some alterations in neurons were observed in control tissues, unlike those found in the animals treated with LQA these were not significant. Thus, the histopathological changes observed can be principally attributed to the administration of sparteine and lupanine present in the alkaloid extracts.

Optical Imaging and Control of Neurons

NASA Astrophysics Data System (ADS)

Song, Yoon-Kyu

Although remarkable progress has been made in our understanding of the function, organization, and development of the brain by various approaches of modern science and technology, how the brain performs its marvelous function remains unsolved or incompletely understood. This is mainly attributed to the insufficient capability of currently available research tools and conceptual frameworks to deal with enormous complexity of the brain. Hence, in the last couple of decades, a significant effort has been made to crack the complexity of brain by utilizing research tools from diverse scientific areas. The research tools include the optical neurotechnology which incorporates the exquisite characteristics of optics, such as multi-parallel access and non-invasiveness, in sensing and stimulating the excitable membrane of a neuron, the basic functional unit of the brain. This chapter is aimed to serve as a short introduction to the optical neurotechnology for those who wish to use optical techniques as one of their brain research tools.

DeepNeuron: an open deep learning toolbox for neuron tracing.

PubMed

Zhou, Zhi; Kuo, Hsien-Chi; Peng, Hanchuan; Long, Fuhui

2018-06-06

Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

A novel Bayesian framework for discriminative feature extraction in Brain-Computer Interfaces.

PubMed

Suk, Heung-Il; Lee, Seong-Whan

2013-02-01

As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.

Effects of the continuous administration of an Agaricus blazei extract to rats on oxidative parameters of the brain and liver during aging.

PubMed

de Sá-Nakanishi, Anacharis B; Soares, Andréia A; Natali, Maria R M; Comar, Jurandir Fernando; Peralta, Rosane M; Bracht, Adelar

2014-11-13

An investigation of the effects of an aqueous extract of Agaricus blazei, a medicinal mushroom, on the oxidative state of the brain and liver of rats during aging (7 to 23 months) was conducted. The treatment consisted in the daily intragastric administration of 50 mg/kg of the extract. The A. blazei treatment tended to maintain the ROS contents of the brain and liver at lower levels, but a significant difference was found only at the age of 23 months and in the brain. The TBARS levels in the brain were maintained at lower levels by the A. blazei treatment during the whole aging process with a specially pronounced difference at the age of 12 months. The total antioxidant capacity in the brain was higher in treated rats only at the age of 12 months. Compared with previous studies in which old rats (21 months) were treated during a short period of 21 days with 200 mg/kg, the effects of the A. blazei extract in the present study tended to be less pronounced. The results also indicate that the long and constant treatment presented a tendency of becoming less effective at ages above 12 months.

Effect of Calea serrata Less. n-hexane extract on acetylcholinesterase of larvae ticks and brain Wistar rats.

PubMed

Ribeiro, Vera Lucia Sardá; Vanzella, Cláudia; Moysés, Felipe dos Santos; Santos, Jaqueline Campiol Dos; Martins, João Ricardo Souza; von Poser, Gilsane Lino; Siqueira, Ionara Rodrigues

2012-10-26

Acetylcholinesterase (AChE), an enzyme that hydrolyses acetylcholine (ACh) at cholinergic synapses, is a target for pesticides and its inhibition by organophosphates leads to paralysis and death of arthropods. It has been demonstrated that the n-hexane extract of Calea serrata had acaricidal activity against larvae of Rhipicephalus (Boophilus) microplus and Rhipicephalus sanguineus. The aim of the present study was to understand the mechanism of the acaricidal action of C. serrata n-hexane extract are specifically to investigate the in vitro anticholinesterase activity on larvae of R. microplus and in brain structures of male Wistar rats. The n-hexane extract significantly inhibited in vitro acetylcholinesterase activity in R. microplus larvae and rat brain structures. The results confirm that inhibition of acetylcholinesterase is a possible mechanism of action of hexane extract at C. serrata. Copyright © 2012 Elsevier B.V. All rights reserved.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

The neuroprotective effects of an ethanolic turmeric (Curcuma longa L.) extract against trimethyltin-induced oxidative stress in rats.

PubMed

Yuliani, Sapto; Mustofa; Partadiredja, Ginus

2018-03-07

Oxidative stress is known to contribute to the pathogenesis of neurodegenerative disorders. An ethanolic turmeric (Curcuma longa L.) extract containing curcumin has been reported to produce antioxidant effects. The present study aims to investigate the possible neuroprotective effects of the ethanolic turmeric extract against trimethyltin (TMT)-induced oxidative stress in Sprague Dawley rats. The ethanolic turmeric extract and citicoline were administered to the TMT exposed rats from day 1 to day 28 of the experiment. The TMT injection was administered on day 8 of the experiment. The plasma and brain malondialdehyde (MDA) and reduced glutathione (GSH) levels, and the activities of the superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) enzymes in the brain were examined at the end of the experiment. The administration of 200 mg/kg bw of the ethanolic turmeric extract prevented oxidative stress by decreasing the plasma and brain MDA levels and increasing the SOD, CAT, and GPx enzyme activities and GSH levels in the brain. These effects seem to be comparable to those of citicoline. The ethanolic turmeric extract at a dose of 200 mg/kg bw may exert neuroprotective effects on TMT-exposed Sprague Dawley rats by preventing them from oxidative stress.

The Potential of Using Brain Images for Authentication

PubMed Central

Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition. PMID:25126604

The potential of using brain images for authentication.

PubMed

Chen, Fanglin; Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer.

PubMed

Bigler, Don C; Aksu, Yaman; Miller, David J; Yang, Qing X

2009-08-01

This paper describes a Software Tool for Automated MRI Post-processing (STAMP) of multiple types of brain MRIs on a workstation and for parallel processing on a supercomputer (STAMPS). This software tool enables the automation of nonlinear registration for a large image set and for multiple MR image types. The tool uses standard brain MRI post-processing tools (such as SPM, FSL, and HAMMER) for multiple MR image types in a pipeline fashion. It also contains novel MRI post-processing features. The STAMP image outputs can be used to perform brain analysis using Statistical Parametric Mapping (SPM) or single-/multi-image modality brain analysis using Support Vector Machines (SVMs). Since STAMPS is PBS-based, the supercomputer may be a multi-node computer cluster or one of the latest multi-core computers.

Assessment of quantitative cortical biomarkers in the developing brain of preterm infants

NASA Astrophysics Data System (ADS)

Moeskops, Pim; Benders, Manon J. N. L.; Pearlman, Paul C.; Kersbergen, Karina J.; Leemans, Alexander; Viergever, Max A.; Išgum, Ivana

2013-02-01

The cerebral cortex rapidly develops its folding during the second and third trimester of pregnancy. In preterm birth, this growth might be disrupted and influence neurodevelopment. The aim of this work is to extract quantitative biomarkers describing the cortex and evaluate them on a set of preterm infants without brain pathology. For this study, a set of 19 preterm - but otherwise healthy - infants scanned coronally with 3T MRI at the postmenstrual age of 30 weeks were selected. In ten patients (test set), the gray and white matter were manually annotated by an expert on the T2-weighted scans. Manual segmentations were used to extract cortical volume, surface area, thickness, and curvature using voxel-based methods. To compute these biomarkers per region in every patient, a template brain image has been generated by iterative registration and averaging of the scans of the remaining nine patients. This template has been manually divided in eight regions, and is transformed to every test image using elastic registration. In the results, gray and white matter volumes and cortical surface area appear symmetric between hemispheres, but small regional differences are visible. Cortical thickness seems slightly higher in the right parietal lobe than in other regions. The parietal lobes exhibit a higher global curvature, indicating more complex folding compared to other regions. The proposed approach can potentially - together with an automatic segmentation algorithm - be applied as a tool to assist in early diagnosis of abnormalities and prediction of the development of the cognitive abilities of these children.

SZDB: A Database for Schizophrenia Genetic Research

PubMed Central

Wu, Yong; Yao, Yong-Gang

2017-01-01

Abstract Schizophrenia (SZ) is a debilitating brain disorder with a complex genetic architecture. Genetic studies, especially recent genome-wide association studies (GWAS), have identified multiple variants (loci) conferring risk to SZ. However, how to efficiently extract meaningful biological information from bulk genetic findings of SZ remains a major challenge. There is a pressing need to integrate multiple layers of data from various sources, eg, genetic findings from GWAS, copy number variations (CNVs), association and linkage studies, gene expression, protein–protein interaction (PPI), co-expression, expression quantitative trait loci (eQTL), and Encyclopedia of DNA Elements (ENCODE) data, to provide a comprehensive resource to facilitate the translation of genetic findings into SZ molecular diagnosis and mechanism study. Here we developed the SZDB database (http://www.szdb.org/), a comprehensive resource for SZ research. SZ genetic data, gene expression data, network-based data, brain eQTL data, and SNP function annotation information were systematically extracted, curated and deposited in SZDB. In-depth analyses and systematic integration were performed to identify top prioritized SZ genes and enriched pathways. Multiple types of data from various layers of SZ research were systematically integrated and deposited in SZDB. In-depth data analyses and integration identified top prioritized SZ genes and enriched pathways. We further showed that genes implicated in SZ are highly co-expressed in human brain and proteins encoded by the prioritized SZ risk genes are significantly interacted. The user-friendly SZDB provides high-confidence candidate variants and genes for further functional characterization. More important, SZDB provides convenient online tools for data search and browse, data integration, and customized data analyses. PMID:27451428

Protective Effects of Flax Seed (Linum Usitatissimum) Hydroalcoholic Extract on Fetus Brain in Aged and Young Mice.

PubMed

Kamali, Mahsa; Bahmanpour, Soghra

2016-05-01

One of the major problems of the aged women or older than 35 is getting pregnant in the late fertility life. Fertility rates begin to decline gradually at the age of 30, more so at 35, and markedly at 40. Even with fertility treatments such as in vitro fertilization, women have more difficulty in getting pregnant or may deliver abnormal fetus. The purpose of this study was to assess the effects of flax seed hydroalcoholic extract on the fetal brain of aged mice and its comparison with young mice. In this experimental study, 32 aged and 32 young mice were divided into 4 groups. Controls received no special treatment. The experimental mice groups, 3 weeks before mating, were fed with flax seed hydroalcoholic extract by oral gavages. After giving birth, the brains of the fetus were removed. Data analysis was performed by statistical test ANOVA using SPSS version 18 (P<0.05). The mean fetus brain weight of aged mother groups compared to the control group was increased significantly (P<0.05). This study showed that flax seed hydroalcoholic extract could improve fetal brain weights in the aged groups.

Dual role of cerebral blood flow in regional brain temperature control in the healthy newborn infant.

PubMed

Iwata, Sachiko; Tachtsidis, Ilias; Takashima, Sachio; Matsuishi, Toyojiro; Robertson, Nicola J; Iwata, Osuke

2014-10-01

Small shifts in brain temperature after hypoxia-ischaemia affect cell viability. The main determinants of brain temperature are cerebral metabolism, which contributes to local heat production, and brain perfusion, which removes heat. However, few studies have addressed the effect of cerebral metabolism and perfusion on regional brain temperature in human neonates because of the lack of non-invasive cot-side monitors. This study aimed (i) to determine non-invasive monitoring tools of cerebral metabolism and perfusion by combining near-infrared spectroscopy and echocardiography, and (ii) to investigate the dependence of brain temperature on cerebral metabolism and perfusion in unsedated newborn infants. Thirty-two healthy newborn infants were recruited. They were studied with cerebral near-infrared spectroscopy, echocardiography, and a zero-heat flux tissue thermometer. A surrogate of cerebral blood flow (CBF) was measured using superior vena cava flow adjusted for cerebral volume (rSVC flow). The tissue oxygenation index, fractional oxygen extraction (FOE), and the cerebral metabolic rate of oxygen relative to rSVC flow (CMRO₂ index) were also estimated. A greater rSVC flow was positively associated with higher brain temperatures, particularly for superficial structures. The CMRO₂ index and rSVC flow were positively coupled. However, brain temperature was independent of FOE and the CMRO₂ index. A cooler ambient temperature was associated with a greater temperature gradient between the scalp surface and the body core. Cerebral oxygen metabolism and perfusion were monitored in newborn infants without using tracers. In these healthy newborn infants, cerebral perfusion and ambient temperature were significant independent variables of brain temperature. CBF has primarily been associated with heat removal from the brain. However, our results suggest that CBF is likely to deliver heat specifically to the superficial brain. Further studies are required to assess the effect of cerebral metabolism and perfusion on regional brain temperature in low-cardiac output conditions, fever, and with therapeutic hypothermia. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Effect of Ginkgo biloba extract on apoptosis of brain tissues in rats with acute cerebral infarction and related gene expression.

PubMed

Wu, C; Zhao, X; Zhang, X; Liu, S; Zhao, H; Chen, Y

2015-06-11

We investigated the effect of Ginkgo biloba extract on apoptosis of brain tissues in rats with acute cerebral infarction and apoptosis-related gene expression. Rat models of acute cerebral infarction were constructed using the suture method, and randomly divided into the control group, model, and treatment groups. In the treatment group, 4 mg/kg G. biloba extract was intravenously injected into the rat tail vein. Phosphate-buffered saline solution was injected in the model group. Seventy-two hours after treatment, rats were euthanized, and brain tissues were removed to analyze the changes in caspase-3, B-cell lymphoma 2 (Bcl-2), and Bcl-2-associated X protein (Bax) mRNA and protein levels, and variation in brain tissue cells' apoptosis indices was measured. Compared with the control group, the model and treatment groups showed significantly upregulated caspase-3, Bcl-2, and Bax mRNA and protein levels in brain tissues, but remarkably downregulated Bcl-2 mRNA and protein levels (P < 0.05). After treatment, in treatment group brain tissues, caspase-3 and Bax mRNA and protein levels were significantly lower than those in the model group, while Bcl-2 mRNA and protein levels were higher than that in the model group (P < 0.05). The model and treatment groups showed increased cell apoptosis indices of brain tissues compared to the control group; after treatment, the apoptosis index in the treatment group was significantly downregulated compared with that in the model group (P < 0.05). In conclusion, G. biloba extract significantly reduced apoptosis in rat brain tissue cells with acute cerebral infarction and thus protected brain tissues.

Cinnamon polyphenol extract exerts neuroprotective activity in traumatic brain injury through modulation of Nfr2 and cytokine expression.

PubMed

Yulug, Burak; Kilic, Ertugrul; Altunay, Serdar; Ersavas, Cenk; Orhan, Cemal; Dalay, Arman; Sahin, Nurhan; Tuzcu, Mehmet; Juturu, Vijaya; Sahin, Kazim

2018-04-30

Cinnamon cinnamon polyphenol extract is a traditional spice commonly used in different areas of the world for treatment of different disease conditions which are associated with inflammation and oxidative stress. Despite many preclinical studies showing the anti-oxidative, anti-inflammatory effects of CN, the underlying mechanisms in signaling pathways via which cinnamon protects the brain after brain trauma remained largely unknown. However, there is still no preclinical study delineating the possible molecular mechanism of neuroprotective effects cinnamon polyphenol extractin TBI.The primary aim of the current study was to test the hypothesis that cinnamon polyphenol extract administration would improve the histopathological outcomes and exert neuroprotective activity through its antioxidative and anti-inflammatory properties following TBI. To investigate the effects of cinnamon, we induced brain injury using a cold trauma model in mice that were treated with cinnamon polyphenol extract (10 mg/kg BW) or vehicle via intraperitoneal administration just after TBI. Mice were divided into two groups: TBI+vehicle group and TBI + cinnamon polyphenol extract group. Brain samples were collected 24 h later for analysis. We have shown that cinnamon polyphenol extract effectively reduced infarct and edema formation which were associated with significant alterations in inflammatory and oxidative parameters, including NF-κB, IL-1, IL-6, GFAP, NCAM and Nfr2 expressions. Our results identify an important neuroprotective role of cinnamon polyphenol extract in TBI which is mediated by its capability to suppress the inflammation and oxidative injury. Further, specially designed experimental studies to understand the molecular cross-talk between signaling pathways would provide valuable evidence for the therapeutic role of cinnamon in TBI and other TBI related conditions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Recommendations for Development of New Standardized Forms of Cocoa Breeds and Cocoa Extract Processing for the Prevention of Alzheimer's Disease: Role of Cocoa in Promotion of Cognitive Resilience and Healthy Brain Aging.

PubMed

Dubner, Lauren; Wang, Jun; Ho, Lap; Ward, Libby; Pasinetti, Giulio M

2015-01-01

It is currently thought that the lackluster performance of translational paradigms in the prevention of age-related cognitive deteriorative disorders, such as Alzheimer's disease (AD), may be due to the inadequacy of the prevailing approach of targeting only a single mechanism. Age-related cognitive deterioration and certain neurodegenerative disorders, including AD, are characterized by complex relationships between interrelated biological phenotypes. Thus, alternative strategies that simultaneously target multiple underlying mechanisms may represent a more effective approach to prevention, which is a strategic priority of the National Alzheimer's Project Act and the National Institute on Aging. In this review article, we discuss recent strategies designed to clarify the mechanisms by which certain brain-bioavailable, bioactive polyphenols, in particular, flavan-3-ols also known as flavanols, which are highly represented in cocoa extracts, may beneficially influence cognitive deterioration, such as in AD, while promoting healthy brain aging. However, we note that key issues to improve consistency and reproducibility in the development of cocoa extracts as a potential future therapeutic agent requires a better understanding of the cocoa extract sources, their processing, and more standardized testing including brain bioavailability of bioactive metabolites and brain target engagement studies. The ultimate goal of this review is to provide recommendations for future developments of cocoa extracts as a therapeutic agent in AD.

A New Disability-related Health Care Needs Assessment Tool for Persons With Brain Disorders

PubMed Central

Kim, Yoon; Eun, Sang June; Kim, Wan Ho; Lee, Bum-Suk; Leigh, Ja-Ho; Kim, Jung-Eun

2013-01-01

Objectives This study aimed to develop a health needs assessment (HNA) tool for persons with brain disorders and to assess the unmet needs of persons with brain disorders using the developed tool. Methods The authors used consensus methods to develop a HNA tool. Using a randomized stratified systematic sampling method adjusted for sex, age, and districts, 57 registered persons (27 severe and 30 mild cases) with brain disorders dwelling in Seoul, South Korea were chosen and medical specialists investigated all of the subjects with the developed tools. Results The HNA tool for brain disorders we developed included four categories: 1) medical interventions and operations, 2) assistive devices, 3) rehabilitation therapy, and 4) regular follow-up. This study also found that 71.9% of the subjects did not receive appropriate medical care, which implies that the severity of their disability is likely to be exacerbated and permanent, and the loss irrecoverable. Conclusions Our results showed that the HNA tool for persons with brain disorders based on unmet needs defined by physicians can be a useful method for evaluating the appropriateness and necessity of medical services offered to the disabled, and it can serve as the norm for providing health care services for disabled persons. Further studies should be undertaken to increase validity and reliability of the tool. Fundamental research investigating the factors generating or affecting the unmet needs is necessary; its results could serve as basis for developing policies to eliminate or alleviate these factors. PMID:24137530

A new disability-related health care needs assessment tool for persons with brain disorders.

PubMed

Kim, Yoon; Eun, Sang June; Kim, Wan Ho; Lee, Bum-Suk; Leigh, Ja-Ho; Kim, Jung-Eun; Lee, Jin Yong

2013-09-01

This study aimed to develop a health needs assessment (HNA) tool for persons with brain disorders and to assess the unmet needs of persons with brain disorders using the developed tool. The authors used consensus methods to develop a HNA tool. Using a randomized stratified systematic sampling method adjusted for sex, age, and districts, 57 registered persons (27 severe and 30 mild cases) with brain disorders dwelling in Seoul, South Korea were chosen and medical specialists investigated all of the subjects with the developed tools. The HNA tool for brain disorders we developed included four categories: 1) medical interventions and operations, 2) assistive devices, 3) rehabilitation therapy, and 4) regular follow-up. This study also found that 71.9% of the subjects did not receive appropriate medical care, which implies that the severity of their disability is likely to be exacerbated and permanent, and the loss irrecoverable. Our results showed that the HNA tool for persons with brain disorders based on unmet needs defined by physicians can be a useful method for evaluating the appropriateness and necessity of medical services offered to the disabled, and it can serve as the norm for providing health care services for disabled persons. Further studies should be undertaken to increase validity and reliability of the tool. Fundamental research investigating the factors generating or affecting the unmet needs is necessary; its results could serve as basis for developing policies to eliminate or alleviate these factors.

Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments.

PubMed

Oubbati, Mohamed; Kord, Bahram; Koprinkova-Hristova, Petia; Palm, Günther

2014-04-01

The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments

NASA Astrophysics Data System (ADS)

Oubbati, Mohamed; Kord, Bahram; Koprinkova-Hristova, Petia; Palm, Günther

2014-04-01

The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

Ameliorative effect of Noni fruit extract on streptozotocin-induced memory impairment in mice.

PubMed

Pachauri, Shakti D; Verma, Priya Ranjan P; Dwivedi, Anil K; Tota, Santoshkumar; Khandelwal, Kiran; Saxena, Jitendra K; Nath, Chandishwar

2013-08-01

This study evaluated the effects of a standardized ethyl acetate extract of Morinda citrifolia L. (Noni) fruit on impairment of memory, brain energy metabolism, and cholinergic function in intracerebral streptozotocin (STZ)-treated mice. STZ (0.5 mg/kg) was administered twice at an interval of 48 h. Noni (50 and 100 mg/kg, postoperatively) was administered for 21 days following STZ administration. Memory function was evaluated using Morris Water Maze and passive avoidance tests, and brain levels of cholinergic function, oxidative stress, energy metabolism, and brain-derived neurotrophic factor (BDNF) were estimated. STZ caused memory impairment in Morris Water Maze and passive avoidance tests along with reduced brain levels of ATP, BDNF, and acetylcholine and increased acetylcholinesterase activity and oxidative stress. Treatment with Noni extract (100 mg/kg) prevented the STZ-induced memory impairment in both behavioral tests along with reduced oxidative stress and acetylcholinesterase activity, and increased brain levels of BDNF, acetylcholine, and ATP level. The study shows the beneficial effects of Noni fruit against STZ-induced memory impairment, which may be attributed to improved brain energy metabolism, cholinergic neurotransmission, BDNF, and antioxidative action.

Regulation of glutamate level in rat brain through activation of glutamate dehydrogenase by Corydalis ternata.

PubMed

Lee, Kwan Ho; Huh, Jae-Wan; Choi, Myung-Min; Yoon, Seung Yong; Yang, Seung-Ju; Hong, Hea Nam; Cho, Sung-Woo

2005-08-31

When treated with protopine and alkalized extracts of the tuber of Corydalis ternata for one year, significant decrease in glutamate level and increase in glutamate dehydrogenase (GDH) activity was observed in rat brains. The expression of GDH between the two groups remained unchanged as determined by Western and Northern blot analysis, suggesting a post-translational regulation of GDH activity in alkalized extracts treated rat brains. The stimulatory effects of alkalized extracts and protopine on the GDH activity was further examined in vitro with two types of human GDH isozymes, hGDH1 (house-keeping GDH) and hGDH2 (nerve-specific GDH). Alkalized extracts and protopine activated the human GDH isozymes up to 4.8-fold. hGDH2 (nerve- specific GDH) was more sensitively affected by 1 mM ADP than hGDH1 (house-keeping GDH) on the activation by alkalized extracts. Studies with cassette mutagenesis at ADP-binding site showed that hGDH2 was more sensitively regulated by ADP than hGDH1 on the activation by Corydalis ternata. Our results suggest that prolonged exposure to Corydalis ternata may be one of the ways to regulate glutamate concentration in brain through the activation of GDH.

Whakawhiti Kōrero, a Method for the Development of a Cultural Assessment Tool, Te Waka Kuaka, in Māori Traumatic Brain Injury.

PubMed

Elder, Hinemoa; Kersten, Paula

2015-01-01

The importance of tools for the measurement of outcomes and needs in traumatic brain injury is well recognised. The development of tools for these injuries in indigenous communities has been limited despite the well-documented disparity of brain injury. The wairua theory of traumatic brain injury (TBI) in Māori proposes that a culturally defined injury occurs in tandem with the physical injury. A cultural response is therefore indicated. This research investigates a Māori method used in the development of cultural needs assessment tool designed to further examine needs associated with the culturally determined injury and in preparation for formal validation. Whakawhiti kōrero is a method used to develop better statements in the development of the assessment tool. Four wānanga (traditional fora) were held including one with whānau (extended family) with experience of traumatic brain injury. The approach was well received. A final version, Te Waka Kuaka, is now ready for validation. Whakawhiti kōrero is an indigenous method used in the development of cultural needs assessment tool in Māori traumatic brain injury. This method is likely to have wider applicability, such as Mental Health and Addictions Services, to ensure robust process of outcome measure and needs assessment development.

Immunomodulatory effect of Hawthorn extract in an experimental stroke model.

PubMed

Elango, Chinnasamy; Devaraj, Sivasithambaram Niranjali

2010-12-30

Recently, we reported a neuroprotective effect for Hawthorn (Crataegus oxyacantha) ethanolic extract in middle cerebral artery occlusion-(MCAO) induced stroke in rats. The present study sheds more light on the extract's mechanism of neuroprotection, especially its immunomodulatory effect. After 15 days of treatment with Hawthorn extract [100 mg/kg, pretreatment (oral)], male Sprague Dawley rats underwent transient MCAO for 75 mins followed by reperfusion (either 3 or 24 hrs). We measured pro-inflammatory cytokines (IL-1β, TNF-α, IL-6), ICAM-1, IL-10 and pSTAT-3 expression in the brain by appropriate methods. We also looked at the cytotoxic T cell sub-population among leukocytes (FACS) and inflammatory cell activation and recruitment in brain (using a myeloperoxidase activity assay) after ischemia and reperfusion (I/R). Apoptosis (TUNEL), and Bcl-xL- and Foxp3- (T(reg) marker) positive cells in the ipsilateral hemisphere of the brain were analyzed separately using immunofluorescence. Our results indicate that occlusion followed by 3 hrs of reperfusion increased pro-inflammatory cytokine and ICAM-1 gene expressions in the ipsilateral hemisphere, and that Hawthorn pre-treatment significantly (p ≤ 0.01) lowered these levels. Furthermore, such pre-treatment was able to increase IL-10 levels and Foxp3-positive cells in brain after 24 hrs of reperfusion. The increase in cytotoxic T cell population in vehicle rats after 24 hrs of reperfusion was decreased by at least 40% with Hawthorn pretreatment. In addition, there was a decrease in inflammatory cell activation and infiltration in pretreated brain. Hawthorn pretreatment elevated pSTAT-3 levels in brain after I/R. We also observed an increase in Bcl-xL-positive cells, which in turn may have influenced the reduction in TUNEL-positive cells compared to vehicle-treated brain. In summary, Hawthorn extract helped alleviate pro-inflammatory immune responses associated with I/R-induced injury, boosted IL-10 levels, and increased Foxp3-positive T(regs) in the brain, which may have aided in suppression of activated inflammatory cells. Such treatment also minimizes apoptotic cell death by influencing STAT-3 phosphorylation and Bcl-xL expression in the brain. Taken together, the immunomodulatory effect of Hawthorn extract may play a critical role in the neuroprotection observed in this MCAO-induced stroke model.

Immunomodulatory effect of Hawthorn extract in an experimental stroke model

PubMed Central

2010-01-01

Background Recently, we reported a neuroprotective effect for Hawthorn (Crataegus oxyacantha) ethanolic extract in middle cerebral artery occlusion-(MCAO) induced stroke in rats. The present study sheds more light on the extract's mechanism of neuroprotection, especially its immunomodulatory effect. Methods After 15 days of treatment with Hawthorn extract [100 mg/kg, pretreatment (oral)], male Sprague Dawley rats underwent transient MCAO for 75 mins followed by reperfusion (either 3 or 24 hrs). We measured pro-inflammatory cytokines (IL-1β, TNF-α, IL-6), ICAM-1, IL-10 and pSTAT-3 expression in the brain by appropriate methods. We also looked at the cytotoxic T cell sub-population among leukocytes (FACS) and inflammatory cell activation and recruitment in brain (using a myeloperoxidase activity assay) after ischemia and reperfusion (I/R). Apoptosis (TUNEL), and Bcl-xL- and Foxp3- (Treg marker) positive cells in the ipsilateral hemisphere of the brain were analyzed separately using immunofluorescence. Results Our results indicate that occlusion followed by 3 hrs of reperfusion increased pro-inflammatory cytokine and ICAM-1 gene expressions in the ipsilateral hemisphere, and that Hawthorn pre-treatment significantly (p ≤ 0.01) lowered these levels. Furthermore, such pre-treatment was able to increase IL-10 levels and Foxp3-positive cells in brain after 24 hrs of reperfusion. The increase in cytotoxic T cell population in vehicle rats after 24 hrs of reperfusion was decreased by at least 40% with Hawthorn pretreatment. In addition, there was a decrease in inflammatory cell activation and infiltration in pretreated brain. Hawthorn pretreatment elevated pSTAT-3 levels in brain after I/R. We also observed an increase in Bcl-xL-positive cells, which in turn may have influenced the reduction in TUNEL-positive cells compared to vehicle-treated brain. Conclusions In summary, Hawthorn extract helped alleviate pro-inflammatory immune responses associated with I/R-induced injury, boosted IL-10 levels, and increased Foxp3-positive Tregs in the brain, which may have aided in suppression of activated inflammatory cells. Such treatment also minimizes apoptotic cell death by influencing STAT-3 phosphorylation and Bcl-xL expression in the brain. Taken together, the immunomodulatory effect of Hawthorn extract may play a critical role in the neuroprotection observed in this MCAO-induced stroke model. PMID:21192826

sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping.

PubMed

Ceschin, Rafael; Panigrahy, Ashok; Gopalakrishnan, Vanathi

2015-01-01

A major challenge in the diagnosis and treatment of brain tumors is tissue heterogeneity leading to mixed treatment response. Additionally, they are often difficult or at very high risk for biopsy, further hindering the clinical management process. To overcome this, novel advanced imaging methods are increasingly being adapted clinically to identify useful noninvasive biomarkers capable of disease stage characterization and treatment response prediction. One promising technique is called functional diffusion mapping (fDM), which uses diffusion-weighted imaging (DWI) to generate parametric maps between two imaging time points in order to identify significant voxel-wise changes in water diffusion within the tumor tissue. Here we introduce serial functional diffusion mapping (sfDM), an extension of existing fDM methods, to analyze the entire tumor diffusion profile along the temporal course of the disease. sfDM provides the tools necessary to analyze a tumor data set in the context of spatiotemporal parametric mapping: the image registration pipeline, biomarker extraction, and visualization tools. We present the general workflow of the pipeline, along with a typical use case for the software. sfDM is written in Python and is freely available as an open-source package under the Berkley Software Distribution (BSD) license to promote transparency and reproducibility.

EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach.

PubMed

Bosl, William J; Tager-Flusberg, Helen; Nelson, Charles A

2018-05-01

Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.

Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool.

PubMed

Liang, Zifei; He, Xiaohai; Ceritoglu, Can; Tang, Xiaoying; Li, Yue; Kutten, Kwame S; Oishi, Kenichi; Miller, Michael I; Mori, Susumu; Faria, Andreia V

2015-01-01

Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer's Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer's disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping.

PubMed

Studholme, Colin

2011-08-15

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Anticholinesterase activities of cold and hot aqueous extracts of F. racemosa stem bark.

PubMed

Ahmed, Faiyaz; Urooj, Asna

2010-04-01

The present study evaluated the anticholinesterase activity of cold and hot aqueous extracts of Ficus racemosa stem bark against rat brain acetylcholinesterase in vitro. Both the cold aqueous extract (FRC) and the hot aqueous extract (FRH) exhibited a dose dependent inhibition of rat brain acetylcholinesterase. FRH showed significantly higher (P

Alzheimer brain-derived tau oligomers propagate pathology from endogenous tau.

PubMed

Lasagna-Reeves, Cristian A; Castillo-Carranza, Diana L; Sengupta, Urmi; Guerrero-Munoz, Marcos J; Kiritoshi, Takaki; Neugebauer, Volker; Jackson, George R; Kayed, Rakez

2012-01-01

Intracerebral injection of brain extracts containing amyloid or tau aggregates in transgenic animals can induce cerebral amyloidosis and tau pathology. We extracted pure populations of tau oligomers directly from the cerebral cortex of Alzheimer disease (AD) brain. These oligomers are potent inhibitors of long term potentiation (LTP) in hippocampal brain slices and disrupt memory in wild type mice. We observed for the first time that these authentic brain-derived tau oligomers propagate abnormal tau conformation of endogenous murine tau after prolonged incubation. The conformation and hydrophobicity of tau oligomers play a critical role in the initiation and spread of tau pathology in the naïve host in a manner reminiscent of sporadic AD.

Segmentation of brain volume based on 3D region growing by integrating intensity and edge for image-guided surgery

NASA Astrophysics Data System (ADS)

Tsagaan, Baigalmaa; Abe, Keiichi; Goto, Masahiro; Yamamoto, Seiji; Terakawa, Susumu

2006-03-01

This paper presents a segmentation method of brain tissues from MR images, invented for our image-guided neurosurgery system under development. Our goal is to segment brain tissues for creating biomechanical model. The proposed segmentation method is based on 3-D region growing and outperforms conventional approaches by stepwise usage of intensity similarities between voxels in conjunction with edge information. Since the intensity and the edge information are complementary to each other in the region-based segmentation, we use them twice by performing a coarse-to-fine extraction. First, the edge information in an appropriate neighborhood of the voxel being considered is examined to constrain the region growing. The expanded region of the first extraction result is then used as the domain for the next processing. The intensity and the edge information of the current voxel only are utilized in the final extraction. Before segmentation, the intensity parameters of the brain tissues as well as partial volume effect are estimated by using expectation-maximization (EM) algorithm in order to provide an accurate data interpretation into the extraction. We tested the proposed method on T1-weighted MR images of brain and evaluated the segmentation effectiveness comparing the results with ground truths. Also, the generated meshes from the segmented brain volume by using mesh generating software are shown in this paper.

Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury, Annual Report 2009

DTIC Science & Technology

2009-01-01

applications for recovering from disaster and trauma Defense and Veterans Brain Injury Center Develops and delivers advanced TBI-specifi c treatment...specifically aimed at developing cognitive and motor therapy tools using videogame technology, game-based PH outreach tools and support tools for children of...Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury Annual Report 2009 Report Documentation Page Form ApprovedOMB No

Protective effect of extract of Cordyceps sinensis in middle cerebral artery occlusion-induced focal cerebral ischemia in rats

PubMed Central

2010-01-01

Background Ischemic hypoxic brain injury often causes irreversible brain damage. The lack of effective and widely applicable pharmacological treatments for ischemic stroke patients may explain a growing interest in traditional medicines. From the point of view of "self-medication" or "preventive medicine," Cordyceps sinensis was used in the prevention of cerebral ischemia in this paper. Methods The right middle cerebral artery occlusion model was used in the study. The effects of Cordyceps sinensis (Caterpillar fungus) extract on mortality rate, neurobehavior, grip strength, lactate dehydrogenase, glutathione content, Lipid Peroxidation, glutathione peroxidase activity, glutathione reductase activity, catalase activity, Na+K+ATPase activity and glutathione S transferase activity in a rat model were studied respectively. Results Cordyceps sinensis extract significantly improved the outcome in rats after cerebral ischemia and reperfusion in terms of neurobehavioral function. At the same time, supplementation of Cordyceps sinensis extract significantly boosted the defense mechanism against cerebral ischemia by increasing antioxidants activity related to lesion pathogenesis. Restoration of the antioxidant homeostasis in the brain after reperfusion may have helped the brain recover from ischemic injury. Conclusions These experimental results suggest that complement Cordyceps sinensis extract is protective after cerebral ischemia in specific way. The administration of Cordyceps sinensis extract significantly reduced focal cerebral ischemic/reperfusion injury. The defense mechanism against cerebral ischemia was by increasing antioxidants activity related to lesion pathogenesis. PMID:20955613

Protective effect of extract of Cordyceps sinensis in middle cerebral artery occlusion-induced focal cerebral ischemia in rats.

PubMed

Liu, Zhenquan; Li, Pengtao; Zhao, Dan; Tang, Huiling; Guo, Jianyou

2010-10-19

Ischemic hypoxic brain injury often causes irreversible brain damage. The lack of effective and widely applicable pharmacological treatments for ischemic stroke patients may explain a growing interest in traditional medicines. From the point of view of "self-medication" or "preventive medicine," Cordyceps sinensis was used in the prevention of cerebral ischemia in this paper. The right middle cerebral artery occlusion model was used in the study. The effects of Cordyceps sinensis (Caterpillar fungus) extract on mortality rate, neurobehavior, grip strength, lactate dehydrogenase, glutathione content, Lipid Peroxidation, glutathione peroxidase activity, glutathione reductase activity, catalase activity, Na+K+ATPase activity and glutathione S transferase activity in a rat model were studied respectively. Cordyceps sinensis extract significantly improved the outcome in rats after cerebral ischemia and reperfusion in terms of neurobehavioral function. At the same time, supplementation of Cordyceps sinensis extract significantly boosted the defense mechanism against cerebral ischemia by increasing antioxidants activity related to lesion pathogenesis. Restoration of the antioxidant homeostasis in the brain after reperfusion may have helped the brain recover from ischemic injury. These experimental results suggest that complement Cordyceps sinensis extract is protective after cerebral ischemia in specific way. The administration of Cordyceps sinensis extract significantly reduced focal cerebral ischemic/reperfusion injury. The defense mechanism against cerebral ischemia was by increasing antioxidants activity related to lesion pathogenesis.

An improved high-throughput lipid extraction method for the analysis of human brain lipids.

PubMed

Abbott, Sarah K; Jenner, Andrew M; Mitchell, Todd W; Brown, Simon H J; Halliday, Glenda M; Garner, Brett

2013-03-01

We have developed a protocol suitable for high-throughput lipidomic analysis of human brain samples. The traditional Folch extraction (using chloroform and glass-glass homogenization) was compared to a high-throughput method combining methyl-tert-butyl ether (MTBE) extraction with mechanical homogenization utilizing ceramic beads. This high-throughput method significantly reduced sample handling time and increased efficiency compared to glass-glass homogenizing. Furthermore, replacing chloroform with MTBE is safer (less carcinogenic/toxic), with lipids dissolving in the upper phase, allowing for easier pipetting and the potential for automation (i.e., robotics). Both methods were applied to the analysis of human occipital cortex. Lipid species (including ceramides, sphingomyelins, choline glycerophospholipids, ethanolamine glycerophospholipids and phosphatidylserines) were analyzed via electrospray ionization mass spectrometry and sterol species were analyzed using gas chromatography mass spectrometry. No differences in lipid species composition were evident when the lipid extraction protocols were compared, indicating that MTBE extraction with mechanical bead homogenization provides an improved method for the lipidomic profiling of human brain tissue.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

A model for brain life history evolution.

PubMed

González-Forero, Mauricio; Faulwasser, Timm; Lehmann, Laurent

2017-03-01

Complex cognition and relatively large brains are distributed across various taxa, and many primarily verbal hypotheses exist to explain such diversity. Yet, mathematical approaches formalizing verbal hypotheses would help deepen the understanding of brain and cognition evolution. With this aim, we combine elements of life history and metabolic theories to formulate a metabolically explicit mathematical model for brain life history evolution. We assume that some of the brain's energetic expense is due to production (learning) and maintenance (memory) of energy-extraction skills (or cognitive abilities, knowledge, information, etc.). We also assume that individuals use such skills to extract energy from the environment, and can allocate this energy to grow and maintain the body, including brain and reproductive tissues. The model can be used to ask what fraction of growth energy should be allocated at each age, given natural selection, to growing brain and other tissues under various biological settings. We apply the model to find uninvadable allocation strategies under a baseline setting ("me vs nature"), namely when energy-extraction challenges are environmentally determined and are overcome individually but possibly with maternal help, and use modern-human data to estimate model's parameter values. The resulting uninvadable strategies yield predictions for brain and body mass throughout ontogeny and for the ages at maturity, adulthood, and brain growth arrest. We find that: (1) a me-vs-nature setting is enough to generate adult brain and body mass of ancient human scale and a sequence of childhood, adolescence, and adulthood stages; (2) large brains are favored by intermediately challenging environments, moderately effective skills, and metabolically expensive memory; and (3) adult skill is proportional to brain mass when metabolic costs of memory saturate the brain metabolic rate allocated to skills.

An Intracranial Electroencephalography (iEEG) Brain Function Mapping Tool with an Application to Epilepsy Surgery Evaluation.

PubMed

Wang, Yinghua; Yan, Jiaqing; Wen, Jianbin; Yu, Tao; Li, Xiaoli

2016-01-01

Before epilepsy surgeries, intracranial electroencephalography (iEEG) is often employed in function mapping and epileptogenic foci localization. Although the implanted electrodes provide crucial information for epileptogenic zone resection, a convenient clinical tool for electrode position registration and Brain Function Mapping (BFM) visualization is still lacking. In this study, we developed a BFM Tool, which facilitates electrode position registration and BFM visualization, with an application to epilepsy surgeries. The BFM Tool mainly utilizes electrode location registration and function mapping based on pre-defined brain models from other software. In addition, the electrode node and mapping properties, such as the node size/color, edge color/thickness, mapping method, can be adjusted easily using the setting panel. Moreover, users may manually import/export location and connectivity data to generate figures for further application. The role of this software is demonstrated by a clinical study of language area localization. The BFM Tool helps clinical doctors and researchers visualize implanted electrodes and brain functions in an easy, quick and flexible manner. Our tool provides convenient electrode registration, easy brain function visualization, and has good performance. It is clinical-oriented and is easy to deploy and use. The BFM tool is suitable for epilepsy and other clinical iEEG applications.

An Intracranial Electroencephalography (iEEG) Brain Function Mapping Tool with an Application to Epilepsy Surgery Evaluation

PubMed Central

Wang, Yinghua; Yan, Jiaqing; Wen, Jianbin; Yu, Tao; Li, Xiaoli

2016-01-01

Objects: Before epilepsy surgeries, intracranial electroencephalography (iEEG) is often employed in function mapping and epileptogenic foci localization. Although the implanted electrodes provide crucial information for epileptogenic zone resection, a convenient clinical tool for electrode position registration and Brain Function Mapping (BFM) visualization is still lacking. In this study, we developed a BFM Tool, which facilitates electrode position registration and BFM visualization, with an application to epilepsy surgeries. Methods: The BFM Tool mainly utilizes electrode location registration and function mapping based on pre-defined brain models from other software. In addition, the electrode node and mapping properties, such as the node size/color, edge color/thickness, mapping method, can be adjusted easily using the setting panel. Moreover, users may manually import/export location and connectivity data to generate figures for further application. The role of this software is demonstrated by a clinical study of language area localization. Results: The BFM Tool helps clinical doctors and researchers visualize implanted electrodes and brain functions in an easy, quick and flexible manner. Conclusions: Our tool provides convenient electrode registration, easy brain function visualization, and has good performance. It is clinical-oriented and is easy to deploy and use. The BFM tool is suitable for epilepsy and other clinical iEEG applications. PMID:27199729

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Unveiling molecular events in the brain by noninvasive imaging.

PubMed

Klohs, Jan; Rudin, Markus

2011-10-01

Neuroimaging allows researchers and clinicians to noninvasively assess structure and function of the brain. With the advances of imaging modalities such as magnetic resonance, nuclear, and optical imaging; the design of target-specific probes; and/or the introduction of reporter gene assays, these technologies are now capable of visualizing cellular and molecular processes in vivo. Undoubtedly, the system biological character of molecular neuroimaging, which allows for the study of molecular events in the intact organism, will enhance our understanding of physiology and pathophysiology of the brain and improve our ability to diagnose and treat diseases more specifically. Technical/scientific challenges to be faced are the development of highly sensitive imaging modalities, the design of specific imaging probe molecules capable of penetrating the CNS and reporting on endogenous cellular and molecular processes, and the development of tools for extracting quantitative, biologically relevant information from imaging data. Today, molecular neuroimaging is still an experimental approach with limited clinical impact; this is expected to change within the next decade. This article provides an overview of molecular neuroimaging approaches with a focus on rodent studies documenting the exploratory state of the field. Concepts are illustrated by discussing applications related to the pathophysiology of Alzheimer's disease.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

The potential role of Morus alba leaves extract on the brain of mice infected with Schistosoma mansoni.

PubMed

Bauomy, Amira A

2014-01-01

Schistosomiasis is a neglected tropical disease which is associated with neuropsychiatric and neuropathological disorders. Herein, the main goal of the presented work is to investigate the effect of Morus alba leaves extract in mice brain infected with Schistosoma mansoni. Since, the resistance of Schistosomes to antischistosomal drug (praziquantel) has been examined, schistosomiasis induced brain oxidative stress as evidenced by the decrease of glutathione level, total antioxidant capacity and the activity of catalase significantly, while a significant elevation in the levels of nitrite/nitrate and malondialdhyde. In addition, the infection resulted in neurochemical disturbances, the main inhibitory amino acid, γ- aminobutyric acid level was decreased. In contrast, the level of chloride ions and acetylcholine esterase activity were significantly increased. Moreover, the histopathological section showed some impairments in the brain. The treatment with Morus alba leaves extract ameliorated the induced disturbances in schistosome-infected mice where the levels of non-enzymatic and enzymatic antioxidants were elevated. On the other hand, the levels of nitrite/nitrate and malondialdhyde were significantly reduced. Likewise, treatment of mice with Morus alba leaves extract improved the altered levels of γ- aminobutyric acid level and chloride ion. Also, it improved the recorded impairments of the histopathological section in the brain of schistosome infected mice.

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

PubMed

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

PubMed Central

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

Learning implicit brain MRI manifolds with deep learning

NASA Astrophysics Data System (ADS)

Bermudez, Camilo; Plassard, Andrew J.; Davis, Larry T.; Newton, Allen T.; Resnick, Susan M.; Landman, Bennett A.

2018-03-01

An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a low-dimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures. In this work, we use deep learning techniques to investigate implicit manifolds of normal brains and generate new, high-quality images. We explore implicit manifolds by addressing the problems of image synthesis and image denoising as important tools in manifold learning. First, we propose the unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) by learning from 528 examples of 2D axial slices of brain MRI. Synthesized images were first shown to be unique by performing a cross-correlation with the training set. Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5. The quality score of the synthetic image showed substantial overlap with that of the real images. Moreover, we use an autoencoder with skip connections for image denoising, showing that the proposed method results in higher PSNR than FSL SUSAN after denoising. This work shows the power of artificial networks to synthesize realistic imaging data, which can be used to improve image processing techniques and provide a quantitative framework to structural changes in the brain.

Brain oxygenation patterns during the execution of tool use demonstration, tool use pantomime, and body-part-as-object tool use.

PubMed

Helmich, Ingo; Holle, Henning; Rein, Robert; Lausberg, Hedda

2015-04-01

Divergent findings exist whether left and right hemispheric pre- and postcentral cortices contribute to the production of tool use related hand movements. In order to clarify the neural substrates of tool use demonstrations with tool in hand, tool use pantomimes without tool in hand, and body-part-as-object presentations of tool use (BPO) in a naturalistic mode of execution, we applied functional Near InfraRed Spectroscopy (fNIRS) in twenty-three right-handed participants. Functional NIRS techniques allow for the investigation of brain oxygenation during the execution of complex hand movements with an unlimited movement range. Brain oxygenation patterns were retrieved from 16 channels of measurement above pre- and postcentral cortices of each hemisphere. The results showed that tool use demonstration with tool in hand leads to increased oxygenation as compared to tool use pantomimes in the left hemispheric somatosensory gyrus. Left hand executions of the demonstration of tool use, pantomime of tool use, and BPO of tool use led to increased oxygenation in the premotor and somatosensory cortices of the left hemisphere as compared to right hand executions of either condition. The results indicate that the premotor and somatosensory cortices of the left hemisphere constitute relevant brain structures for tool related hand movement production when using the left hand, whereas the somatosensory cortex of the left hemisphere seems to provide specific mental representations when performing tool use demonstrations with the tool in hand. Copyright © 2015 Elsevier B.V. All rights reserved.

Video-based eye tracking for neuropsychiatric assessment.

PubMed

Adhikari, Sam; Stark, David E

2017-01-01

This paper presents a video-based eye-tracking method, ideally deployed via a mobile device or laptop-based webcam, as a tool for measuring brain function. Eye movements and pupillary motility are tightly regulated by brain circuits, are subtly perturbed by many disease states, and are measurable using video-based methods. Quantitative measurement of eye movement by readily available webcams may enable early detection and diagnosis, as well as remote/serial monitoring, of neurological and neuropsychiatric disorders. We successfully extracted computational and semantic features for 14 testing sessions, comprising 42 individual video blocks and approximately 17,000 image frames generated across several days of testing. Here, we demonstrate the feasibility of collecting video-based eye-tracking data from a standard webcam in order to assess psychomotor function. Furthermore, we were able to demonstrate through systematic analysis of this data set that eye-tracking features (in particular, radial and tangential variance on a circular visual-tracking paradigm) predict performance on well-validated psychomotor tests. © 2017 New York Academy of Sciences.

Multimodal optical analysis discriminates freshly extracted human sample of gliomas, metastases and meningiomas from their appropriate controls

NASA Astrophysics Data System (ADS)

Zanello, Marc; Poulon, Fanny; Pallud, Johan; Varlet, Pascale; Hamzeh, H.; Abi Lahoud, Georges; Andreiuolo, Felipe; Ibrahim, Ali; Pages, Mélanie; Chretien, Fabrice; di Rocco, Federico; Dezamis, Edouard; Nataf, François; Turak, Baris; Devaux, Bertrand; Abi Haidar, Darine

2017-02-01

Delineating tumor margins as accurately as possible is of primordial importance in surgical oncology: extent of resection is associated with survival but respect of healthy surrounding tissue is necessary for preserved quality of life. The real-time analysis of the endogeneous fluorescence signal of brain tissues is a promising tool for defining margins of brain tumors. The present study aims to demonstrate the feasibility of multimodal optical analysis to discriminate fresh samples of gliomas, metastases and meningiomas from their appropriate controls. Tumor samples were studied on an optical fibered endoscope using spectral and fluorescence lifetime analysis and then on a multimodal set-up for acquiring spectral, one and two-photon fluorescence images, second harmonic generation signals and two-photon fluorescence lifetime datasets. The obtained data allowed us to differentiate healthy samples from tumor samples. These results confirmed the possible clinical relevance of this real-time multimodal optical analysis. This technique can be easily applied to neurosurgical procedures for a better delineation of surgical margins.

Neuroimaging with functional near infrared spectroscopy: From formation to interpretation

NASA Astrophysics Data System (ADS)

Herrera-Vega, Javier; Treviño-Palacios, Carlos G.; Orihuela-Espina, Felipe

2017-09-01

Functional Near Infrared Spectroscopy (fNIRS) is gaining momentum as a functional neuroimaging modality to investigate the cerebral hemodynamics subsequent to neural metabolism. As other neuroimaging modalities, it is neuroscience's tool to understand brain systems functions at behaviour and cognitive levels. To extract useful knowledge from functional neuroimages it is critical to understand the series of transformations applied during the process of the information retrieval and how they bound the interpretation. This process starts with the irradiation of the head tissues with infrared light to obtain the raw neuroimage and proceeds with computational and statistical analysis revealing hidden associations between pixels intensities and neural activity encoded to end up with the explanation of some particular aspect regarding brain function.To comprehend the overall process involved in fNIRS there is extensive literature addressing each individual step separately. This paper overviews the complete transformation sequence through image formation, reconstruction and analysis to provide an insight of the final functional interpretation.

A New MRI Masking Technique Based on Multi-Atlas Brain Segmentation in Controls and Schizophrenia: A Rapid and Viable Alternative to Manual Masking.

PubMed

Del Re, Elisabetta C; Gao, Yi; Eckbo, Ryan; Petryshen, Tracey L; Blokland, Gabriëlla A M; Seidman, Larry J; Konishi, Jun; Goldstein, Jill M; McCarley, Robert W; Shenton, Martha E; Bouix, Sylvain

2016-01-01

Brain masking of MRI images separates brain from surrounding tissue and its accuracy is important for further imaging analyses. We implemented a new brain masking technique based on multi-atlas brain segmentation (MABS) and compared MABS to masks generated using FreeSurfer (FS; version 5.3), Brain Extraction Tool (BET), and Brainwash, using manually defined masks (MM) as the gold standard. We further determined the effect of different masking techniques on cortical and subcortical volumes generated by FreeSurfer. Images were acquired on a 3-Tesla MR Echospeed system General Electric scanner on five control and five schizophrenia subjects matched on age, sex, and IQ. Automated masks were generated from MABS, FS, BET, and Brainwash, and compared to MM using these metrics: a) volume difference from MM; b) Dice coefficients; and c) intraclass correlation coefficients. Mean volume difference between MM and MABS masks was significantly less than the difference between MM and FS or BET masks. Dice coefficient between MM and MABS was significantly higher than Dice coefficients between MM and FS, BET, or Brainwash. For subcortical and left cortical regions, MABS volumes were closer to MM volumes than were BET or FS volumes. For right cortical regions, MABS volumes were closer to MM volumes than were BET volumes. Brain masks generated using FreeSurfer, BET, and Brainwash are rapidly obtained, but are less accurate than manually defined masks. Masks generated using MABS, in contrast, resemble more closely the gold standard of manual masking, thereby offering a rapid and viable alternative. Copyright © 2015 by the American Society of Neuroimaging.

A model for brain life history evolution

PubMed Central

Lehmann, Laurent

2017-01-01

Complex cognition and relatively large brains are distributed across various taxa, and many primarily verbal hypotheses exist to explain such diversity. Yet, mathematical approaches formalizing verbal hypotheses would help deepen the understanding of brain and cognition evolution. With this aim, we combine elements of life history and metabolic theories to formulate a metabolically explicit mathematical model for brain life history evolution. We assume that some of the brain’s energetic expense is due to production (learning) and maintenance (memory) of energy-extraction skills (or cognitive abilities, knowledge, information, etc.). We also assume that individuals use such skills to extract energy from the environment, and can allocate this energy to grow and maintain the body, including brain and reproductive tissues. The model can be used to ask what fraction of growth energy should be allocated at each age, given natural selection, to growing brain and other tissues under various biological settings. We apply the model to find uninvadable allocation strategies under a baseline setting (“me vs nature”), namely when energy-extraction challenges are environmentally determined and are overcome individually but possibly with maternal help, and use modern-human data to estimate model’s parameter values. The resulting uninvadable strategies yield predictions for brain and body mass throughout ontogeny and for the ages at maturity, adulthood, and brain growth arrest. We find that: (1) a me-vs-nature setting is enough to generate adult brain and body mass of ancient human scale and a sequence of childhood, adolescence, and adulthood stages; (2) large brains are favored by intermediately challenging environments, moderately effective skills, and metabolically expensive memory; and (3) adult skill is proportional to brain mass when metabolic costs of memory saturate the brain metabolic rate allocated to skills. PMID:28278153

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer's Disease.

PubMed

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K

2016-11-30

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer's disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer's disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer's disease brains. The biological pathways associated with Alzheimer's disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.

Dimension reduction: additional benefit of an optimal filter for independent component analysis to extract event-related potentials.

PubMed

Cong, Fengyu; Leppänen, Paavo H T; Astikainen, Piia; Hämäläinen, Jarmo; Hietanen, Jari K; Ristaniemi, Tapani

2011-09-30

The present study addresses benefits of a linear optimal filter (OF) for independent component analysis (ICA) in extracting brain event-related potentials (ERPs). A filter such as the digital filter is usually considered as a denoising tool. Actually, in filtering ERP recordings by an OF, the ERP' topography should not be changed by the filter, and the output should also be able to be modeled by the linear transformation. Moreover, an OF designed for a specific ERP source or component may remove noise, as well as reduce the overlap of sources and even reject some non-targeted sources in the ERP recordings. The OF can thus accomplish both the denoising and dimension reduction (reducing the number of sources) simultaneously. We demonstrated these effects using two datasets, one containing visual and the other auditory ERPs. The results showed that the method including OF and ICA extracted much more reliable components than the sole ICA without OF did, and that OF removed some non-targeted sources and made the underdetermined model of EEG recordings approach to the determined one. Thus, we suggest designing an OF based on the properties of an ERP to filter recordings before using ICA decomposition to extract the targeted ERP component. Copyright © 2011 Elsevier B.V. All rights reserved.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

PubMed Central

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K.

2016-01-01

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer’s disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer’s disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer’s disease brains. The biological pathways associated with Alzheimer’s disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature. PMID:27901073

Freeze-Drying as Sample Preparation for Micellar Electrokinetic Capillary Chromatography – Electrochemical Separations of Neurochemicals in Drosophila Brains

PubMed Central

Berglund, E. Carina; Kuklinski, Nicholas J.; Karagündüz, Ekin; Ucar, Kubra; Hanrieder, Jörg; Ewing, Andrew G.

2013-01-01

Micellar electrokinetic capillary chromatography with electrochemical detection has been used to quantify biogenic amines in freeze-dried Drosophila melanogaster brains. Freeze drying samples offers a way to preserve the biological sample while making dissection of these tiny samples easier and faster. Fly samples were extracted in cold acetone and dried in a rotary evaporator. Extraction and drying times were optimized in order to avoid contamination by red-pigment from the fly eyes and still have intact brain structures. Single freeze-dried fly-brain samples were found to produce representative electropherograms as a single hand-dissected brain sample. Utilizing the faster dissection time that freeze drying affords, the number of brains in a fixed homogenate volume can be increased to concentrate the sample. Thus, concentrated brain samples containing five or fifteen preserved brains were analyzed for their neurotransmitter content, and five analytes; dopamine N-acetyloctopamine, Nacetylserotonin, N-acetyltyramine, N-acetyldopamine were found to correspond well with previously reported values. PMID:23387977

Ensembles of NLP Tools for Data Element Extraction from Clinical Notes

PubMed Central

Kuo, Tsung-Ting; Rao, Pallavi; Maehara, Cleo; Doan, Son; Chaparro, Juan D.; Day, Michele E.; Farcas, Claudiu; Ohno-Machado, Lucila; Hsu, Chun-Nan

2016-01-01

Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort. PMID:28269947

Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

PubMed

Kuo, Tsung-Ting; Rao, Pallavi; Maehara, Cleo; Doan, Son; Chaparro, Juan D; Day, Michele E; Farcas, Claudiu; Ohno-Machado, Lucila; Hsu, Chun-Nan

2016-01-01

Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.

The muscle protein dysferlin accumulates in the Alzheimer brain

PubMed Central

Palamand, Divya; Strider, Jeff; Milone, Margherita; Pestronk, Alan

2006-01-01

Dysferlin is a transmembrane protein that is highly expressed in muscle. Dysferlin mutations cause limb-girdle dystrophy type 2B, Miyoshi myopathy and distal anterior compartment myopathy. Dysferlin has also been described in neural tissue. We studied dysferlin distribution in the brains of patients with Alzheimer disease (AD) and controls. Twelve brains, staged using the Clinical Dementia Rating were examined: 9 AD cases (mean age: 85.9 years and mean disease duration: 8.9 years), and 3 age-matched controls (mean age: 87.5 years). Dysferlin is a cytoplasmic protein in the pyramidal neurons of normal and AD brains. In addition, there were dysferlin-positive dystrophic neurites within Aβ plaques in the AD brain, distinct from tau-positive neurites. Western blots of total brain protein (RIPA) and sequential extraction buffers (high salt, high salt/Triton X-100, SDS and formic acid) of increasing protein extraction strength were performed to examine solubility state. In RIPA fractions, dysferlin was seen as 230–272 kDa bands in normal and AD brains. In serial extractions, there was a shift of dysferlin from soluble phase in high salt/Triton X-100 to the more insoluble SDS fraction in AD. Dysferlin is a new protein described in the AD brain that accumulates in association with neuritic plaques. In muscle, dysferlin plays a role in the repair of muscle membrane damage. The accumulation of dysferlin in the AD brain may be related to the inability of neurons to repair damage due to Aβ deposits accumulating in the AD brain. PMID:17024495

Transcranial magnetic stimulation in brain injury.

PubMed

Castel-Lacanal, E; Tarri, M; Loubinoux, I; Gasq, D; de Boissezon, X; Marque, P; Simonetta-Moreau, M

2014-02-01

Transcranial magnetic stimulations (TMS) have been used for many years as a diagnostic tool to explore changes in cortical excitability, and more recently as a tool for therapeutic neuromodulation. We are interested in their applications following brain injury: stroke, traumatic and anoxic brain injury. Following brain injury, there is decreased cortical excitability and changes in interhemispheric interactions depending on the type, the severity, and the time-lapse between the injury and the treatment implemented. rTMS (repetitive TMS) is a therapeutic neuromodulation tool which restores the interhemispheric interactions following stroke by inhibiting the healthy cortex with frequencies ≤1Hz, or by exciting the lesioned cortex with frequencies between 3 and 50Hz. Results in motor recovery are promising and those in improving aphasia or visuospatial neglect are also encouraging. Finally, the use of TMS is mainly limited by the risk of seizure, and is therefore contraindicated for many patients. TMS is a useful non-invasive brain stimulation tool to diagnose the effects of brain injury, to study the mechanisms of recovery and a non-invasive neuromodulation promising tool to influence the post-lesional recovery. Copyright © 2013 Société française d’anesthésie et de réanimation (Sfar). Published by Elsevier SAS. All rights reserved.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

PubMed Central

Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

2015-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. PMID:25987366

Microstates in resting-state EEG: current status and future directions.

PubMed

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M; Farzan, Faranak

2015-02-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

Microstates in Resting-State EEG: Current Status and Future Directions

PubMed Central

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M.; Farzan, Faranak

2015-01-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable “microstates” that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. PMID:25526823

Metabolic and vascular origins of the BOLD effect: Implications for imaging pathology and resting-state brain function.

PubMed

Mark, Clarisse I; Mazerolle, Erin L; Chen, J Jean

2015-08-01

The blood oxygenation level-dependent (BOLD) phenomenon has profoundly revolutionized neuroscience, with applications ranging from normal brain development and aging, to brain disorders and diseases. While the BOLD effect represents an invaluable tool to map brain function, it does not measure neural activity directly; rather, it reflects changes in blood oxygenation resulting from the relative balance between cerebral oxygen metabolism (through neural activity) and oxygen supply (through cerebral blood flow and volume). As such, there are cases in which BOLD signals might be dissociated from neural activity, leading to misleading results. The emphasis of this review is to develop a critical perspective for interpreting BOLD results, through a comprehensive consideration of BOLD's metabolic and vascular underpinnings. We demonstrate that such an understanding is especially important under disease or resting conditions. We also describe state-of-the-art acquisition and analytical techniques to reveal physiological information on the mechanisms underlying measured BOLD signals. With these goals in mind, this review is structured to provide a fundamental understanding of: 1) the physiological and physical sources of the BOLD contrast; 2) the extraction of information regarding oxidative metabolism and cerebrovascular reactivity from the BOLD signal, critical to investigating neuropathology; and 3) the fundamental importance of metabolic and vascular mechanisms for interpreting resting-state BOLD measurements. © 2015 Wiley Periodicals, Inc.

Frontal and Temporal Structural Connectivity Is Associated with Social Communication Impairment Following Traumatic Brain Injury

PubMed Central

Rigon, Arianna; Voss, Michelle W.; Turkstra, Lyn S.; Mutlu, Bilge; Duff, Melissa C.

2018-01-01

Objectives Although it has been well documented that traumatic brain injury (TBI) can result in communication impairment, little work to date has examined the relationship between social communication skills and structural brain integrity in patients with TBI. The aim of the current study was to investigate the association between self- and other-perceived communication problems and white matter integrity in patients with mild to severe TBI. Methods Forty-four individuals (TBI = 24) and people with whom they frequently communicate, as well as demographically matched normal healthy comparisons (NC) and their frequent communication partners, were administered, respectively, the La-Trobe Communication Questionnaire Self form (LCQ-SELF) and Other form (LCQ-OTHER). In addition, diffusion tensor imaging data were collected, and fractional anisotropy (FA) measures were extracted for each lobe in both hemispheres. Results Within the TBI group, but not within the NC group, participants who were perceived by their close others as having more communication problems had lower FA in the left frontal and temporal lobes (p < .01), but not in other brain regions. Conclusions Frontotemporal white matter microstructural integrity is associated with social communication abilities in adults with TBI. This finding contributes to our understanding of the mechanisms leading to communication impairment following TBI and can inform the development of new neuromodulation therapies as well as diagnostic tools. PMID:27405965

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

PubMed

Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2015-08-01

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2015-08-01

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

Assessing and inducing neuroplasticity with transcranial magnetic stimulation and robotics for motor function.

PubMed

O'Malley, Marcia K; Ro, Tony; Levin, Harvey S

2006-12-01

To describe 2 new ways of assessing and inducing neuroplasticity in the human brain--transcranial magnetic stimulation (TMS) and robotics--and to investigate and promote the recovery of motor function after brain damage. We identified recent articles and books directly bearing on TMS and robotics. Articles using these tools for purposes other than rehabilitation were excluded. From these studies, we emphasize the methodologic and technical details of these tools as applicable for assessing and inducing plasticity. Because both tools have only recently been used for rehabilitation, the majority of the articles selected for this review have been published only within the last 10 years. We used the PubMed and Compendex databases to find relevant peer-reviewed studies for this review. The studies were required to be relevant to rehabilitation and to use TMS or robotics methodologies. Guidelines were applied via independent extraction by multiple observers. Despite the limited amount of research using these procedures for assessing and inducing neuroplasticity, there is growing evidence that both TMS and robotics can be very effective, inexpensive, and convenient ways for assessing and inducing rehabilitation. Although TMS has primarily been used as an assessment tool for motor function, an increasing number of studies are using TMS as a tool to directly induce plasticity and improve motor function. Similarly, robotic devices have been used for rehabilitation because of their suitability for delivery of highly repeatable training. New directions in robotics-assisted rehabilitation are taking advantage of novel measurements that can be acquired via the devices, enabling unique methods of assessment of motor recovery. As refinements in technology and advances in our knowledge continue, TMS and robotics should play an increasing role in assessing and promoting the recovery of function. Ongoing and future studies combining TMS and robotics within the same populations may prove fruitful for a more detailed and comprehensive assessment of the central and peripheral changes in the nervous system during precisely induced recovery.

Protective role of Scoparia dulcis plant extract on brain antioxidant status and lipidperoxidation in STZ diabetic male Wistar rats

PubMed Central

Pari, Leelavinothan; Latha, Muniappan

2004-01-01

Background The aim of the study was to investigate the effect of aqueous extract of Scoparia dulcis on the occurrence of oxidative stress in the brain of rats during diabetes by measuring the extent of oxidative damage as well as the status of the antioxidant defense system. Methods Aqueous extract of Scoparia dulcis plant was administered orally (200 mg/kg body weight) and the effect of extract on blood glucose, plasma insulin and the levels of thiobarbituric acid reactive substances (TBARS), hydroperoxides, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione-S-transferase (GST) and reduced glutathione (GSH) were estimated in streptozotocin (STZ) induced diabetic rats. Glibenclamide was used as standard reference drug. Results A significant increase in the activities of plasma insulin, superoxide dismutase, catalase, glutathione peroxidase, glutathione-S-transferase and reduced glutathione was observed in brain on treatment with 200 mg/kg body weight of Scoparia dulcis plant extract (SPEt) and glibenclamide for 6 weeks. Both the treated groups showed significant decrease in TBARS and hydroperoxides formation in brain, suggesting its role in protection against lipidperoxidation induced membrane damage. Conclusions Since the study of induction of the antioxidant enzymes is considered to be a reliable marker for evaluating the antiperoxidative efficacy of the medicinal plant, these findings suggest a possible antiperoxidative role for Scoparia dulcis plant extract. Hence, in addition to antidiabetic effect, Scoparia dulcis possess antioxidant potential that may be used for therapeutic purposes. PMID:15522116

Protective role of Scoparia dulcis plant extract on brain antioxidant status and lipidperoxidation in STZ diabetic male Wistar rats.

PubMed

Pari, Leelavinothan; Latha, Muniappan

2004-11-02

The aim of the study was to investigate the effect of aqueous extract of Scoparia dulcis on the occurrence of oxidative stress in the brain of rats during diabetes by measuring the extent of oxidative damage as well as the status of the antioxidant defense system. Aqueous extract of Scoparia dulcis plant was administered orally (200 mg/kg body weight) and the effect of extract on blood glucose, plasma insulin and the levels of thiobarbituric acid reactive substances (TBARS), hydroperoxides, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione-S-transferase (GST) and reduced glutathione (GSH) were estimated in streptozotocin (STZ) induced diabetic rats. Glibenclamide was used as standard reference drug. A significant increase in the activities of plasma insulin, superoxide dismutase, catalase, glutathione peroxidase, glutathione-S-transferase and reduced glutathione was observed in brain on treatment with 200 mg/kg body weight of Scoparia dulcis plant extract (SPEt) and glibenclamide for 6 weeks. Both the treated groups showed significant decrease in TBARS and hydroperoxides formation in brain, suggesting its role in protection against lipidperoxidation induced membrane damage. Since the study of induction of the antioxidant enzymes is considered to be a reliable marker for evaluating the antiperoxidative efficacy of the medicinal plant, these findings suggest a possible antiperoxidative role for Scoparia dulcis plant extract. Hence, in addition to antidiabetic effect, Scoparia dulcis possess antioxidant potential that may be used for therapeutic purposes.

brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets.

PubMed

Freytag, Saskia; Burgess, Rosemary; Oliver, Karen L; Bahlo, Melanie

2017-06-08

The pathogenesis of neurological and mental health disorders often involves multiple genes, complex interactions, as well as brain- and development-specific biological mechanisms. These characteristics make identification of disease genes for such disorders challenging, as conventional prioritisation tools are not specifically tailored to deal with the complexity of the human brain. Thus, we developed a novel web-application-brain-coX-that offers gene prioritisation with accompanying visualisations based on seven gene expression datasets in the post-mortem human brain, the largest such resource ever assembled. We tested whether our tool can correctly prioritise known genes from 37 brain-specific KEGG pathways and 17 psychiatric conditions. We achieved average sensitivity of nearly 50%, at the same time reaching a specificity of approximately 75%. We also compared brain-coX's performance to that of its main competitors, Endeavour and ToppGene, focusing on the ability to discover novel associations. Using a subset of the curated SFARI autism gene collection we show that brain-coX's prioritisations are most similar to SFARI's own curated gene classifications. brain-coX is the first prioritisation and visualisation web-tool targeted to the human brain and can be freely accessed via http://shiny.bioinf.wehi.edu.au/freytag.s/ .

Anti-amnesic effects of Ganoderma species: A possible cholinergic and antioxidant mechanism.

PubMed

Kaur, Ravneet; Singh, Varinder; Shri, Richa

2017-08-01

Mushrooms are valued for their nutritional as well as medicinal properties. Ganoderma species are used traditionally to treat neurological disorders but scientific evidence for this is insufficient. The present study was designed to systematically evaluate the anti-amnesic effect of selected Ganoderma species i.e. G. mediosinense and G. ramosissimum. Extracts of selected mushroom species were evaluated for their antioxidant activity and acetylcholinesterase (AChE) inhibition using in-vitro assays (DPPH and Ellman tests respectively). The anti-amnesic potential of the most active extract (i.e. 70% methanol extract of G. mediosinense) was confirmed using mouse model of scopolamine-induced amnesia. Mice were treated with bioactive extract and donepezil once orally before the induction of amnesia. Cognitive functions were evaluated using passive shock avoidance (PSA) and novel object recognition (NOR) tests. The effect on brain AChE activity, brain oxidative stress (TBARS level) and neuronal damage (H & E staining) were also assessed. In-vitro results showed strong antioxidant and AChE inhibitory activities by G. mediosinense extract (GME). Therefore, it was selected for in-vivo studies. GME pre-treatment (800mg/kg, p.o.) reversed the effect of scopolamine in mice, evident by significant decrease (p <0.05) in the transfer latency time and increase in object recognition index in PSA and NOR, respectively. GME significantly reduced the brain AChE activity and oxidative stress. Histopathological examination of brain tissues showed decrease in vacuolated cytoplasm and increase in pyramidal cells in brain hippocampal and cortical regions. GME exerts anti-amnesic effect through AChE inhibition and antioxidant mechanisms. Copyright © 2017. Published by Elsevier Masson SAS.

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

PubMed

Xu, Shanzhi; Hu, Hai; Ji, Linhong; Wang, Peng

2018-02-26

The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

Bayesian decoding using unsorted spikes in the rat hippocampus

PubMed Central

Layton, Stuart P.; Chen, Zhe; Wilson, Matthew A.

2013-01-01

A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces. PMID:24089403

Shared brain lateralization patterns in language and Acheulean stone tool production: a functional transcranial Doppler ultrasound study.

PubMed

Uomini, Natalie Thaïs; Meyer, Georg Friedrich

2013-01-01

The popular theory that complex tool-making and language co-evolved in the human lineage rests on the hypothesis that both skills share underlying brain processes and systems. However, language and stone tool-making have so far only been studied separately using a range of neuroimaging techniques and diverse paradigms. We present the first-ever study of brain activation that directly compares active Acheulean tool-making and language. Using functional transcranial Doppler ultrasonography (fTCD), we measured brain blood flow lateralization patterns (hemodynamics) in subjects who performed two tasks designed to isolate the planning component of Acheulean stone tool-making and cued word generation as a language task. We show highly correlated hemodynamics in the initial 10 seconds of task execution. Stone tool-making and cued word generation cause common cerebral blood flow lateralization signatures in our participants. This is consistent with a shared neural substrate for prehistoric stone tool-making and language, and is compatible with language evolution theories that posit a co-evolution of language and manual praxis. In turn, our results support the hypothesis that aspects of language might have emerged as early as 1.75 million years ago, with the start of Acheulean technology.

Automated Design Tools for Integrated Mixed-Signal Microsystems (NeoCAD)

DTIC Science & Technology

2005-02-01

method, Model Order Reduction (MOR) tools, system-level, mixed-signal circuit synthesis and optimization tools, and parsitic extraction tools. A unique...Mission Area: Command and Control mixed signal circuit simulation parasitic extraction time-domain simulation IC design flow model order reduction... Extraction 1.2 Overall Program Milestones CHAPTER 2 FAST TIME DOMAIN MIXED-SIGNAL CIRCUIT SIMULATION 2.1 HAARSPICE Algorithms 2.1.1 Mathematical Background

Matching spatial with ontological brain regions using Java tools for visualization, database access, and integrated data analysis.

PubMed

Bezgin, Gleb; Reid, Andrew T; Schubert, Dirk; Kötter, Rolf

2009-01-01

Brain atlases are widely used in experimental neuroscience as tools for locating and targeting specific brain structures. Delineated structures in a given atlas, however, are often difficult to interpret and to interface with database systems that supply additional information using hierarchically organized vocabularies (ontologies). Here we discuss the concept of volume-to-ontology mapping in the context of macroscopical brain structures. We present Java tools with which we have implemented this concept for retrieval of mapping and connectivity data on the macaque brain from the CoCoMac database in connection with an electronic version of "The Rhesus Monkey Brain in Stereotaxic Coordinates" authored by George Paxinos and colleagues. The software, including our manually drawn monkey brain template, can be downloaded freely under the GNU General Public License. It adds value to the printed atlas and has a wider (neuro-)informatics application since it can read appropriately annotated data from delineated sections of other species and organs, and turn them into 3D registered stacks. The tools provide additional features, including visualization and analysis of connectivity data, volume and centre-of-mass estimates, and graphical manipulation of entire structures, which are potentially useful for a range of research and teaching applications.

Zingiber officinale Mitigates Brain Damage and Improves Memory Impairment in Focal Cerebral Ischemic Rat

PubMed Central

Wattanathorn, Jintanaporn; Jittiwat, Jinatta; Tongun, Terdthai; Muchimapura, Supaporn; Ingkaninan, Kornkanok

2011-01-01

Cerebral ischemia is known to produce brain damage and related behavioral deficits including memory. Recently, accumulating lines of evidence showed that dietary enrichment with nutritional antioxidants could reduce brain damage and improve cognitive function. In this study, possible protective effect of Zingiber officinale, a medicinal plant reputed for neuroprotective effect against oxidative stress-related brain damage, on brain damage and memory deficit induced by focal cerebral ischemia was elucidated. Male adult Wistar rats were administrated an alcoholic extract of ginger rhizome orally 14 days before and 21 days after the permanent occlusion of right middle cerebral artery (MCAO). Cognitive function assessment was performed at 7, 14, and 21 days after MCAO using the Morris water maze test. The brain infarct volume and density of neurons in hippocampus were also determined. Furthermore, the level of malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) in cerebral cortex, striatum, and hippocampus was also quantified at the end of experiment. The results showed that cognitive function and neurons density in hippocampus of rats receiving ginger rhizome extract were improved while the brain infarct volume was decreased. The cognitive enhancing effect and neuroprotective effect occurred partly via the antioxidant activity of the extract. In conclusion, our study demonstrated the beneficial effect of ginger rhizome to protect against focal cerebral ischemia. PMID:21197427

Mapping population-based structural connectomes.

PubMed

Zhang, Zhengwu; Descoteaux, Maxime; Zhang, Jingwen; Girard, Gabriel; Chamberland, Maxime; Dunson, David; Srivastava, Anuj; Zhu, Hongtu

2018-05-15

Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects' brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects. Copyright © 2018 Elsevier Inc. All rights reserved.

The impact of unilateral brain damage on anticipatory grip force scaling when lifting everyday objects.

PubMed

Eidenmüller, S; Randerath, J; Goldenberg, G; Li, Y; Hermsdörfer, J

2014-08-01

The scaling of our finger forces according to the properties of manipulated objects is an elementary prerequisite of skilled motor behavior. Lesions of the motor-dominant left brain may impair several aspects of motor planning. For example, limb-apraxia, a tool-use disorder after left brain damage is thought to be caused by deficient recall or integration of tool-use knowledge into an action plan. The aim of the present study was to investigate whether left brain damage affects anticipatory force scaling when lifting everyday objects. We examined 26 stroke patients with unilateral brain damage (16 with left brain damage, ten with right brain damage) and 21 healthy control subjects. Limb apraxia was assessed by testing pantomime of familiar tool-use and imitation of meaningless hand postures. Participants grasped and lifted twelve randomly presented everyday objects. Grip force was measured with help of sensors fixed on thumb, index and middle-finger. The maximum rate of grip force was determined to quantify the precision of anticipation of object properties. Regression analysis yielded clear deficits of anticipation in the group of patients with left brain damage, while the comparison of patient with right brain damage with their respective control group did not reveal comparable deficits. Lesion-analyses indicate that brain structures typically associated with a tool-use network in the left hemisphere play an essential role for anticipatory grip force scaling, especially the left inferior frontal gyrus (IFG) and the premotor cortex (PMC). Furthermore, significant correlations of impaired anticipation with limb apraxia scores suggest shared representations. However, the presence of dissociations, implicates also independent processes. Overall, our findings suggest that the left hemisphere is engaged in anticipatory grip force scaling for lifting everyday objects. The underlying neural substrate is not restricted to a single region or stream; instead it may rely on the intact functioning of a left hemisphere network that may overlap with the left hemisphere dominant tool-use network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Facilitation of Function and Manipulation Knowledge of Tools Using Transcranial Direct Current Stimulation (tDCS).

PubMed

Ishibashi, Ryo; Mima, Tatsuya; Fukuyama, Hidenao; Pobric, Gorana

2017-01-01

Using a variety of tools is a common and essential component of modern human life. Patients with brain damage or neurological disorders frequently have cognitive deficits in their recognition and manipulation of tools. In this study, we focused on improving tool-related cognition using transcranial direct current stimulation (tDCS). Converging evidence from neuropsychology, neuroimaging and non- invasive brain stimulation has identified the anterior temporal lobe (ATL) and inferior parietal lobule (IPL) as brain regions supporting action semantics. We observed enhanced performance in tool cognition with anodal tDCS over ATL and IPL in two cognitive tasks that require rapid access to semantic knowledge about the function or manipulation of common tools. ATL stimulation improved access to both function and manipulation knowledge of tools. The effect of IPL stimulation showed a trend toward better manipulation judgments. Our findings support previous studies of tool semantics and provide a novel approach for manipulation of underlying circuits.

Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism

PubMed Central

Ghiassian, Sina; Greiner, Russell; Jin, Ping; Brown, Matthew R. G.

2016-01-01

A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis. PMID:28030565

Mesoscale brain explorer, a flexible python-based image analysis and visualization tool.

PubMed

Haupt, Dirk; Vanni, Matthieu P; Bolanos, Federico; Mitelut, Catalin; LeDue, Jeffrey M; Murphy, Tim H

2017-07-01

Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE.

Large-scale extraction of brain connectivity from the neuroscientific literature

PubMed Central

Richardet, Renaud; Chappelier, Jean-Cédric; Telefont, Martin; Hill, Sean

2015-01-01

Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity. Results: NERs and connectivity extractors are evaluated against a manually annotated corpus. The complete in litero extraction models are also evaluated against in vivo connectivity data from ABA with an estimated precision of 78%. The resulting database contains over 4 million brain region mentions and over 100 000 (ABA) and 122 000 (BAMS) potential brain region connections. This database drastically accelerates connectivity literature review, by providing a centralized repository of connectivity data to neuroscientists. Availability and implementation: The resulting models are publicly available at github.com/BlueBrain/bluima. Contact: renaud.richardet@epfl.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25609795

Tools for studying drug transport and metabolism in the brain.

PubMed

Pitcher, Meagan R; Quevedo, João

2016-01-01

Development of xenobiotics that cross the blood-brain barrier in therapeutically-relevant quantities is an expensive and time-consuming undertaking. However, central nervous system diseases are an under-addressed cause of high mortality and morbidity, and drug development in this field is a worthwhile venture. We aim to familiarize the reader with available methodologies for studying drug transport into the brain. Current understanding of the blood-brain barrier structure has been well-described in other manuscripts, and first we briefly review the path that xenobiotics take through the brain - from bloodstream, to endothelial cells of the blood-brain barrier, to interstitial space, to brain parenchymal cells, and then to an exit point from the central nervous system. The second half of the review discusses research tools available to determine if xenobiotics are making the journey through the brain successfully and offers commentary on the translational utility of each methodology. Theoretically, non-human mammalian and human blood-brain barriers are similar in composition; however, some findings demonstrate important differences across species. Translational methodologies may provide more reliable information about how a drug may act across species. The recent finding of lymphatic vessels within the central nervous system may provide new tools and strategies for drug delivery to the brain.

Tool Wear Feature Extraction Based on Hilbert Marginal Spectrum

NASA Astrophysics Data System (ADS)

Guan, Shan; Song, Weijie; Pang, Hongyang

2017-09-01

In the metal cutting process, the signal contains a wealth of tool wear state information. A tool wear signal’s analysis and feature extraction method based on Hilbert marginal spectrum is proposed. Firstly, the tool wear signal was decomposed by empirical mode decomposition algorithm and the intrinsic mode functions including the main information were screened out by the correlation coefficient and the variance contribution rate. Secondly, Hilbert transform was performed on the main intrinsic mode functions. Hilbert time-frequency spectrum and Hilbert marginal spectrum were obtained by Hilbert transform. Finally, Amplitude domain indexes were extracted on the basis of the Hilbert marginal spectrum and they structured recognition feature vector of tool wear state. The research results show that the extracted features can effectively characterize the different wear state of the tool, which provides a basis for monitoring tool wear condition.

[Factor structure of regional CBF and CMRglu values as a tool for the study of default mode of the brain].

PubMed

Kataev, G V; Korotkov, A D; Kireev, M V; Medvedev, S V

2013-01-01

In the present article it was shown that the functional connectivity of brain structures, revealed by factor analysis of resting PET CBF and rCMRglu data, is an adequate tool to study the default mode of the human brain. The identification of neuroanatomic systems of default mode (default mode network) during routine clinical PET investigations is important for further studying the functional organization of the normal brain and its reorganizations in pathological conditions.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2012-01-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2011-12-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Using UMLS to construct a generalized hierarchical concept-based dictionary of brain functions for information extraction from the fMRI literature.

PubMed

Hsiao, Mei-Yu; Chen, Chien-Chung; Chen, Jyh-Horng

2009-10-01

With a rapid progress in the field, a great many fMRI studies are published every year, to the extent that it is now becoming difficult for researchers to keep up with the literature, since reading papers is extremely time-consuming and labor-intensive. Thus, automatic information extraction has become an important issue. In this study, we used the Unified Medical Language System (UMLS) to construct a hierarchical concept-based dictionary of brain functions. To the best of our knowledge, this is the first generalized dictionary of this kind. We also developed an information extraction system for recognizing, mapping and classifying terms relevant to human brain study. The precision and recall of our system was on a par with that of human experts in term recognition, term mapping and term classification. Our approach presented in this paper presents an alternative to the more laborious, manual entry approach to information extraction.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2015-11-01

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.

Functional source separation and hand cortical representation for a brain–computer interface feature extraction

PubMed Central

Tecchio, Franca; Porcaro, Camillo; Barbati, Giulia; Zappasodi, Filippo

2007-01-01

A brain–computer interface (BCI) can be defined as any system that can track the person's intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non-invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require set up of the appropriate experimental protocol while recording the brain signal and then to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named functional source separation (FSS). As it comes from the blind source separation algorithms, it exploits the most valuable information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as the optimization procedure allowing the exploit of functional constraints non-differentiable. Moreover, a minor section is included, devoted to information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behaviour in these patients. Relevant BCI features – spatial and time-frequency properties – are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigate the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. This review provides a description of the FSS technique, a promising tool for the BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients. PMID:17331989

[Initial diagnosis of Parkinson's disease - neuroradiological diagnosis].

PubMed

Orimo, Satoshi

2013-01-01

Brain MRI is essential for differentiating Parkinson's disease (PD) from other parkinsonian syndromes. The purpose of performing brain MRI is not to make a diagnosis of PD but is to exclude other parkinsonian syndromes. Recently, several new MRI techniques such as voxel based morphometry, relaxometry, magnetization transfer, spectroscopy, tractography, and functional MRI have been introduced in the diagnosis of PD. Neuromelanin imaging is one of the new techniques and can be useful to make an initial diagnosis of PD. MIBG myocardial scintigraphy is a sensitive imaging tool to differentiate PD from other parkinsonian syndromes and is one of the good tools to make an initial diagnosis of PD. Brain perfusion imaging is sometimes useful to make an initial diagnosis of PD, because reduced brain perfusion area can be detected before brain MRI detects morphological changes of the brain. Dopamine transporter imaging, not available in Japan, is a sensitive tool to detect very early parkinsonism and is useful to make an initial diagnosis of PD. However, it is difficult to differentiate PD from other parkinsonian syndromes.

Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality.

PubMed

Mongerson, Chandler R L; Jennings, Russell W; Borsook, David; Becerra, Lino; Bajic, Dusica

2017-01-01

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

Graphical Neuroimaging Informatics: Application to Alzheimer’s Disease

PubMed Central

Bowman, Ian; Joshi, Shantanu H.; Greer, Vaughan

2013-01-01

The Informatics Visualization for Neuroimaging (INVIZIAN) framework allows one to graphically display image and meta-data information from sizeable collections of neuroimaging data as a whole using a dynamic and compelling user interface. Users can fluidly interact with an entire collection of cortical surfaces using only their mouse. In addition, users can cluster and group brains according in multiple ways for subsequent comparison using graphical data mining tools. In this article, we illustrate the utility of INVIZIAN for simultaneous exploration and mining a large collection of extracted cortical surface data arising in clinical neuroimaging studies of patients with Alzheimer’s Disease, mild cognitive impairment, as well as healthy control subjects. Alzheimer’s Disease is particularly interesting due to the wide-spread effects on cortical architecture and alterations of volume in specific brain areas associated with memory. We demonstrate INVIZIAN’s ability to render multiple brain surfaces from multiple diagnostic groups of subjects, showcase the interactivity of the system, and showcase how INVIZIAN can be employed to generate hypotheses about the collection of data which would be suitable for direct access to the underlying raw data and subsequent formal statistical analysis. Specifically, we use INVIZIAN show how cortical thickness and hippocampal volume differences between group are evident even in the absence of more formal hypothesis testing. In the context of neurological diseases linked to brain aging such as AD, INVIZIAN provides a unique means for considering the entirety of whole brain datasets, look for interesting relationships among them, and thereby derive new ideas for further research and study. PMID:24203652

Toward a workbench for rodent brain image data: systems architecture and design.

PubMed

Moene, Ivar A; Subramaniam, Shankar; Darin, Dmitri; Leergaard, Trygve B; Bjaalie, Jan G

2007-01-01

We present a novel system for storing and manipulating microscopic images from sections through the brain and higher-level data extracted from such images. The system is designed and built on a three-tier paradigm and provides the research community with a web-based interface for facile use in neuroscience research. The Oracle relational database management system provides the ability to store a variety of objects relevant to the images and provides the framework for complex querying of data stored in the system. Further, the suite of applications intimately tied into the infrastructure in the application layer provide the user the ability not only to query and visualize the data, but also to perform analysis operations based on the tools embedded into the system. The presentation layer uses extant protocols of the modern web browser and this provides ease of use of the system. The present release, named Functional Anatomy of the Cerebro-Cerebellar System (FACCS), available through The Rodent Brain Workbench (http:// rbwb.org/), is targeted at the functional anatomy of the cerebro-cerebellar system in rats, and holds axonal tracing data from these projections. The system is extensible to other circuits and projections and to other categories of image data and provides a unique environment for analysis of rodent brain maps in the context of anatomical data. The FACCS application assumes standard animal brain atlas models and can be extended to future models. The system is available both for interactive use from a remote web-browser client as well as for download to a local server machine.

Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

PubMed Central

Mongerson, Chandler R. L.; Jennings, Russell W.; Borsook, David; Becerra, Lino; Bajic, Dusica

2017-01-01

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used. PMID:28856131

Automatic brain tumor detection in MRI: methodology and statistical validation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

2005-04-01

Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

Biobibliometrics (UGDH-TP53-BRCA1) Genes Connections in the Possible Relationship Between Breast Cancer and EEG.

PubMed

Martzoukos, Yannis; Papavlasopoulos, Sozon; Poulos, Marios; Syrrou, Maria

2017-01-01

In recent years there has been an increasingly amount of data stored in biomedical Databases due to the breakthroughs in biology and bioinformatics, biomedical information is growing exponentially making efficient information retrieval from scientist more and more challenging. New Scientific fields as Bioinformatics seem to be the tool needed to extract scientifically important data based on experimental results and information provided by papers and journals. In this paper we are going to implement a custom made IT system in order to find connections between genes in the breast cancer pathways such the BRCA1 with the electrical energy in the human brain with UGDH gene via the TP53 tumor gene. The proposed system will be able to identify the appearance of each gene ID and compare the coexistence of two genes in PubMed articles/papers. The final system could become a useful tool against the struggle of scientists and medical professionals in the near future.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

The effects of dexamethasone on rat brain cortical nuclear factor kappa B (NF-{kappa}B) in endotoxic shock

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Zhi; Kang Jinsong; Li Yang

2006-08-01

To explore the molecular mechanism of brain tissue injury induced by lipopolysaccharide (LPS), we studied the effects of endotoxic shock on rat brain cortex NF-{kappa}B and the effects of dexamethasone on these changes. Rats were randomly divided into LPS, LPS + dexamethasone, and control groups. The DNA-binding activity of NF-{kappa}B was observed using electrophoretic mobility shift assay (EMSA). Protein expression in nuclear extracts was studied using Western blots, and nuclear translocation was observed using immunohistochemistry. These indices were assayed at 1 h and 4 h after intravenous injection of LPS (4 mg.kg{sup -1}). EMSA showed significantly increased NF-{kappa}B DNA-binding activitymore » in nuclear extracts from the LPS group at both 1 h and 4 h after LPS injection, compared with the control group (P < 0.01). For the LPS group, the NF-{kappa}B DNA-binding activity was greater at 1 h than at 4 h (P < 0.05). The expression of p65 and p50 protein in the nuclear extracts was also increased, as compared with the control group. However, the expression of p65 and p50 protein from cytosolic extracts did not show any significant change. Dexamethasone down-regulated not only NF-{kappa}B DNA-binding activity but also the expression of p65 protein in the nuclear extracts. From these data, we have concluded that NF-{kappa}B activation and nuclear translocation of NF-{kappa}B play a key role in the molecular mechanism of brain tissue injury in endotoxic shock. Dexamethasone may alleviate brain injury by inhibiting NF-{kappa}B activation.« less

Using Brain–Computer Interfaces and Brain-State Dependent Stimulation as Tools in Cognitive Neuroscience

PubMed Central

Jensen, Ole; Bahramisharif, Ali; Oostenveld, Robert; Klanke, Stefan; Hadjipapas, Avgis; Okazaki, Yuka O.; van Gerven, Marcel A. J.

2011-01-01

Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work. PMID:21687463

Changes in event-related potential functional networks predict traumatic brain injury in piglets.

PubMed

Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

2018-06-01

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Ethanol extract of Oenanthe javanica increases cell proliferation and neuroblast differentiation in the adolescent rat dentate gyrus

PubMed Central

Chen, Bai Hui; Park, Joon Ha; Cho, Jeong Hwi; Kim, In Hye; Shin, Bich Na; Ahn, Ji Hyeon; Hwang, Seok Joon; Yan, Bing Chun; Tae, Hyun Jin; Lee, Jae Chul; Bae, Eun Joo; Lee, Yun Lyul; Kim, Jong Dai; Won, Moo-Ho; Kang, Il Jun

2015-01-01

Oenanthe javanica is an aquatic perennial herb that belongs to the Oenanthe genus in Apiaceae family, and it displays well-known medicinal properties such as protective effects against glutamate-induced neurotoxicity. However, few studies regarding effects of Oenanthe javanica on neurogenesis in the brain have been reported. In this study, we examined the effects of a normal diet and a diet containing ethanol extract of Oenanthe javanica on cell proliferation and neuroblast differentiation in the subgranular zone of the hippocampal dentate gyrus of adolescent rats using Ki-67 (an endogenous marker for cell proliferation) and doublecortin (a marker for neuroblast). Our results showed that Oenanthe javanica extract significantly increased the number of Ki-67-immunoreactive cells and doublecortin-immunoreactive neuroblasts in the subgranular zone of the dentate gyrus in the adolescent rats. In addition, the immunoreactivity of brain-derived neurotrophic factor was significantly increased in the dentate gyrus of the Oenanthe javanica extract-treated group compared with the control group. However, we did not find that vascular endothelial growth factor expression was increased in the Oenanthe javanica extract-treated group compared with the control group. These results indicate that Oenanthe javanica extract improves cell proliferation and neuroblast differentiation by increasing brain-derived neurotrophic factor immunoreactivity in the rat dentate gyrus. PMID:25883627

Changes in Brain Metallome/Metabolome Pattern due to a Single i.v. Injection of Manganese in Rats

PubMed Central

Neth, Katharina; Lucio, Marianna; Walker, Alesia; Zorn, Julia; Schmitt-Kopplin, Philippe; Michalke, Bernhard

2015-01-01

Exposure to high concentrations of Manganese (Mn) is known to potentially induce an accumulation in the brain, leading to a Parkinson related disease, called manganism. Versatile mechanisms of Mn-induced brain injury are discussed, with inactivation of mitochondrial defense against oxidative stress being a major one. So far, studies indicate that the main Mn-species entering the brain are low molecular mass (LMM) compounds such as Mn-citrate. Applying a single low dose MnCl2 injection in rats, we observed alterations in Mn-species pattern within the brain by analysis of aqueous brain extracts by size-exclusion chromatography—inductively coupled plasma mass spectrometry (SEC-ICP-MS). Additionally, electrospray ionization—ion cyclotron resonance-Fourier transform-mass spectrometry (ESI-ICR/FT-MS) measurement of methanolic brain extracts revealed a comprehensive analysis of changes in brain metabolisms after the single MnCl2 injection. Major alterations were observed for amino acid, fatty acid, glutathione, glucose and purine/pyrimidine metabolism. The power of this metabolomic approach is the broad and detailed overview of affected brain metabolisms. We also correlated results from the metallomic investigations (Mn concentrations and Mn-species in brain) with the findings from metabolomics. This strategy might help to unravel the role of different Mn-species during Mn-induced alterations in brain metabolism. PMID:26383269

Development of the Aboriginal Communication Assessment After Brain Injury (ACAABI): A screening tool for identifying acquired communication disorders in Aboriginal Australians.

PubMed

Armstrong, Elizabeth M; Ciccone, Natalie; Hersh, Deborah; Katzenellebogen, Judith; Coffin, Juli; Thompson, Sandra; Flicker, Leon; Hayward, Colleen; Woods, Deborah; McAllister, Meaghan

2017-06-01

Acquired communication disorders (ACD), following stroke and traumatic brain injury, may not be correctly identified in Aboriginal Australians due to a lack of linguistically and culturally appropriate assessment tools. Within this paper we explore key issues that were considered in the development of the Aboriginal Communication Assessment After Brain Injury (ACAABI) - a screening tool designed to assess the presence of ACD in Aboriginal populations. A literature review and consultation with key stakeholders were undertaken to explore directions needed to develop a new tool, based on existing tools and recommendations for future developments. The literature searches revealed no existing screening tool for ACD in these populations, but identified tools in the areas of cognition and social-emotional wellbeing. Articles retrieved described details of the content and style of these tools, with recommendations for the development and administration of a new tool. The findings from the interview and focus group views were consistent with the approach recommended in the literature. There is a need for a screening tool for ACD to be developed but any tool must be informed by knowledge of Aboriginal language, culture and community input in order to be acceptable and valid.

Development and psychometric properties of an informant assessment scale of theory of mind for adults with traumatic brain injury.

PubMed

Zhang, Dengke; Pang, Yanxia; Cai, Weixiong; Fazio, Rachel L; Ge, Jianrong; Su, Qiaorong; Xu, Shuiqin; Pan, Yinan; Chen, Sanmei; Zhang, Hongwei

2016-08-01

Impairment of theory of mind (ToM) is a common phenomenon following traumatic brain injury (TBI) that has clear effects on patients' social functioning. A growing body of research has focused on this area, and several methods have been developed to assess ToM deficiency. Although an informant assessment scale would be useful for examining individuals with TBI, very few studies have adopted this approach. The purpose of the present study was to develop an informant assessment scale of ToM for adults with traumatic brain injury (IASToM-aTBI) and to test its reliability and validity with 196 adults with TBI and 80 normal adults. A 44-item scale was developed following a literature review, interviews with patient informants, consultations with experts, item analysis, and exploratory factor analysis (EFA). The following three common factors were extracted: social interaction, understanding of beliefs, and understanding of emotions. The psychometric analyses indicate that the scale has good internal consistency reliability, split-half reliability, test-retest reliability, inter-rater reliability, structural validity, discriminate validity and criterion validity. These results provide preliminary evidence that supports the reliability and validity of the IASToM-aTBI as a ToM assessment tool for adults with TBI.

Feature study of hysterical blindness EEG based on FastICA with combined-channel information.

PubMed

Qin, Xuying; Wang, Wei; Hu, Lintao; Wang, Xu; Yuan, Xiaojie

2015-01-01

An appropriate feature study of hysteria electroencephalograms (EEG) would provide new insights into neural mechanisms of the disease, and also make improvements in patient diagnosis and management. The objective of this paper is to provide an explanation for what causes a particular visual loss, by associating the features of hysterical blindness EEG with brain function. An idea for the novel feature extraction for hysterical blindness EEG, utilizing combined-channel information, was applied in this paper. After channels had been combined, the sliding-window-FastICA was applied to process the combined normal EEG and hysteria EEG, respectively. Kurtosis features were calculated from the processed signals. As the comparison feature, the power spectral density of normal and hysteria EEG were computed. According to the feature analysis results, a region of brain dysfunction was located at the occipital lobe, O1 and O2. Furthermore, new abnormality was found at the parietal lobe, C3, C4, P3, and P4, that provided us with a new perspective for understanding hysterical blindness. Indicated by the kurtosis results which were consistent with brain function and the clinical diagnosis, our method was found to be a useful tool to capture features in hysterical blindness EEG.

"My Heart Die in Me": Idioms of Distress and the Development of a Screening Tool for Mental Suffering in Southeast Liberia.

PubMed

Fabian, Katrin; Fannoh, Josiah; Washington, George G; Geninyan, Wilfred B; Nyachienga, Bethuel; Cyrus, Garmai; Hallowanger, Joyce N; Beste, Jason; Rao, Deepa; Wagenaar, Bradley H

2018-05-04

The integration of culturally salient idioms of distress into mental healthcare delivery is essential for effective screening, diagnosis, and treatment. This study systematically explored idioms, explanatory models, and conceptualizations in Maryland County, Liberia to develop a culturally-resonant screening tool for mental distress. We employed a sequential mixed-methods process of: (1) free-lists and semi-structured interviews (n = 20); patient chart reviews (n = 315); (2) pile-sort exercises, (n = 31); and (3) confirmatory focus group discussions (FGDs); (n = 3) from June to December 2017. Free-lists identified 64 idioms of distress, 36 of which were eliminated because they were poorly understood, stigmatizing, irrelevant, or redundant. The remaining 28 terms were used in pile-sort exercises to visualize the interrelatedness of idioms. Confirmatory FDGs occurred before and after the pile-sort exercise to explain findings. Four categories of idioms resulted, the most substantial of which included terms related to the heart and to the brain/mind. The final screening tool took into account 11 idioms and 6 physical symptoms extracted from patient chart reviews. This study provides the framework for culturally resonant mental healthcare by cataloguing language around mental distress and designing an emic screening tool for validation in a clinical setting.

Morus alba leaf extract mediates neuroprotection against glyphosate-induced toxicity and biochemical alterations in the brain.

PubMed

Rebai, Olfa; Belkhir, Manel; Boujelben, Adnen; Fattouch, Sami; Amri, Mohamed

2017-04-01

Recent studies demonstrate that glyphosate exposure is associated with oxidative stress and some neurological disorders such as Parkinson's pathology. Therefore, phytochemicals, in particular phenolic compounds, have attracted increasing attention as potential agents for neuroprotection. In the present study, we investigate the impact of glyphosate on the rat brain following i.p. injection and the possible molecular target of neuroprotective activity of the phenolic fraction from Morus alba leaf extract (MALE) and its ability to reduce oxidative damage in the brain. Wistar rats from 180 to 240 g were i.p. treated with a single dose of glyphosate (100 mg kg -1 b.w.) or MALE (100 μg mL -1  kg -1 b.w.) for 2 weeks. Brain homogenates were used to evaluate neurotoxicity induced by the pesticide. For this, biochemical parameters were measured. Data shows that MALE regulated oxidative stress and counteracted glyphosate-induced deleterious effects and oxidative damage in the brain, as it abrogated LDH, protein carbonyls, and malonyldialdehyde. MALE also appears to be able to scavenge H 2 O 2 levels, maintain iron and Ca 2+ homeostasis, and increase SOD activity. Thus, in vivo results showed that mulberry leaf extract is a potent protector against glyphosate-induced toxicity, and its protective effect could result from synergism or antagonism between the various bioactive phenolic compounds in the acetonic fraction from M. alba leaf extract.

Tool use in left brain damage and Alzheimer's disease: What about function and manipulation knowledge?

PubMed

Jarry, Christophe; Osiurak, François; Besnard, Jérémy; Baumard, Josselin; Lesourd, Mathieu; Croisile, Bernard; Etcharry-Bouyx, Frédérique; Chauviré, Valérie; Le Gall, Didier

2016-03-01

Tool use disorders are usually associated with difficulties in retrieving function and manipulation knowledge. Here, we investigate tool use (Real Tool Use, RTU), function (Functional Association, FA) and manipulation knowledge (Gesture Recognition, GR) in 17 left-brain-damaged (LBD) patients and 14 AD patients (Alzheimer disease). LBD group exhibited predicted deficit on RTU but not on FA and GR while AD patients showed deficits on GR and FA with preserved tool use skills. These findings question the role played by function and manipulation knowledge in actual tool use. © 2016 The British Psychological Society.

Antioxidant and inhibitory effect of red ginger (Zingiber officinale var. Rubra) and white ginger (Zingiber officinale Roscoe) on Fe(2+) induced lipid peroxidation in rat brain in vitro.

PubMed

Oboh, Ganiyu; Akinyemi, Ayodele J; Ademiluyi, Adedayo O

2012-01-01

Neurodegerative diseases have been linked to oxidative stress arising from peroxidation of membrane biomolecules and high levels of Fe have been reported to play an important role in neurodegenerative diseases and other brain disorder. Malondialdehyde (MDA) is the end-product of lipid peroxidation and the production of this aldehyde is used as a biomarker to measure the level of oxidative stress in an organism. The present study compares the protective properties of two varieties of ginger [red ginger (Zingiber officinale var. Rubra) and white ginger (Zingiber officinale Roscoe)] on Fe(2+) induced lipid peroxidation in rat brain in vitro. Incubation of the brain tissue homogenate in the presence of Fe caused a significant increase in the malondialdehyde (MDA) contents of the brain. However, the aqueous extract from both varieties of ginger caused a significant decrease in the MDA contents of the brain in a dose-dependent manner. However, the aqueous extract of red ginger had a significantly higher inhibitory effect on both Fe(2+)-induced lipid peroxidation in the rat brain homogenates than that of white ginger. This higher inhibitory effect of red ginger could be attributed to its significantly higher phytochemical content, Fe(2+) chelating ability, OH scavenging ability and reducing power. However, part of the mechanisms through which the extractable phytochemicals in ginger (red and white) protect the brain may be through their antioxidant activity, Fe(2+) chelating and OH scavenging ability. Therefore, oxidative stress in the brain could be potentially managed/prevented by dietary intake of ginger varieties (red ginger and white ginger rhizomes). Copyright © 2010 Elsevier GmbH. All rights reserved.

Brain aluminium accumulation and oxidative stress in the presence of calcium silicate dental cements.

PubMed

Demirkaya, K; Demirdöğen, B Can; Torun, Z Öncel; Erdem, O; Çırak, E; Tunca, Y M

2017-10-01

Mineral trioxide aggregate (MTA) is a calcium silicate dental cement used for various applications in dentistry. This study was undertaken to test whether the presence of three commercial brands of calcium silicate dental cements in the dental extraction socket of rats would affect the brain aluminium (Al) levels and oxidative stress parameters. Right upper incisor was extracted and polyethylene tubes filled with MTA Angelus, MTA Fillapex or Theracal LC, or left empty for the control group, were inserted into the extraction socket. Rats were killed 7, 30 or 60 days after operation. Brain tissues were obtained before killing. Al levels were measured by atomic absorption spectrometry. Thiobarbituric acid reactive substances (TBARS) levels, catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities were determined using spectrophotometry. A transient peak was observed in brain Al level of MTA Angelus group on day 7, while MTA Fillapex and Theracal LC groups reached highest brain Al level on day 60. Brain TBARS level, CAT, SOD and GPx activities transiently increased on day 7 and then returned to almost normal levels. This in vivo study for the first time indicated that initial washout may have occurred in MTA Angelus, while element leaching after the setting is complete may have taken place for MTA Fillapex and Theracal LC. Moreover, oxidative stress was induced and antioxidant enzymes were transiently upregulated. Further studies to search for oxidative neuronal damage should be done to completely understand the possible toxic effects of calcium silicate cements on the brain.

Orbital penetration associated with tooth extraction.

PubMed

Smith, Mark M; Smith, Eric M; La Croix, Noelle; Mould, John

2003-03-01

Three cats and 2 dogs were evaluated for ophthalmologic complications associated with tooth extraction procedures. Orbital penetration leading to ocular and, in one case, brain trauma was secondary to iatrogenic injury from a dental elevator. Outcomes included enucleation of the affected eye in 3 cases, and death from brain abscessation in 1 case. Early treatment or, preferably, referral to a veterinary ophthalmology specialist may prevent such outcomes. Awareness of the anatomical proximity of caudal maxillary tooth roots and the orbit, appropriate interpretation of diagnostic intraoral dental radiographs, and technical proficiency in tooth extraction techniques will minimize these complications in veterinary dental practice.

Cholecystokinin-converting enzymes in brain.

PubMed Central

Malesci, A; Straus, E; Yalow, R S

1980-01-01

Crude extracts of porcine cerebral cortical tissue convert cholecystokinin (CCK) to its COOH-terminal fragments, the dodecapeptide (CCK-12) and the octapeptide (CCK-8). The Sephadex G-75 void volume eluate of the crude extract cleaves the arginine-isoleucine bond and effects conversion only to CCK-12; the Sephadex G-50 void volume eluate of the same extract cleaves the arginine-aspartate bond as well, so that both CCK-12 and CCK-8 are end products. Thus, there are at least two enzymes; the one involved in the conversion to CCK-12 is of larger molecular radius than the other. The Km for the cleavage of CCK at the arginine-isoleucine bond by the Sephadex G-75 void volume eluate enzyme is 1.1 X 10(-6) M; the Km for trypsin cleavage of the same bond is 4.7 x 10(-6) M. The lower Vmax for the brain enzyme (1.5 x 10(-11) mol/min per g of extract) compared with trypsin (66 x 10(-11) mol/min per g of trypsin) simply reflects the lesser degree of purify of the brain extract than of the highly purified trypsin. Images PMID:6987659

Standardized Bacopa monnieri extract ameliorates acute paraquat-induced oxidative stress, and neurotoxicity in prepubertal mice brain.

PubMed

Hosamani, Ravikumar; Krishna, Gokul; Muralidhara

2016-12-01

Bacopa monnieri (BM), an ayurvedic medicinal plant, has attracted considerable interest owing to its diverse neuropharmacological properties. Epidemiological studies have shown significant correlation between paraquat (PQ) exposure and increased risk for Parkinson's disease in humans. In this study, we examined the propensity of standardized extract of BM to attenuate acute PQ-induced oxidative stress, mitochondrial dysfunctions, and neurotoxicity in the different brain regions of prepubertal mice. To test this hypothesis, prepubertal mice provided orally with standardized BM extract (200 mg/kg body weight/day for 4 weeks) were challenged with an acute dose (15 mg/kg body weight, intraperitoneally) of PQ after 3 hours of last dose of extract. Mice were sacrificed after 48 hours of PQ injection, and different brain regions were isolated and subjected to biochemical determinations/quantification of central monoamine (dopamine, DA) levels (by high-performance liquid chromatography). Oral supplementation of BM for 4 weeks resulted in significant reduction in the basal levels of oxidative markers such as reactive oxygen species (ROS), malondialdehyde (MDA), and hydroperoxides (HP) in various brain regions. PQ at the administered dose elicited marked oxidative stress within 48 hours in various brain regions of mice. However, BM prophylaxis significantly improved oxidative homeostasis by restoring PQ-induced ROS, MDA, and HP levels and also by attenuating mitochondrial dysfunction. Interestingly, BM supplementation restored the activities of cholinergic enzymes along with the restoration of striatal DA levels among the PQ-treated mice. Based on these findings, we infer that BM prophylaxis renders the brain resistant to PQ-mediated oxidative perturbations and thus may be better exploited as a preventive approach to protect against oxidative-mediated neuronal dysfunctions.

Development of the Sydney Falls Risk Screening Tool in brain injury rehabilitation: A multisite prospective cohort study.

PubMed

McKechnie, Duncan; Fisher, Murray J; Pryor, Julie; Bonser, Melissa; Jesus, Jhoven De

2018-03-01

To develop a falls risk screening tool (FRST) sensitive to the traumatic brain injury rehabilitation population. Falls are the most frequently recorded patient safety incident within the hospital context. The inpatient traumatic brain injury rehabilitation population is one particular population that has been identified as at high risk of falls. However, no FRST has been developed for this patient population. Consequently in the traumatic brain injury rehabilitation population, there is the real possibility that nurses are using falls risk screening tools that have a poor clinical utility. Multisite prospective cohort study. Univariate and multiple logistic regression modelling techniques (backward elimination, elastic net and hierarchical) were used to examine each variable's association with patients who fell. The resulting FRST's clinical validity was examined. Of the 140 patients in the study, 41 (29%) fell. Through multiple logistic regression modelling, 11 variables were identified as predictors for falls. Using hierarchical logistic regression, five of these were identified for inclusion in the resulting falls risk screening tool: prescribed mobility aid (such as, wheelchair or frame), a fall since admission to hospital, impulsive behaviour, impaired orientation and bladder and/or bowel incontinence. The resulting FRST has good clinical validity (sensitivity = 0.9; specificity = 0.62; area under the curve = 0.87; Youden index = 0.54). The tool was significantly more accurate (p = .037 on DeLong test) in discriminating fallers from nonfallers than the Ontario Modified STRATIFY FRST. A FRST has been developed using a comprehensive statistical framework, and evidence has been provided of this tool's clinical validity. The developed tool, the Sydney Falls Risk Screening Tool, should be considered for use in brain injury rehabilitation populations. © 2017 John Wiley & Sons Ltd.

Measurement and Finite Element Model Validation of Immature Porcine Brain–Skull Displacement during Rapid Sagittal Head Rotations

PubMed Central

Pasquesi, Stephanie A.; Margulies, Susan S.

2018-01-01

Computational models are valuable tools for studying tissue-level mechanisms of traumatic brain injury, but to produce more accurate estimates of tissue deformation, these models must be validated against experimental data. In this study, we present in situ measurements of brain–skull displacement in the neonatal piglet head (n = 3) at the sagittal midline during six rapid non-impact rotations (two rotations per specimen) with peak angular velocities averaging 51.7 ± 1.4 rad/s. Marks on the sagittally cut brain and skull/rigid potting surfaces were tracked, and peak values of relative brain–skull displacement were extracted and found to be significantly less than values extracted from a previous axial plane model. In a finite element model of the sagittally transected neonatal porcine head, the brain–skull boundary condition was matched to the measured physical experiment data. Despite smaller sagittal plane displacements at the brain–skull boundary, the corresponding finite element boundary condition optimized for sagittal plane rotations is far less stiff than its axial counterpart, likely due to the prominent role of the boundary geometry in restricting interface movement. Finally, bridging veins were included in the finite element model. Varying the bridging vein mechanical behavior over a previously reported range had no influence on the brain–skull boundary displacements. This direction-specific sagittal plane boundary condition can be employed in finite element models of rapid sagittal head rotations. PMID:29515995

CD38-dependent ADP-ribosyl cyclase activity in developing and adult mouse brain.

PubMed Central

Ceni, Claire; Pochon, Nathalie; Brun, Virginie; Muller-Steffner, Hélène; Andrieux, Annie; Grunwald, Didier; Schuber, Francis; De Waard, Michel; Lund, Frances; Villaz, Michel; Moutin, Marie-Jo

2003-01-01

CD38 is a transmembrane glycoprotein that is expressed in many tissues throughout the body. In addition to its major NAD+-glycohydrolase activity, CD38 is also able to synthesize cyclic ADP-ribose, an endogenous calcium-regulating molecule, from NAD+. In the present study, we have compared ADP-ribosyl cyclase and NAD+-glycohydrolase activities in protein extracts of brains from developing and adult wild-type and Cd38 -/- mice. In extracts from wild-type brain, cyclase activity was detected spectrofluorimetrically, using nicotinamide-guanine dinucleotide as a substrate (GDP-ribosyl cyclase activity), as early as embryonic day 15. The level of cyclase activity was similar in the neonate brain (postnatal day 1) and then increased greatly in the adult brain. Using [14C]NAD+ as a substrate and HPLC analysis, we found that ADP-ribose is the major product formed in the brain at all developmental stages. Under the same experimental conditions, neither NAD+-glycohydrolase nor GDP-ribosyl cyclase activity could be detected in extracts of brains from developing or adult Cd38 -/- mice, demonstrating that CD38 is the predominant constitutive enzyme endowed with these activities in brain at all developmental stages. The activity measurements correlated with the level of CD38 transcripts present in the brains of developing and adult wild-type mice. Using confocal microscopy we showed, in primary cultures of hippocampal cells, that CD38 is expressed by both neurons and glial cells, and is enriched in neuronal perikarya. Intracellular NAD+-glycohydrolase activity was measured in hippocampal cell cultures, and CD38-dependent cyclase activity was higher in brain fractions enriched in intracellular membranes. Taken together, these results lead us to speculate that CD38 might have an intracellular location in neural cells in addition to its plasma membrane location, and may play an important role in intracellular cyclic ADP-ribose-mediated calcium signalling in brain tissue. PMID:12403647

An independent SSVEP-based brain-computer interface in locked-in syndrome.

PubMed

Lesenfants, D; Habbal, D; Lugo, Z; Lebeau, M; Horki, P; Amico, E; Pokorny, C; Gómez, F; Soddu, A; Müller-Putz, G; Laureys, S; Noirhomme, Q

2014-06-01

Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS). Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based 'interlaced squares' stimulation pattern. Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online. We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

Patient-tailored multimodal neuroimaging, visualization and quantification of human intra-cerebral hemorrhage

NASA Astrophysics Data System (ADS)

Goh, Sheng-Yang M.; Irimia, Andrei; Vespa, Paul M.; Van Horn, John D.

2016-03-01

In traumatic brain injury (TBI) and intracerebral hemorrhage (ICH), the heterogeneity of lesion sizes and types necessitates a variety of imaging modalities to acquire a comprehensive perspective on injury extent. Although it is advantageous to combine imaging modalities and to leverage their complementary benefits, there are difficulties in integrating information across imaging types. Thus, it is important that efforts be dedicated to the creation and sustained refinement of resources for multimodal data integration. Here, we propose a novel approach to the integration of neuroimaging data acquired from human patients with TBI/ICH using various modalities; we also demonstrate the integrated use of multimodal magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) data for TBI analysis based on both visual observations and quantitative metrics. 3D models of healthy-appearing tissues and TBIrelated pathology are generated, both of which are derived from multimodal imaging data. MRI volumes acquired using FLAIR, SWI, and T2 GRE are used to segment pathology. Healthy tissues are segmented using user-supervised tools, and results are visualized using a novel graphical approach called a `connectogram', where brain connectivity information is depicted within a circle of radially aligned elements. Inter-region connectivity and its strength are represented by links of variable opacities drawn between regions, where opacity reflects the percentage longitudinal change in brain connectivity density. Our method for integrating, analyzing and visualizing structural brain changes due to TBI and ICH can promote knowledge extraction and enhance the understanding of mechanisms underlying recovery.

Intraoperative brain hemodynamic response assessment with real-time hyperspectral optical imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Laurence, Audrey; Pichette, Julien; Angulo-Rodríguez, Leticia M.; Saint Pierre, Catherine; Lesage, Frédéric; Bouthillier, Alain; Nguyen, Dang Khoa; Leblond, Frédéric

2016-03-01

Following normal neuronal activity, there is an increase in cerebral blood flow and cerebral blood volume to provide oxygenated hemoglobin to active neurons. For abnormal activity such as epileptiform discharges, this hemodynamic response may be inadequate to meet the high metabolic demands. To verify this hypothesis, we developed a novel hyperspectral imaging system able to monitor real-time cortical hemodynamic changes during brain surgery. The imaging system is directly integrated into a surgical microscope, using the white-light source for illumination. A snapshot hyperspectral camera is used for detection (4x4 mosaic filter array detecting 16 wavelengths simultaneously). We present calibration experiments where phantoms made of intralipid and food dyes were imaged. Relative concentrations of three dyes were recovered at a video rate of 30 frames per second. We also present hyperspectral recordings during brain surgery of epileptic patients with concurrent electrocorticography recordings. Relative concentration maps of oxygenated and deoxygenated hemoglobin were extracted from the data, allowing real-time studies of hemodynamic changes with a good spatial resolution. Finally, we present preliminary results on phantoms obtained with an integrated spatial frequency domain imaging system to recover tissue optical properties. This additional module, used together with the hyperspectral imaging system, will allow quantification of hemoglobin concentrations maps. Our hyperspectral imaging system offers a new tool to analyze hemodynamic changes, especially in the case of epileptiform discharges. It also offers an opportunity to study brain connectivity by analyzing correlations between hemodynamic responses of different tissue regions.

Artifact suppression and analysis of brain activities with electroencephalography signals.

PubMed

Rashed-Al-Mahfuz, Md; Islam, Md Rabiul; Hirose, Keikichi; Molla, Md Khademul Islam

2013-06-05

Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

Brain mechanisms of perceiving tools and imagining tool use acts: a functional MRI study.

PubMed

Wadsworth, Heather M; Kana, Rajesh K

2011-06-01

The ability to conceptualize and manipulate tools in a complex manner is a distinguishing characteristic of humans, and forms a promising milestone in human evolution. While using tools is a motor act, proposals for executing such acts may be evoked by the mere perception of a tool. Imagining an action using a tool may invoke mental readjustment of body posture, planning motor movements, and matching such plans with the model action. This fMRI study examined the brain response in 32 healthy adults when they either viewed a tool or imagined using it. While both viewing and imagining tasks recruited similar regions, imagined tool use showed greater activation in motor areas, and in areas around the bilateral temporoparietal junction. Viewing tools, on the other hand, produced robust activation in the inferior frontal, occipital, parietal, and ventral temporal areas. Analysis of gender differences indicated males recruiting medial prefrontal and anterior cingulate cortices and females, left supramarginal gyrus and left anterior insula. While tool viewing seems to generate prehensions about using them, the imagined action using a tool mirrored brain responses underlying functional use of it. The findings of this study may suggest that perception and imagination of tools may form precursors to overt actions. Published by Elsevier Ltd.

Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions.

PubMed

Yu, Kaixin; Wang, Xuetong; Li, Qiongling; Zhang, Xiaohui; Li, Xinwei; Li, Shuyu

2018-01-01

Morphological brain network plays a key role in investigating abnormalities in neurological diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, most of the morphological brain network construction methods only considered a single morphological feature. Each type of morphological feature has specific neurological and genetic underpinnings. A combination of morphological features has been proven to have better diagnostic performance compared with a single feature, which suggests that an individual morphological brain network based on multiple morphological features would be beneficial in disease diagnosis. Here, we proposed a novel method to construct individual morphological brain networks for two datasets by calculating the exponential function of multivariate Euclidean distance as the evaluation of similarity between two regions. The first dataset included 24 healthy subjects who were scanned twice within a 3-month period. The topological properties of these brain networks were analyzed and compared with previous studies that used different methods and modalities. Small world property was observed in all of the subjects, and the high reproducibility indicated the robustness of our method. The second dataset included 170 patients with MCI (86 stable MCI and 84 progressive MCI cases) and 169 normal controls (NC). The edge features extracted from the individual morphological brain networks were used to distinguish MCI from NC and separate MCI subgroups (progressive vs. stable) through the support vector machine in order to validate our method. The results showed that our method achieved an accuracy of 79.65% (MCI vs. NC) and 70.59% (stable MCI vs. progressive MCI) in a one-dimension situation. In a multiple-dimension situation, our method improved the classification performance with an accuracy of 80.53% (MCI vs. NC) and 77.06% (stable MCI vs. progressive MCI) compared with the method using a single feature. The results indicated that our method could effectively construct an individual morphological brain network based on multiple morphological features and could accurately discriminate MCI from NC and stable MCI from progressive MCI, and may provide a valuable tool for the investigation of individual morphological brain networks.

Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

PubMed Central

Su, Hai; Xing, Fuyong; Yang, Lin

2016-01-01

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96. PMID:26812706

Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

PubMed

Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

2013-07-01

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

A simple procedure for the extraction of DNA from long-term formalin-preserved brain tissues for the detection of EBV by PCR.

PubMed

Hassani, Asma; Khan, Gulfaraz

2015-12-01

Long-term formalin fixed brain tissues are potentially an important source of material for molecular studies. Ironically, very few protocols have been published describing DNA extraction from such material for use in PCR analysis. In our attempt to investigate the role of Epstein-Barr virus (EBV) in the pathogenesis of multiple sclerosis (MS), extracting PCR quality DNA from brain samples fixed in formalin for 2-22 years, proved to be very difficult and challenging. As expected, DNA extracted from these samples was not only of poor quality and quantity, but more importantly, it was frequently found to be non-amplifiable due to the presence of PCR inhibitors. Here, we describe a simple and reproducible procedure for extracting DNA using a modified proteinase K and phenol-chloroform methodology. Central to this protocol is the thorough pre-digestion washing of the tissues in PBS, extensive digestion with proteinase K in low SDS containing buffer, and using low NaCl concentration during DNA precipitation. The optimized protocol was used in extracting DNA from meninges of 26 MS and 6 non-MS cases. Although the quality of DNA from these samples was generally poor, small size amplicons (100-200 nucleotides) of the house-keeping gene, β-globin could be reliably amplified from all the cases. PCR for EBV revealed positivity in 35% (9/26) MS cases, but 0/6 non-MS cases. These findings indicate that the method described here is suitable for PCR detection of viral sequences in long-term formalin persevered brain tissues. Our findings also support a possible role for EBV in the pathogenesis of MS. Copyright © 2015 Elsevier Inc. All rights reserved.

A Nth-order linear algorithm for extracting diffuse correlation spectroscopy blood flow indices in heterogeneous tissues.

PubMed

Shang, Yu; Yu, Guoqiang

2014-09-29

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a N th-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD B ). The purpose of this study is to extend the capability of the N th-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD B in the brain layer with a step decrement of 10% while maintaining αD B values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order ( N  ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The N th-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.

Anti-epileptogenic and antioxidant effect of Lavandula officinalis aerial part extract against pentylenetetrazol-induced kindling in male mice.

PubMed

Rahmati, Batool; Khalili, Mohsen; Roghani, Mehrdad; Ahghari, Parisa

2013-06-21

Repeated application of Lavandula officinalis (L. officinalis) has been recommended for a long time in Iranian traditional medicine for some of nervous disorders like epilepsy and dementia. However, there is no available report for the effect of chronic administration of Lavandula extract in development (acquisition) of epilepsy. Therefore, this study was designed to investigate the anti-epileptogenic and antioxidant activity of repeated administration of Lavandula officinalis extract on pentylenetetrazol (PTZ) kindling seizures in mice model. Lavandula officinalis was tested for its ability (i) to suppress the seizure intensity and lethal effects of PTZ in kindled mice (anti-epileptogenic effect), (ii) to attenuate the PTZ-induced oxidative injury in the brain tissue (antioxidant effect) when given as a pretreatment prior to each PTZ injection during kindling development. Valproate (Val), a major antiepileptic drug, was also tested for comparison. Val and Lavandula officinalis extract showed anti-epileptogenic properties as they reduced seizure score of kindled mice and PTZ-induced mortality. In this regard, Lavandula officinalis was more effective than Val. Both Lavandula officinalis and Val suppressed brain nitric oxide (NO) level of kindled mice in comparison with the control and PTZ group. Meanwhile, Lavandula officinalis suppressed NO level more than Val and Lavandula officinalis also decreased brain MDA level relative to PTZ group. This is the first report to demonstrate NO suppressing and anti-epileptogenic effect of chronic administration of Lavandula officinalis extract on acquisition of epilepsy in PTZ kindling mice model. In this regard, Lavandula officinalis extract was more effective than Val, possibly and in part via brain NO suppression. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Context-dependent ‘safekeeping’ of foraging tools in New Caledonian crows

PubMed Central

Klump, Barbara C.; van der Wal, Jessica E. M.; St Clair, James J. H.; Rutz, Christian

2015-01-01

Several animal species use tools for foraging, such as sticks to extract embedded arthropods and honey, or stones to crack open nuts and eggs. While providing access to nutritious foods, these behaviours may incur significant costs, such as the time and energy spent searching for, manufacturing and transporting tools. These costs can be reduced by re-using tools, keeping them safe when not needed. We experimentally investigated what New Caledonian crows do with their tools between successive prey extractions, and whether they express tool ‘safekeeping’ behaviours more often when the costs (foraging at height), or likelihood (handling of demanding prey), of tool loss are high. Birds generally took care of their tools (84% of 176 prey extractions, nine subjects), either trapping them underfoot (74%) or storing them in holes (26%)—behaviours we also observed in the wild (19 cases, four subjects). Moreover, tool-handling behaviour was context-dependent, with subjects: keeping their tools safe significantly more often when foraging at height; and storing tools significantly more often in holes when extracting more demanding prey (under these conditions, foot-trapping proved challenging). In arboreal environments, safekeeping can prevent costly tool losses, removing a potentially important constraint on the evolution of habitual and complex tool behaviour. PMID:25994674

Online Motor Imagery Training Effect for the Appearance of Event Related Desynchronization (ERD)

NASA Astrophysics Data System (ADS)

Takahashi, Mitsuru; Gouko, Manabu; Ito, Koji

Stroke patients have some motor deficits, but they can regain their motor abilities by rehabilitation. In the aspect of rehabilitation, voluntary movement is very important. We propose a system which can make a closed loop in brain for stroke patients like voluntary movement. Event Related Desynchronization (ERD) is used to extract patients' motor intention, and then Functional Electrical Stimulation (FES) stimuls their paralyzed muscles. In many Brain Computer Interface (BCI) researches, subjects are trained for several months or years to do the task, because of the difficulty to extract clear ERD without training. Thinking about applying for stroke patients, motor imagery training should be shorter, because of the brain plasticity. We did a pilot study about the effect of visual feedback training for three days with healthy subjects. The result indicated that ERD could be clearly extracted in three days, but the training effect differs in each subjects.

The antiepileptic effect of Centella asiatica on the activities of Na+/K+, Mg2+ and Ca2+-ATPases in rat brain during pentylenetetrazol–induced epilepsy

PubMed Central

G., Visweswari; K., Siva Prasad; V., Lokanatha; Rajendra, W.

2010-01-01

Background: To study the anticonvulsant effect of different extracts of Centella asiatica (CA) in male albino rats with reference to Na+/K+, Mg2+ and Ca2+-ATPase activities. Materials and Methods: Male Wistar rats (150±25 g b.w.) were divided into seven groups of six each i.e. (a) control rats treated with saline, (b) pentylenetetrazol (PTZ)-induced epileptic group (60 mg/kg, i.p.), (c) epileptic group pretreated with n-hexane extract (n-HE), (d) epileptic group pretreated with chloroform extract (CE), (e) epileptic group pretreated with ethyl acetate extract (EAE), (f) epileptic group pretreated with n-butanol extract (n-BE), and (g) epileptic group pretreated with aqueous extract (AE). Results: The activities of three ATPases were decreased in different regions of brain during PTZ-induced epilepsy and were increased in epileptic rats pretreated with different extracts of CA except AE. Conclusion: The extracts of C. asiatica, except AE, possess anticonvulsant and neuroprotective activity and thus can be used for effective management in treatment of epileptic seizures. PMID:20711371

In vitro and In vivo Antioxidant Evaluation and Estimation of Total Phenolic, Flavonoidal Content of Mimosa pudica L

PubMed Central

Patro, Ganesh; Bhattamisra, Subrat Kumar; Mohanty, Bijay Kumar; Sahoo, Himanshu Bhusan

2016-01-01

Objective: Mimosa pudica Linn. (Mimosaceae) is traditionally used as a folk medicine to treat various ailments including convulsions, alopecia, diarrhea, dysentery, insomnia, tumor, wound healing, snake bite, etc., Here, the study was aimed to evaluate the antioxidant potential of M. pudica leaves extract against 2, 2-diphenyl-1-picrylhydrazyl (DPPH) (in vitro) and its modulatory effect on rat brain enzymes. Materials and Methods: Total phenolic, flavonoid contents, and in vitro antioxidant potential against DPPH radical were evaluated from various extracts of M. pudica leaves. In addition, ethyl acetate extract of Mimosa pudica leaves (EAMP) in doses of 100, 200, and 400 mg/kg/day were administered orally for 7 consecutive days to albino rats and evaluated for the oxidative stress markers as thiobarbituric acid reactive substances (TBARS), superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) from rat brain homogenate. Results: The ethyl acetate extract showed the highest total phenolic content and total flavonoid content among other extracts of M. pudica leaves. The percentage inhibition and IC50 value of all the extracts were followed dose-dependency and found significant (P < 0.01) as compared to standard (ascorbic acid). The oxidative stress markers as SOD, CAT, and GSH were increased significantly (P < 0.01) at 200 and 400 mg/kg of EAMP treated animals and decreased significantly the TBARS level at 400 mg/kg of EAMP as compared to control group. Conclusion: These results revealed that the ethyl acetate extract of M. pudica exhibits both in vitro antioxidant activity against DPPH and in vivo antioxidant activity by modulating brain enzymes in the rat. This could be further correlated with its potential to neuroprotective activity due to the presence of flavonoids and phenolic contents in the extract. SUMMARY Total phenolic, flavonoid contents and in-vitro antioxidant potential were evaluated from various extracts of M. pudica leaves. Again, in-vivo antioxidant evaluation from brain homogenate on oxidative stress markers as TBARS, SOD, CAT and GSH from rat was investigated. Our findings revealed that M. pudica possesses both in-vitro and in-vivo antioxidant activity due to presence of phenolics and flavonoids. PMID:26941532

Tool use, aye-ayes, and sensorimotor intelligence.

PubMed

Sterling, E J; Povinelli, D J

1999-01-01

Humans, chimpanzees, capuchins and aye-ayes all display an unusually high degree of encephalization and diverse omnivorous extractive foraging. It has been suggested that the high degree of encephalization in aye-ayes may be the result of their diverse, omnivorous extractive foraging behaviors. In combination with certain forms of tool use, omnivorous extractive foraging has been hypothesized to be linked to higher levels of sensorimotor intelligence (stages 5 or 6). Although free-ranging aye-ayes have not been observed to use tools directly in the context of their extractive foraging activities, they have recently been reported to use lianas as tools in a manner that independently suggests that they may possess stage 5 or 6 sensorimotor intelligence. Although other primate species which display diverse, omnivorous extractive foraging have been tested for sensorimotor intelligence, aye-ayes have not. We report a test of captive aye-ayes' comprehension of tool use in a situation designed to simulate natural conditions. The results support the view that aye-ayes do not achieve stage 6 comprehension of tool use, but rather may use trial-and-error learning to develop tool-use behaviors. Other theories for aye-aye encephalization are considered.

Allen Brain Atlas-Driven Visualizations: a web-based gene expression energy visualization tool.

PubMed

Zaldivar, Andrew; Krichmar, Jeffrey L

2014-01-01

The Allen Brain Atlas-Driven Visualizations (ABADV) is a publicly accessible web-based tool created to retrieve and visualize expression energy data from the Allen Brain Atlas (ABA) across multiple genes and brain structures. Though the ABA offers their own search engine and software for researchers to view their growing collection of online public data sets, including extensive gene expression and neuroanatomical data from human and mouse brain, many of their tools limit the amount of genes and brain structures researchers can view at once. To complement their work, ABADV generates multiple pie charts, bar charts and heat maps of expression energy values for any given set of genes and brain structures. Such a suite of free and easy-to-understand visualizations allows for easy comparison of gene expression across multiple brain areas. In addition, each visualization links back to the ABA so researchers may view a summary of the experimental detail. ABADV is currently supported on modern web browsers and is compatible with expression energy data from the Allen Mouse Brain Atlas in situ hybridization data. By creating this web application, researchers can immediately obtain and survey numerous amounts of expression energy data from the ABA, which they can then use to supplement their work or perform meta-analysis. In the future, we hope to enable ABADV across multiple data resources.

Molecular weights and metabolism of rat brain proteins

PubMed Central

Vrba, R.; Cannon, Wendy

1970-01-01

1. Rats were injected with [U-14C]glucose and after various intervals extracts of whole brain proteins (and in some cases proteins from liver, blood and heart) were prepared by high-speed centrifugation of homogenates in 0.9% sodium chloride or 0.5% sodium deoxycholate. 2. The extracts were subjected to gel filtration on columns of Sephadex G-200 equilibrated with 0.9% sodium chloride or 0.5% sodium deoxycholate. 3. Extracts prepared with both solvents displayed on gel filtration a continuous range of proteins of approximate molecular weights ranging from less than 2×104 to more than 8×105. 4. The relative amount of the large proteins (mol.wt.>8×105) was conspicuously higher in brain and liver than in blood. 5. At 15min after the injection of [U-14C]glucose the smaller protein molecules (mol.wt.<2×104) were significantly radioactive, whereas no 14C could be detected in the larger (mol.wt.>2×104) protein molecules. The labelling of all protein samples was similar within 4h after injection of [U-14C]glucose. Fractionation of brain proteins into distinctly different groups by the methods used in the present work yielded protein samples with a specific radioactivity comparable with that of total brain protein. 6. No evidence could be obtained by the methods used in the present and previous work to indicate the presence of a significant amount of `metabolically inert protein' in the brain. 7. It is concluded that: (a) most or all of the brain proteins are in a dynamic state of equilibrium between continuous catabolism and anabolism; (b) the continuous conversion of glucose into protein is an important part of the maintenance of this equilibrium and of the homoeostasis of brain proteins in vivo. PMID:5435499

Computational and mathematical methods in brain atlasing.

PubMed

Nowinski, Wieslaw L

2017-12-01

Brain atlases have a wide range of use from education to research to clinical applications. Mathematical methods as well as computational methods and tools play a major role in the process of brain atlas building and developing atlas-based applications. Computational methods and tools cover three areas: dedicated editors for brain model creation, brain navigators supporting multiple platforms, and atlas-assisted specific applications. Mathematical methods in atlas building and developing atlas-aided applications deal with problems in image segmentation, geometric body modelling, physical modelling, atlas-to-scan registration, visualisation, interaction and virtual reality. Here I overview computational and mathematical methods in atlas building and developing atlas-assisted applications, and share my contribution to and experience in this field.

Optogenetics and the future of neuroscience.

PubMed

Boyden, Edward S

2015-09-01

Over the last 10 years, optogenetics has become widespread in neuroscience for the study of how specific cell types contribute to brain functions and brain disorder states. The full impact of optogenetics will emerge only when other toolsets mature, including neural connectivity and cell phenotyping tools and neural recording and imaging tools. The latter tools are rapidly improving, in part because optogenetics has helped galvanize broad interest in neurotechnology development.

Morinda citrifolia L. Leaf Extract Protects against Cerebral Ischemia and Osteoporosis in an In Vivo Experimental Model of Menopause

PubMed Central

Thipkaew, Cholathip; Thukham-mee, Wipawee; Wannanon, Panakaporn

2018-01-01

We aimed to determine the protective effects against cerebral ischemia and osteoporosis of Morinda citrifolia extract in experimental menopause. The neuroprotective effect was assessed by giving M. citrifolia leaf extract at doses of 2, 10, and 50 mg/kg BW to the bilateral ovariectomized (OVX) rats for 7 days. Then, they were occluded in the right middle cerebral artery (MCAO) for 90 minutes. The neurological score, brain infarction volume, oxidative stress status, and ERK1/2 and eNOS activities were assessed 24 hours later. M. citrifolia improved neurological score, brain infarction, and brain oxidative stress status in the cortex of OVX rats plus the MCAO. No changes in ERK 1/2 signal pathway and NOS expression were observed in this area. Our data suggested that the neuroprotective effect of the extract might occur partly via the improvement of oxidative stress status in the cortex. The antiosteoporotic effect in OVX rats was also assessed after an 84-day intervention of M. citrifolia. The serum levels of calcium, osteocalcin, and alkaline phosphatase and osteoblast density in the tibia were increased, but the density of osteoclast was decreased in OVX rats which received the extract. Therefore, the current data suggested that the extract possessed antiosteoporotic effect by increasing bone formation but decreasing bone resorption. PMID:29765488

Morinda citrifolia L. Leaf Extract Protects against Cerebral Ischemia and Osteoporosis in an In Vivo Experimental Model of Menopause.

PubMed

Wattanathorn, Jintanaporn; Thipkaew, Cholathip; Thukham-Mee, Wipawee; Muchimapura, Supaporn; Wannanon, Panakaporn; Tong-Un, Terdthai

2018-01-01

We aimed to determine the protective effects against cerebral ischemia and osteoporosis of Morinda citrifolia extract in experimental menopause. The neuroprotective effect was assessed by giving M. citrifolia leaf extract at doses of 2, 10, and 50 mg/kg BW to the bilateral ovariectomized (OVX) rats for 7 days. Then, they were occluded in the right middle cerebral artery (MCAO) for 90 minutes. The neurological score, brain infarction volume, oxidative stress status, and ERK1/2 and eNOS activities were assessed 24 hours later. M. citrifolia improved neurological score, brain infarction, and brain oxidative stress status in the cortex of OVX rats plus the MCAO. No changes in ERK 1/2 signal pathway and NOS expression were observed in this area. Our data suggested that the neuroprotective effect of the extract might occur partly via the improvement of oxidative stress status in the cortex. The antiosteoporotic effect in OVX rats was also assessed after an 84-day intervention of M. citrifolia . The serum levels of calcium, osteocalcin, and alkaline phosphatase and osteoblast density in the tibia were increased, but the density of osteoclast was decreased in OVX rats which received the extract. Therefore, the current data suggested that the extract possessed antiosteoporotic effect by increasing bone formation but decreasing bone resorption.

SIGNALING PATHWAYS REGULATED BY BRASSICACEAE EXTRACT INHIBIT THE FORMATION OF ADVANCED GLYCATED END PRODUCTS IN RAT BRAIN.

PubMed

Al-Malki, Abdulrahman L; Barbour, Elie K; Ea, Huwait; Moselhy, Said S; ALZahrani, Anas Hassan Saeed; Kumosani, Taha A

2017-01-01

The goal of this study was identification signaling molecules mediated the formation of AGEs in brain of rats injected with CdCl2 and the role of camel whey proteins and Brassicaceae extract on formation of AGEs in brain. Ninety male rats were randomly grouped into five groups; Normal control (GpI) and the other rats (groups II-V) were received a single dose of cadmium chloride i.p (5 μg/kg/b.w) for induction of neurodegeneration. Rats in groups III-V were treated daily with whey protein (1g/kg b.w) or Brassicaceae extract (1mg/kg b.w) or combined respectively for 12 weeks. It was found that whey protein combined with Brassicaceae extract prevented the formation of AGEs and enhance the antioxidant activity compared with untreated group (p <0.001). Serum tumor necrosis factor (TNF-α) and interleukine (IL-6) levels were significantly decreased (p<0.01) in rats treated with whey protein and Brassicaceae extract formation compared with untreated. The combined treatment showed a better impact than individual ones (p<0.001). The level of cAMP but not cGMP were lowered in combined treatment than individual (p<0.01). It can be postulated that Whey protein + Brassicaceae extract formation could have potential benefits in the prevention of the onset and progression of neuropathy in patients.

SIGNALING PATHWAYS REGULATED BY BRASSICACEAE EXTRACT INHIBIT THE FORMATION OF ADVANCED GLYCATED END PRODUCTS IN RAT BRAIN

PubMed Central

Al-Malki, Abdulrahman L.; Barbour, Elie K.; EA, Huwait; Moselhy, Said S.; ALZahrani, Anas Hassan Saeed; Kumosani, Taha A.

2017-01-01

Background: The goal of this study was identification signaling molecules mediated the formation of AGEs in brain of rats injected with CdCl2 and the role of camel whey proteins and Brassicaceae extract on formation of AGEs in brain. Methods: Ninety male rats were randomly grouped into five groups; Normal control (GpI) and the other rats (groups II-V) were received a single dose of cadmium chloride i.p (5 μg/kg/b.w) for induction of neurodegeneration. Rats in groups III-V were treated daily with whey protein (1g/kg b.w) or Brassicaceae extract (1mg/kg b.w) or combined respectively for 12 weeks. Results: It was found that whey protein combined with Brassicaceae extract prevented the formation of AGEs and enhance the antioxidant activity compared with untreated group (p <0.001). Serum tumor necrosis factor (TNF-α) and interleukine (IL-6) levels were significantly decreased (p<0.01) in rats treated with whey protein and Brassicaceae extract formation compared with untreated. The combined treatment showed a better impact than individual ones (p<0.001). The level of cAMP but not cGMP were lowered in combined treatment than individual (p<0.01). Conclusion: It can be postulated that Whey protein + Brassicaceae extract formation could have potential benefits in the prevention of the onset and progression of neuropathy in patients. PMID:28573240

Monoamine reuptake inhibition and mood-enhancing potential of a specified oregano extract.

PubMed

Mechan, Annis O; Fowler, Ann; Seifert, Nicole; Rieger, Henry; Wöhrle, Tina; Etheve, Stéphane; Wyss, Adrian; Schüler, Göde; Colletto, Biagio; Kilpert, Claus; Aston, James; Elliott, J Martin; Goralczyk, Regina; Mohajeri, M Hasan

2011-04-01

A healthy, balanced diet is essential for both physical and mental well-being. Such a diet must include an adequate intake of micronutrients, essential fatty acids, amino acids and antioxidants. The monoamine neurotransmitters, serotonin, dopamine and noradrenaline, are derived from dietary amino acids and are involved in the modulation of mood, anxiety, cognition, sleep regulation and appetite. The capacity of nutritional interventions to elevate brain monoamine concentrations and, as a consequence, with the potential for mood enhancement, has not been extensively evaluated. The present study investigated an extract from oregano leaves, with a specified range of active constituents, identified via an unbiased, high-throughput screening programme. The oregano extract was demonstrated to inhibit the reuptake and degradation of the monoamine neurotransmitters in a dose-dependent manner, and microdialysis experiments in rats revealed an elevation of extracellular serotonin levels in the brain. Furthermore, following administration of oregano extract, behavioural responses were observed in mice that parallel the beneficial effects exhibited by monoamine-enhancing compounds when used in human subjects. In conclusion, these data show that an extract prepared from leaves of oregano, a major constituent of the Mediterranean diet, is brain-active, with moderate triple reuptake inhibitory activity, and exhibits positive behavioural effects in animal models. We postulate that such an extract may be effective in enhancing mental well-being in humans.

Preattentive extraction of abstract feature conjunctions from auditory stimulation as reflected by the mismatch negativity (MMN).

PubMed

Paavilainen, P; Simola, J; Jaramillo, M; Näätänen, R; Winkler, I

2001-03-01

Brain mechanisms extracting invariant information from varying auditory inputs were studied using the mismatch-negativity (MMN) brain response. We wished to determine whether the preattentive sound-analysis mechanisms, reflected by MMN, are capable of extracting invariant relationships based on abstract conjunctions between two sound features. The standard stimuli varied over a large range in frequency and intensity dimensions following the rule that the higher the frequency, the louder the intensity. The occasional deviant stimuli violated this frequency-intensity relationship and elicited an MMN. The results demonstrate that preattentive processing of auditory stimuli extends to unexpectedly complex relationships between the stimulus features.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

PubMed

Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

2016-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. Copyright © 2015 Elsevier Inc. All rights reserved.

Recovery correction technique for NMR spectroscopy of perchloric acid extracts using DL-valine-2,3-d2: validation and application to 5-fluorouracil-induced brain damage.

PubMed

Nakagami, Ryutaro; Yamaguchi, Masayuki; Ezawa, Kenji; Kimura, Sadaaki; Hamamichi, Shusei; Sekine, Norio; Furukawa, Akira; Niitsu, Mamoru; Fujii, Hirofumi

2014-01-01

We explored a recovery correction technique that can correct metabolite loss during perchloric acid (PCA) extraction and minimize inter-assay variance in quantitative (1)H nuclear magnetic resonance (NMR) spectroscopy of the brain and evaluated its efficacy in 5-fluorouracil (5-FU)- and saline-administered rats. We measured the recovery of creatine and dl-valine-2,3-d2 from PCA extract containing both compounds (0.5 to 8 mM). We intravenously administered either 5-FU for 4 days (total, 100 mg/kg body weight) or saline into 2 groups of 11 rats each. We subsequently performed PCA extraction of the whole brain on Day 9, externally adding 7 µmol of dl-valine-2,3-d2. We estimated metabolite concentrations using an NMR spectrometer with recovery correction, correcting metabolite concentrations based on the recovery factor of dl-valine-2,3-d2. For each metabolite concentration, we calculated the coefficient of variation (CEV) and compared differences between the 2 groups using unpaired t-test. Equivalent recoveries of dl-valine-2,3-d2 (89.4 ± 3.9%) and creatine (89.7 ± 3.9%) in the PCA extract of the mixed solution indicated the suitability of dl-valine-2,3-d2 as an internal reference. In the rat study, recovery of dl-valine-2,3-d2 was 90.6 ± 9.2%. Nine major metabolite concentrations adjusted by recovery of dl-valine-2,3-d2 in saline-administered rats were comparable to data in the literature. CEVs of these metabolites were reduced from 10 to 17% before to 7 to 16% after correction. The significance of differences in alanine and taurine between the 5-FU- and saline-administered groups was determined only after recovery correction (0.75 ± 0.12 versus 0.86 ± 0.07 for alanine; 5.17 ± 0.59 versus 5.66 ± 0.42 for taurine [µmol/g brain tissue]; P < 0.05). A new recovery correction technique corrected metabolite loss during PCA extraction, minimized inter-assay variance in quantitative (1)H NMR spectroscopy of brain tissue, and effectively detected inter-group differences in concentrations of brain metabolites between 5-FU- and saline-administered rats.

The Effect of Rosa Damascena Extract on Expression of Neurotrophic Factors in the CA1 Neurons of Adult Rat Hippocampus Following Ischemia.

PubMed

Moniri, Seyedeh Farzaneh; Hedayatpour, Azim; Hassanzadeh, Gholamreza; Vazirian, Mahdi; Karimian, Morteza; Belaran, Maryam; Ejtemaie Mehr, Shahram; Akbari, Mohamad

2017-12-01

Ischemic stroke is an important cause of death and disability in the world. Brain ischemia causes damage to brain cell, and among brain neurons, pyramidal neurons of the hippocampal CA1 region are more susceptive to ischemic injury. Recent findings suggest that neurotrophic factors protect against ischemic cell death. A dietary component of Rosa damascene extract possibly is associated with expression of neurotrophic factors mRNA following ischemia, so it can have therapeutic effect on cerebral ischemia. The present study attempts to evaluate the neuroprotective effect of Rosa damascene extract on adult rat hippocampal neurons following ischemic brain injury. Forty-eight adult male Wistar rats (weighing 250±20 gr and ages 10-12 weeks) used in this study, animals randomly were divided into 6 groups including Control, ischemia/ reperfusion (IR), vehicle and three treated groups (IR+0.5, 1, 2 mg/ml extract). Global ischemia was induced by bilateral common carotid arteries occlusion for 20 minutes. The treatment was done by different doses of Rosa damascena extract for 30 days. After 30 days cell death and gene expression in neurons of the CA1 region of the hippocampus were evaluated by Nissl staining and real time PCR assay. We found a significant decrease in NGF, BDNF and NT3 mRNA expression in neurons of CA1 region of the hippocampus in ischemia group compared to control group (P<0.0001). Our results also revealed that the number of dark neurons significantly increases in ischemia group compared to control group (P<0.0001). Following treatment with Rosa damascene extract reduced the number of dark neurons that was associated with NGF, NT3, and BDNF mRNA expression. All doses level had positive effects, but the most effective dose of Rosa damascena extract was 1 mg/ml. Our results suggest that neuroprotective activity of Rosa damascena can enhance hippocampal CA1 neuronal survival after global ischemia.

Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristopher; Brown, Jesse A.; ...

2016-05-09

Here, we present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams, and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parametersmore » gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. In order to demonstrate the utility of our tool, we also present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval« less

Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murugesan, Sugeerth; Bouchard, Kristopher; Brown, Jesse A.

Here, we present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams, and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parametersmore » gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. In order to demonstrate the utility of our tool, we also present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval« less

Methanol extract of Nigella sativa seed induces changes in the levels of neurotransmitter amino acids in male rat brain regions.

PubMed

El-Naggar, Tarek; Carretero, María Emilia; Arce, Carmen; Gómez-Serranillos, María Pilar

2017-12-01

Nigella sativa L. (Ranunculaceae) (NS) has been used for medicinal and culinary purposes. Different parts of the plant are used to treat many disorders. This study investigates the effects of NS methanol extract on brain neurotransmitter amino acid levels. We measured the changes in aspartate, glutamate, glycine and γ-aminobutyric acid in five brain regions of male Wistar rats after methanol extract treatment. Animals were injected intraperitoneally with saline solution (controls) or NS methanol extract (equivalent of 2.5 g/kg body weight) and sacrificed 1 h later or after administering 1 daily dose for 8 days. The neurotransmitters were measured in the hypothalamus, cortex, striatum, hippocampus and thalamus by HPLC. Results showed significant changes in amino acids compared to basal values. Glutamate increased significantly (16-36%) in the regions analyzed except the striatum. Aspartate in the hypothalamus (50 and 76%) and glycine in hippocampus (32 and 25%), thalamus (66 and 29%) and striatum (75 and 48%) also increased with the two treatment intervals. γ-Aminobutyric acid significantly increased in the hippocampus (38 and 32%) and thalamus (22 and 40%) but decreased in the cortex and hypothalamus although in striatum only after eight days of treatment (24%). Our results suggest that injected methanol extract modifies amino acid levels in the rat brain regions. These results could be of interest since some neurodegenerative diseases are related to amino acid level imbalances in the central nervous system, suggesting the prospect for therapeutic use of NS against these disorders.

Mucuna pruriens seed extract reduces oxidative stress in nigrostriatal tissue and improves neurobehavioral activity in paraquat-induced Parkinsonian mouse model.

PubMed

Yadav, Satyndra Kumar; Prakash, Jay; Chouhan, Shikha; Singh, Surya Pratap

2013-06-01

Parkinson's disease (PD) is a neurodegenerative disease which causes rigidity, resting tremor and postural instability. Treatment for this disease is still under investigation. Mucuna pruriens (L.), is a traditional herbal medicine, used in India since 1500 B.C., as a neuroprotective agent. In this present study, we evaluated the therapeutic effects of aqueous extract of M. pruriens (Mp) seed in Parkinsonian mouse model developed by chronic exposure to paraquat (PQ). Results of our study revealed that the nigrostriatal portion of Parkinsonian mouse brain showed significantly increased levels of nitrite, malondialdehyde (MDA) and reduced levels of catalase compared to the control. In the Parkinsonian mice hanging time was decreased, whereas narrow beam walk time and foot printing errors were increased. Treatment with aqueous seed extract of Mp significantly increased the catalase activity and decreased the MDA and nitrite level, compared to untreated Parkinsonian mouse brain. Mp treatment also improved the behavioral abnormalities. It increased hanging time, whereas it decreased narrow beam walk time and foot printing error compared to untreated Parkinsonian mouse brain. Furthermore, we observed a significant reduction in tyrosine hydroxylase (TH) immunoreactivity in the substantia nigra (SN) and striatum region of the brain, after treatment with PQ which was considerably restored by the use of Mp seed extract. Our result suggested that Mp seed extract treatment significantly reduced the PQ induced neurotoxicity as evident by decrease in oxidative damage, physiological abnormalities and immunohistochemical changes in the Parkinsonian mouse. Copyright © 2013 Elsevier Ltd. All rights reserved.

Neuroprotection and enhanced neurogenesis by extract from the tropical plant Knema laurina after inflammatory damage in living brain tissue.

PubMed

Häke, Ines; Schönenberger, Silvia; Neumann, Jens; Franke, Katrin; Paulsen-Merker, Katrin; Reymann, Klaus; Ismail, Ghazally; Bin Din, Laily; Said, Ikram M; Latiff, A; Wessjohann, Ludger; Zipp, Frauke; Ullrich, Oliver

2009-01-03

Inflammatory reactions in the CNS, resulting from a loss of control and involving a network of non-neuronal and neuronal cells, are major contributors to the onset and progress of several major neurodegenerative diseases. Therapeutic strategies should therefore keep or restore the well-controlled and finely-tuned balance of immune reactions, and protect neurons from inflammatory damage. In our study, we selected plants of the Malaysian rain forest by an ethnobotanic survey, and investigated them in cell-based-assay-systems and in living brain tissue cultures in order to identify anti-inflammatory and neuroprotective effects. We found that alcoholic extracts from the tropical plant Knema laurina (Black wild nutmeg) exhibited highly anti-inflammatory and neuroprotective effects in cell culture experiments, reduced NO- and IL-6-release from activated microglia cells dose-dependently, and protected living brain tissue from microglia-mediated inflammatory damage at a concentration of 30 microg/ml. On the intracellular level, the extract inhibited ERK-1/2-phosphorylation, IkB-phosphorylation and subsequently NF-kB-translocation in microglia cells. K. laurina belongs to the family of Myristicaceae, which have been used for centuries for treatment of digestive and inflammatory diseases and is also a major food plant of the Giant Hornbill. Moreover, extract from K. laurina promotes also neurogenesis in living brain tissue after oxygen-glucose deprivation. In conclusion, extract from K. laurina not only controls and limits inflammatory reaction after primary neuronal damage, it promotes moreover neurogenesis if given hours until days after stroke-like injury.

Feature extraction inspired by V1 in visual cortex

NASA Astrophysics Data System (ADS)

Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang

2018-04-01

Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.

Near-infrared spectroscopy versus magnetic resonance imaging to study brain perfusion in newborns with hypoxic-ischemic encephalopathy treated with hypothermia.

PubMed

Wintermark, P; Hansen, A; Warfield, S K; Dukhovny, D; Soul, J S

2014-01-15

The measurement of brain perfusion may provide valuable information for assessment and treatment of newborns with hypoxic-ischemic encephalopathy (HIE). While arterial spin labeled perfusion (ASL) magnetic resonance imaging (MRI) provides noninvasive and direct measurements of regional cerebral blood flow (CBF) values, it is logistically challenging to obtain. Near-infrared spectroscopy (NIRS) might be an alternative, as it permits noninvasive and continuous monitoring of cerebral hemodynamics and oxygenation at the bedside. The purpose of this study is to determine the correlation between measurements of brain perfusion by NIRS and by MRI in term newborns with HIE treated with hypothermia. In this prospective cohort study, ASL-MRI and NIRS performed during hypothermia were used to assess brain perfusion in these newborns. Regional cerebral blood flow (CBF) values, measured from 1-2 MRI scans for each patient, were compared to mixed venous saturation values (SctO2) recorded by NIRS just before and after each MRI. Analysis included groupings into moderate versus severe HIE based on their initial background pattern of amplitude-integrated electroencephalogram. Twelve concomitant recordings were obtained of seven neonates. Strong correlation was found between SctO2 and CBF in asphyxiated newborns with severe HIE (r=0.88; p value=0.0085). Moreover, newborns with severe HIE had lower CBF (likely lower oxygen supply) and extracted less oxygen (likely lower oxygen demand or utilization) when comparing SctO2 and CBF to those with moderate HIE. NIRS is an effective bedside tool to monitor and understand brain perfusion changes in term asphyxiated newborns, which in conjunction with precise measurements of CBF obtained by MRI at particular times, may help tailor neuroprotective strategies in term newborns with HIE. Copyright © 2013 Elsevier Inc. All rights reserved.

Near-Infrared Spectroscopy versus Magnetic Resonance Imaging To Study Brain Perfusion in Newborns with Hypoxic-Ischemic Encephalopathy Treated with Hypothermia

PubMed Central

Wintermark, P.; Hansen, A.; Warfield, SK.; Dukhovny, D.; Soul, JS.

2014-01-01

Background The measurement of brain perfusion may provide valuable information for assessment and treatment of newborns with hypoxic-ischemic encephalopathy (HIE). While arterial spin labeled perfusion (ASL) magnetic resonance imaging (MRI) provides noninvasive and direct measurements of regional cerebral blood flow (CBF) values, it is logistically challenging to obtain. Near-infrared spectroscopy (NIRS) might be an alternative, as it permits noninvasive and continuous monitoring of cerebral hemodynamics and oxygenation at the bedside. Objective The purpose of this study is to determine the correlation between measurements of brain perfusion by NIRS and by MRI in term newborns with HIE treated with hypothermia. Design/Methods In this prospective cohort study, ASL-MRI and NIRS performed during hypothermia were used to assess brain perfusion in these newborns. Regional cerebral blood flow values (CBF), measured from 1–2 MRI scans for each patient, were compared to mixed venous saturation values (SctO2) recorded by NIRS just before and after each MRI. Analysis included groupings into moderate versus severe HIE based on their initial background pattern of amplitude-integrated electroencephalogram. Results Twelve concomitant recordings were obtained of seven neonates. Strong correlation was found between SctO2 and CBF in asphyxiated newborns with severe HIE (r = 0.88; p value = 0.0085). Moreover, newborns with severe HIE had lower CBF (likely lower oxygen supply) and extracted less oxygen (likely lower oxygen demand or utilization) when comparing SctO2 and CBF to those with moderate HIE. Conclusions NIRS is an effective bedside tool to monitor and understand brain perfusion changes in term asphyxiated newborns, which in conjunction with precise measurements of CBF obtained by MRI at particular times, may help tailor neuroprotective strategies in term newborns with HIE. PMID:23631990

Nanomedicine in Central Nervous System (CNS) Disorders: A Present and Future Prospective

PubMed Central

Soni, Shringika; Ruhela, Rakesh Kumar; Medhi, Bikash

2016-01-01

Purpose: For the past few decades central nervous system disorders were considered as a major strike on human health and social system of developing countries. The natural therapeutic methods for CNS disorders limited for many patients. Moreover, nanotechnology-based drug delivery to the brain may an exciting and promising platform to overcome the problem of BBB crossing. In this review, first we focused on the role of the blood-brain barrier in drug delivery; and second, we summarized synthesis methods of nanomedicine and their role in different CNS disorder. Method: We reviewed the PubMed databases and extracted several kinds of literature on neuro nanomedicines using keywords, CNS disorders, nanomedicine, and nanotechnology. The inclusion criteria included chemical and green synthesis methods for synthesis of nanoparticles encapsulated drugs and, their in-vivo and in-vitro studies. We excluded nanomedicine gene therapy and nanomaterial in brain imaging. Results: In this review, we tried to identify a highly efficient method for nanomedicine synthesis and their efficacy in neuronal disorders. SLN and PNP encapsulated drugs reported highly efficient by easily crossing BBB. Although, these neuro-nanomedicine play significant role in therapeutics but some metallic nanoparticles reported the adverse effect on developing the brain. Conclusion: Although impressive advancement has made via innovative potential drug development, but their efficacy is still moderate due to limited brain permeability. To overcome this constraint,powerful tool in CNS therapeutic intervention provided by nanotechnology-based drug delivery methods. Due to its small and biofunctionalization characteristics, nanomedicine can easily penetrate and facilitate the drug through the barrier. But still, understanding of their toxicity level, optimization and standardization are a long way to go. PMID:27766216

Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics

PubMed Central

Banaei, Maryam; Hatami, Javad; Yazdanfar, Abbas; Gramann, Klaus

2017-01-01

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants’ emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers’ affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types (rs (14) = 0.525, p = 0.037) and geometry (rs (14) = −0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces. PMID:29033807

Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics.

PubMed

Banaei, Maryam; Hatami, Javad; Yazdanfar, Abbas; Gramann, Klaus

2017-01-01

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants' emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers' affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types ( r s (14) = 0.525, p = 0.037) and geometry ( r s (14) = -0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces.

Seven challenges for neuroscience.

PubMed

Markram, Henry

2013-01-01

Although twenty-first century neuroscience is a major scientific enterprise, advances in basic research have not yet translated into benefits for society. In this paper, I outline seven fundamental challenges that need to be overcome. First, neuroscience has to become "big science" - we need big teams with the resources and competences to tackle the big problems. Second, we need to create interlinked sets of data providing a complete picture of single areas of the brain at their different levels of organization with "rungs" linking the descriptions for humans and other species. Such "data ladders" will help us to meet the third challenge - the development of efficient predictive tools, enabling us to drastically increase the information we can extract from expensive experiments. The fourth challenge goes one step further: we have to develop novel hardware and software sufficiently powerful to simulate the brain. In the future, supercomputer-based brain simulation will enable us to make in silico manipulations and recordings, which are currently completely impossible in the lab. The fifth and sixth challenges are translational. On the one hand we need to develop new ways of classifying and simulating brain disease, leading to better diagnosis and more effective drug discovery. On the other, we have to exploit our knowledge to build new brain-inspired technologies, with potentially huge benefits for industry and for society. This leads to the seventh challenge. Neuroscience can indeed deliver huge benefits but we have to be aware of widespread social concern about our work. We need to recognize the fears that exist, lay them to rest, and actively build public support for neuroscience research. We have to set goals for ourselves that the public can recognize and share. And then we have to deliver on our promises. Only in this way, will we receive the support and funding we need.

Effects of an Agaricus blazei aqueous extract pretreatment on paracetamol-induced brain and liver injury in rats.

PubMed

Soares, Andréia A; de Oliveira, Andrea L; Sá-Nakanishi, Anacharis B; Comar, Jurandir F; Rampazzo, Ana P S; Vicentini, Fernando A; Natali, Maria R M; Gomes da Costa, Sandra M; Bracht, Adelar; Peralta, Rosane M

2013-01-01

The action of an Agaricus blazei aqueous extract pretreatment on paracetamol injury in rats was examined not only in terms of the classical indicators (e.g., levels of hepatic enzymes in the plasma) but also in terms of functional and metabolic parameters (e.g., gluconeogenesis). Considering solely the classical indicators for tissue damage, the results can be regarded as an indication that the A. blazei extract is able to provide a reasonable degree of protection against the paracetamol injury in both the hepatic and brain tissues. The A. blazei pretreatment largely prevented the increased levels of hepatic enzymes in the plasma (ASP, ALT, LDH, and ALP) and practically normalized the TBARS levels in both liver and brain tissues. With respect to the functional and metabolic parameters of the liver, however, the extract provided little or no protection. This includes morphological signs of inflammation and the especially important functional parameter gluconeogenesis, which was impaired by paracetamol. Considering these results and the long list of extracts and substances that are said to have hepatoprotective effects, it would be useful to incorporate evaluations of functional parameters into the experimental protocols of studies aiming to attribute or refute effective hepatoprotective actions to natural products.

Effects of an Agaricus blazei Aqueous Extract Pretreatment on Paracetamol-Induced Brain and Liver Injury in Rats

PubMed Central

Soares, Andréia A.; de Oliveira, Andrea L.; Sá-Nakanishi, Anacharis B.; Comar, Jurandir F.; Rampazzo, Ana P. S.; Vicentini, Fernando A.; Natali, Maria R. M.; Gomes da Costa, Sandra M.; Peralta, Rosane M.

2013-01-01

The action of an Agaricus blazei aqueous extract pretreatment on paracetamol injury in rats was examined not only in terms of the classical indicators (e.g., levels of hepatic enzymes in the plasma) but also in terms of functional and metabolic parameters (e.g., gluconeogenesis). Considering solely the classical indicators for tissue damage, the results can be regarded as an indication that the A. blazei extract is able to provide a reasonable degree of protection against the paracetamol injury in both the hepatic and brain tissues. The A. blazei pretreatment largely prevented the increased levels of hepatic enzymes in the plasma (ASP, ALT, LDH, and ALP) and practically normalized the TBARS levels in both liver and brain tissues. With respect to the functional and metabolic parameters of the liver, however, the extract provided little or no protection. This includes morphological signs of inflammation and the especially important functional parameter gluconeogenesis, which was impaired by paracetamol. Considering these results and the long list of extracts and substances that are said to have hepatoprotective effects, it would be useful to incorporate evaluations of functional parameters into the experimental protocols of studies aiming to attribute or refute effective hepatoprotective actions to natural products. PMID:23984368

Diverse action of lipoteichoic acid and lipopolysaccharide on neuroinflammation, blood-brain barrier disruption, and anxiety in mice.

PubMed

Mayerhofer, Raphaela; Fröhlich, Esther E; Reichmann, Florian; Farzi, Aitak; Kogelnik, Nora; Fröhlich, Eleonore; Sattler, Wolfgang; Holzer, Peter

2017-02-01

Microbial metabolites are known to affect immune system, brain, and behavior via activation of pattern recognition receptors such as Toll-like receptor 4 (TLR4). Unlike the effect of the TLR4 agonist lipopolysaccharide (LPS), the role of other TLR agonists in immune-brain communication is insufficiently understood. We therefore hypothesized that the TLR2 agonist lipoteichoic acid (LTA) causes immune activation in the periphery and brain, stimulates the hypothalamic-pituitary-adrenal (HPA) axis and has an adverse effect on blood-brain barrier (BBB) and emotional behavior. Since LTA preparations may be contaminated by LPS, an extract of LTA (LTA extract ), purified LTA (LTA pure ), and pure LPS (LPS ultrapure ) were compared with each other in their effects on molecular and behavioral parameters 3h after intraperitoneal (i.p.) injection to male C57BL/6N mice. The LTA extract (20mg/kg) induced anxiety-related behavior in the open field test, enhanced the circulating levels of particular cytokines and the cerebral expression of cytokine mRNA, and blunted the cerebral expression of tight junction protein mRNA. A dose of LPS ultrapure matching the amount of endotoxin/LPS contaminating the LTA extract reproduced several of the molecular and behavioral effects of LTA extract . LTA pure (20mg/kg) increased plasma levels of tumor necrosis factor-α (TNF-α), interleukin-6 and interferon-γ, and enhanced the transcription of TNF-α, interleukin-1β and other cytokines in the amygdala and prefrontal cortex. These neuroinflammatory effects of LTA pure were associated with transcriptional down-regulation of tight junction-associated proteins (claudin 5, occludin) in the brain. LTA pure also enhanced circulating corticosterone, but failed to alter locomotor and anxiety-related behavior in the open field test. These data disclose that TLR2 agonism by LTA causes peripheral immune activation and initiates neuroinflammatory processes in the brain that are associated with down-regulation of BBB components and activation of the HPA axis, although emotional behavior (anxiety) is not affected. The results obtained with an LTA preparation contaminated with LPS hint at a facilitatory interaction between TLR2 and TLR4, the adverse impact of which on long-term neuroinflammation, disruption of the BBB and mental health warrants further analysis. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

The development of cortical sensitivity to visual word forms.

PubMed

Ben-Shachar, Michal; Dougherty, Robert F; Deutsch, Gayle K; Wandell, Brian A

2011-09-01

The ability to extract visual word forms quickly and efficiently is essential for using reading as a tool for learning. We describe the first longitudinal fMRI study to chart individual changes in cortical sensitivity to written words as reading develops. We conducted four annual measurements of brain function and reading skills in a heterogeneous group of children, initially 7-12 years old. The results show age-related increase in children's cortical sensitivity to word visibility in posterior left occipito-temporal sulcus (LOTS), nearby the anatomical location of the visual word form area. Moreover, the rate of increase in LOTS word sensitivity specifically correlates with the rate of improvement in sight word efficiency, a measure of speeded overt word reading. Other cortical regions, including V1, posterior parietal cortex, and the right homologue of LOTS, did not demonstrate such developmental changes. These results provide developmental support for the hypothesis that LOTS is part of the cortical circuitry that extracts visual word forms quickly and efficiently and highlight the importance of developing cortical sensitivity to word visibility in reading acquisition.

The Development of Cortical Sensitivity to Visual Word Forms

PubMed Central

Ben-Shachar, Michal; Dougherty, Robert F.; Deutsch, Gayle K.; Wandell, Brian A.

2011-01-01

The ability to extract visual word forms quickly and efficiently is essential for using reading as a tool for learning. We describe the first longitudinal fMRI study to chart individual changes in cortical sensitivity to written words as reading develops. We conducted four annual measurements of brain function and reading skills in a heterogeneous group of children, initially 7–12 years old. The results show age-related increase in children's cortical sensitivity to word visibility in posterior left occipito-temporal sulcus (LOTS), nearby the anatomical location of the visual word form area. Moreover, the rate of increase in LOTS word sensitivity specifically correlates with the rate of improvement in sight word efficiency, a measure of speeded overt word reading. Other cortical regions, including V1, posterior parietal cortex, and the right homologue of LOTS, did not demonstrate such developmental changes. These results provide developmental support for the hypothesis that LOTS is part of the cortical circuitry that extracts visual word forms quickly and efficiently and highlight the importance of developing cortical sensitivity to word visibility in reading acquisition. PMID:21261451

Selection and application of familiar and novel tools in patients with left and right hemispheric stroke: Psychometrics and normative data.

PubMed

Buchmann, Ilka; Randerath, Jennifer

2017-09-01

Frequently left brain damage (LBD) leads to limb apraxia, a disorder that can affect tool-use. Despite its impact on daily life, classical tests examining the pantomime of tool-use and imitation of gestures are seldom applied in clinical practice. The study's aim was to present a diagnostic approach which appears more strongly related to actions in daily life in order to sensitize applicants and patients about the relevance of the disorder before patients are discharged. Two tests were introduced that evaluate actual tool selection and tool-object-application: the Novel Tools (NTT) and the Familiar Tools (FTT) Test (parts of the DILA-S: Diagnostic Instrument for Limb Apraxia - Short Version). Normative data in healthy subjects (N = 82) was collected. Then the tests were applied in stroke patients with unilateral left brain damage (LBD: N = 33), a control right brain damage group (RBD: N = 20) as well as healthy age and gender matched controls (CL: N = 28, and CR, N = 18). The tests showed appropriate interrater-reliability and internal consistency as well as concurrent and divergent validity. To examine criterion validity based on the well-known left lateralization of limb apraxia, group comparisons were run. As expected, the LBD group demonstrated a high prevalence of tool-use apraxia (NTT: 36.4%, FTT: 48.5%) ranging from mild to severe impairment and scored worse than their control group (CL). A few RBD patients did demonstrate impairments in tool-use (NTT: 15%, FTT: 15%). On a group level they did not differ from their healthy controls (CR). Further, it was demonstrated that the selection and application of familiar and novel tools can be impaired selectively. Our study results suggest that real tool-use tests evaluating tool selection and tool application should be considered for standard diagnosis of limb apraxia in left as well as right brain damaged patients. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

PubMed

Li, Wan; Yang, Chunlan; Shi, Feng; Wang, Qun; Wu, Shuicai; Lu, Wangsheng; Li, Shaowu; Nie, Yingnan; Zhang, Xin

2018-04-16

Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

The Lateralizer: A Tool for Students to Explore the Divided Brain

ERIC Educational Resources Information Center

Motz, Benjamin A.; James, Karin H.; Busey, Thomas A.

2012-01-01

Despite a profusion of popular misinformation about the left brain and right brain, there are functional differences between the left and right cerebral hemispheres in humans. Evidence from split-brain patients, individuals with unilateral brain damage, and neuroimaging studies suggest that each hemisphere may be specialized for certain cognitive…

Grape seed and skin extract prevents high-fat diet-induced brain lipotoxicity in rat.

PubMed

Charradi, Kamel; Elkahoui, Salem; Karkouch, Ines; Limam, Ferid; Hassine, Fethy Ben; Aouani, Ezzedine

2012-09-01

Obesity is related to an elevated risk of dementia and the physiologic mechanisms whereby fat adversely affects the brain are poorly understood. The present investigation analyzed the effect of a high fat diet (HFD) on brain steatosis and oxidative stress and the intracellular mediators involved in signal transduction, as well as the protection offered by grape seed and skin extract (GSSE). HFD induced ectopic deposition of cholesterol and phospholipid but not triglyceride. Moreover brain lipotoxicity is linked to an oxidative stress characterized by increased lipoperoxidation and carbonylation, inhibition of glutathione peroxidase and superoxide dismutase activities, depletion of manganese and a concomitant increase in ionizable calcium and acetylcholinesterase activity. Importantly GSSE alleviated all the deleterious effects of HFD treatment. Altogether our data indicated that HFD could find some potential application in the treatment of manganism and that GSSE should be used as a safe anti-lipotoxic agent in the prevention and treatment of fat-induced brain injury.

GPR139, an Orphan Receptor Highly Enriched in the Habenula and Septum, Is Activated by the Essential Amino Acids L-Tryptophan and L-Phenylalanine.

PubMed

Liu, Changlu; Bonaventure, Pascal; Lee, Grace; Nepomuceno, Diane; Kuei, Chester; Wu, Jiejun; Li, Qingqin; Joseph, Victory; Sutton, Steven W; Eckert, William; Yao, Xiang; Yieh, Lynn; Dvorak, Curt; Carruthers, Nicholas; Coate, Heather; Yun, Sujin; Dugovic, Christine; Harrington, Anthony; Lovenberg, Timothy W

2015-11-01

GPR139 is an orphan G-protein-coupled receptor expressed in the central nervous system. To identify its physiologic ligand, we measured GPR139 receptor activity from recombinant cells after treatment with amino acids, orphan ligands, serum, and tissue extracts. GPR139 activity was measured using guanosine 5'-O-(3-[(35)S]thio)-triphosphate binding, calcium mobilization, and extracellular signal-regulated kinases phosphorylation assays. Amino acids L-tryptophan (L-Trp) and L-phenylalanine (L-Phe) activated GPR139, with EC50 values in the 30- to 300-μM range, consistent with the physiologic concentrations of L-Trp and L-Phe in tissues. Chromatography of rat brain, rat serum, and human serum extracts revealed two peaks of GPR139 activity, which corresponded to the elution peaks of L-Trp and L-Phe. With the purpose of identifying novel tools to study GPR139 function, a high-throughput screening campaign led to the identification of a selective small-molecule agonist [JNJ-63533054, (S)-3-chloro-N-(2-oxo-2-((1-phenylethyl)amino)ethyl) benzamide]. The tritium-labeled JNJ-63533054 bound to cell membranes expressing GPR139 and could be specifically displaced by L-Trp and L-Phe. Sequence alignment revealed that GPR139 is highly conserved across species, and RNA sequencing studies of rat and human tissues indicated its exclusive expression in the brain and pituitary gland. Immunohistochemical analysis showed specific expression of the receptor in circumventricular regions of the habenula and septum in mice. Together, these findings suggest that L-Trp and L-Phe are candidate physiologic ligands for GPR139, and we hypothesize that this receptor may act as a sensor to detect dynamic changes of L-Trp and L-Phe in the brain. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Assessment and modulation of neuroplasticity in rehabilitation with transcranial magnetic stimulation

PubMed Central

Bashir, Shahid; Mizrahi, Ilan; Weaver, Kayleen; Fregni, Felipe; Pascual-Leone, Alvaro

2013-01-01

Despite intensive efforts towards the improvement of outcomes after acquired brain injury functional recovery is often limited. One reasons is the challenge in assessing and guiding plasticity after brain injury. In this context, Transcranial Magnetic Stimulation (TMS) - a noninvasive tool of brain stimulation - could play a major role. TMS has shown to be a reliable tool to measure plastic changes in the motor cortex associated with interventions in the motor system; such as motor training and motor cortex stimulation. In addition, as illustrated by the experience in promoting recovery from stroke, TMS a promising therapeutic tool to minimize motor, speech, cognitive, and mood deficits. In this review, we will focus on stroke to discuss how TMS can provide insights into the mechanisms of neurological recovery, and can be used for measurement and modulation of plasticity after an acquired brain insult. PMID:21172687

Real-time simulation and visualization of volumetric brain deformation for image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Ferrant, Matthieu; Nabavi, Arya; Macq, Benoit M. M.; Kikinis, Ron; Warfield, Simon K.

2001-05-01

During neurosurgery, the challenge for the neurosurgeon is to remove as much as possible of a tumor without destroying healthy tissue. This can be difficult because healthy and diseased tissue can have the same visual appearance. To this aim, and because the surgeon cannot see underneath the brain surface, image-guided neurosurgery systems are being increasingly used. However, during surgery, deformation of the brain occurs (due to brain shift and tumor resection), therefore causing errors in the surgical planning with respect to preoperative imaging. In our previous work, we developed software for capturing the deformation of the brain during neurosurgery. The software also allows preoperative data to be updated according to the intraoperative imaging so as to reflect the shape changes of the brain during surgery. Our goal in this paper was to rapidly visualize and characterize this deformation over the course of surgery with appropriate tools. Therefore, we developed tools allowing the doctor to visualize (in 2D and 3D) deformations, as well as the stress tensors characterizing the deformation along with the updated preoperative and intraoperative imaging during the course of surgery. Such tools significantly add to the value of intraoperative imaging and hence could improve surgical outcomes.

Cinnamon extract inhibits tau aggregation associated with Alzheimer’s Disease in vitro

USDA-ARS?s Scientific Manuscript database

An aqueous extract of Ceylon cinnamon (C. zeylanicum) was found to inhibit tau aggregation and filament formation, hallmarks of Alzheimer’s disease (AD) in vitro using brain cells taken from patients who died with AD. The extract also promoted complete disassembly of recombinant tau filaments, and ...

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

PubMed Central

Bridwell, David A.; Cavanagh, James F.; Collins, Anne G. E.; Nunez, Michael D.; Srinivasan, Ramesh; Stober, Sebastian; Calhoun, Vince D.

2018-01-01

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function. PMID:29632480

PHIT for Duty, a Personal Health Intervention Tool for Psychological Health and Traumatic Brain Injury

DTIC Science & Technology

2015-04-01

Award Number: W81XWH-11-2-0129 TITLE: PHIT for Duty, a Personal Health Intervention Tool for Psychological Health and Traumatic Brain Injury...TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-11-2-0129 PHIT for Duty, a Personal Health Intervention Tool for Psychological Health and Traumatic...health problems. PHIT for Duty integrates self-report and physiological sensor instruments to assess health status via brief weekly screening

Real time system design of motor imagery brain-computer interface based on multi band CSP and SVM

NASA Astrophysics Data System (ADS)

Zhao, Li; Li, Xiaoqin; Bian, Yan

2018-04-01

Motion imagery (MT) is an effective method to promote the recovery of limbs in patients after stroke. Though an online MT brain computer interface (BCT) system, which apply MT, can enhance the patient's participation and accelerate their recovery process. The traditional method deals with the electroencephalogram (EEG) induced by MT by common spatial pattern (CSP), which is used to extract information from a frequency band. Tn order to further improve the classification accuracy of the system, information of two characteristic frequency bands is extracted. The effectiveness of the proposed feature extraction method is verified by off-line analysis of competition data and the analysis of online system.

The effects of Mucuna pruriens extract on histopathological and biochemical features in the rat model of ischemia.

PubMed

Nayak, Vanishri S; Kumar, Nitesh; D'Souza, Antony S; Nayak, Sunil S; Cheruku, Sri P; Pai, K Sreedhara Ranganath

2017-12-13

Stroke is considered to be one of the most important causes of death worldwide. Global ischemia causes widespread brain injury and infarctions in various regions of the brain. Oxidative stress can be considered an important factor in the development of tissue damage, which is caused because of arterial occlusion with subsequent reperfusion. Kapikacchu or Mucuna pruriens, commonly known as velvet bean, is well known for its aphrodisiac activities. It is also used in the treatment of snakebites, depressive neurosis, and Parkinson's disease. Although this plant has different pharmacological actions, its neuroprotective activity has received minimal attention. Thus, this study was carried out with the aim of evaluating the neuroprotective action of M. pruriens in bilateral carotid artery occlusion-induced global cerebral ischemia in Wistar rats. The carotid arteries of both sides were occluded for 30 min and reperfused to induce global cerebral ischemia. The methanolic plant extract was administered to the study animals for 10 days. The brains of the Wistar rats were isolated by decapitation and observed for histopathological and biochemical changes. Cerebral ischemia resulted in significant neurological damage in the brains of the rats that were not treated by M. pruriens. The group subjected to treatment by the M. pruriens extract showed significant protection against brain damage compared with the negative control group, which indicates the therapeutic potential of this plant in ischemia.

Protective Effects of Crocus Sativus L. Extract and Crocin against Chronic-Stress Induced Oxidative Damage of Brain, Liver and Kidneys in Rats

PubMed Central

Bandegi, Ahmad Reza; Rashidy-Pour, Ali; Vafaei, Abbas Ali; Ghadrdoost, Behshid

2014-01-01

Purpose: Chronic stress has been reported to induce oxidative damage of the brain. A few studies have shown that Crocus Sativus L., commonly known as saffron and its active constituent crocin may have a protective effect against oxidative stress. The present work was designed to study the protective effects of saffron extract and crocin on chronic – stress induced oxidative stress damage of the brain, liver and kidneys. Methods: Rats were injected with a daily dose of saffron extract (30 mg/kg, IP) or crocin (30 mg/kg, IP) during a period of 21 days following chronic restraint stress (6 h/day). In order to determine the changes of the oxidative stress parameters following chronic stress, the levels of the lipid peroxidation product, malondialdehyde (MDA), the total antioxidant reactivity (TAR), as well as antioxidant enzyme activities glutathione peroxidase (GPx), glutathione reductase (GR) and superoxide dismutase (SOD) were measured in the brain, liver and kidneys tissues after the end of chronic stress. Results: In the stressed animals that receiving of saline, levels of MDA, and the activities of GPx, GR, and SOD were significantly higher (P<0.0001) and the TAR capacity were significantly lower than those of the non-stressed animals (P<0.0001). Both saffron extract and crocin were able to reverse these changes in the stressed animals as compared with the control groups (P<0.05). Conclusion: These observations indicate that saffron and its active constituent crocin can prevent chronic stress–induced oxidative stress damage of the brain, liver and kidneys and suggest that these substances may be useful against oxidative stress. PMID:25671180

'Prion-like' propagation of the synucleinopathy of M83 transgenic mice depends on the mouse genotype and type of inoculum.

PubMed

Sargent, Dorian; Verchère, Jérémy; Lazizzera, Corinne; Gaillard, Damien; Lakhdar, Latifa; Streichenberger, Nathalie; Morignat, Eric; Bétemps, Dominique; Baron, Thierry

2017-10-01

The M83 transgenic mouse is a model of human synucleinopathies that develops severe motor impairment correlated with accumulation of the pathological Ser129-phosphorylated α-synuclein (α-syn P ) in the brain and spinal cord. M83 disease can be accelerated by intracerebral inoculation of brain extracts from sick M83 mice. This has also recently been described using peripheral routes, injecting recombinant preformed α-syn fibrils into the muscle or the peritoneum. Here, we inoculated homozygous and/or hemizygous M83 neonates via the intraperitoneal and/or intracerebral routes with two different brain extracts: one from sick M83 mice inoculated with brain extract from other sick M83 mice, and the other derived from a human multiple system atrophy source passaged in M83 mice. Detection of α-syn P using ELISA and western blot confirmed the disease in mice. The distribution of α-syn P in the central nervous system was similar, independently of the inoculum or inoculation route, consistent with previous studies describing M83 disease. ELISA tests revealed higher levels of α-syn P in homozygous than in hemizygous sick M83 mice, at least after IC inoculation. Interestingly, the immunoreactivity of α-syn P detected by ELISA was significantly lower in M83 mice inoculated with the multiple system atrophy inoculum than in M83 mice inoculated with the M83 inoculum, at the first two passages. 'Prion-like' propagation of the synucleinopathy up to the clinical disease was accelerated by both intracerebral and intraperitoneal inoculations of brain extracts from sick mice. This acceleration, however, depends on the levels of α-syn expression by the mouse and the type of inoculum. © 2017 International Society for Neurochemistry.

Evidence for a Phe-Gly-Leu-amide-like allatostatin in the beetle Tenebrio molitor.

PubMed

Elliott, Karen L; Chan, Kuen Kuen; Stay, Barbara

2010-03-01

The allatostatins (ASTs) with Phe-Gly-Leu-amide C-terminal sequence are multifunctional neuropeptides discovered as inhibitors of juvenile hormone (JH) synthesis by corpora allata (CA) of cockroaches. Although these ASTs inhibit JH synthesis only in cockroaches, crickets, termites and locusts, isolation of peptides or of cDNA/genomic DNA or analysis of genomes indicates their occurrence in many orders of insects with the exception of coleopterans. The gene for these ASTs has not been found in the genome of the red flour beetle Tribolium castaneum (Family Tenebrionidae). Yet, in view of widespread occurrence of these peptides in insects, crustaceans and nematodes, they would be expected to occur in beetles. This study provides evidence for the presence of FGLa-like ASTs in the tenebrionid beetle, Tenebrio molitor, and scarabid beetle, Popillia japonica. Extract of brain from both beetles inhibited JH synthesis by cockroach CA dose dependently and reversibly. 20 brain equivalents of T. molitor and P. japonica extracts inhibited JH synthesis 64+/-5 and 65+/-0.6% respectively. Antibody against cockroach allatostatin (Diploptera punctata AST-7) used in an enzyme-linked immunosorbent assay reacted with brain extract of these beetles. Antibody against D. punctata AST-5 localized FGLa-like ASTs in the brain and subesophageal ganglion of T. molitor and P. japonica. In addition, pretreatment of T. molitor brain extract with anti-D. punctata AST-5 reduced the inhibition of JH synthesis and pretreatment of anti-D. punctata AST-5 with D. punctata AST-5 diminished the immunoreactivity of the antibody. Thus we predict that FGLa-like allatostatins will be found in beetles. (c) 2009 Elsevier Inc. All rights reserved.

MRI as a tool to study brain structure from mouse models for mental retardation

NASA Astrophysics Data System (ADS)

Verhoye, Marleen; Sijbers, Jan; Kooy, R. F.; Reyniers, E.; Fransen, E.; Oostra, B. A.; Willems, Peter; Van der Linden, Anne-Marie

1998-07-01

Nowadays, transgenic mice are a common tool to study brain abnormalities in neurological disorders. These studies usually rely on neuropathological examinations, which have a number of drawbacks, including the risk of artefacts introduced by fixation and dehydration procedures. Here we present 3D Fast Spin Echo Magnetic Resonance Imaging (MRI) in combination with 2D and 3D segmentation techniques as a powerful tool to study brain anatomy. We set up MRI of the brain in mouse models for the fragile X syndrome (FMR1 knockout) and Corpus callosum hypoplasia, mental Retardation, Adducted thumbs, Spastic paraplegia and Hydrocephalus (CRASH) syndrome (L1CAM knockout). Our major goal was to determine qualitative and quantitative differences in specific brain structures. MRI of the brain of fragile X and CRASH patients has revealed alterations in the size of specific brain structures, including the cerebellar vermis and the ventricular system. In the present MRI study of the brain from fragile X knockout mice, we have measured the size of the brain, cerebellum and 4th ventricle, which were reported as abnormal in human fragile X patients, but found no evidence for altered brain regions in the mouse model. In CRASH syndrome, the most specific brain abnormalities are vermis hypoplasia and abnormalities of the ventricular system with some degree of hydrocephalus. With the MRI study of L1CAM knockout mice we found vermis hypoplasia, abnormalities of the ventricular system including dilatation of the lateral and the 4th ventricles. These subtle abnormalities were not detected upon standard neuropathological examination. Here we proved that this sensitive MRI technique allows to measure small differences which can not always be detected by means of pathology.

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

PubMed

Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

2018-05-01

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

An independent SSVEP-based brain-computer interface in locked-in syndrome

NASA Astrophysics Data System (ADS)

Lesenfants, D.; Habbal, D.; Lugo, Z.; Lebeau, M.; Horki, P.; Amico, E.; Pokorny, C.; Gómez, F.; Soddu, A.; Müller-Putz, G.; Laureys, S.; Noirhomme, Q.

2014-06-01

Objective. Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS). Approach. Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based ‘interlaced squares' stimulation pattern. Main results. Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online. Significance. We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

Grape seed polyphenolic extract specifically decreases aβ*56 in the brains of Tg2576 mice.

PubMed

Liu, Peng; Kemper, Lisa J; Wang, Jun; Zahs, Kathleen R; Ashe, Karen H; Pasinetti, Giulio M

2011-01-01

Amyloid-β (Aβ) oligomers, found in the brains of Alzheimer's disease (AD) patients and transgenic mouse models of AD, cause synaptotoxicity and memory impairment. Grape seed polyphenolic extract (GSPE) inhibits Aβ oligomerization in vitro and attenuates cognitive impairment and AD-related neuropathology in the brains of transgenic mice. In the current study, GSPE was administered to Tg2576 mice for a period of five months. Treatment significantly decreased brain levels of Aβ*56, a 56-kDa Aβ oligomer previously shown to induce memory dysfunction in rodents, without changing the levels of transgenic amyloid-β protein precursor, monomeric Aβ, or other Aβ oligomers. These results thus provide the first demonstration that a safe and affordable intervention can lower the levels of a memory-impairing Aβ oligomer in vivo and strongly suggest that GSPE should be further tested as a potential prevention and/or therapy for AD.

Determination of nitrosourea compounds in brain tissue by gas chromatography and electron capture detection.

PubMed

Hassenbusch, S J; Colvin, O M; Anderson, J H

1995-07-01

A relatively simple, high-sensitivity gas chromatographic assay is described for nitrosourea compounds, such as BCNU [1,3-bis(2-chloroethyl)-1-nitrosourea] and MeCCNU [1-(2-chloroethyl)-3-(trans-4-methylcyclohexyl)-1-nitrosourea], in small biopsy samples of brain and other tissues. After extraction with ethyl acetate, secondary amines in BCNU and MeCCNU are derivatized with trifluoroacetic anhydride. Compounds are separated and quantitated by gas chromatography using a capillary column with temperature programming and an electron capture detector. Standard curves of BCNU indicate a coefficient of variance of 0.066 +/- 0.018, a correlation coefficient of 0.929, and an extraction efficiency from whole brain of 68% with a minimum detectable amount of 20 ng in 5-10 mg samples. The assay has been facile and sensitive in over 1000 brain biopsy specimens after intravenous and intraarterial infusions of BCNU.

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

PubMed

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

Method and apparatus for extraction of low-frequency artifacts from brain waves for alertness detection

DOEpatents

Clapp, Ned E.; Hively, Lee M.

1997-01-01

Methods and apparatus automatically detect alertness in humans by monitoring and analyzing brain wave signals. Steps include: acquiring the brain wave (EEG or MEG) data from the subject, digitizing the data, separating artifact data from raw data, and comparing trends in f-data to alertness indicators, providing notification of inadequate alertness.

Protective effect of Uncaria tomentosa extract against oxidative stress and genotoxicity induced by glyphosate-Roundup® using zebrafish (Danio rerio) as a model.

PubMed

Santo, Glaucia Dal; Grotto, Alan; Boligon, Aline A; Da Costa, Bárbara; Rambo, Cassiano L; Fantini, Emily A; Sauer, Elisa; Lazzarotto, Luan M V; Bertoncello, Kanandra T; Júnior, Osmar Tomazelli; Garcia, Solange C; Siebel, Anna M; Rosemberg, Denis B; Magro, Jacir Dal; Conterato, Greicy M M; Zanatta, Leila

2018-04-01

Oxidative stress and DNA damage are involved in the glyphosate-based herbicide toxicity. Uncaria tomentosa (UT; Rubiaceae) is a plant species from South America containing bioactive compounds with known beneficial properties. The objective of this work was to evaluate the antioxidant and antigenotoxic potential of UT extract in a model of acute exposure to glyphosate-Roundup® (GR) in zebrafish (Danio rerio). We showed that UT (1.0 mg/mL) prevented the decrease of brain total thiols, the increase of lipid peroxidation in both brain and liver, and the decrease of liver GPx activity caused after 96 h of GR (5.0 mg/L) exposure. In addition, UT partially protected against the increase of micronucleus frequency induced by GR exposure in fish brain. Overall, our results indicate that UT protects against damage induced by a glyphosate-based herbicide by providing antioxidant and antigenotoxic effects, which may be related to the phenolic compounds identified in the extract.

Effect of Piper betle leaf extract on alcoholic toxicity in the rat brain.

PubMed

Saravanan, R; Rajendra Prasad, N; Pugalendi, K V

2003-01-01

The protective effect of Piper betle, a commonly used masticatory, has been examined in the brain of ethanol-administered Wistar rats. Brain of ethanol-treated rats exhibited increased levels of lipids, lipid peroxidation, and disturbances in antioxidant defense. Subsequent to the experimental induction of toxicity (i.e., the initial period of 30 days), aqueous P. betle extract was simultaneously administered in three different doses (100, 200, and 300 mg kg(-1)) for 30 days along with the daily dose of alcohol. P. betle coadministration resulted in significant reduction of lipid levels (free fatty acids, cholesterol, and phospholipids) and lipid peroxidation markers such as thiobarbituric acid reactive substances and hydroperoxides. Further, antioxidants, like reduced glutathione, vitamin C, vitamin E, superoxide dismutase, catalase, and glutathione peroxidase, were increased in P. betle-coadministered rats. The higher dose of extract (300 mg kg(-1)) was more effective, and these results indicate the neuroprotective effect of P. betle in ethanol-treated rats.

Antidepressant-like deliverables from the sea: evidence on the efficacy of three different brown seaweeds via involvement of monoaminergic system.

PubMed

Siddiqui, Pirzada Jamal Ahmed; Khan, Adnan; Uddin, Nizam; Khaliq, Saima; Rasheed, Munawwer; Nawaz, Shazia; Hanif, Muhammad; Dar, Ahsana

2017-07-01

Brown seaweeds exhibit several health benefits in treating and managing wide array of ailments. In this study, the antidepressant-like effect of methaolic extracts from Sargassum swartzii (SS), Stoechospermum marginatum (SM), and Nizamuddinia zanardinii (NZ) was examined in forced swimming test (FST), in rats. Oral administration of SS, SM, and NZ extract (30-60 mg/kg) exhibited antidepressant-like activity in FST by reducing immobility time as compared to control group, without inducing significant change in ambulatory behavior in open field test. In order to evaluate the involvement of monoaminergic system, rats were pretreated with the inhibitor of brain serotonin stores p-chlorophenylalanin (PCPA), dopamine (SCH23390 and sulpiride), and adrenoceptor (prazosin and propranolol) antagonists. Rats receiving treatment for 28 days were decapitated and brains were analyzed for monoamine levels. It may be concluded that the extracts of SS, SM, and NZ produces antidepressant-like activity via modulation of brain monoaminergic system in a rat model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shang, Yu; Yu, Guoqiang, E-mail: guoqiang.yu@uky.edu

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD{sub B}). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different typesmore » of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD{sub B} in the brain layer with a step decrement of 10% while maintaining αD{sub B} values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (N ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The Nth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.« less

The Effects of Capillary Transit Time Heterogeneity (CTH) on the Cerebral Uptake of Glucose and Glucose Analogs: Application to FDG and Comparison to Oxygen Uptake

PubMed Central

Angleys, Hugo; Jespersen, Sune N.; Østergaard, Leif

2016-01-01

Glucose is the brain's principal source of ATP, but the extent to which cerebral glucose consumption (CMRglc) is coupled with its oxygen consumption (CMRO2) remains unclear. Measurements of the brain's oxygen-glucose index OGI = CMRO2/CMRglc suggest that its oxygen uptake largely suffices for oxidative phosphorylation. Nevertheless, during functional activation and in some disease states, brain tissue seemingly produces lactate although cerebral blood flow (CBF) delivers sufficient oxygen, so-called aerobic glycolysis. OGI measurements, in turn, are method-dependent in that estimates based on glucose analog uptake depend on the so-called lumped constant (LC) to arrive at CMRglc. Capillary transit time heterogeneity (CTH), which is believed to change during functional activation and in some disease states, affects the extraction efficacy of oxygen from blood. We developed a three-compartment model of glucose extraction to examine whether CTH also affects glucose extraction into brain tissue. We then combined this model with our previous model of oxygen extraction to examine whether differential glucose and oxygen extraction might favor non-oxidative glucose metabolism under certain conditions. Our model predicts that glucose uptake is largely unaffected by changes in its plasma concentration, while changes in CBF and CTH affect glucose and oxygen uptake to different extents. Accordingly, functional hyperemia facilitates glucose uptake more than oxygen uptake, favoring aerobic glycolysis during enhanced energy demands. Applying our model to glucose analogs, we observe that LC depends on physiological state, with a risk of overestimating relative increases in CMRglc during functional activation by as much as 50%. PMID:27790110

Mentha piperita as a pivotal neuro-protective agent against gamma irradiation induced DNA fragmentation and apoptosis : Mentha extract as a neuroprotective against gamma irradiation.

PubMed

Hassan, Hanaa A; Hafez, Hani S; Goda, Mona S

2013-01-01

Ionizing radiation is classified as a potent carcinogen, and its injury to living cells, in particular to DNA, is due to oxidative stress enhancing apoptotic cell death. Our present study aimed to characterize and semi-quantify the radiation-induced apoptosis in CNS and the activity of Mentha extracts as neuron-protective agent. Our results through flow cytometry exhibited the significant disturbance and arrest in cell cycle in % of M1: SubG1 phase, M2: G0/1 phase of diploid cycle, M3: S phase and M4: G2/M phase of cell cycle in brain tissue (p < 0.05). Significant increase in % of apoptosis and P53 protein expression as apoptotic biomarkers were coincided with significant decrease in Bcl(2) as an anti-apoptotic marker. The biochemical analysis recorded a significant decrease in the levels of reduced glutathione, superoxide dismutase, deoxyribonucleic acid (DNA) and ribonucleic acid contents. Moreover, numerous histopathological alterations were detected in brain tissues of gamma irradiated mice such as signs of chromatolysis in pyramidal cells of cortex, nuclear vacuolation, numerous apoptotic cell, and neural degeneration. On the other hand, gamma irradiated mice pretreated with Mentha extract showed largely an improvement in all the above tested parameters through a homeostatic state for the content of brain apoptosis and stabilization of DNA cycle with a distinct improvement in cell cycle analysis and antioxidant defense system. Furthermore, the aforementioned effects of Mentha extracts through down-regulation of P53 expression and up-regulation of Bcl(2) domain protected brain structure from extensive damage. Therefore, Mentha extract seems to have a significant role to ameliorate the neuronal injury induced by gamma irradiation.

Tool compounds robustly increase turnover of an artificial substrate by glucocerebrosidase in human brain lysates.

PubMed

Berger, Zdenek; Perkins, Sarah; Ambroise, Claude; Oborski, Christine; Calabrese, Matthew; Noell, Stephen; Riddell, David; Hirst, Warren D

2015-01-01

Mutations in glucocerebrosidase (GBA1) cause Gaucher disease and also represent a common risk factor for Parkinson's disease and Dementia with Lewy bodies. Recently, new tool molecules were described which can increase turnover of an artificial substrate 4MUG when incubated with mutant N370S GBA1 from human spleen. Here we show that these compounds exert a similar effect on the wild-type enzyme in a cell-free system. In addition, these tool compounds robustly increase turnover of 4MUG by GBA1 derived from human cortex, despite substantially lower glycosylation of GBA1 in human brain, suggesting that the degree of glycosylation is not important for compound binding. Surprisingly, these tool compounds failed to robustly alter GBA1 turnover of 4MUG in the mouse brain homogenate. Our data raise the possibility that in vivo models with humanized glucocerebrosidase may be needed for efficacy assessments of such small molecules.

Therapeutic effect of methanolic extract of Laportea aestuans (L.) Chew, on oxidative stress in the brain of male Wistar rats

NASA Astrophysics Data System (ADS)

Elizabeth, Omotosho Omolola; Olawumi, Ogunlade Oladipupo

2018-04-01

The aim of this study was to assess the effect of diclofenac-induced oxidative stress in the brain of Wistar rats. The experiment was carried out using thirty-six rats. Six groups contained six rats in each. The first group being the control group received 1ml of gum acacia which is the vehicle. Groups 2 to 6 were induced with oxidative stress by oral administration of 40 mg/kg body weight of diclofenac and pretreated as follows: group 2 received only diclofenac, group 3 with 200 mg/kg body weight of methanolic extract of Laportea aestuans (L.) Chew, group 4 with 400 mg/kg body weight of Laportea aestuans extract, group 5 with 800 mg/kg body weight of Laportea aestuans and group 6 with 50 mg/kg body weight of cimetidine. The pretreatment was carried out for a period of seven days after which oxidative stress was induced. The animals were thereafter sacrificed and brain was excised. Antioxidant enzymes and molecules such as superoxide dismutase, catalase, glutathione, levels of malondialdehyde and protein carbonyl were assayed by standard methods. The results showed significant increases in glutathione level and activities of catalase, superoxide dismutase and significant decrease in lipid peroxidation and protein carbonyl in groups 3 to 5 when compared to group 2. This shows that the methanolic extract of Laportea aestuans has a protective effect on the brain against oxidative stress.

Lesion correlates of impairments in actual tool use following unilateral brain damage.

PubMed

Salazar-López, E; Schwaiger, B J; Hermsdörfer, J

2016-04-01

To understand how the brain controls actions involving tools, tests have been developed employing different paradigms such as pantomime, imitation and real tool use. The relevant areas have been localized in the premotor cortex, the middle temporal gyrus and the superior and inferior parietal lobe. This study employs Voxel Lesion Symptom Mapping to relate the functional impairment in actual tool use with extent and localization of the structural damage in the left (LBD, N=31) and right (RBD, N=19) hemisphere in chronic stroke patients. A series of 12 tools was presented to participants in a carousel. In addition, a non-tool condition tested the prescribed manipulation of a bar. The execution was scored according to an apraxic error scale based on the dimensions grasp, movement, direction and space. Results in the LBD group show that the ventro-dorsal stream constitutes the core of the defective network responsible for impaired tool use; it is composed of the inferior parietal lobe, the supramarginal and angular gyrus and the dorsal premotor cortex. In addition, involvement of regions in the temporal lobe, the rolandic operculum, the ventral premotor cortex and the middle occipital gyrus provide evidence of the role of the ventral stream in this task. Brain areas related to the use of the bar largely overlapped with this network. For patients with RBD data were less conclusive; however, a trend for the involvement of the temporal lobe in apraxic errors was manifested. Skilled bar manipulation depended on the same temporal area in these patients. Therefore, actual tool use depends on a well described left fronto-parietal-temporal network. RBD affects actual tool use, however the underlying neural processes may be more widely distributed and more heterogeneous. Goal directed manipulation of non-tool objects seems to involve very similar brain areas as tool use, suggesting that both types of manipulation share identical processes and neural representations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neuromodulation for mood and memory: from the engineering bench to the patient bedside

PubMed Central

Deng, Zhi-De; McClintock, Shawn M.; Oey, Nicodemus E.; Luber, Bruce; Lisanby, Sarah H.

2014-01-01

Brain stimulation, in the form of electroconvulsive therapy (ECT), has long been a gold standard treatment for depression, but today, the field of neuromodulation is rapidly changing with the advent of newer and more precise tools to alter neuroplasticity and to treat brain-based disorders. Now there are new means to induce focal seizures, as with magnetic seizure therapy (MST), or modifications to ECT. There are also surgical approaches to target brain circuits via implanted stimulators placed in the brain or on cranial nerves. Finally, there are noninvasive subconvulsive approaches for the transcranial application of either electric or magnetic fields. Collectively, these tools have transformed the face of neurotherapeutics and informed our understanding of the brain basis of complex neurobehavioral conditions. PMID:25222617

Basic and functional effects of transcranial Electrical Stimulation (tES)-An introduction.

PubMed

Yavari, Fatemeh; Jamil, Asif; Mosayebi Samani, Mohsen; Vidor, Liliane Pinto; Nitsche, Michael A

2018-02-01

Non-invasive brain stimulation (NIBS) has been gaining increased popularity in human neuroscience research during the last years. Among the emerging NIBS tools is transcranial electrical stimulation (tES), whose main modalities are transcranial direct, and alternating current stimulation (tDCS, tACS). In tES, a small current (usually less than 3mA) is delivered through the scalp. Depending on its shape, density, and duration, the applied current induces acute or long-lasting effects on excitability and activity of cerebral regions, and brain networks. tES is increasingly applied in different domains to (a) explore human brain physiology with regard to plasticity, and brain oscillations, (b) explore the impact of brain physiology on cognitive processes, and (c) treat clinical symptoms in neurological and psychiatric diseases. In this review, we give a broad overview of the main mechanisms and applications of these brain stimulation tools. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

In vivo and In vitro neurochemical-based assessments of wastewater effluents from the Maumee River area of concern.

PubMed

Arini, Adeline; Cavallin, Jenna E; Berninger, Jason P; Marfil-Vega, Ruth; Mills, Marc; Villeneuve, Daniel L; Basu, Niladri

2016-04-01

Wastewater treatment plant (WWTP) effluents contain potentially neuroactive chemicals though few methods are available to screen for the presence of such agents. Here, two parallel approaches (in vivo and in vitro) were used to assess WWTP exposure-related changes to neurochemistry. First, fathead minnows (FHM, Pimephales promelas) were caged for four days along a WWTP discharge zone into the Maumee River (Ohio, USA). Grab water samples were collected and extracts obtained for the detection of alkylphenols, bisphenol A (BPA) and steroid hormones. Second, the extracts were then used as a source of in vitro exposure to brain tissues from FHM and four additional species relevant to the Great Lakes ecosystem (rainbow trout (RT), river otter (RO), bald eagle (BE) and human (HU)). The ability of the wastewater (in vivo) or extracts (in vitro) to interact with enzymes (monoamine oxidase (MAO) and glutamine synthetase (GS)) and receptors (dopamine (D2) and N-methyl-D-aspartate receptor (NMDA)) involved in dopamine and glutamate-dependent neurotransmission were examined on brain homogenates. In vivo exposure of FHM led to significant decreases of NMDA receptor binding in females (24-42%), and increases of MAO activity in males (2.8- to 3.2-fold). In vitro, alkylphenol-targeted extracts significantly inhibited D2 (66% in FHM) and NMDA (24-54% in HU and RT) receptor binding, and induced MAO activity in RT, RO, and BE brains. Steroid hormone-targeted extracts inhibited GS activity in all species except FHM. BPA-targeted extracts caused a MAO inhibition in FHM, RT and BE brains. Using both in vivo and in vitro approaches, this study shows that WWTP effluents contain agents that can interact with neurochemicals important in reproduction and other neurological functions. Additional work is needed to better resolve in vitro to in vivo extrapolations (IVIVE) as well as cross-species differences. Copyright © 2016 Elsevier Ltd. All rights reserved.

Understanding the brain through its spatial structure

NASA Astrophysics Data System (ADS)

Morrison, Will Zachary

The spatial location of cells in neural tissue can be easily extracted from many imaging modalities, but the information contained in spatial relationships between cells is seldom utilized. This is because of a lack of recognition of the importance of spatial relationships to some aspects of brain function, and the reflection in spatial statistics of other types of information. The mathematical tools necessary to describe spatial relationships are also unknown to many neuroscientists, and biologists in general. We analyze two cases, and show that spatial relationships can be used to understand the role of a particular type of cell, the astrocyte, in Alzheimer's disease, and that the geometry of axons in the brain's white matter sheds light on the process of establishing connectivity between areas of the brain. Astrocytes provide nutrients for neuronal metabolism, and regulate the chemical environment of the brain, activities that require manipulation of spatial distributions (of neurotransmitters, for example). We first show, through the use of a correlation function, that inter-astrocyte forces determine the size of independent regulatory domains in the cortex. By examining the spatial distribution of astrocytes in a mouse model of Alzheimer's Disease, we determine that astrocytes are not actively transported to fight the disease, as was previously thought. The paths axons take through the white matter determine which parts of the brain are connected, and how quickly signals are transmitted. The rules that determine these paths (i.e. shortest distance) are currently unknown. By measurement of axon orientation distributions using three-point correlation functions and the statistics of axon turning and branching, we reveal that axons are restricted to growth in three directions, like a taxicab traversing city blocks, albeit in three-dimensions. We show how geometric restrictions at the small scale are related to large-scale trajectories. Finally we discuss the implications of this finding for experimental and theoretical connectomics.

Brain modulation of Dufour's gland ester biosynthesis in vitro in the honeybee ( Apis mellifera)

NASA Astrophysics Data System (ADS)

Katzav-Gozansky, Tamar; Hefetz, Abraham; Soroker, Victoria

2007-05-01

Caste-specific pheromone biosynthesis is a prerequisite for reproductive skew in the honeybee. Nonetheless, this process is not hardwired but plastic, in that egg-laying workers produce a queen-like pheromone. Studies with Dufour’s gland pheromone revealed that, in vivo, workers’ gland biosynthesis matches the social status of the worker, i.e., sterile workers showed a worker-like pattern whereas fertile workers showed a queen-like pattern (production of the queen-specific esters). However, when incubated in vitro, the gland spontaneously exhibits the queen-like pattern, irrespective of its original worker type, prompting the notion that ester production in workers is under inhibitory control that is queen-dependent. We tested this hypothesis by exposing queen or worker Dufour’s glands in vitro to brain extracts of queens, queenright (sterile) workers and males. Unexpectedly, worker brain extracts activated the queen-like esters biosynthesis in workers’ Dufour’s gland. This stimulation was gender-specific; queen or worker brains demonstrated a stimulatory activity, but male brains did not. Queen gland could not be further stimulated. Bioassays with heated and filtered extracts indicate that the stimulatory brain factor is below 3,000 Da. We suggest that pheromone production in Dufour’s gland is under dual, negative positive control. Under queenright conditions, the inhibitor is released and blocks ester biosynthesis, whereas under queenless conditions, the activator is released, activating ester biosynthesis in the gland. This is consistent with the hypothesis that queenright workers are unequivocally recognized as non-fertile, whereas queenless workers try to become “false queens” as part of the reproductive competition.

Effects of Grape Skin Extract on Age-Related Mitochondrial Dysfunction, Memory and Life Span in C57BL/6J Mice.

PubMed

Asseburg, Heike; Schäfer, Carmina; Müller, Madeleine; Hagl, Stephanie; Pohland, Maximilian; Berressem, Dirk; Borchiellini, Marta; Plank, Christina; Eckert, Gunter P

2016-09-01

Dementia contributes substantially to the burden of disability experienced at old age, and mitochondrial dysfunction (MD) was identified as common final pathway in brain aging and Alzheimer's disease. Due to its early appearance, MD is a promising target for nutritional prevention strategies and polyphenols as potential neurohormetic inducers may be strong neuroprotective candidates. This study aimed to investigate the effects of a polyphenol-rich grape skin extract (PGE) on age-related dysfunctions of brain mitochondria, memory, life span and potential hormetic pathways in C57BL/6J mice. PGE was administered at a dose of 200 mg/kg body weight/d in a 3-week short-term, 6-month long-term and life-long study. MD in the brains of aged mice (19-22 months old) compared to young mice (3 months old) was demonstrated by lower ATP levels and by impaired mitochondrial respiratory complex activity (except for mice treated with antioxidant-depleted food pellets). Long-term PGE feeding partly enhanced brain mitochondrial respiration with only minor beneficial effect on brain ATP levels and memory of aged mice. Life-long PGE feeding led to a transient but significant shift of survival curve toward higher survival rates but without effect on the overall survival. The moderate effects of PGE were associated with elevated SIRT1 but not SIRT3 mRNA expressions in brain and liver tissue. The beneficial effects of the grape extract may have been influenced by the profile of bioavailable polyphenols and the starting point of interventions.

Sulci segmentation using geometric active contours

NASA Astrophysics Data System (ADS)

Torkaman, Mahsa; Zhu, Liangjia; Karasev, Peter; Tannenbaum, Allen

2017-02-01

Sulci are groove-like regions lying in the depth of the cerebral cortex between gyri, which together, form a folded appearance in human and mammalian brains. Sulci play an important role in the structural analysis of the brain, morphometry (i.e., the measurement of brain structures), anatomical labeling and landmark-based registration.1 Moreover, sulcal morphological changes are related to cortical thickness, whose measurement may provide useful information for studying variety of psychiatric disorders. Manually extracting sulci requires complying with complex protocols, which make the procedure both tedious and error prone.2 In this paper, we describe an automatic procedure, employing geometric active contours, which extract the sulci. Sulcal boundaries are obtained by minimizing a certain energy functional whose minimum is attained at the boundary of the given sulci.

Atypical Balance between Occipital and Fronto-Parietal Activation for Visual Shape Extraction in Dyslexia

PubMed Central

Zhang, Ying; Whitfield-Gabrieli, Susan; Christodoulou, Joanna A.; Gabrieli, John D. E.

2013-01-01

Reading requires the extraction of letter shapes from a complex background of text, and an impairment in visual shape extraction would cause difficulty in reading. To investigate the neural mechanisms of visual shape extraction in dyslexia, we used functional magnetic resonance imaging (fMRI) to examine brain activation while adults with or without dyslexia responded to the change of an arrow’s direction in a complex, relative to a simple, visual background. In comparison to adults with typical reading ability, adults with dyslexia exhibited opposite patterns of atypical activation: decreased activation in occipital visual areas associated with visual perception, and increased activation in frontal and parietal regions associated with visual attention. These findings indicate that dyslexia involves atypical brain organization for fundamental processes of visual shape extraction even when reading is not involved. Overengagement in higher-order association cortices, required to compensate for underengagment in lower-order visual cortices, may result in competition for top-down attentional resources helpful for fluent reading. PMID:23825653

Restoration of stressor-induced calcium dysregulation and autophagy inhibition by polyphenol-rich açaí (Euterpe spp.) fruit pulp extracts in rodent brain cells in vitro.

PubMed

Poulose, Shibu M; Fisher, Derek R; Bielinski, Donna F; Gomes, Stacey M; Rimando, Agnes M; Schauss, Alexander G; Shukitt-Hale, Barbara

2014-01-01

Oxidative damage to lipids, proteins, and nucleic acids in the brain often causes progressive neuronal degeneration and death that are the focal traits of chronic and acute pathologies, including those involving cognitive decline. The aim of this study was to investigate the specific effects of both Euterpe oleracea and Euterpe precatoria açaí fruit pulp on restoring stressor-induced calcium dysregulation, stunted growth of basal dendrites, and autophagy inhibition using embryonic hippocampal and HT22 hippocampal neurons. Water-soluble whole fruit pulp extracts from two açaí species were applied to rat primary neurons and HT22 hippocampal neurons with varied time and concentrations. Recovery of neurons from dopamine-induced Ca(2+) dysregulation was measured by live cell imaging using fluorescent microscopy. The effect of açaí fruit pulp extracts on neurons following chemically-induced autophagy inhibition was measured using both immunofluorescence and immunohistochemical techniques. It has been postulated that at least part of the loss of cognitive function in aging may depend on a dysregulation in calcium ion (Ca(2+)) homeostasis and a loss of autophagy function in the brain, which affects numerous signaling pathways and alters protein homeostasis. In the present study, polyphenol-rich fruit pulp extracts from two species of açaí, Euterpe precatoria and Euterpe oleracea, when applied to rat hippocampal primary neuronal cells (E18), caused a significant (P < 0.05) recovery of depolarized brain cells from dopamine-induced Ca(2+) influx. Autophagy, a protein homeostasis mechanism in brain, when blocked by known inhibitors such as bafilomycin A1 or wortmannin, caused a significant reduction in the growth of primary basal dendrites in rodent primary hippocampal neurons and significant accumulation of polyubiquitinated proteins in mouse HT22 hippocampal neurons in culture. However, pretreatment with açaí extracts up to 1 mg/mL significantly increased the length of basal dendrites and attenuated the inhibitor-induced autophagy dysfunction. Açaí extracts activated the phosphorylation of mammalian target of rapamycin, increased the turnover of autophagosomes and MAP1 B LC3-II, and decreased accumulation of LC3-ubiquitin binding P62/SQSTM1. Although the polyphenol profile of Euterpe precatoria showed substantially higher concentrations of major flavonoids han Euterpe oleracea, the relative effects were essentially similar for both species. The study adds to growing evidence that supports the putative health effects of açaí fruit species on brain cells. Published by Elsevier Inc.

Fuzzy logic system able to detect interesting areas of a video sequence

NASA Astrophysics Data System (ADS)

De Vleeschouwer, Christophe; Marichal, Xavier; Delmot, Thierry; Macq, Benoit M. M.

1997-06-01

This paper introduces an automatic tool able to analyze the picture according to the semantic interest an observer attributes to its content. Its aim is to give a 'level of interest' to the distinct areas of the picture extracted by any segmentation tool. For the purpose of dealing with semantic interpretation of images, a single criterion is clearly insufficient because the human brain, due to its a priori knowledge and its huge memory of real-world concrete scenes, combines different subjective criteria in order to assess its final decision. The developed method permits such combination through a model using assumptions to express some general subjective criteria. Fuzzy logic enables the user to encode knowledge in a form that is very close the way experts think about the decision process. This fuzzy modeling is also well suited to represent multiple collaborating or even conflicting experts opinions. Actually, the assumptions are verified through a non-hierarchical strategy that considers them in a random order, each partial result contributing to the final one. Presented results prove that the tool is effective for a wide range of natural pictures. It is versatile and flexible in that it can be used stand-alone or can take into account any a priori knowledge about the scene.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

NASA Astrophysics Data System (ADS)

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects.

PubMed

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

The use and impact of quality of life assessment tools in clinical care settings for cancer patients, with a particular emphasis on brain cancer: insights from a systematic review and stakeholder consultations.

PubMed

King, Sarah; Exley, Josephine; Parks, Sarah; Ball, Sarah; Bienkowska-Gibbs, Teresa; MacLure, Calum; Harte, Emma; Stewart, Katherine; Larkin, Jody; Bottomley, Andrew; Marjanovic, Sonja

2016-09-01

Patient-reported data are playing an increasing role in health care. In oncology, data from quality of life (QoL) assessment tools may be particularly important for those with limited survival prospects, where treatments aim to prolong survival while maintaining or improving QoL. This paper examines the use and impact of using QoL measures on health care of cancer patients within a clinical setting, particularly those with brain cancer. It also examines facilitators and challenges, and provides implications for policy and practice. We conducted a systematic literature review, 15 expert interviews and a consultation at an international summit. The systematic review found no relevant intervention studies specifically in brain cancer patients, and after expanding our search to include other cancers, 15 relevant studies were identified. The evidence on the effectiveness of using QoL tools was inconsistent for patient management, but somewhat more consistent in favour of improving patient-physician communication. Interviews identified unharnessed potential and growing interest in QoL tool use and associated challenges to address. Our findings suggest that the use of QoL tools in cancer patients may improve patient-physician communication and have the potential to improve care, but the tools are not currently widely used in clinical practice (in brain cancer nor some other cancer contexts) although they are in clinical trials. There is a need for further research and stakeholder engagement on how QoL tools can achieve most impact across cancer and patient contexts. There is also a need for policy, health professional, research and patient communities to strengthen information exchange and debate, support awareness raising and provide training on tool design, use and interpretation.

Ethanolic extracts of Alstonia Scholaris and Bacopa Monniera possess neuroleptic activity due to anti-dopaminergic effect

PubMed Central

Jash, Rajiv; Chowdary, K. Appana

2014-01-01

Background: An increased inclination has been observed for the use of herbal drugs in chronic and incurable diseases. Treatment of psychiatric diseases like schizophrenia is largely palliative and more importantly, a prominent adverse effect prevails with the majority of anti-psychotic drugs, which are the extrapyramidal motor disorders. Existing anti-psychotic drug therapy is not so promising, and their adverse effect is a matter of concern for continuing the therapy for long duration. Objective: This experimental study was done to evaluate the neuroleptic activity of the ethanolic extracts of two plants Alstonia Scholaris and Bacopa Monnieri with different anti-psychotic animal models with a view that these plant extracts shall have no or at least reduced adverse effect so that it can be used for long duration. Materials and Methods: Two doses of both the extracts (100 and 200 mg/kg) and also standard drug haloperidol (0.2 mg/kg) were administered to their respective groups once daily with 5 different animal models. After that, the concentration of the dopamine neurotransmitter was estimated in two different regions of the brain viz. frontal cortex and striatum. Results: The result of the study indicated a significant reduction of amphetamine-induced stereotype and conditioned avoidance response for both the extracts compared with the control group, but both did not have any significant effect in phencyclidine-induced locomotor activity and social interaction activity. However, both the extracts showed minor signs of catalepsy compared to the control group. The study also revealed that the neuroleptic effect was due to the reduction of the dopamine concentration in the frontal cortex region of the rat brain. The results largely pointed out the fact that both the extract may be having the property to alleviate the positive symptoms of schizophrenia by reducing the dopamine levels of dopaminergic neurons of the brain. Conclusion: The estimation of dopamine in the two major regions of brain indicated the alteration of dopamine levels was the reason for the anti-psychotic activity as demonstrated by the different animal models. PMID:24497742

Functional brain networks for learning predictive statistics.

PubMed

Giorgio, Joseph; Karlaftis, Vasilis M; Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew; Kourtzi, Zoe

2017-08-18

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Method and apparatus for extraction of low-frequency artifacts from brain waves for alertness detection

DOEpatents

Clapp, N.E.; Hively, L.M.

1997-05-06

Methods and apparatus automatically detect alertness in humans by monitoring and analyzing brain wave signals. Steps include: acquiring the brain wave (EEG or MEG) data from the subject, digitizing the data, separating artifact data from raw data, and comparing trends in f-data to alertness indicators, providing notification of inadequate alertness. 4 figs.

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

In vivo antioxidant activity of total flavonoids from indocalamus leaves in aging mice caused by D-galactose.

PubMed

Jin, Sheng-lang; Yin, Yong-guang

2012-10-01

The aim of this thesis is to explore antioxidant activity of total flavonoids extracted from indocalamus leaves. Aging mice model was established by D-galactose induction. Three groups of mice were treated with total flavonoids extracted from indocalamus leaves at doses of 20, 40 and 80 mg/kg d bw respectively. The antioxidant status in the aging mice was measured by determining the activities of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT) and total anti-oxidant capability (T-AOC) in the serum and liver and malondialdehyde (MDA) content in the serum, liver and brain. Compared with control group, extracts of indocalamus leaves significantly enhanced activities of SOD, GSH-Px, CAT in the serum and liver, and decreased MDA content in the serum, liver and brain at the tested doses. Total flavonoids extracted from indocalamus leaves demonstrated the potent antioxidant activity. Copyright © 2012 Elsevier Ltd. All rights reserved.

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

Crocus sativus Extract Tightens the Blood-Brain Barrier, Reduces Amyloid β Load and Related Toxicity in 5XFAD Mice.

PubMed

Batarseh, Yazan S; Bharate, Sonali S; Kumar, Vikas; Kumar, Ajay; Vishwakarma, Ram A; Bharate, Sandip B; Kaddoumi, Amal

2017-08-16

Crocus sativus, commonly known as saffron or Kesar, is used in Ayurveda and other folk medicines for various purposes as an aphrodisiac, antispasmodic, and expectorant. Previous evidence suggested that Crocus sativus is linked to improving cognitive function in Alzheimer's disease (AD) patients. The aim of this study was to in vitro and in vivo investigate the mechanism(s) by which Crocus sativus exerts its positive effect against AD. The effect of Crocus sativus extract on Aβ load and related toxicity was evaluated. In vitro results showed that Crocus sativus extract increases the tightness of a cell-based blood-brain barrier (BBB) model and enhances transport of Aβ. Further in vivo studies confirmed the effect of Crocus sativus extract (50 mg/kg/day, added to mice diet) on the BBB tightness and function that was associated with reduced Aβ load and related pathological changes in 5XFAD mice used as an AD model. Reduced Aβ load could be explained, at least in part, by Crocus sativus extract effect to enhance Aβ clearance pathways including BBB clearance, enzymatic degradation and ApoE clearance pathway. Furthermore, Crocus sativus extract upregulated synaptic proteins and reduced neuroinflammation associated with Aβ pathology in the brains of 5XFAD mice. Crocin, a major active constituent of Crocus sativus and known for its antioxidant and anti-inflammatory effect, was also tested separately in vivo in 5XFAD mice. Crocin (10 mg/kg/day) was able to reduce Aβ load but to a lesser extent when compared to Crocus sativus extract. Collectively, findings from this study support the positive effect of Crocus sativus against AD by reducing Aβ pathological manifestations.

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

PubMed Central

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin MD.; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md. Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis. PMID:25093211

TheHiveDB image data management and analysis framework.

PubMed

Muehlboeck, J-Sebastian; Westman, Eric; Simmons, Andrew

2014-01-06

The hive database system (theHiveDB) is a web-based brain imaging database, collaboration, and activity system which has been designed as an imaging workflow management system capable of handling cross-sectional and longitudinal multi-center studies. It can be used to organize and integrate existing data from heterogeneous projects as well as data from ongoing studies. It has been conceived to guide and assist the researcher throughout the entire research process, integrating all relevant types of data across modalities (e.g., brain imaging, clinical, and genetic data). TheHiveDB is a modern activity and resource management system capable of scheduling image processing on both private compute resources and the cloud. The activity component supports common image archival and management tasks as well as established pipeline processing (e.g., Freesurfer for extraction of scalar measures from magnetic resonance images). Furthermore, via theHiveDB activity system algorithm developers may grant access to virtual machines hosting versioned releases of their tools to collaborators and the imaging community. The application of theHiveDB is illustrated with a brief use case based on organizing, processing, and analyzing data from the publically available Alzheimer Disease Neuroimaging Initiative.

Role of EEG as biomarker in the early detection and classification of dementia.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin Md; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

TheHiveDB image data management and analysis framework

PubMed Central

Muehlboeck, J-Sebastian; Westman, Eric; Simmons, Andrew

2014-01-01

The hive database system (theHiveDB) is a web-based brain imaging database, collaboration, and activity system which has been designed as an imaging workflow management system capable of handling cross-sectional and longitudinal multi-center studies. It can be used to organize and integrate existing data from heterogeneous projects as well as data from ongoing studies. It has been conceived to guide and assist the researcher throughout the entire research process, integrating all relevant types of data across modalities (e.g., brain imaging, clinical, and genetic data). TheHiveDB is a modern activity and resource management system capable of scheduling image processing on both private compute resources and the cloud. The activity component supports common image archival and management tasks as well as established pipeline processing (e.g., Freesurfer for extraction of scalar measures from magnetic resonance images). Furthermore, via theHiveDB activity system algorithm developers may grant access to virtual machines hosting versioned releases of their tools to collaborators and the imaging community. The application of theHiveDB is illustrated with a brief use case based on organizing, processing, and analyzing data from the publically available Alzheimer Disease Neuroimaging Initiative. PMID:24432000

Integrating research and clinical neuroimaging for the evaluation of traumatic brain injury recovery

NASA Astrophysics Data System (ADS)

Senseney, Justin; Ollinger, John; Graner, John; Lui, Wei; Oakes, Terry; Riedy, Gerard

2015-03-01

Advanced MRI research and other imaging modalities may serve as biomarkers for the evaluation of traumatic brain injury (TBI) recovery. However, these advanced modalities typically require off-line processing which creates images that are incompatible with radiologist viewing software sold commercially. AGFA Impax is an example of such a picture archiving and communication system(PACS) that is used by many radiology departments in the United States Military Health System. By taking advantage of Impax's use of the Digital Imaging and Communications in Medicine (DICOM) standard, we developed a system that allows for advanced medical imaging to be incorporated into clinical PACS. Radiology research can now be conducted using existing clinical imaging display platforms resources in combination with image processingtechniques that are only available outside of the clinical scanning environment. We extracted the spatial and identification elements of theDICOM standard that are necessary to allow research images to be incorporatedinto a clinical radiology system, and developed a tool that annotates research images with the proper tags. This allows for the evaluation of imaging representations of biological markers that may be useful in theevaluation of TBI and TBI recovery.

Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

NASA Astrophysics Data System (ADS)

Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

2015-01-01

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

The preservation of substance p by lysergic acid diethylamide

PubMed Central

Krivoy, W. A.

1957-01-01

Lysergic acid diethylamide (LSD) potentiated the response of guinea-pig ileum to substance P but not to histamine. It also inhibited the disappearance of substance P when incubated with guinea-pig brain extract but not when incubated with chymotrypsin. Eserine, morphine, mescaline, chlorpromazine, ergometrine, strychnine and 2 bromo-LSD did not have this effect. Oxytocin was not destroyed by brain extract. The inhibition of the destruction of substance P by LSD could be antagonized by 2 bromo-LSD. This effect of LSD may have some relation to its pharmacological actions. PMID:13460245

The preservation of substance P by lysergic acid diethylamide.

PubMed

KRIVOY, W A

1957-09-01

Lysergic acid diethylamide (LSD) potentiated the response of guinea-pig ileum to substance P but not to histamine. It also inhibited the disappearance of substance P when incubated with guinea-pig brain extract but not when incubated with chymotrypsin. Eserine, morphine, mescaline, chlorpromazine, ergometrine, strychnine and 2 bromo-LSD did not have this effect. Oxytocin was not destroyed by brain extract. The inhibition of the destruction of substance P by LSD could be antagonized by 2 bromo-LSD. This effect of LSD may have some relation to its pharmacological actions.

The effect of lead exposure on fatty acid composition in mouse brain analyzed using pseudo-catalytic derivatization.

PubMed

Jung, Jong-Min; Lee, Jechan; Kim, Ki-Hyun; Jang, In Geon; Song, Jae Gwang; Kang, Kyeongjin; Tack, Filip M G; Oh, Jeong-Ik; Kwon, Eilhann E; Kim, Hyung-Wook

2017-03-01

We performed toxicological study of mice exposed to lead by quantifying fatty acids in brain of the mice. This study suggests that the introduced analytical method had an extremely high tolerance against impurities such as water and extractives; thus, it led to the enhanced resolution in visualizing the spectrum of fatty acid profiles in animal brain. Furthermore, one of the biggest technical advantages achieved in this study was the quantitation of fatty acid methyl ester profiles of mouse brain using a trace amount of sample (e.g., 100 μL mixture). Methanol was screened as the most effective extraction solvent for mouse brain. The behavioral test of the mice before and after lead exposure was conducted to see the effect of lead exposure on fatty acid composition of the mice' brain. The lead exposure led to changes in disease-related behavior of the mice. Also, the lead exposure induced significant alterations of fatty acid profile (C16:0, C 18:0, and C 18:1) in brain of the mice, implicated in pathology of psychiatric diseases. The alteration of fatty acid profile of brain of the mice suggests that the derivatizing technique can be applicable to most research fields associated with the environmental neurotoxins with better resolution in a short time, as compared to the current protocols for lipid analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Non-invasive imaging of oxygen extraction fraction in adults with sickle cell anaemia

PubMed Central

Gindville, Melissa C.; Scott, Allison O.; Juttukonda, Meher R.; Strother, Megan K.; Kassim, Adetola A.; Chen, Sheau-Chiann; Lu, Hanzhang; Pruthi, Sumit; Shyr, Yu; Donahue, Manus J.

2016-01-01

Sickle cell anaemia is a monogenetic disorder with a high incidence of stroke. While stroke screening procedures exist for children with sickle cell anaemia, no accepted screening procedures exist for assessing stroke risk in adults. The purpose of this study is to use novel magnetic resonance imaging methods to evaluate physiological relationships between oxygen extraction fraction, cerebral blood flow, and clinical markers of cerebrovascular impairment in adults with sickle cell anaemia. The specific goal is to determine to what extent elevated oxygen extraction fraction may be uniquely present in patients with higher levels of clinical impairment and therefore may represent a candidate biomarker of stroke risk. Neurological evaluation, structural imaging, and the non-invasive T2-relaxation-under-spin-tagging magnetic resonance imaging method were applied in sickle cell anaemia (n = 34) and healthy race-matched control (n = 11) volunteers without sickle cell trait to assess whole-brain oxygen extraction fraction, cerebral blood flow, degree of vasculopathy, severity of anaemia, and presence of prior infarct; findings were interpreted in the context of physiological models. Cerebral blood flow and oxygen extraction fraction were elevated (P < 0.05) in participants with sickle cell anaemia (n = 27) not receiving monthly blood transfusions (interquartile range cerebral blood flow = 46.2–56.8 ml/100 g/min; oxygen extraction fraction = 0.39–0.50) relative to controls (interquartile range cerebral blood flow = 40.8–46.3 ml/100 g/min; oxygen extraction fraction = 0.33–0.38). Oxygen extraction fraction (P < 0.0001) but not cerebral blood flow was increased in participants with higher levels of clinical impairment. These data provide support for T2-relaxation-under-spin-tagging being able to quickly and non-invasively detect elevated oxygen extraction fraction in individuals with sickle cell anaemia with higher levels of clinical impairment. Our results support the premise that magnetic resonance imaging-based assessment of elevated oxygen extraction fraction might be a viable screening tool for evaluating stroke risk in adults with sickle cell anaemia. PMID:26823369

Triadic (ecological, neural, cognitive) niche construction: a scenario of human brain evolution extrapolating tool use and language from the control of reaching actions

PubMed Central

Iriki, Atsushi; Taoka, Miki

2012-01-01

Hominin evolution has involved a continuous process of addition of new kinds of cognitive capacity, including those relating to manufacture and use of tools and to the establishment of linguistic faculties. The dramatic expansion of the brain that accompanied additions of new functional areas would have supported such continuous evolution. Extended brain functions would have driven rapid and drastic changes in the hominin ecological niche, which in turn demanded further brain resources to adapt to it. In this way, humans have constructed a novel niche in each of the ecological, cognitive and neural domains, whose interactions accelerated their individual evolution through a process of triadic niche construction. Human higher cognitive activity can therefore be viewed holistically as one component in a terrestrial ecosystem. The brain's functional characteristics seem to play a key role in this triadic interaction. We advance a speculative argument about the origins of its neurobiological mechanisms, as an extension (with wider scope) of the evolutionary principles of adaptive function in the animal nervous system. The brain mechanisms that subserve tool use may bridge the gap between gesture and language—the site of such integration seems to be the parietal and extending opercular cortices. PMID:22106423

Triadic (ecological, neural, cognitive) niche construction: a scenario of human brain evolution extrapolating tool use and language from the control of reaching actions.

PubMed

Iriki, Atsushi; Taoka, Miki

2012-01-12

Hominin evolution has involved a continuous process of addition of new kinds of cognitive capacity, including those relating to manufacture and use of tools and to the establishment of linguistic faculties. The dramatic expansion of the brain that accompanied additions of new functional areas would have supported such continuous evolution. Extended brain functions would have driven rapid and drastic changes in the hominin ecological niche, which in turn demanded further brain resources to adapt to it. In this way, humans have constructed a novel niche in each of the ecological, cognitive and neural domains, whose interactions accelerated their individual evolution through a process of triadic niche construction. Human higher cognitive activity can therefore be viewed holistically as one component in a terrestrial ecosystem. The brain's functional characteristics seem to play a key role in this triadic interaction. We advance a speculative argument about the origins of its neurobiological mechanisms, as an extension (with wider scope) of the evolutionary principles of adaptive function in the animal nervous system. The brain mechanisms that subserve tool use may bridge the gap between gesture and language--the site of such integration seems to be the parietal and extending opercular cortices.

Effects of Cannabis sativa extract on haloperidol-induced catalepsy and oxidative stress in the mice

PubMed Central

Abdel-Salam, Omar M.E.; El-Sayed El-Shamarka, Marawa; Salem, Neveen A.; El-Din M. Gaafar, Alaa

2012-01-01

Haloperidol is a classic antipsychotic drug known for its propensity to cause extrapyramidal symptoms due to blockade of dopamine D2 receptors in the striatum. Interest in medicinal uses of cannabis is growing. Cannabis sativa has been suggested as a possible adjunctive in treatment of Parkinson's disease. The present study aimed to investigate the effect of repeated administration of an extract of Cannabis sativa on catalepsy and brain oxidative stress induced by haloperidol administration in mice. Cannabis extract was given by subcutaneous route at 5, 10 or 20 mg/kg (expressed as Δ9-tetrahydrocannabinol) once daily for 18 days and the effect on haloperidol (1 mg/kg, i.p.)-induced catalepsy was examined at selected time intervals using the bar test. Mice were euthanized 18 days after starting cannabis injection when biochemical assays were carried out. Malondialdehyde (MDA), reduced glutathione (GSH) and nitric oxide (the concentrations of nitrite/nitrate) were determined in brain and liver. In saline-treated mice, no catalepsy was observed at doses of cannabis up to 20 mg/kg. Mice treated with haloperidol at the dose of 1 mg/kg, exhibited significant cataleptic response. Mice treated with cannabis and haloperidol showed significant decrease in catalepsy duration, compared with the haloperidol only treated group. This decrease in catalepsy duration was evident on days 1-12 after starting cannabis injection. Later the effect of cannabis was not apparent. The administration of only cannabis (10 or 20 mg/kg) decreased brain MDA by 17.5 and 21.8 %, respectively. The level of nitric oxide decreased by 18 % after cannabis at 20 mg/kg. Glucose in brain decreased by 20.1 % after 20 mg/kg of cannabis extract. The administration of only haloperidol increased MDA (22.2 %), decreased GSH (25.7 %) and increased brain nitric oxide by 44.1 %. The administration of cannabis (10 or 20 mg/kg) to haloperidol-treated mice resulted in a significant decrease in brain MDA and nitric oxide as well as a significant increase in GSH and glucose compared with the haloperidol-control group. Cannabis had no significant effects on liver MDA, GSH, nitric oxide in saline or haloperidol-treated mice. It is concluded that cannabis improves catalepsy induced by haloperidol though the effect is not maintained on repeated cannabis administration. Cannabis alters the oxidative status of the brain in favor of reducing lipid peroxidation, but reduces brain glucose, which would impair brain energetics. PMID:27366134

Effects of Cannabis sativa extract on haloperidol-induced catalepsy and oxidative stress in the mice.

PubMed

Abdel-Salam, Omar M E; El-Sayed El-Shamarka, Marawa; Salem, Neveen A; El-Din M Gaafar, Alaa

2012-01-01

Haloperidol is a classic antipsychotic drug known for its propensity to cause extrapyramidal symptoms due to blockade of dopamine D2 receptors in the striatum. Interest in medicinal uses of cannabis is growing. Cannabis sativa has been suggested as a possible adjunctive in treatment of Parkinson's disease. The present study aimed to investigate the effect of repeated administration of an extract of Cannabis sativa on catalepsy and brain oxidative stress induced by haloperidol administration in mice. Cannabis extract was given by subcutaneous route at 5, 10 or 20 mg/kg (expressed as Δ(9)-tetrahydrocannabinol) once daily for 18 days and the effect on haloperidol (1 mg/kg, i.p.)-induced catalepsy was examined at selected time intervals using the bar test. Mice were euthanized 18 days after starting cannabis injection when biochemical assays were carried out. Malondialdehyde (MDA), reduced glutathione (GSH) and nitric oxide (the concentrations of nitrite/nitrate) were determined in brain and liver. In saline-treated mice, no catalepsy was observed at doses of cannabis up to 20 mg/kg. Mice treated with haloperidol at the dose of 1 mg/kg, exhibited significant cataleptic response. Mice treated with cannabis and haloperidol showed significant decrease in catalepsy duration, compared with the haloperidol only treated group. This decrease in catalepsy duration was evident on days 1-12 after starting cannabis injection. Later the effect of cannabis was not apparent. The administration of only cannabis (10 or 20 mg/kg) decreased brain MDA by 17.5 and 21.8 %, respectively. The level of nitric oxide decreased by 18 % after cannabis at 20 mg/kg. Glucose in brain decreased by 20.1 % after 20 mg/kg of cannabis extract. The administration of only haloperidol increased MDA (22.2 %), decreased GSH (25.7 %) and increased brain nitric oxide by 44.1 %. The administration of cannabis (10 or 20 mg/kg) to haloperidol-treated mice resulted in a significant decrease in brain MDA and nitric oxide as well as a significant increase in GSH and glucose compared with the haloperidol-control group. Cannabis had no significant effects on liver MDA, GSH, nitric oxide in saline or haloperidol-treated mice. It is concluded that cannabis improves catalepsy induced by haloperidol though the effect is not maintained on repeated cannabis administration. Cannabis alters the oxidative status of the brain in favor of reducing lipid peroxidation, but reduces brain glucose, which would impair brain energetics.

1,4-Dioxane Remediation by Extreme Soil Vapor Extraction (XSVE). Screening-Level Feasibility Assessment and Design Tool in Support of 1,4-Dioxane Remediation by Extreme Soil Vapor Extraction (XSVE) ESTCP Project ER 201326

DTIC Science & Technology

2017-10-01

USER GUIDE 1,4-Dioxane Remediation by Extreme Soil Vapor Extraction (XSVE) Screening-Level Feasibility Assessment and Design Tool in...Support of 1,4-Dioxane Remediation by Extreme Soil Vapor Extraction (XSVE) ESTCP Project ER-201326 OCTOBER 2017 Rob Hinchee Integrated Science...Technology, Inc. 1509 Coastal Highway Panacea, FL 32346 8/8/2013 - 8/8/2018 10-2017 1,4-Dioxane Remediation by Extreme Soil Vapor Extraction (XSVE) Screening

Reach for Reference. BrainPOP--A Teaching Tool Library Media Specialists Should Know

ERIC Educational Resources Information Center

Safford, Barbara Ripp

2005-01-01

This column describes a new teaching tool, BrainPOP, which is a database that blurs the distinction between classroom and library media center. This collection of more than 300 short, concept-based, animated movies is intended primarily for use by teachers in classroom instruction. It is reminiscent of the single-concept film cartridges that used…

The brain is not a tool.

PubMed

Gaete, Alfredo; Cornejo, Carlos

2014-03-01

Some psychologists claim that the brain is a tool. This claim can be construed either literally or figuratively. We argue that, in the former case, it is false, whereas in the latter case it has no place in scientific psychology. We also try to show why this discussion is relevant and suggest how a metaphor should behave to be of use in science.

Phenethylamines in brain and liver of rats with experimentally induced phenylketonuria-like characteristics

PubMed Central

Edwards, David J.; Blau, Karl

1973-01-01

1. Phenethylamines were extracted from brain and liver of rats with phenylketonuria-like characteristics produced in vivo by inhibition of phenylalanine hydroxylase (EC 1.14.3.1) with p-chlorophenylalanine, with or without phenylalanine administration. To protect amines against oxidation by monoamine oxidase, pargyline was also administered. 2. β-Phenethylamine was the major compound found in brain and liver. β-Phenethanolamine and octopamine were also present, in lesser amounts, and the concentrations of these three amines paralleled blood phenylalanine concentrations. By comparison, tissues from control animals had only very low concentrations of these amines. 3. Small amounts of normetadrenaline, m-tyramine and 3-methoxytyramine were also found. 4. The inhibitors used, p-chlorophenylalanine and pargyline, gave rise to p-chlorophenethylamine and benzylamine respectively, the first via decarboxylation, the second probably by breakdown during extraction. 5. Distribution of phenethylamines in different brain regions and in subcellular fractions of rat brain cells was also investigated. The content of phenethylamine was highest in the striatum. 6. These findings are discussed in the light of changes occurring in human patients with uncontrolled phenylketonuria. PMID:4269184

Amateur boxing and risk of chronic traumatic brain injury: systematic review of observational studies

PubMed Central

Knowles, Charles H; Whyte, Greg P

2007-01-01

Objective To evaluate the risk of chronic traumatic brain injury from amateur boxing. Setting Secondary research performed by combination of sport physicians and clinical academics. Design, data sources, and methods Systematic review of observational studies in which chronic traumatic brain injury was defined as any abnormality on clinical neurological examination, psychometric testing, neuroimaging studies, and electroencephalography. Studies were identified through database (1950 to date) and bibliographic searches without language restrictions. Two reviewers extracted study characteristics, quality, and data, with adherence to a protocol developed from a widely recommended method for systematic review of observational studies (MOOSE). Results 36 papers had relevant extractable data (from a detailed evaluation of 93 studies of 943 identified from the initial search). Quality of evidence was generally poor. The best quality studies were those with a cohort design and those that used psychometric tests. These yielded the most negative results: only four of 17 (24%) better quality studies found any indication of chronic traumatic brain injury in a minority of boxers studied. Conclusion There is no strong evidence to associate chronic traumatic brain injury with amateur boxing. PMID:17916811

Amateur boxing and risk of chronic traumatic brain injury: systematic review of observational studies.

PubMed

Loosemore, Mike; Knowles, Charles H; Whyte, Greg P

2007-10-20

To evaluate the risk of chronic traumatic brain injury from amateur boxing. Secondary research performed by combination of sport physicians and clinical academics. DESIGN, DATA SOURCES, AND METHODS: Systematic review of observational studies in which chronic traumatic brain injury was defined as any abnormality on clinical neurological examination, psychometric testing, neuroimaging studies, and electroencephalography. Studies were identified through database (1950 to date) and bibliographic searches without language restrictions. Two reviewers extracted study characteristics, quality, and data, with adherence to a protocol developed from a widely recommended method for systematic review of observational studies (MOOSE). 36 papers had relevant extractable data (from a detailed evaluation of 93 studies of 943 identified from the initial search). Quality of evidence was generally poor. The best quality studies were those with a cohort design and those that used psychometric tests. These yielded the most negative results: only four of 17 (24%) better quality studies found any indication of chronic traumatic brain injury in a minority of boxers studied. There is no strong evidence to associate chronic traumatic brain injury with amateur boxing.

Optimization of Evans blue quantitation in limited rat tissue samples

PubMed Central

Wang, Hwai-Lee; Lai, Ted Weita

2014-01-01

Evans blue dye (EBD) is an inert tracer that measures plasma volume in human subjects and vascular permeability in animal models. Quantitation of EBD can be difficult when dye concentration in the sample is limited, such as when extravasated dye is measured in the blood-brain barrier (BBB) intact brain. The procedure described here used a very small volume (30 µl) per sample replicate, which enabled high-throughput measurements of the EBD concentration based on a standard 96-well plate reader. First, ethanol ensured a consistent optic path length in each well and substantially enhanced the sensitivity of EBD fluorescence spectroscopy. Second, trichloroacetic acid (TCA) removed false-positive EBD measurements as a result of biological solutes and partially extracted EBD into the supernatant. Moreover, a 1:2 volume ratio of 50% TCA ([TCA final] = 33.3%) optimally extracted EBD from the rat plasma protein-EBD complex in vitro and in vivo, and 1:2 and 1:3 weight-volume ratios of 50% TCA optimally extracted extravasated EBD from the rat brain and liver, respectively, in vivo. This procedure is particularly useful in the detection of EBD extravasation into the BBB-intact brain, but it can also be applied to detect dye extravasation into tissues where vascular permeability is less limiting. PMID:25300427

Optimization of Evans blue quantitation in limited rat tissue samples

NASA Astrophysics Data System (ADS)

Wang, Hwai-Lee; Lai, Ted Weita

2014-10-01

Evans blue dye (EBD) is an inert tracer that measures plasma volume in human subjects and vascular permeability in animal models. Quantitation of EBD can be difficult when dye concentration in the sample is limited, such as when extravasated dye is measured in the blood-brain barrier (BBB) intact brain. The procedure described here used a very small volume (30 µl) per sample replicate, which enabled high-throughput measurements of the EBD concentration based on a standard 96-well plate reader. First, ethanol ensured a consistent optic path length in each well and substantially enhanced the sensitivity of EBD fluorescence spectroscopy. Second, trichloroacetic acid (TCA) removed false-positive EBD measurements as a result of biological solutes and partially extracted EBD into the supernatant. Moreover, a 1:2 volume ratio of 50% TCA ([TCA final] = 33.3%) optimally extracted EBD from the rat plasma protein-EBD complex in vitro and in vivo, and 1:2 and 1:3 weight-volume ratios of 50% TCA optimally extracted extravasated EBD from the rat brain and liver, respectively, in vivo. This procedure is particularly useful in the detection of EBD extravasation into the BBB-intact brain, but it can also be applied to detect dye extravasation into tissues where vascular permeability is less limiting.

Apium graveolens extract influences mood and cognition in healthy mice.

PubMed

Boonruamkaew, Phetcharat; Sukketsiri, Wanida; Panichayupakaranant, Pharkphoom; Kaewnam, Wijittra; Tanasawet, Supita; Tipmanee, Varomyalin; Hutamekalin, Pilaiwanwadee; Chonpathompikunlert, Pennapa

2017-07-01

Apium graveolens is a food flavoring which possesses various health promoting effects. This study investigates the effect of a sub-acute administration of A. graveolens on cognition and anti-depression behaviors via antioxidant and related neurotransmitter systems in mice brains. Cognition and depression was assessed by various models of behavior. The antioxidant system of glutathione peroxidase (GPx), % inhibition of superoxide anion (O 2 - ), and lipid peroxidation were studied. In addition, neurochemical parameters including acetylcholinesterase (AChE) and monoamine oxidase-type A (MAO-A) were also evaluated. Nine groups of male mice were fed for 30 days with different substances-a control, vehicle, A. graveolens extract (65-500 mg/kg), and reference drugs (donepezil and fluoxetine). The results indicated that the effect of the intake of A. graveolens extract (125-500 mg/kg) was similar to the reference drugs, as it improved both spatial and non-spatial memories. Moreover, there was a decrease in immobility time in both the forced swimming and tail suspension tests. In addition, the A. graveolens extract reduced lipid peroxidation of the brain and increased GPx activity and the % inhibition of O 2 - , whereas the activities of AChE and MAO-A were decreased. Thus, our data have shown that the consumption of A. graveolens extract improved cognitive function and anti-depression activities as well as modulating the endogenous antioxidant and neurotransmitter systems in the brain, resulting in increased neuronal density. This result indicated an important role for A. graveolens extract in preventing age-associated decline in cognitive function associated with depression.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Extracting the inclination angle of nerve fibers within the human brain with 3D-PLI independent of system properties

NASA Astrophysics Data System (ADS)

Reckfort, Julia; Wiese, Hendrik; Dohmen, Melanie; Grässel, David; Pietrzyk, Uwe; Zilles, Karl; Amunts, Katrin; Axer, Markus

2013-09-01

The neuroimaging technique 3D-polarized light imaging (3D-PLI) has opened up new avenues to study the complex nerve fiber architecture of the human brain at sub-millimeter spatial resolution. This polarimetry technique is applicable to histological sections of postmortem brains utilizing the birefringence of nerve fibers caused by the regular arrangement of lipids and proteins in the myelin sheaths surrounding axons. 3D-PLI provides a three-dimensional description of the anatomical wiring scheme defined by the in-section direction angle and the out-of-section inclination angle. To date, 3D-PLI is the only available method that allows bridging the microscopic and the macroscopic description of the fiber architecture of the human brain. Here we introduce a new approach to retrieve the inclination angle of the fibers independently of the properties of the used polarimeters. This is relevant because the image resolution and the signal transmission inuence the measured birefringent signal (retardation) significantly. The image resolution was determined using the USAF- 1951 testchart applying the Rayleigh criterion. The signal transmission was measured by elliptical polarizers applying the Michelson contrast and histological slices of the optic tract of a postmortem brain. Based on these results, a modified retardation-inclination transfer function was proposed to extract the fiber inclination. The comparison of the actual and the inclination angles calculated with the theoretically proposed and the modified transfer function revealed a significant improvement in the extraction of the fiber inclinations.

Uniform distributions of glucose oxidation and oxygen extraction in gray matter of normal human brain: No evidence of regional differences of aerobic glycolysis.

PubMed

Hyder, Fahmeed; Herman, Peter; Bailey, Christopher J; Møller, Arne; Globinsky, Ronen; Fulbright, Robert K; Rothman, Douglas L; Gjedde, Albert

2016-05-01

Regionally variable rates of aerobic glycolysis in brain networks identified by resting-state functional magnetic resonance imaging (R-fMRI) imply regionally variable adenosine triphosphate (ATP) regeneration. When regional glucose utilization is not matched to oxygen delivery, affected regions have correspondingly variable rates of ATP and lactate production. We tested the extent to which aerobic glycolysis and oxidative phosphorylation power R-fMRI networks by measuring quantitative differences between the oxygen to glucose index (OGI) and the oxygen extraction fraction (OEF) as measured by positron emission tomography (PET) in normal human brain (resting awake, eyes closed). Regionally uniform and correlated OEF and OGI estimates prevailed, with network values that matched the gray matter means, regardless of size, location, and origin. The spatial agreement between oxygen delivery (OEF≈0.4) and glucose oxidation (OGI ≈ 5.3) suggests that no specific regions have preferentially high aerobic glycolysis and low oxidative phosphorylation rates, with globally optimal maximum ATP turnover rates (VATP ≈ 9.4 µmol/g/min), in good agreement with (31)P and (13)C magnetic resonance spectroscopy measurements. These results imply that the intrinsic network activity in healthy human brain powers the entire gray matter with ubiquitously high rates of glucose oxidation. Reports of departures from normal brain-wide homogeny of oxygen extraction fraction and oxygen to glucose index may be due to normalization artefacts from relative PET measurements. © The Author(s) 2016.

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

Distribution of the hallucinogens N,N-dimethyltryptamine and 5-methoxy-N,N-dimethyltryptamine in rat brain following intraperitoneal injection: application of a new solid-phase extraction LC-APcI-MS-MS-isotope dilution method.

PubMed

Barker, S A; Littlefield-Chabaud, M A; David, C

2001-02-10

A method for the solid-phase extraction (SPE) and liquid chromatographic-atmospheric pressure chemical ionization-mass spectrometric-mass spectrometric-isotope dilution (LC-APcI-MS-MS-ID) analysis of the indole hallucinogens N,N-dimethyltryptamine (DMT) and 5-methoxy DMT (or O-methyl bufotenin, OMB) from rat brain tissue is reported. Rats were administered DMT or OMB by the intraperitoneal route at a dose of 5 mg/kg and sacrificed 15 min post treatment. Brains were dissected into discrete areas and analyzed by the methods described as a demonstration of the procedure's applicability. The synthesis and use of two new deuterated internal standards for these purposes are also reported.

BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

PubMed Central

Vidaurre, Carmen; Sander, Tilmann H.; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals. PMID:21437227

BioSig: the free and open source software library for biomedical signal processing.

PubMed

Vidaurre, Carmen; Sander, Tilmann H; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

Automatic segmentation of brain hemispheres by midplane detection in class images

NASA Astrophysics Data System (ADS)

Wagenknecht, Gudrun; Kaiser, Hans-Juergen; Sabri, Osama; Buell, Udalrich

2000-06-01

Segmentation of brain hemispheres is necessary to study left- right differences in structure and function. For extraction of a 3D individual region-of-interest atlas of the human brain, detection of the midplane is the sine qua non as it provides the reference plane for determining other anatomical objects. Extraction of the sagittal midplane is done in two main steps. First, a 2D filter is used to give a first approximation of the midplane position. To model symmetry properties of the midplane neighborhood, the different filter columns contain class-dependent weights for cerebrospinal fluid, gray and white matter. The filter can be rotated in a range of angles. In a user-defined range of planes, the global maximum of the filter response is searched for and the resulting position is utilized to restrict the search in the remaining planes. In a second step, midplane extraction is refined by searching for the optimal path of the midplane within the filter mask at optimum position. Symmetry properties are modeled analogous to the first step with class-dependent weights of the filter columns. The extraction of the midplane gives accurate and reliable results in simulated data sets and patient studies even if asymmetric artifacts are simulated.

Spirulina non-protein components induce BDNF gene transcription via HO-1 activity in C6 glioma cells.

PubMed

Morita, Kyoji; Itoh, Mari; Nishibori, Naoyoshi; Her, Song; Lee, Mi-Sook

2015-01-01

Blue-green algae are known to contain biologically active proteins and non-protein substances and considered as useful materials for manufacturing the nutritional supplements. Particularly, Spirulina has been reported to contain a variety of antioxidants, such as flavonoids, carotenoids, and vitamin C, thereby exerting their protective effects against the oxidative damage to the cells. In addition to their antioxidant actions, polyphenolic compounds have been speculated to cause the protection of neuronal cells and the recovery of neurologic function in the brain through the production of brain-derived neurotrophic factor (BDNF) in glial cells. Then, the protein-deprived extract was prepared by removing the most part of protein components from aqueous extract of Spirulina platensis, and the effect of this extract on BDNF gene transcription was examined in C6 glioma cells. Consequently, the protein-deprived extract was shown to cause the elevation of BDNF mRNA levels following the expression of heme oxygenase-1 (HO-1) in the glioma cells. Therefore, the non-protein components of S. platensis are considered to stimulate BDNF gene transcription through the HO-1 induction in glial cells, thus proposing a potential ability of the algae to indirectly modulate the brain function through the glial cell activity.

Restoration of stressor-induced calcium dysregulation and autophagy inhibition by polyphenol-rich acai (Euterpe sps.) fruit pulp extracts in rodent brain cells in vitro

USDA-ARS?s Scientific Manuscript database

Oxidative damage to lipids, proteins and nucleic acids in brain often causes progressive neuronal degeneration and death which are the focal traits of chronic and acute pathologies in the brain, including those involving cognitive decline. It has been postulated that at least part of the loss of cog...

The Challenge of Connecting the Dots in the B.R.A.I.N

PubMed Central

Devor, Anna; Bandettini, Peter A.; Boas, David A.; Bower, James M.; Buxton, Richard B.; Cohen, Lawrence B.; Dale, Anders M.; Einevoll, Gaute T.; Fox, Peter T.; Franceschini, Maria Angela; Friston, Karl J.; Fujimoto, James G.; Geyer, Marc A.; Greenberg, Joel H.; Halgren, Eric; Hämäläinen, Matti S.; Helmchen, Fritjof; Hyman, Bradley T.; Jasanoff, Alan; Jernigan, Terry L.; Judd, Lewis L.; Kim, Seong-Gi; Kleinfeld, David; Kopell, Nancy J.; Kutas, Marta; Kwong, Kenneth K.; Larkum, Matthew E.; Lo, Eng H.; Magistretti, Pierre J.; Mandeville, Joseph B.; Masliah, Eliezer; Mitra, Partha P.; Mobley, William C.; Moskowitz, Michael A.; Nimmerjahn, Axel; Reynolds, John H.; Rosen, Bruce R.; Salzberg, Brian M.; Schaffer, Chris B.; Silva, Gabriel A.; So, Peter T. C.; Spitzer, Nicholas C.; Tootell, Roger B.; Van Essen, David C.; Vanduffel, Wim; Vinogradov, Sergei A.; Wald, Larry L.; Wang, Lihong V.; Weber, Bruno; Yodh, Arjun G.

2013-01-01

The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative has focused scientific attention on the necessary tools to understand the human brain and mind. Here, we outline our collective vision for what we can achieve within a decade with properly targeted efforts, and discuss likely technological deliverables and neuroscience progress. PMID:24139032

TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE.

PubMed

Prasad, Gautam; Nir, Talia M; Toga, Arthur W; Thompson, Paul M

2013-04-01

Brain connectivity declines in Alzheimer's disease (AD), both functionally and structurally. Connectivity maps and networks derived from diffusion-based tractography offer new ways to track disease progression and to understand how AD affects the brain. Here we set out to identify (1) which fiber network measures show greatest differences between AD patients and controls, and (2) how these effects depend on the density of fibers extracted by the tractography algorithm. We computed brain networks from diffusion-weighted images (DWI) of the brain, in 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD). We derived connectivity matrices and network topology measures, for each subject, from whole-brain tractography and cortical parcellations. We used an ODF lookup table to speed up fiber extraction, and to exploit the full information in the orientation distribution function (ODF). This made it feasible to compute high density connectivity maps. We used accelerated tractography to compute a large number of fibers to understand what effect fiber density has on network measures and in distinguishing different disease groups in our data. We focused on global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity measures computed from weighted and binary undirected connectivity matrices. Of all these measures, the mean nodal degree best distinguished diagnostic groups. High-density fiber matrices were most helpful for picking up the more subtle clinical differences, e.g. between mild cognitively impaired (MCI) and normals, or for distinguishing subtypes of MCI (early versus late). Care is needed in clinical analyses of brain connectivity, as the density of extracted fibers may affect how well a network measure can pick up differences between patients and controls.

Extracts and constituents of Leontopodium alpinum enhance cholinergic transmission: Brain ACh increasing and memory improving properties

PubMed Central

Hornick, Ariane; Schwaiger, Stefan; Rollinger, Judith M.; Vo, Nguyen Phung; Prast, Helmut; Stuppner, Hermann

2012-01-01

Leontopodium alpinum (‘Edelweiss’) was phytochemically investigated for constituents that might enhance cholinergic neurotransmission. The potency to increase synaptic availability of acetylcholine (ACh) in rat brain served as key property for the bioguided isolation of cholinergically active compounds using different chromatographic techniques. The dichlormethane (DCM) extract of the root, fractions and isolated constituents were injected i.c.v. and the effect on brain ACh was detected via the push–pull technique. The DCM extract enhanced extracellular ACh concentration in rat brain and inhibited acetylcholinesterase (AChE) in vitro. The extracellular level of brain ACh was significantly increased by the isolated sesquiterpenes, isocomene and 14-acetoxyisocomene, while silphiperfolene acetate and silphinene caused a small increasing tendency. Only silphiperfolene acetate showed in vitro AChE inhibitory activity, thus suggesting the other sesquiterpenes to stimulate cholinergic transmission by an alternative mechanism of action. Isocomene was further investigated with behavioural tasks in mice. It restored object recognition in scopolamine-impaired mice and showed nootropic effects in the T-maze alternation task in normal and scopolamine-treated mice. Additionally, this sesquiterpene reduced locomotor activity of untreated mice in the open field task, while the activity induced by scopolamine was abolished. The enhancement of synaptic availability of ACh, the promotion of alternation, and the amelioration of scopolamine-induced deficit are in accordance with a substance that amplifies cholinergic transmission. Whether the mechanism of action is inhibition of AChE or another pro-cholinergic property remains to be elucidated. Taken together, isocomene and related constituents of L. alpinum deserve further interest as potential antidementia agents in brain diseases associated with cholinergic deficits. PMID:18541221

Bioluminescence imaging of c-fos gene expression accompanying filial imprinting in the newly hatched chick brain.

PubMed

Yamaguchi, Shinji; Iikubo, Eiji; Hirose, Naoki; Kitajima, Takaaki; Katagiri, Sachiko; Kawamori, Ai; Fujii-Taira, Ikuko; Matsushima, Toshiya; Homma, Koichi J

2010-06-01

Bioluminescence imaging is a powerful tool for examining gene expression in living animals. Previously, we reported that exogenous DNA could be successfully delivered into neurons in the newly hatched chick brain using electroporation. Here, we show the in vivo bioluminescence imaging of c-fos promoter activity and its upregulation, which is associated with filial imprinting. The upregulation of c-fos gene expression correlated with both the strength of the chicks' approach activity to the training object and the acquisition of memory. The present technique should be a powerful tool for analyzing the time changes in neural activity of certain brain areas in real-time during memory formation, using brains of living animals.

Anticonvulsant activity of Aloe vera leaf extract in acute and chronic models of epilepsy in mice.

PubMed

Rathor, Naveen; Arora, Tarun; Manocha, Sachin; Patil, Amol N; Mediratta, Pramod K; Sharma, Krishna K

2014-03-01

The effect of Aloe vera in epilepsy has not yet been explored. This study was done to explore the effect of aqueous extract of Aloe vera leaf powder on three acute and one chronic model of epilepsy. In acute study, aqueous extract of Aloe vera leaf (extract) powder was administered in doses 100, 200 and 400 mg/kg p.o. Dose of 400 mg/kg of Aloe vera leaf extract was chosen for chronic administration. Oxidative stress parameters viz. malondialdehyde (MDA) and reduced glutathione (GSH) were also estimated in brain of kindled animals. In acute study, Aloe vera leaf (extract) powder in a dose-dependent manner significantly decreased duration of tonic hind limb extension in maximal electroshock seizure model, increased seizure threshold current in increasing current electroshock seizure model, and increased latency to onset and decreased duration of clonic convulsion in pentylenetetrazole (PTZ) model as compared with control group. In chronic study, Aloe vera leaf (extract) powder prevented progression of kindling in PTZ-kindled mice. Aloe vera leaf (extract) powder 400 mg/kg p.o. also reduced brain levels of MDA and increased GSH levels as compared to the PTZ-kindled non-treated group. The results of study showed that Aloe vera leaf (extract) powder possessed significant anticonvulsant and anti-oxidant activity. © 2013 Royal Pharmaceutical Society.

Population based MRI and DTI templates of the adult ferret brain and tools for voxelwise analysis.

PubMed

Hutchinson, E B; Schwerin, S C; Radomski, K L; Sadeghi, N; Jenkins, J; Komlosh, M E; Irfanoglu, M O; Juliano, S L; Pierpaoli, C

2017-05-15

Non-invasive imaging has the potential to play a crucial role in the characterization and translation of experimental animal models to investigate human brain development and disorders, especially when employed to study animal models that more accurately represent features of human neuroanatomy. The purpose of this study was to build and make available MRI and DTI templates and analysis tools for the ferret brain as the ferret is a well-suited species for pre-clinical MRI studies with folded cortical surface, relatively high white matter volume and body dimensions that allow imaging with pre-clinical MRI scanners. Four ferret brain templates were built in this study - in-vivo MRI and DTI and ex-vivo MRI and DTI - using brain images across many ferrets and region of interest (ROI) masks corresponding to established ferret neuroanatomy were generated by semi-automatic and manual segmentation. The templates and ROI masks were used to create a web-based ferret brain viewing software for browsing the MRI and DTI volumes with annotations based on the ROI masks. A second objective of this study was to provide a careful description of the imaging methods used for acquisition, processing, registration and template building and to demonstrate several voxelwise analysis methods including Jacobian analysis of morphometry differences between the female and male brain and bias-free identification of DTI abnormalities in an injured ferret brain. The templates, tools and methodological optimization presented in this study are intended to advance non-invasive imaging approaches for human-similar animal species that will enable the use of pre-clinical MRI studies for understanding and treating brain disorders. Published by Elsevier Inc.

Sex steroid hormones and brain function: PET imaging as a tool for research.

PubMed

Moraga-Amaro, R; van Waarde, A; Doorduin, J; de Vries, E F J

2018-02-01

Sex steroid hormones are major regulators of sexual characteristic among species. These hormones, however, are also produced in the brain. Steroidal hormone-mediated signalling via the corresponding hormone receptors can influence brain function at the cellular level and thus affect behaviour and higher brain functions. Altered steroid hormone signalling has been associated with psychiatric disorders, such as anxiety and depression. Neurosteroids are also considered to have a neuroprotective effect in neurodegenerative diseases. So far, the role of steroid hormone receptors in physiological and pathological conditions has mainly been investigated post mortem on animal or human brain tissues. To study the dynamic interplay between sex steroids, their receptors, brain function and behaviour in psychiatric and neurological disorders in a longitudinal manner, however, non-invasive techniques are needed. Positron emission tomography (PET) is a non-invasive imaging tool that is used to quantitatively investigate a variety of physiological and biochemical parameters in vivo. PET uses radiotracers aimed at a specific target (eg, receptor, enzyme, transporter) to visualise the processes of interest. In this review, we discuss the current status of the use of PET imaging for studying sex steroid hormones in the brain. So far, PET has mainly been investigated as a tool to measure (changes in) sex hormone receptor expression in the brain, to measure a key enzyme in the steroid synthesis pathway (aromatase) and to evaluate the effects of hormonal treatment by imaging specific downstream processes in the brain. Although validated radiotracers for a number of targets are still warranted, PET can already be a useful technique for steroid hormone research and facilitate the translation of interesting findings in animal studies to clinical trials in patients. © 2017 The Authors. Journal of Neuroendocrinology published by John Wiley & Sons Ltd on behalf of British Society for Neuroendocrinology.

An SPM12 extension for multiple sclerosis lesion segmentation

NASA Astrophysics Data System (ADS)

Roura, Eloy; Oliver, Arnau; Cabezas, Mariano; Valverde, Sergi; Pareto, Deborah; Vilanova, Joan C.; Ramió-Torrentà, Lluís.; Rovira, Àlex; Lladó, Xavier

2016-03-01

Purpose: Magnetic resonance imaging is nowadays the hallmark to diagnose multiple sclerosis (MS), characterized by white matter lesions. Several approaches have been recently presented to tackle the lesion segmentation problem, but none of them have been accepted as a standard tool in the daily clinical practice. In this work we present yet another tool able to automatically segment white matter lesions outperforming the current-state-of-the-art approaches. Methods: This work is an extension of Roura et al. [1], where external and platform dependent pre-processing libraries (brain extraction, noise reduction and intensity normalization) were required to achieve an optimal performance. Here we have updated and included all these required pre-processing steps into a single framework (SPM software). Therefore, there is no need of external tools to achieve the desired segmentation results. Besides, we have changed the working space from T1w to FLAIR, reducing interpolation errors produced in the registration process from FLAIR to T1w space. Finally a post-processing constraint based on shape and location has been added to reduce false positive detections. Results: The evaluation of the tool has been done on 24 MS patients. Qualitative and quantitative results are shown with both approaches in terms of lesion detection and segmentation. Conclusion: We have simplified both installation and implementation of the approach, providing a multiplatform tool1 integrated into the SPM software, which relies only on using T1w and FLAIR images. We have reduced with this new version the computation time of the previous approach while maintaining the performance.

Assessment of Mexican Arnica (Heterotheca inuloides Cass) and Rosemary (Rosmarinus officinalis) Extracts on Dopamine and Selected Biomarkers of Oxidative Stress in Stomach and Brain of Salmonella typhimurium Infected rats.

PubMed

Guzmàn, David Calderón; Herrera, Maribel Ortiz; Brizuela, Norma Osnaya; Mejía, Gerardo Barragàn; García, Ernestina Hernàndez; Olguín, Hugo Juàrez; Peraza, Armando Valenzuela; Ruíz, Norma Labra; Del Angel, Daniel Santamaría

2017-01-01

The effects of some natural products on dopamine (DA) and 5-hydroxyindole acetic acid (5-HIAA) in brain of infected models are still unclear. The purpose of this study was to measure the effect of Mexican arnica/rosemary (MAR) water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Female Wistar rats (weight 80 g) in the presence of MAR or absence (no-MAR) were treated as follows: group 1, buffer solution (controls); oseltamivir (100 mg/kg), group 2; culture of Salmonella typhimurium ( S.Typh ) (1 × 10 6 colony-forming units/rat) group 3; oseltamivir (100 mg/kg) + S.Typh (same dose) group 4. Drug and extracts were administered intraperitoneally every 24 h for 5 days, and S.Typh was given orally on days 1 and 3. On the fifth day, blood was collected to measure glucose and hemoglobin. The brains and stomachs were obtained to measure levels of DA, 5-HIAA, glutathione (GSH), TBARS, H 2 O 2 , and total ATPase activity using validated methods. DA levels increased in MAR group treated with oseltamivir alone but decreased in no-MAR group treated with oseltamivir plus S.Typh . 5-HIAA, GSH, and H 2 O 2 decreased in this last group, and ATPase activity increased in MAR group treated with oseltamivir plus S.Typh . TBARS (lipid peroxidation) increased in MAR group that received oseltamivir alone. Most of the biomarkers were not altered significantly in the stomach. MAR extract alters DA and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. The purpose of this study was to measure the effect of Mexican arnica/rosemary water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Results: Mexican arnica and rosemary extract alter dopamine and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. Abbreviations used: AS: Automated system, ATP: Adenosine triphosphate, CNS: Central nervous system, CFU: Colony-forming unit, DA: Dopamine EDTA: Ethylenediaminetetraacetic acid, 5-HIAA: Äcido 5-hidroxindolacético (serotonina), GABA: γ-aminobutyric acid, GSH: Glutathione, H2O2: Hidrogen peroxide, HCLO4: Perchloric acid, iNOS: Inducible nitric oxide synthase, LPS: Lipopolysaccharides, MAR: Arnica/Rosemary, NaCl: Sodium Chloride, NOGSH: nitrosoglutathione, NOS: Nitric oxide, OPT: Ortho-phtaldialdehyde, Pbs: Phosphate buffered saline, pH: potential of Hydrogen, Pi: Inorganic phosphate, ROS: Reactive oxygen species, RNSs: Reactive nitrogen species Tba: Thiobarbaturic acid, TBARS: Thiobarbituric aid reactive, Tca: Trichloroacetic, Tris-HCL: Tris hydrochloride, TSA: Trypticasein Soya Agar.

Assessment of Mexican Arnica (Heterotheca inuloides Cass) and Rosemary (Rosmarinus officinalis) Extracts on Dopamine and Selected Biomarkers of Oxidative Stress in Stomach and Brain of Salmonella typhimurium Infected rats

PubMed Central

Guzmàn, David Calderón; Herrera, Maribel Ortiz; Brizuela, Norma Osnaya; Mejía, Gerardo Barragàn; García, Ernestina Hernàndez; Olguín, Hugo Juàrez; Peraza, Armando Valenzuela; Ruíz, Norma Labra; Del Angel, Daniel Santamaría

2017-01-01

Background: The effects of some natural products on dopamine (DA) and 5-hydroxyindole acetic acid (5-HIAA) in brain of infected models are still unclear. Objective: The purpose of this study was to measure the effect of Mexican arnica/rosemary (MAR) water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Methods: Female Wistar rats (weight 80 g) in the presence of MAR or absence (no-MAR) were treated as follows: group 1, buffer solution (controls); oseltamivir (100 mg/kg), group 2; culture of Salmonella typhimurium (S.Typh) (1 × 106 colony-forming units/rat) group 3; oseltamivir (100 mg/kg) + S.Typh (same dose) group 4. Drug and extracts were administered intraperitoneally every 24 h for 5 days, and S.Typh was given orally on days 1 and 3. On the fifth day, blood was collected to measure glucose and hemoglobin. The brains and stomachs were obtained to measure levels of DA, 5-HIAA, glutathione (GSH), TBARS, H2O2, and total ATPase activity using validated methods. Results: DA levels increased in MAR group treated with oseltamivir alone but decreased in no-MAR group treated with oseltamivir plus S.Typh. 5-HIAA, GSH, and H2O2 decreased in this last group, and ATPase activity increased in MAR group treated with oseltamivir plus S.Typh. TBARS (lipid peroxidation) increased in MAR group that received oseltamivir alone. Most of the biomarkers were not altered significantly in the stomach. Conclusion: MAR extract alters DA and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. SUMMARY The purpose of this study was to measure the effect of Mexican arnica/rosemary water extract and oseltamivir on both biogenic amines and some oxidative biomarkers in the brain and stomach of young rats under infection condition. Results: Mexican arnica and rosemary extract alter dopamine and metabolism of 5-HIAA in the brain of young animals infected. Antioxidant capacity may be involved in these effects. Abbreviations used: AS: Automated system, ATP: Adenosine triphosphate, CNS: Central nervous system, CFU: Colony-forming unit, DA: Dopamine EDTA: Ethylenediaminetetraacetic acid, 5-HIAA: Äcido 5-hidroxindolacético (serotonina), GABA: γ-aminobutyric acid, GSH: Glutathione, H2O2: Hidrogen peroxide, HCLO4: Perchloric acid, iNOS: Inducible nitric oxide synthase, LPS: Lipopolysaccharides, MAR: Arnica/Rosemary, NaCl: Sodium Chloride, NOGSH: nitrosoglutathione, NOS: Nitric oxide, OPT: Ortho-phtaldialdehyde, Pbs: Phosphate buffered saline, pH: potential of Hydrogen, Pi: Inorganic phosphate, ROS: Reactive oxygen species, RNSs: Reactive nitrogen species Tba: Thiobarbaturic acid, TBARS: Thiobarbituric aid reactive, Tca: Trichloroacetic, Tris-HCL: Tris hydrochloride, TSA: Trypticasein Soya Agar PMID:28539708

Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

PubMed Central

Jiang, Yang; Abiri, Reza; Zhao, Xiaopeng

2017-01-01

Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population. PMID:28348527

A Method for Automatic Extracting Intracranial Region in MR Brain Image

NASA Astrophysics Data System (ADS)

Kurokawa, Keiji; Miura, Shin; Nishida, Makoto; Kageyama, Yoichi; Namura, Ikuro

It is well known that temporal lobe in MR brain image is in use for estimating the grade of Alzheimer-type dementia. It is difficult to use only region of temporal lobe for estimating the grade of Alzheimer-type dementia. From the standpoint for supporting the medical specialists, this paper proposes a data processing approach on the automatic extraction of the intracranial region from the MR brain image. The method is able to eliminate the cranium region with the laplacian histogram method and the brainstem with the feature points which are related to the observations given by a medical specialist. In order to examine the usefulness of the proposed approach, the percentage of the temporal lobe in the intracranial region was calculated. As a result, the percentage of temporal lobe in the intracranial region on the process of the grade was in agreement with the visual sense standards of temporal lobe atrophy given by the medical specialist. It became clear that intracranial region extracted by the proposed method was good for estimating the grade of Alzheimer-type dementia.

Cerebral Oximetry as an Auxiliary Diagnostic Tool in the Diagnosis of Brain Death.

PubMed

Tatli, O; Bekar, O; Imamoglu, M; Gonenc Cekic, O; Aygun, A; Eryigit, U; Karaca, Y; Sahin, A; Turkmen, S; Turedi, S

2017-10-01

To investigate the efficacy of cerebral oximetry (CO) as an auxiliary diagnostic tool in brain death (BD). This observational case-control study was performed on patients with suspected BD. Patients with diagnosis of BD confirmed by the brain death committee were enrolled as the BD group and other patients as the non-BD group. CO monitoring was performed at least 6 h, and cerebral tissue oxygen saturation (ScO 2 ) parameters were compared. Mean ScO 2 level in the BD group was lower than non-brain-dead patients: mean difference for right lobe = 6.48 (95% confidence interval [CI] 0.08-12.88) and for left lobe = 6.09 (95% CI -0.22-12.41). Maximum ScO 2 values in the BD group were significantly lower than the non-BD group: mean difference for right lobe = 8.20 (95% CI 1.64-14.77) and for left lobe = 9.54 (95% CI 3.06-16.03). The area under the curve for right lobe maximum ScO 2 was 0.69 (95% CI 0.55-0.81) and for left lobe was 0.72 (95% CI 0.58-0.84). Maximum ScO 2 in brain-dead patients at CO monitoring is significantly low. However, this cannot be used to differentiate brain-dead and non-brain-dead patients. CO monitoring is therefore not an appropriate auxiliary diagnostic tool for confirming BD. Copyright © 2017 Elsevier Inc. All rights reserved.

Sodium-dependent transport of sugars and iodide from the cerebral venticles of the rabbit.

PubMed

Bradbury, M W; Brondsted, H E

1973-10-01

1. The objective was to discover whether the extraction of sugars and iodide from the perfused cerebral ventricles is Na(+)-dependent.2. In the ventriculo-aqueductal and ventriculo-cisternal perfusion systems in the rabbit the extraction of (14)C-labelled D-hexoses (glucose, 3-O-methyl-glucose, alpha-methyl-glucoside and galactose), (131)I(-) and (24)Na was inhibited when 82% of the Na(+) in the perfusion fluid was replaced by choline. The extraction returned to control levels when the Na(+) concentration in the perfusion fluid was returned to normal.3. Ouabain, 5 x 10(-5)M in the perfusion fluid inhibited the extraction of the above (14)C sugars and (131)I(-), but hardly affected that of [(3)H]2-deoxy-D-glucose. It enhanced the extraction of (24)Na. C.s.f. production was usually totally inhibited.4. The extraction of [(14)C]urea remained unchanged during perfusion with low Na(+) fluid or ouabain.5. Recovery from brain of [(14)C]3-O-methyl-glucose, [(3)H]2-deoxy-glucose and (131)I(-) was low while recovery of [(14)C]alpha-methyl-glucoside and (24)Na was high. On an equal weight basis recovery of [(14)C]3-O-methyl-glucose was about twelve times higher from the choroid plexus than from the brain.6. Part of the movement of (14)C sugars may be explained on basis of a Na(+)-gradient hypothesis with involvement of the Na(+) pump at the blood-c.s.f. or blood-brain barriers.7. The rate of c.s.f. production from the first three ventricles comprised about 40% of the rate from all four ventricles. The extraction of sugars, urea and cations was similar in both perfusion systems while the extraction of (131)I(-) was higher in the ventriculo-cisternal system than in the ventriculo-aqueductal system.

Multimodal 2D Brain Computer Interface.

PubMed

Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

2015-08-01

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

Precise and accurate assay of pregnenolone and five other neurosteroids in monkey brain tissue by LC-MS/MS.

PubMed

Dury, Alain Y; Ke, Yuyong; Labrie, Fernand

2016-09-01

A series of steroids present in the brain have been named "neurosteroids" following the possibility of their role in the central nervous system impairments such as anxiety disorders, depression, premenstrual dysphoric disorder (PMDD), addiction, or even neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Study of their potential role requires a sensitive and accurate assay of their concentration in the monkey brain, the closest model to the human. We have thus developed a robust, precise and accurate liquid chromatography-tandem mass spectrometry method for the assay of pregnenolone, pregnanolone, epipregnanolone, allopregnanolone, epiallopregnanolone, and androsterone in the cynomolgus monkey brain. The extraction method includes a thorough sample cleanup using protein precipitation and phospholipid removal, followed by hexane liquid-liquid extraction and a Girard T ketone-specific derivatization. This method opens the possibility of investigating the potential implication of these six steroids in the most suitable animal model for neurosteroid-related research. Copyright © 2016 Elsevier Inc. All rights reserved.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

PubMed

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

2016-09-01

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

PubMed Central

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B.; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes. PMID:28731430

Validation of ICDPIC software injury severity scores using a large regional trauma registry.

PubMed

Greene, Nathaniel H; Kernic, Mary A; Vavilala, Monica S; Rivara, Frederick P

2015-10-01

Administrative or quality improvement registries may or may not contain the elements needed for investigations by trauma researchers. International Classification of Diseases Program for Injury Categorisation (ICDPIC), a statistical program available through Stata, is a powerful tool that can extract injury severity scores from ICD-9-CM codes. We conducted a validation study for use of the ICDPIC in trauma research. We conducted a retrospective cohort validation study of 40,418 patients with injury using a large regional trauma registry. ICDPIC-generated AIS scores for each body region were compared with trauma registry AIS scores (gold standard) in adult and paediatric populations. A separate analysis was conducted among patients with traumatic brain injury (TBI) comparing the ICDPIC tool with ICD-9-CM embedded severity codes. Performance in characterising overall injury severity, by the ISS, was also assessed. The ICDPIC tool generated substantial correlations in thoracic and abdominal trauma (weighted κ 0.87-0.92), and in head and neck trauma (weighted κ 0.76-0.83). The ICDPIC tool captured TBI severity better than ICD-9-CM code embedded severity and offered the advantage of generating a severity value for every patient (rather than having missing data). Its ability to produce an accurate severity score was consistent within each body region as well as overall. The ICDPIC tool performs well in classifying injury severity and is superior to ICD-9-CM embedded severity for TBI. Use of ICDPIC demonstrates substantial efficiency and may be a preferred tool in determining injury severity for large trauma datasets, provided researchers understand its limitations and take caution when examining smaller trauma datasets. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Extraction efficiency and implications for absolute quantitation of propranolol in mouse brain, liver and kidney thin tissue sections using droplet-based liquid microjunction surface sampling-HPLC ESI-MS/MS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kertesz, Vilmos; Weiskittel, Taylor M.; Vavek, Marissa

Currently, absolute quantitation aspects of droplet-based surface sampling for thin tissue analysis using a fully automated autosampler/HPLC-ESI-MS/MS system are not fully evaluated. Knowledge of extraction efficiency and its reproducibility is required to judge the potential of the method for absolute quantitation of analytes from thin tissue sections. Methods: Adjacent thin tissue sections of propranolol dosed mouse brain (10- μm-thick), kidney (10- μm-thick) and liver (8-, 10-, 16- and 24- μm-thick) were obtained. Absolute concentration of propranolol was determined in tissue punches from serial sections using standard bulk tissue extraction protocols and subsequent HPLC separations and tandem mass spectrometric analysis. Thesemore » values were used to determine propranolol extraction efficiency from the tissues with the droplet-based surface sampling approach. Results: Extraction efficiency of propranolol using 10- μm-thick brain, kidney and liver thin tissues using droplet-based surface sampling varied between ~45-63%. Extraction efficiency decreased from ~65% to ~36% with liver thickness increasing from 8 μm to 24 μm. Randomly selecting half of the samples as standards, precision and accuracy of propranolol concentrations obtained for the other half of samples as quality control metrics were determined. Resulting precision ( ±15%) and accuracy ( ±3%) values, respectively, were within acceptable limits. In conclusion, comparative quantitation of adjacent mouse thin tissue sections of different organs and of various thicknesses by droplet-based surface sampling and by bulk extraction of tissue punches showed that extraction efficiency was incomplete using the former method, and that it depended on the organ and tissue thickness. However, once extraction efficiency was determined and applied, the droplet-based approach provided the required quantitation accuracy and precision for assay validations. Furthermore, this means that once the extraction efficiency was calibrated for a given tissue type and drug, the droplet-based approach provides a non-labor intensive and high-throughput means to acquire spatially resolved quantitative analysis of multiple samples of the same type.« less

Extraction efficiency and implications for absolute quantitation of propranolol in mouse brain, liver and kidney thin tissue sections using droplet-based liquid microjunction surface sampling-HPLC ESI-MS/MS

DOE PAGES

Kertesz, Vilmos; Weiskittel, Taylor M.; Vavek, Marissa; ...

2016-06-22

Currently, absolute quantitation aspects of droplet-based surface sampling for thin tissue analysis using a fully automated autosampler/HPLC-ESI-MS/MS system are not fully evaluated. Knowledge of extraction efficiency and its reproducibility is required to judge the potential of the method for absolute quantitation of analytes from thin tissue sections. Methods: Adjacent thin tissue sections of propranolol dosed mouse brain (10- μm-thick), kidney (10- μm-thick) and liver (8-, 10-, 16- and 24- μm-thick) were obtained. Absolute concentration of propranolol was determined in tissue punches from serial sections using standard bulk tissue extraction protocols and subsequent HPLC separations and tandem mass spectrometric analysis. Thesemore » values were used to determine propranolol extraction efficiency from the tissues with the droplet-based surface sampling approach. Results: Extraction efficiency of propranolol using 10- μm-thick brain, kidney and liver thin tissues using droplet-based surface sampling varied between ~45-63%. Extraction efficiency decreased from ~65% to ~36% with liver thickness increasing from 8 μm to 24 μm. Randomly selecting half of the samples as standards, precision and accuracy of propranolol concentrations obtained for the other half of samples as quality control metrics were determined. Resulting precision ( ±15%) and accuracy ( ±3%) values, respectively, were within acceptable limits. In conclusion, comparative quantitation of adjacent mouse thin tissue sections of different organs and of various thicknesses by droplet-based surface sampling and by bulk extraction of tissue punches showed that extraction efficiency was incomplete using the former method, and that it depended on the organ and tissue thickness. However, once extraction efficiency was determined and applied, the droplet-based approach provided the required quantitation accuracy and precision for assay validations. Furthermore, this means that once the extraction efficiency was calibrated for a given tissue type and drug, the droplet-based approach provides a non-labor intensive and high-throughput means to acquire spatially resolved quantitative analysis of multiple samples of the same type.« less

Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation

PubMed Central

2015-01-01

This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from rats (n = 9) experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool. PMID:26380297

Automated extraction of subdural electrode grid from post-implant MRI scans for epilepsy surgery

NASA Astrophysics Data System (ADS)

Pozdin, Maksym A.; Skrinjar, Oskar

2005-04-01

This paper presents an automated algorithm for extraction of Subdural Electrode Grid (SEG) from post-implant MRI scans for epilepsy surgery. Post-implant MRI scans are corrupted by the image artifacts caused by implanted electrodes. The artifacts appear as dark spherical voids and given that the cerebrospinal fluid is also dark in T1-weigthed MRI scans, it is a difficult and time-consuming task to manually locate SEG position relative to brain structures of interest. The proposed algorithm reliably and accurately extracts SEG from post-implant MRI scan, i.e. finds its shape and position relative to brain structures of interest. The algorithm was validated against manually determined electrode locations, and the average error was 1.6mm for the three tested subjects.

Consumer nueroscience: a new area of study for biomedical engineers.

PubMed

Babiloni, Fabio

2012-01-01

In scientific literature, the most accepted definition of consumer neuroscience or neuromarketing is that it is a field of study concerning the application of neuroscience methods to analyze and understand human behavior related to markets and marketing exchanges. First, it might seem strange that marketers would be interested in using neuroscience to understand consumer's preferences. Yet in practice, the basic goal of marketers is to guide the design and presentation of products in such a way that they are highly compatible with consumer preferences. To understand consumers preferences, several standard research tools are commonly used by marketers, such as personal interviews with the consumers, scoring questionnaries gathered from consumers, and focus groups. The reason marketing researchers are interested in using brain imaging tools instead of simply asking people for their preferences in front of marketing stimuli, arises from the assumption that people cannot (or do not want to) fully explain their preference when explicitly asked. Researchers in the field hypothesize that neuroimaging tools can access information within the consumer's brain during the generation of a preference or the observation of a commercial advertisement. The question of will this information be useful in further promoting the product is still up for debate in marketing literature. From the marketing researchers point of view, there is a hope that this body of brain imaging techniques will provide an efficient tradeoff between costs and benefits of the research. Currently, neuroscience methodology includes powerful brain imaging tools based on the gathering of hemodynamic or electromagnetic signals related to the human brain activity during the performance of a relevant task for marketing objectives. These tools are briefly reviewed in this article.

Network neuroscience

PubMed Central

Bassett, Danielle S; Sporns, Olaf

2017-01-01

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

Graph Theory at the Service of Electroencephalograms.

PubMed

Iakovidou, Nantia D

2017-04-01

The brain is one of the largest and most complex organs in the human body and EEG is a noninvasive electrophysiological monitoring method that is used to record the electrical activity of the brain. Lately, the functional connectivity in human brain has been regarded and studied as a complex network using EEG signals. This means that the brain is studied as a connected system where nodes, or units, represent different specialized brain regions and links, or connections, represent communication pathways between the nodes. Graph theory and theory of complex networks provide a variety of measures, methods, and tools that can be useful to efficiently model, analyze, and study EEG networks. This article is addressed to computer scientists who wish to be acquainted and deal with the study of EEG data and also to neuroscientists who would like to become familiar with graph theoretic approaches and tools to analyze EEG data.

A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.

PubMed

Panta, Sandeep R; Wang, Runtang; Fries, Jill; Kalyanam, Ravi; Speer, Nicole; Banich, Marie; Kiehl, Kent; King, Margaret; Milham, Michael; Wager, Tor D; Turner, Jessica A; Plis, Sergey M; Calhoun, Vince D

2016-01-01

In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.

γ-H2AX as a Marker for Dose Deposition in the Brain of Wistar Rats after Synchrotron Microbeam Radiation

PubMed Central

Fernandez-Palomo, Cristian; Mothersill, Carmel; Bräuer-Krisch, Elke; Laissue, Jean; Seymour, Colin; Schültke, Elisabeth

2015-01-01

Objective Synchrotron radiation has shown high therapeutic potential in small animal models of malignant brain tumours. However, more studies are needed to understand the radiobiological effects caused by the delivery of high doses of spatially fractionated x-rays in tissue. The purpose of this study was to explore the use of the γ-H2AX antibody as a marker for dose deposition in the brain of rats after synchrotron microbeam radiation therapy (MRT). Methods Normal and tumour-bearing Wistar rats were exposed to 35, 70 or 350 Gy of MRT to their right cerebral hemisphere. The brains were extracted either at 4 or 8 hours after irradiation and immediately placed in formalin. Sections of paraffin-embedded tissue were incubated with anti γ-H2AX primary antibody. Results While the presence of the C6 glioma does not seem to modulate the formation of γ-H2AX in normal tissue, the irradiation dose and the recovery versus time are the most important factors affecting the development of γ-H2AX foci. Our results also suggest that doses of 350 Gy can trigger the release of bystander signals that significantly amplify the DNA damage caused by radiation and that the γ-H2AX biomarker does not only represent DNA damage produced by radiation, but also damage caused by bystander effects. Conclusion In conclusion, we suggest that the γ-H2AX foci should be used as biomarker for targeted and non-targeted DNA damage after synchrotron radiation rather than a tool to measure the actual physical doses. PMID:25799425

Understanding neuromotor strategy during functional upper extremity tasks using symbolic dynamics.

PubMed

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.

Isolation, structure, synthesis, and activity of a new member of the calcitonin gene-related peptide family from frog skin and molecular cloning of its precursor.

PubMed

Seon, A A; Pierre, T N; Redeker, V; Lacombe, C; Delfour, A; Nicolas, P; Amiche, M

2000-02-25

Calcitonin gene-related peptide has been extracted from the skin exudate of a single living specimen of the frog Phyllomedusa bicolor and purified to homogeneity by a two-step protocol. A total volume of 250 microl of exudate yielded 380 microg of purified peptide. Mass spectrometric analysis and gas phase sequencing of the purified peptide as well as chemical synthesis and cDNA analysis were consistent with the structure SCDTSTCATQRLADFLSRSGGIGSPDFVPTDVSANSF amide and the presence of a disulfide bridge linking Cys(2) and Cys(7). The skin peptide, named skin calcitonin gene-related peptide, differs significantly from all other members of the calcitonin gene-related peptide family of peptides at nine positions but binds with high affinity to calcitonin gene-related peptide receptors in the rat brain and acts as an agonist in the rat vas deferens bioassay with potencies equal to those of human CGRP. Reverse transcriptase-polymerase chain reaction coupled with cDNA cloning and sequencing demonstrated that skin calcitonin gene-related peptide isolated in the skin is identical to that present in the frog's central and enteric nervous systems. These data, which indicate for the first time the existence of calcitonin gene-related peptide in the frog skin, add further support to the brain-skin-gut triangle hypothesis as a useful tool in the identification and/or isolation of mammalian peptides that are present in the brain and other tissues in only minute quantities.

Embryonic Alcohol Exposure Impairs the Dopaminergic System and Social Behavioral Responses in Adult Zebrafish

PubMed Central

Fernandes, Yohaan; Rampersad, Mindy

2015-01-01

Background: The zebrafish is a powerful neurobehavioral genetics tool with which complex human brain disorders including alcohol abuse and fetal alcohol spectrum disorders may be modeled and investigated. Zebrafish innately form social groups called shoals. Previously, it has been demonstrated that a single bath exposure (24 hours postfertilization) to low doses of alcohol (0, 0.25, 0.50, 0.75, and 1% vol/vol) for a short duration (2 hours) leads to impaired group forming, or shoaling, in adult zebrafish. Methods: In the current study, we immersed zebrafish eggs in a low concentration of alcohol (0.5% or 1% vol/vol) for 2 hours at 24 hours postfertilization and let the fish grow and reach adulthood. In addition to quantifying the behavioral response of the adult fish to an animated shoal, we also measured the amount of dopamine and its metabolite 3,4-dihydroxyphenylacetic acid from whole brain extracts of these fish using high-pressure liquid chromatograph. Results: Here we confirm that embryonic alcohol exposure makes adult zebrafish increase their distance from the shoal stimulus in a dose-dependent manner. We also show that the shoal stimulus increases the amount of dopamine and 3,4-dihydroxyphenylacetic acid in the brain of control zebrafish but not in fish previously exposed to alcohol during their embryonic development. Conclusions: We speculate that one of the mechanisms that may explain the embryonic alcohol-induced impaired shoaling response in zebrafish is dysfunction of reward mechanisms subserved by the dopaminergic system. PMID:25568285

Mutagenicity, anticancer activity and blood brain barrier: similarity and dissimilarity of molecular alerts.

PubMed

Toropov, Andrey A; Toropova, Alla P; Benfenati, Emilio; Salmona, Mario

2018-06-01

The aim of the present work is an attempt to define computable measure of similarity between different endpoints. The similarity of structural alerts of different biochemical endpoints can be used to solve tasks of medicinal chemistry. Optimal descriptors are a tool to build up models for different endpoints. The optimal descriptor is calculated with simplified molecular input-line entry system (SMILES). A group of elements (single symbol or pair of symbols) can represent any SMILES. Each element of SMILES can be represented by so-called correlation weight i.e. coefficient that should be used to calculate descriptor. Numerical data on the correlation weights are calculated by the Monte Carlo method, i.e. by optimization procedure, which gives maximal correlation coefficient between the optimal descriptor and endpoint for the training set. Statistically stable correlation weights observed in several runs of the optimization can be examined as structural alerts, which are promoters of the increase or the decrease of a biochemical activity of a substance. Having data on several runs of the optimization correlation weights, one can extract list of promoters of increase and list of promoters of decrease for an endpoint. The study of similarity and dissimilarity of the above lists has been carried out for the following pairs of endpoints: (i) mutagenicity and anticancer activity; (ii) mutagenicity and blood brain barrier; and (iii) blood brain barrier and anticancer activity. The computational experiment confirms that similarity and dissimilarity for pairs of endpoints can be measured.

Keyword extraction, ranking, and organization for the neuroinformatics platform.

PubMed

Usui, S; Palmes, P; Nagata, K; Taniguchi, T; Ueda, N

2007-04-01

Brain-related researches encompass many fields of studies and usually involve worldwide collaborations. Recognizing the value of these international collaborations for efficient use of resources and improving the quality of brain research, the International Neuroinformatics Coordinating Facility (INCF) started to coordinate the effort of establishing neuroinformatics (NI) centers and portal sites among the different participating countries. These NI centers and portal sites will serve as the conduit for the interchange of information and brain-related resources among different countries. In Japan, several NI platforms under the support of NIJC (NI Japan Center) are being developed with one platform called, Visiome, already operating and publicly accessible at "http://www.platform.visiome.org". Each of these platforms requires their own set of keywords that represent important terms covering their respective fields of study. One important function of this predefined keyword list is to help contributors classify the contents of their contributions and group related resources. It is vital, therefore, that this predefined list should be properly chosen to cover the necessary areas. Currently, the process of identifying these appropriate keywords relies on the availability of human experts which does not scale well considering that different areas are rapidly evolving. This problem prompted us to develop a tool to automatically filter the most likely terms preferred by human experts. We tested the effectiveness of the proposed approach using the abstracts of the Vision Research Journal (VR) and Investigative Ophthalmology and Visual Science Journal (IOVS) as source files.

Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?

PubMed

Varoquaux, G; Gramfort, A; Poline, J B; Thirion, B

2012-01-01

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

Brain Tumors - Multiple Languages

MedlinePlus

... FAQs Customer Support Health Topics Drugs & Supplements Videos & Tools You Are Here: Home → Multiple Languages → All Health Topics → Brain Tumors URL of this page: https://medlineplus.gov/ ...

Brain Diseases - Multiple Languages

MedlinePlus

... FAQs Customer Support Health Topics Drugs & Supplements Videos & Tools You Are Here: Home → Multiple Languages → All Health Topics → Brain Diseases URL of this page: https://medlineplus.gov/ ...

[The algorithms and development for the extraction of evoked potentials].

PubMed

Niu, Jie; Qiu, Tianshuang

2004-06-01

The extraction of evoked potentials is a main subject in the area of brain signal processing. In recent years, the single-trial extraction of evoked potentials has been focused on by many studies. In this paper, the approaches based on the wavelet transform, the neural network, the high order acumulants and the independent component analysis are briefly reviewed.

BioSimplify: an open source sentence simplification engine to improve recall in automatic biomedical information extraction.

PubMed

Jonnalagadda, Siddhartha; Gonzalez, Graciela

2010-11-13

BioSimplify is an open source tool written in Java that introduces and facilitates the use of a novel model for sentence simplification tuned for automatic discourse analysis and information extraction (as opposed to sentence simplification for improving human readability). The model is based on a "shot-gun" approach that produces many different (simpler) versions of the original sentence by combining variants of its constituent elements. This tool is optimized for processing biomedical scientific literature such as the abstracts indexed in PubMed. We tested our tool on its impact to the task of PPI extraction and it improved the f-score of the PPI tool by around 7%, with an improvement in recall of around 20%. The BioSimplify tool and test corpus can be downloaded from https://biosimplify.sourceforge.net.

Combinations of Ashwagandha Leaf Extracts Protect Brain-Derived Cells against Oxidative Stress and Induce Differentiation

PubMed Central

Shah, Navjot; Singh, Rumani; Sarangi, Upasana; Saxena, Nishant; Chaudhary, Anupama; Kaur, Gurcharan; Kaul, Sunil C.; Wadhwa, Renu

2015-01-01

Background Ashwagandha, a traditional Indian herb, has been known for its variety of therapeutic activities. We earlier demonstrated anticancer activities in the alcoholic and water extracts of the leaves that were mediated by activation of tumor suppressor functions and oxidative stress in cancer cells. Low doses of these extracts were shown to possess neuroprotective activities in vitro and in vivo assays. Methodology/Principal Findings We used cultured glioblastoma and neuroblastoma cells to examine the effect of extracts (alcoholic and water) as well as their bioactive components for neuroprotective activities against oxidative stress. Various biochemical and imaging assays on the marker proteins of glial and neuronal cells were performed along with their survival profiles in control, stressed and recovered conditions. We found that the extracts and one of the purified components, withanone, when used at a low dose, protected the glial and neuronal cells from oxidative as well as glutamate insult, and induced their differentiation per se. Furthermore, the combinations of extracts and active component were highly potent endorsing the therapeutic merit of the combinational approach. Conclusion Ashwagandha leaf derived bioactive compounds have neuroprotective potential and may serve as supplement for brain health. PMID:25789768

Oxidative stress induced by 1.8 GHz radio frequency electromagnetic radiation and effects of garlic extract in rats.

PubMed

Avci, Bahattin; Akar, Ayşegül; Bilgici, Birşen; Tunçel, Özgür Korhan

2012-11-01

We aimed to study the oxidative damage induced by radiofrequency electromagnetic radiation (RF-EMR) emitted by mobile telephones and the protective effect of garlic extract used as an anti-oxidant against this damage. A total of 66 albino Wistar rats were divided into three groups. The first group of rats was given 1.8 GHz, 0.4 W/kg specific absorption rate (SAR) for 1 h a day for three weeks. The second group was given 500 mg/kg garlic extract in addition to RF-EMR. The third group of rats was used as the control group. At the end of the study, blood and brain tissue samples were collected from the rats. After the RF-EMR exposed, the advanced oxidation protein product (AOPP) levels of brain tissue increased compared with the control group (p < 0.001). Garlic administration accompanying the RF-EMR, on the other hand, significantly reduced AOPP levels in brain tissue (p < 0.001). The serum nitric oxide (NO) levels significantly increased both in the first and second group (p < 0.001). However, in the group for which garlic administration accompanied that of RF-EMR, there was no difference in serum NO levels compared with the RF-EMR exposed group (p > 0.05). There was no significant difference among the groups with respect to malondialdehyde (MDA) levels in brain tissue and blood samples (p > 0.05). Similarly, no difference was detected among the groups regarding serum paroxonase (PON) levels (p > 0.05). We did not detect any PON levels in the brain tissue. The exposure of RF-EMR similar to 1.8 GHz Global system for mobile communication (GSM) leads to protein oxidation in brain tissue and an increase in serum NO. We observed that garlic administration reduced protein oxidation in brain tissue and that it did not have any effects on serum NO levels.

Carbon Dioxide Fluctuations Are Associated with Changes in Cerebral Oxygenation and Electrical Activity in Infants Born Preterm.

PubMed

Dix, Laura Marie Louise; Weeke, Lauren Carleen; de Vries, Linda Simone; Groenendaal, Floris; Baerts, Willem; van Bel, Frank; Lemmers, Petra Maria Anna

2017-08-01

To evaluate the effects of acute arterial carbon dioxide partial pressure changes on cerebral oxygenation and electrical activity in infants born preterm. This retrospective observational study included ventilated infants born preterm with acute fluctuations of continuous end-tidal CO 2 (etCO 2 ) as a surrogate marker for arterial carbon dioxide partial pressure, during the first 72 hours of life. Regional cerebral oxygen saturation and fractional tissue oxygen extraction were monitored with near-infrared spectroscopy. Brain activity was monitored with 2-channel electroencephalography. Spontaneous activity transients (SATs) rate (SATs/minute) and interval between SATs (in seconds) were calculated. Ten-minute periods were selected for analysis: before, during, and after etCO 2 fluctuations of ≥5  mm Hg. Thirty-eight patients (mean ± SD gestational age of 29 ± 1.8 weeks) were included, with 60 episodes of etCO 2 increase and 70 episodes of etCO 2 decrease. During etCO 2 increases, brain oxygenation increased (regional cerebral oxygen saturation increased, fractional tissue oxygen extraction decreased; P < .01) and electrical activity decreased (SATs/minute decreased, interval between SATs increased; P < .01). All measures recovered when etCO 2 returned to baseline. During etCO 2 decreases, brain oxygenation decreased (regional cerebral oxygen saturation decreased, fractional tissue oxygen extraction decreased; P < .01) and brain activity increased (SATs/minute increased, P < .05), also with recovery after return of etCO 2 to baseline. An acute increase in etCO 2 is associated with increased cerebral oxygenation and decreased brain activity, whereas an acute decrease is associated with decreased cerebral oxygenation and slightly increased brain activity. Combining continuous CO 2 monitoring with near-infrared spectroscopy may enable the detection of otherwise undetected fluctuations in arterial carbon dioxide partial pressure that may be harmful to the neonatal brain. Copyright © 2017 Elsevier Inc. All rights reserved.

Multifractal texture estimation for detection and segmentation of brain tumors.

PubMed

Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

2013-11-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

PubMed Central

Islam, Atiq; Reza, Syed M. S.

2016-01-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

Segmentation, feature extraction, and multiclass brain tumor classification.

PubMed

Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

2013-12-01

Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %. PCA-ANN has delivered high accuracy for each class: AS-90.74 %, GBM-88.46 %, MED-85 %, MEN-90.70 %, MET-96.67 %, and NR-93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS-86.15 %, GBM-65.1 %, MED-63.36 %, MEN-91.5 %, MET-65.21 %, and NR-93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

PubMed Central

Wang, Yaping; Nie, Jingxin; Yap, Pew-Thian; Li, Gang; Shi, Feng; Geng, Xiujuan; Guo, Lei; Shen, Dinggang

2014-01-01

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness. PMID:24489639

Systematic review and narrative summary: Treatments for and risk factors associated with respiratory tract secretions (death rattle) in the dying adult.

PubMed

Kolb, Hildegard; Snowden, Austyn; Stevens, Elaine

2018-03-01

To identify effective treatments and risk factors associated with death rattle in adults at the end of life. The presence of noisy, pooled respiratory tract secretions is among the most common symptoms in dying patients around the world. It is unknown if "death rattle" distresses patients, but it can distress relatives and clinicians. Treatments appear unsatisfactory, so prophylaxis would be ideal if possible. Quantitative systematic review and narrative summary following Cochrane Collaboration guidelines. CINAHL, MEDLINE, Health Source Nursing and Web of Science were searched for international literature in any language published from 1993 - 2016 using MeSH headings and iterative interchangeable terms for "death rattle". Randomized controlled trials were appraised using the Cochrane Collaboration's tool for assessing risk of bias. Non-randomized studies were assessed using ROBINS-I tool for assessing risk of bias in non-randomized studies of interventions. Instances of treatment and risk were extracted and relevant key findings extracted in line with Cochrane methods. Five randomized trials and 23 non-randomized studies were analysed. No pharmacological or non-pharmacological treatment was found superior to placebo. There was a weak association between lung or brain metastases and presence of death rattle, but otherwise inconsistent empirical support for a range of potential risk factors. Clinicians have no clear evidence to follow in either treating death rattle or preventing it occurring. However, several risk factors look promising candidates for prospective analysis, so this review concludes with clear recommendations for further research. © 2018 John Wiley & Sons Ltd.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

UBO Detector - A cluster-based, fully automated pipeline for extracting white matter hyperintensities.

PubMed

Jiang, Jiyang; Liu, Tao; Zhu, Wanlin; Koncz, Rebecca; Liu, Hao; Lee, Teresa; Sachdev, Perminder S; Wen, Wei

2018-07-01

We present 'UBO Detector', a cluster-based, fully automated pipeline for extracting and calculating variables for regions of white matter hyperintensities (WMH) (available for download at https://cheba.unsw.edu.au/group/neuroimaging-pipeline). It takes T1-weighted and fluid attenuated inversion recovery (FLAIR) scans as input, and SPM12 and FSL functions are utilised for pre-processing. The candidate clusters are then generated by FMRIB's Automated Segmentation Tool (FAST). A supervised machine learning algorithm, k-nearest neighbor (k-NN), is applied to determine whether the candidate clusters are WMH or non-WMH. UBO Detector generates both image and text (volumes and the number of WMH clusters) outputs for whole brain, periventricular, deep, and lobar WMH, as well as WMH in arterial territories. The computation time for each brain is approximately 15 min. We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI) = 0.848) and volumetric (intraclass correlation coefficient (ICC) = 0.985) agreement between the UBO Detector-derived and manually traced WMH; b) highly correlated (r 2  > 0.9) and a steady increase of WMH volumes over time; and c) significant associations of periventricular (t = 22.591, p < 0.001) and deep (t = 14.523, p < 0.001) WMH volumes generated by UBO Detector with Fazekas rating scores. With parallel computing enabled in UBO Detector, the processing can take advantage of multi-core CPU's that are commonly available on workstations. In conclusion, UBO Detector is a reliable, efficient and fully automated WMH segmentation pipeline. Copyright © 2018 Elsevier Inc. All rights reserved.

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

PubMed Central

Sadeh, Boaz; Yovel, Galit

2014-01-01

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator's coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes. PMID:24893706

Development of Mackintosh Probe Extractor

NASA Astrophysics Data System (ADS)

Rahman, Noor Khazanah A.; Kaamin, Masiri; Suwandi, Amir Khan; Sahat, Suhaila; Jahaya Kesot, Mohd

2016-11-01

Dynamic probing is a continuous soil investigation technique, which is one of the simplest soil penetration test. It basically consist of repeatedly driving a metal tipped probe into the ground using a drop weight of fixed mass and travel. Testing was carried out continuously from ground level to the final penetration depth. Once the soil investigation work done, it is difficult to pull out the probe rod from the ground, due to strong soil structure grip against probe cone and prevent the probe rod out from the ground. Thus, in this case, a tool named Extracting Probe was created to assist in the process of retracting the probe rod from the ground. In addition, Extracting Probe also can reduce the time to extract the probe rod from the ground compare with the conventional method. At the same time, it also can reduce manpower cost because only one worker involve to handle this tool compare with conventional method used two or more workers. From experiment that have been done we found that the time difference between conventional tools and extracting probe is significant, average time difference is 155 minutes. In addition the extracting probe can reduce manpower usage, and also labour cost for operating the tool. With all these advantages makes this tool has the potential to be marketed.

Can we replace curation with information extraction software?

PubMed

Karp, Peter D

2016-01-01

Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL. © The Author(s) 2016. Published by Oxford University Press.

A brief understanding of process optimisation in microwave-assisted extraction of botanical materials: options and opportunities with chemometric tools.

PubMed

Das, Anup Kumar; Mandal, Vivekananda; Mandal, Subhash C

2014-01-01

Extraction forms the very basic step in research on natural products for drug discovery. A poorly optimised and planned extraction methodology can jeopardise the entire mission. To provide a vivid picture of different chemometric tools and planning for process optimisation and method development in extraction of botanical material, with emphasis on microwave-assisted extraction (MAE) of botanical material. A review of studies involving the application of chemometric tools in combination with MAE of botanical materials was undertaken in order to discover what the significant extraction factors were. Optimising a response by fine-tuning those factors, experimental design or statistical design of experiment (DoE), which is a core area of study in chemometrics, was then used for statistical analysis and interpretations. In this review a brief explanation of the different aspects and methodologies related to MAE of botanical materials that were subjected to experimental design, along with some general chemometric tools and the steps involved in the practice of MAE, are presented. A detailed study on various factors and responses involved in the optimisation is also presented. This article will assist in obtaining a better insight into the chemometric strategies of process optimisation and method development, which will in turn improve the decision-making process in selecting influential extraction parameters. Copyright © 2013 John Wiley & Sons, Ltd.

Retractor Tool for Brain Surgery

NASA Technical Reports Server (NTRS)

Helms, R.; Hayes, T.

1982-01-01

Proposed brain-surgery tool has an octogonal fixture for positioning latex tube over incision. Eight stainless-steel wires embedded in latex extend to hold positioning fixture. Another eight are also embedded in the latex. Concentric sleeves are successively inserted into expandable latex tube. The first sleeve is placed over a solid rod. Last sleeve is a stainless-steel tube 1 inch in diameter. It is overcoated with Teflon (or equivalent) material.

Trans3D: a free tool for dynamical visualization of EEG activity transmission in the brain.

PubMed

Blinowski, Grzegorz; Kamiński, Maciej; Wawer, Dariusz

2014-08-01

The problem of functional connectivity in the brain is in the focus of attention nowadays, since it is crucial for understanding information processing in the brain. A large repertoire of measures of connectivity have been devised, some of them being capable of estimating time-varying directed connectivity. Hence, there is a need for a dedicated software tool for visualizing the propagation of electrical activity in the brain. To this aim, the Trans3D application was developed. It is an open access tool based on widely available libraries and supporting both Windows XP/Vista/7(™), Linux and Mac environments. Trans3D can create animations of activity propagation between electrodes/sensors, which can be placed by the user on the scalp/cortex of a 3D model of the head. Various interactive graphic functions for manipulating and visualizing components of the 3D model and input data are available. An application of the Trans3D tool has helped to elucidate the dynamics of the phenomena of information processing in motor and cognitive tasks, which otherwise would have been very difficult to observe. Trans3D is available at: http://www.eeg.pl/. Copyright © 2014 Elsevier Ltd. All rights reserved.

Tool-use: An open window into body representation and its plasticity

PubMed Central

Martel, Marie; Cardinali, Lucilla; Roy, Alice C.; Farnè, Alessandro

2016-01-01

ABSTRACT Over the last decades, scientists have questioned the origin of the exquisite human mastery of tools. Seminal studies in monkeys, healthy participants and brain-damaged patients have primarily focused on the plastic changes that tool-use induces on spatial representations. More recently, we focused on the modifications tool-use must exert on the sensorimotor system and highlighted plastic changes at the level of the body representation used by the brain to control our movements, i.e., the Body Schema. Evidence is emerging for tool-use to affect also more visually and conceptually based representations of the body, such as the Body Image. Here we offer a critical review of the way different tool-use paradigms have been, and should be, used to try disentangling the critical features that are responsible for tool incorporation into different body representations. We will conclude that tool-use may offer a very valuable means to investigate high-order body representations and their plasticity. PMID:27315277

Tool-use: An open window into body representation and its plasticity.

PubMed

Martel, Marie; Cardinali, Lucilla; Roy, Alice C; Farnè, Alessandro

2016-01-01

Over the last decades, scientists have questioned the origin of the exquisite human mastery of tools. Seminal studies in monkeys, healthy participants and brain-damaged patients have primarily focused on the plastic changes that tool-use induces on spatial representations. More recently, we focused on the modifications tool-use must exert on the sensorimotor system and highlighted plastic changes at the level of the body representation used by the brain to control our movements, i.e., the Body Schema. Evidence is emerging for tool-use to affect also more visually and conceptually based representations of the body, such as the Body Image. Here we offer a critical review of the way different tool-use paradigms have been, and should be, used to try disentangling the critical features that are responsible for tool incorporation into different body representations. We will conclude that tool-use may offer a very valuable means to investigate high-order body representations and their plasticity.

Traumatic Brain Injury - Multiple Languages

MedlinePlus

... FAQs Customer Support Health Topics Drugs & Supplements Videos & Tools You Are Here: Home → Multiple Languages → All Health Topics → Traumatic Brain Injury URL of this page: https://medlineplus.gov/ ...

Antidepressant-Like Behavioral, Anatomical, and Biochemical Effects of Petroleum Ether Extract from Maca (Lepidium meyenii) in Mice Exposed to Chronic Unpredictable Mild Stress

PubMed Central

Ai, Zhong; Cheng, Ai-Fang; Yu, Yuan-Tao; Yu, Long-Jiang

2014-01-01

Abstract Maca has been consumed as a medical food in Peru for thousands of years, and exerts anxiolytic and antidepressant effects. Our present study aimed to evaluate the behavior and anatomical and biochemical effects of petroleum ether extract from maca (ME) in the chronic unpredictable mild stress (CUMS) model of depression in mice. Three different doses of maca extract (125, 250, and 500 mg/kg) were orally administrated in the six-week CUMS procedure. Fluoxetine (10 mg/kg) was used as a positive control drug. Maca extract (250 and 500 mg/kg) significantly decreased the duration of immobility time in the tail suspension test. After treatment with maca extract (250 and 500 mg/kg), the granule cell layer in the dentate gyrus appeared thicker. Maca extract (250 and 500 mg/kg) also induced a significant reduction in corticosterone levels in mouse serum. In mouse brain tissue, after six weeks of treatment, noradrenaline and dopamine levels were increased by maca extract, and the activity of reactive oxygen species was significantly inhibited. Serotonin levels were not significantly altered. These results demonstrated that maca extract (250 and 500 mg/kg) showed antidepressant-like effects and was related to the activation of both noradrenergic and dopaminergic systems, as well as attenuation of oxidative stress in mouse brain. PMID:24730393

Antidepressant-like behavioral, anatomical, and biochemical effects of petroleum ether extract from maca (Lepidium meyenii) in mice exposed to chronic unpredictable mild stress.

PubMed

Ai, Zhong; Cheng, Ai-Fang; Yu, Yuan-Tao; Yu, Long-Jiang; Jin, Wenwen

2014-05-01

Maca has been consumed as a medical food in Peru for thousands of years, and exerts anxiolytic and antidepressant effects. Our present study aimed to evaluate the behavior and anatomical and biochemical effects of petroleum ether extract from maca (ME) in the chronic unpredictable mild stress (CUMS) model of depression in mice. Three different doses of maca extract (125, 250, and 500 mg/kg) were orally administrated in the six-week CUMS procedure. Fluoxetine (10 mg/kg) was used as a positive control drug. Maca extract (250 and 500 mg/kg) significantly decreased the duration of immobility time in the tail suspension test. After treatment with maca extract (250 and 500 mg/kg), the granule cell layer in the dentate gyrus appeared thicker. Maca extract (250 and 500 mg/kg) also induced a significant reduction in corticosterone levels in mouse serum. In mouse brain tissue, after six weeks of treatment, noradrenaline and dopamine levels were increased by maca extract, and the activity of reactive oxygen species was significantly inhibited. Serotonin levels were not significantly altered. These results demonstrated that maca extract (250 and 500 mg/kg) showed antidepressant-like effects and was related to the activation of both noradrenergic and dopaminergic systems, as well as attenuation of oxidative stress in mouse brain.

Nootropic potential of Ashwagandha leaves: Beyond traditional root extracts.

PubMed

Wadhwa, Renu; Konar, Arpita; Kaul, Sunil C

2016-05-01

Rapidly increasing aging population and environmental stressors are the two main global concerns of the modern society. These have brought in light rapidly increasing incidence of a variety of pathological conditions including brain tumors, neurodegenerative & neuropsychiatric disorders, and new challenges for their treatment. The overlapping symptoms, complex etiology and lack of full understanding of the brain structure and function to-date further complicate these tasks. On the other hand, several herbal reagents with a long history of their use have been asserted to possess neurodifferentiation, neuroregenerative and neuroprotective potentials, and hence been recommended as supplement to enhance and maintain brain health and function. Although they have been claimed to function by holistic approach resulting in maintaining body homeostasis and brain health, there are not enough laboratory studies in support to these and mechanism(s) of such beneficial activities remain largely undefined. One such herb is Ashwagandha, also called "Queen of Ayurveda" for its popular use in Indian traditional home medicine because of its extensive benefits including anticancer, anti-stress and remedial potential for aging and neurodegenerative pathologies. However, active principles and underlying mechanism(s) of action remain largely unknown. Here we provide a review on the effects of Ashwagandha extracts and active principles, and underlying molecular mechanism(s) for brain pathologies. We highlight our findings on the nootropic potential of Ashwagandha leaves. The effects of Ashwagandha leaf extracts are multidimensional ranging from differentiation of neuroblastoma and glioma cells, reversal of Alzheimer and Parkinson's pathologies, protection against environmental neurotoxins and enhancement of memory. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating individual contribution from group-based structural correlation networks.

PubMed

Saggar, Manish; Hosseini, S M Hadi; Bruno, Jennifer L; Quintin, Eve-Marie; Raman, Mira M; Kesler, Shelli R; Reiss, Allan L

2015-10-15

Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Valeriana wallichii root extract improves sleep quality and modulates brain monoamine level in rats.

PubMed

Sahu, Surajit; Ray, Koushik; Yogendra Kumar, M S; Gupta, Shilpa; Kauser, Hina; Kumar, Sanjeev; Mishra, Kshipra; Panjwani, Usha

2012-07-15

The present study was performed to investigate the effects of Valeriana wallichi (VW) aqueous root extract on sleep-wake profile and level of brain monoamines on Sprague-Dawley rats. Electrodes and transmitters were implanted to record EEG and EMG in freely moving condition and the changes were recorded telemetrically after oral administration of VW in the doses of 100, 200 and 300 mg/kg body weight. Sleep latency was decreased and duration of non-rapid eye movement (NREM) sleep was increased in a dose dependent manner. A significant decrease of sleep latency and duration of wakefulness were observed with VW at doses of 200 and 300 mg/kg. Duration of NREM sleep as well as duration of total sleep was increased significantly after treatment with VW at the doses of 200 and 300 mg/kg. VW also increased EEG slow wave activity during NREM sleep at the doses of 200 and 300 mg/kg. Level of norepinephrine (NE), dopamine (DA), dihydroxyphenylacetic acid (DOPAC), serotonin (5-HT) and hydroxy indole acetic acid (HIAA) were measured in frontal cortex and brain stem after VW treatment at the dose of 200mg/kg. NE and 5HT level were decreased significantly in both frontal cortex and brain stem. DA and HIAA level significantly decreased only in cortex. DOPAC level was not changed in any brain region studied. In conclusion it can be said that VW water extract has a sleep quality improving effect which may be dependent upon levels of monoamines in cortex and brainstem. Copyright © 2012 Elsevier GmbH. All rights reserved.

Synchrotron radiation imaging is a powerful tool to image brain microvasculature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Mengqi; Sun, Danni; Xie, Yuanyuan

2014-03-15

Synchrotron radiation (SR) imaging is a powerful experimental tool for micrometer-scale imaging of microcirculation in vivo. This review discusses recent methodological advances and findings from morphological investigations of cerebral vascular networks during several neurovascular pathologies. In particular, it describes recent developments in SR microangiography for real-time assessment of the brain microvasculature under various pathological conditions in small animal models. It also covers studies that employed SR-based phase-contrast imaging to acquire 3D brain images and provide detailed maps of brain vasculature. In addition, a brief introduction of SR technology and current limitations of SR sources are described in this review. Inmore » the near future, SR imaging could transform into a common and informative imaging modality to resolve subtle details of cerebrovascular function.« less

Synchrotron radiation imaging is a powerful tool to image brain microvasculature.

PubMed

Zhang, Mengqi; Peng, Guanyun; Sun, Danni; Xie, Yuanyuan; Xia, Jian; Long, Hongyu; Hu, Kai; Xiao, Bo

2014-03-01

Synchrotron radiation (SR) imaging is a powerful experimental tool for micrometer-scale imaging of microcirculation in vivo. This review discusses recent methodological advances and findings from morphological investigations of cerebral vascular networks during several neurovascular pathologies. In particular, it describes recent developments in SR microangiography for real-time assessment of the brain microvasculature under various pathological conditions in small animal models. It also covers studies that employed SR-based phase-contrast imaging to acquire 3D brain images and provide detailed maps of brain vasculature. In addition, a brief introduction of SR technology and current limitations of SR sources are described in this review. In the near future, SR imaging could transform into a common and informative imaging modality to resolve subtle details of cerebrovascular function.

Brain-Compatible Assessments. Second Edition

ERIC Educational Resources Information Center

Ronis, Diane L.

2007-01-01

Diane Ronis, a recognized expert in brain-compatible learning and assessment, goes beyond the world of standardized testing to show educators how to build and use targeted assessments based on the latest neuroscientific research. Updated to reflect recent findings about how the brain learns, this book provides readers with revised tools for…

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

On characterizing population commonalities and subject variations in brain networks.

PubMed

Ghanbari, Yasser; Bloy, Luke; Tunc, Birkan; Shankar, Varsha; Roberts, Timothy P L; Edgar, J Christopher; Schultz, Robert T; Verma, Ragini

2017-05-01

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of Thymus vulgaris ethanolic extract on chronic toxoplasmosis in a mouse model.

PubMed

Eraky, Maysa Ahmad; El-Fakahany, Amany Farouk; El-Sayed, Nagwa Mostafa; Abou-Ouf, Eman Abdel-Rahman; Yaseen, Doaa Ibrahim

2016-07-01

The current work was undertaken to investigate the potential effectiveness of Thymus vulgaris ethanolic extract (TVE) against Toxoplasma gondii infection in chronic experimental toxoplasmosis. To evaluate prophylactic effects, mice received 500 mg/kg TVE for 5 days before they were infected by an avirulent Me49 T. gondii strain. To investigate the therapeutic effects of the extract postinfection, daily treatment with TVE was initiated at 6 weeks postinfection and continued for 10 days. The following groups of animals were used as controls: uninfected/non-treated, infected/non-treated, and infected/treated with a combination of pyrimethamine and sulfadiazine. Brain cyst count and histopathological changes using H&E and Feulgen stains were used to evaluate the efficacy of TVE. The mean number of brain cysts was significantly decreased by 24 % in mice treated prophylactically with TVE. TVE also significantly reduced the mean number of brain cysts when administered to animals already chronically infected with T. gondii. The effect of TVE was comparable to that of treatment with a mixture of sulfadiazine and pyrimethamine (46 and 51 % reduction, respectively). Moreover, considerable amelioration of the pathological lesions in the brain and retina was observed. The results demonstrate the potential efficacy of T. vulgaris as a new natural therapeutic and prophylactic agent for use in the treatment of chronic toxoplasmosis.

Transport of cargo from periphery to brain by circulating monocytes.

PubMed

Cintron, Amarallys F; Dalal, Nirjari V; Dooyema, Jeromy; Betarbet, Ranjita; Walker, Lary C

2015-10-05

The misfolding and aggregation of the Aβ peptide - a fundamental event in the pathogenesis of Alzheimer׳s disease - can be instigated in the brains of experimental animals by the intracranial infusion of brain extracts that are rich in aggregated Aβ. Recent experiments have found that the peripheral (intraperitoneal) injection of Aβ seeds induces Aβ deposition in the brains of APP-transgenic mice, largely in the form of cerebral amyloid angiopathy. Macrophage-type cells normally are involved in pathogen neutralization and antigen presentation, but under some circumstances, circulating monocytes have been found to act as vectors for the transport of pathogenic agents such as viruses and prions. The present study assessed the ability of peripheral monocytes to transport Aβ aggregates from the peritoneal cavity to the brain. Our initial experiments showed that intravenously delivered macrophages that had previously ingested fluorescent nanobeads as tracers migrate primarily to peripheral organs such as spleen and liver, but that a small number also reach the brain parenchyma. We next injected CD45.1-expressing monocytes from donor mice intravenously into CD45.2-expressing host mice; after 24h, analysis by fluorescence-activated cell sorting (FACS) and histology confirmed that some CD45.1 monocytes enter the brain, particularly in the superficial cortex and around blood vessels. When the donor monocytes are first exposed to Aβ-rich brain extracts from human AD cases, a subset of intravenously delivered Aβ-containing cells migrate to the brain. These experiments indicate that, in mouse models, circulating monocytes are potential vectors by which exogenously delivered, aggregated Aβ travels from periphery to brain, and more generally support the hypothesis that macrophage-type cells can participate in the dissemination of proteopathic seeds. Copyright © 2015 Elsevier B.V. All rights reserved.

Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

NASA Astrophysics Data System (ADS)

Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

2012-04-01

A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

An easy-to-use liquid chromatography assay for the analysis of lamotrigine in rat plasma and brain samples using microextraction by packed sorbent: Application to a pharmacokinetic study.

PubMed

Ventura, Sandra; Rodrigues, Márcio; Pousinho, Sarah; Falcão, Amílcar; Alves, Gilberto

2016-11-01

A simple and rapid high-performance liquid chromatography method with diode-array detection (HPLC-DAD) using microextraction by packed sorbent (MEPS) during the sample preparation step was developed and validated to quantify lamotrigine (LTG) in rat plasma and brain samples. MEPS variables such as pH, number of draw-eject cycles, and washing and desorption conditions were optimized. The chromatographic resolution of LTG and chloramphenicol, used as internal standard (IS), was accomplished in less than 5min on a C18 column, at 35°C, using an isocratic elution with acetonitrile (13%), methanol (13%) and water-triethylamine (99.7:0.3, v/v; pH 6.0) pumped at a flow rate of 1mL/min. Detection was performed at 215nm. Calibration curves were linear over the range of 0.1-20μg/mL (r 2 ≥0.9947) for LTG in both rat plasma and brain homogenate samples. The intra and interday imprecision did not exceed 8.6% and the intra and interday inaccuracy ranged from -8.1 to 13.5%. LTG was extracted from rat plasma and brain homogenate samples with an average absolute recovery ranging from 68.0 to 86.7%, and its stability was demonstrated in the assayed conditions. No interferences were observed at the retention times of the analyte (LTG) and IS. To the best of our knowledge, this is the first bioanalytical assay that uses MEPS procedure for the determination of LTG not only in rat plasma but also in tissue (brain) samples. This novel method was successfully applied to a preliminary pharmacokinetic study in rats and it seems to be a cost-effective tool to support non-clinical pharmacokinetic-based studies involving LTG treatment. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative Evaluation of Automated Skull-Stripping Methods Applied to Contemporary and Legacy Images: Effects of Diagnosis, Bias Correction, and Slice Location

PubMed Central

Fennema-Notestine, Christine; Ozyurt, I. Burak; Clark, Camellia P.; Morris, Shaunna; Bischoff-Grethe, Amanda; Bondi, Mark W.; Jernigan, Terry L.; Fischl, Bruce; Segonne, Florent; Shattuck, David W.; Leahy, Richard M.; Rex, David E.; Toga, Arthur W.; Zou, Kelly H.; BIRN, Morphometry; Brown, Gregory G.

2008-01-01

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143–155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060–1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41–54; Shattuck et al. [2001] Neuroimage 13:856 – 876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer’s, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies. PMID:15986433

Evidences for the involvement of monoaminergic and GABAergic systems in antidepressant-like activity of garlic extract in mice

PubMed Central

Dhingra, Dinesh; Kumar, Vaibhav

2008-01-01

Objectives: The present study was undertaken to investigate the effect of the ethanolic extract of Allium sativum L. (Family: Lilliaceae), commonly known as garlic, on depression in mice. Materials and Methods: Ethanolic extract of garlic (25, 50 and 100 mg/kg) was administered orally for 14 successive days to young Swiss albino mice of either sex and antidepressant-like activity was evaluated employing tail suspension test (TST) and forced swim test (FST). The efficacy of the extract was compared with standard antidepressant drugs like fluoxetine and imipramine. The mechanism of action of the extract was investigated by co-administration of prazosin (α1-adrenoceptor antagonist), sulpiride (selective D2-receptor antagonist), baclofen (GABAB agonist) and p-CPA (serotonin antagonist) separately with the extract and by studying the effect of the extract on brain MAO-A and MAO-B levels. Results: Garlic extract (25, 50 and 100 mg/kg) significantly decreased immobility time in a dose-dependent manner in both TST and FST, indicating significant antidepressant-like activity. The efficacy of the extract was found to be comparable to fluoxetine (20 mg/kg p.o.) and imipramine (15 mg/kg p.o.) in both TST and FST. The extract did not show any significant effect on the locomotor activity of the mice. Prazosin, sulpiride, baclofen and p-CPA significantly attenuated the extract-induced antidepressant-like effect in TST. Garlic extract (100 mg/kg) administered orally for 14 successive days significantly decreased brain MAO-A and MAO-B levels, as compared to the control group. Conclusion: Garlic extract showed significant antidepressant-like activity probably by inhibiting MAO-A and MAO-B levels and through interaction with adrenergic, dopaminergic, serotonergic and GABAergic systems. PMID:20040952

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

Human brain distinctiveness based on EEG spectral coherence connectivity.

PubMed

Rocca, D La; Campisi, P; Vegso, B; Cserti, P; Kozmann, G; Babiloni, F; Fallani, F De Vico

2014-09-01

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.

Chronic Ethanol Consumption Profoundly Alters Regional Brain Ceramide and Sphingomyelin Content in Rodents

PubMed Central

2015-01-01

Ceramides (CER) are involved in alcohol-induced neuroinflammation. In a mouse model of chronic alcohol exposure, 16 CER and 18 sphingomyelin (SM) concentrations from whole brain lipid extracts were measured using electrospray mass spectrometry. All 18 CER concentrations in alcohol exposed adults increased significantly (range: 25–607%); in juveniles, 6 CER decreased (range: −9 to −37%). In contrast, only three SM decreased in adult and one increased significantly in juvenile. Next, regional identification at 50 μm spatial resolution from coronal sections was obtained with matrix implanted laser desorption/ionization mass spectrometry imaging (MILDI-MSI) by implanting silver nanoparticulate matrices followed by focused laser desorption. Most of the CER and SM quantified in whole brain extracts were detected in MILDI images. Coronal sections from three brain levels show qualitative regional changes in CER-SM ion intensities, as a function of group and brain region, in cortex, striatum, accumbens, habenula, and hippocampus. Highly correlated changes in certain white matter CER-SM pairs occur in regions across all groups, including the hippocampus and the lateral (but not medial) cerebellar cortex of adult mice. Our data provide the first microscale MS evidence of regional lipid intensity variations induced by alcohol. PMID:25387107

Nootropic activity of Celastrus paniculatus seed.

PubMed

Bhanumathy, M; Harish, M S; Shivaprasad, H N; Sushma, G

2010-03-01

The effect of Celastrus paniculatus Willd. (Celastraceae) seed aqueous extract on learning and memory was studied using elevated plus maze and passive avoidance test (sodium nitrite induced amnesia rodent model). The aqueous seed extract was administered orally in two different doses to rats (350 and 1050 mg/kg) and to mice (500 and 1500 mg/kg). The results were compared to piracetam (100 mg/kg, p.o.) used as a standard drug. Chemical hypoxia was induced by subcutaneous administration of sodium nitrite (35 mg/kg), immediately after acquisition training. In elevated plus maze and sodium nitrite-induced amnesia model, Celastrus paniculatus extract has showed statistically significant improvement in memory process when compared to control. The estimation of acetylcholinesterase enzyme in rat brain supports the plus maze and passive avoidance test by reducing acetylcholinesterase activity which helps in memory performance. The study reveals that the aqueous extract of Celastrus paniculatus seed has dose-dependent cholinergic activity, thereby improving memory performance. The mechanism by which Celastrus paniculatus enhances cognition may be due to increased acetylcholine level in rat brain.

Molecular Neuroanatomy: A Generation of Progress

PubMed Central

Pollock, Jonathan D.; Wu, Da-Yu; Satterlee, John

2014-01-01

The neuroscience research landscape has changed dramatically over the past decade. An impressive array of neuroscience tools and technologies have been generated, including brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity and function, cost effective genome sequencing, new technologies enabling genome manipulation, new imaging methods and new tools for mapping neuronal circuits. However, despite these technological advances, several significant scientific challenges must be overcome in the coming decade to enable a better understanding of brain function and to develop next generation cell type-targeted therapeutics to treat brain disorders. For example, we do not have an inventory of the different types of cells that exist in the brain, nor do we know how to molecularly phenotype them. We also lack robust technologies to map connections between cells. This review will provide an overview of some of the tools and technologies neuroscientists are currently using to move the field of molecular neuroanatomy forward and also discuss emerging technologies that may enable neuroscientists to address these critical scientific challenges over the coming decade. PMID:24388609

The Hidden Lives of Nurses' Cognitive Artifacts.

PubMed

Blaz, Jacquelyn W; Doig, Alexa K; Cloyes, Kristin G; Staggers, Nancy

2016-09-07

Standardizing nursing handoffs at shift change is recommended to improve communication, with electronic tools as the primary approach. However, nurses continue to rely on personally created paper-based cognitive artifacts - their "paper brains" - to support handoffs, indicating a deficiency in available electronic versions. The purpose of this qualitative study was to develop a deep understanding of nurses' paper-based cognitive artifacts in the context of a cancer specialty hospital. After completing 73 hours of hospital unit field observations, 13 medical oncology nurses were purposively sampled, shadowed for a single shift and interviewed using a semi-structured technique. An interpretive descriptive study design guided analysis of the data corpus of field notes, transcribed interviews, images of nurses' paper-based cognitive artifacts, and analytic memos. Findings suggest nurses' paper brains are personal, dynamic, living objects that undergo a life cycle during each shift and evolve over the course of a nurse's career. The life cycle has four phases: Creation, Application, Reproduction, and Destruction. Evolution in a nurse's individually styled, paper brain is triggered by a change in the nurse's environment that reshapes cognitive needs. If a paper brain no longer provides cognitive support in the new environment, it is modified into (adapted) or abandoned (made extinct) for a different format that will provide the necessary support. The "hidden lives" - the life cycle and evolution - of paper brains have implications for the design of successful electronic tools to support nursing practice, including handoff. Nurses' paper brains provide cognitive support beyond the context of handoff. Information retrieval during handoff is undoubtedly an important function of nurses' paper brains, but tools designed to standardize handoff communication without accounting for cognitive needs during all phases of the paper brain life cycle or the ability to evolve with changes to those cognitive needs will be underutilized.

Neurodegeneration with brain iron accumulation (NBIA)

MedlinePlus

... FAQs Customer Support Health Topics Drugs & Supplements Videos & Tools Español You Are Here: Home → Medical Encyclopedia → Neurodegeneration with brain iron accumulation (NBIA) URL of this page: //medlineplus. ...

3D Feature Extraction for Unstructured Grids

NASA Technical Reports Server (NTRS)

Silver, Deborah

1996-01-01

Visualization techniques provide tools that help scientists identify observed phenomena in scientific simulation. To be useful, these tools must allow the user to extract regions, classify and visualize them, abstract them for simplified representations, and track their evolution. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This article explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and those from Finite Element Analysis.

(1)H NMR-based metabonomics revealed protective effect of Naodesheng bioactive extract on ischemic stroke rats.

PubMed

Luo, Lan; Zhen, Lifeng; Xu, Yatao; Yang, Yongxia; Feng, Suxiang; Wang, Shumei; Liang, Shengwang

2016-06-20

Stroke is a leading cause of death and disability in the world. However, current therapies are limited. Naodesheng, a widely used traditional Chinese medicine prescription, has shown a good clinical curative effect on ischemic stroke. Also, Naodesheng has been suggested to have neuroprotective effect on focal cerebral ischemia rats, but the underlying molecular mechanism remains unclear. The present study was designed to evaluate the effect of Naodesheng bioactive extract on the metabolic changes in brain tissue, plasma and urine induced by cerebral ischemia perfusion injury, and explore the possible metabolic mechanisms by using a (1)H NMR-based metabonomics approach. A middle cerebral artery occlusion rat model was established and confirmed by the experiments of neurobehavioral abnormality evaluation, brain tissue TTC staining and pathological examination. The metabolic changes in brain tissue, plasma and urine were then assessed by a (1)H NMR technique combined with multivariate statistical analysis method. These NMR data showed that cerebral ischemia reperfusion induced great metabolic disorders in brain tissue, plasma and urine metabolisms. However, Naodesheng bioactive extract could reverse most of the imbalanced metabolites. Meanwhile, it was found that both the medium and high dosages of Naodesheng bioactive extract were more effective on the metabolic changes than the low dosage, consistent with histopathological assessments. These results revealed that Naodesheng had protective effect on ischemic stroke rats and the underlying mechanisms involved multiple metabolic pathways, including energy metabolism, amino acid metabolism, oxidative stress and inflammatory injury. The present study could provide evidence that metabonomics revealed its capacity to evaluate the holistic efficacy of traditional Chinese medicine and explore the underlying mechanisms. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Antidepressant, antioxidant and neurotrophic properties of the standardized extract of Cocos nucifera husk fiber in mice.

PubMed

Lima, Eliane Brito Cortez; de Sousa, Caren Nádia Soares; Vasconcelos, Germana Silva; Meneses, Lucas Nascimento; E Silva Pereira, Yuri Freitas; Ximenes, Naiara Coelho; Santos Júnior, Manuel Alves; Matos, Natália Castelo Branco; Brito, Rayanne; Miron, Diogo; Leal, Luzia Kalyne Almeida Moreira; Macêdo, Danielle; Vasconcelos, Silvânia Maria Mendes

2016-07-01

The plant Cocos nucifera and its derivatives have shown antidepressant-like effects, although its hydroalcoholic extract has not been studied with this end in mind. Therefore, we decided to determine the antidepressant-like effects of the standardized hydroalcoholic extract of Cocos nucifera husk fiber (HECN) as well as oxidative alterations in the prefrontal cortex (PFC), hippocampus (HC) and striatum (ST), and the levels of brain-derived neurotrophic factor (BDNF) in the HC of mice. The extract was characterized based on the content of total polyphenols as well as two phenol compounds-catechin and chlorogenic acid-by HPLC-PDA. Male animals were treated per os (p.o.) for 7 days with distilled water or HECN (50, 100 or 200 mg/kg), or intraperitoneally with vitamin E (Vit E 400 mg/kg). One hour after the last drug administration, the animals were submitted to the open field test, forced swimming test (FST), tail suspension test (TST) and, immediately after the behavioral tests, had their brain removed for neurochemical determinations. The results showed that HECN100 decreased the immobility time in the FST and TST presenting, thus demonstrating an antidepressant-like effect. The administration of HECN decreased malondialdehyde levels in all doses and brain areas studied with the exception of HECN50 in the HC. The administration of HECN also decreased nitrite levels in all doses and brain regions studied. HECN100 also increased the levels of BDNF in HC of mice. In conclusion, we demonstrated that HECN has antidepressant-like properties, probably based on its antioxidant and neurotrophic effects, and is thus relevant for the treatment of depression.

Location of core diagnostic information across various sequences in brain MRI and implications for efficiency of MRI scanner utilization.

PubMed

Sharma, Aseem; Chatterjee, Arindam; Goyal, Manu; Parsons, Matthew S; Bartel, Seth

2015-04-01

Targeting redundancy within MRI can improve its cost-effective utilization. We sought to quantify potential redundancy in our brain MRI protocols. In this retrospective review, we aggregated 207 consecutive adults who underwent brain MRI and reviewed their medical records to document clinical indication, core diagnostic information provided by MRI, and its clinical impact. Contributory imaging abnormalities constituted positive core diagnostic information whereas absence of imaging abnormalities constituted negative core diagnostic information. The senior author selected core sequences deemed sufficient for extraction of core diagnostic information. For validating core sequences selection, four readers assessed the relative ease of extracting core diagnostic information from the core sequences. Potential redundancy was calculated by comparing the average number of core sequences to the average number of sequences obtained. Scanning had been performed using 9.4±2.8 sequences over 37.3±12.3 minutes. Core diagnostic information was deemed extractable from 2.1±1.1 core sequences, with an assumed scanning time of 8.6±4.8 minutes, reflecting a potential redundancy of 74.5%±19.1%. Potential redundancy was least in scans obtained for treatment planning (14.9%±25.7%) and highest in scans obtained for follow-up of benign diseases (81.4%±12.6%). In 97.4% of cases, all four readers considered core diagnostic information to be either easily extractable from core sequences or the ease to be equivalent to that from the entire study. With only one MRI lacking clinical impact (0.48%), overutilization did not seem to contribute to potential redundancy. High potential redundancy that can be targeted for more efficient scanner utilization exists in brain MRI protocols.

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.

Effects of traditionally used anxiolytic botanicals on enzymes of the gamma-aminobutyric acid (GABA) system.

PubMed

Awad, R; Levac, D; Cybulska, P; Merali, Z; Trudeau, V L; Arnason, J T

2007-09-01

In Canada, the use of botanical natural health products (NHPs) for anxiety disorders is on the rise, and a critical evaluation of their safety and efficacy is required. The purpose of this study was to determine whether commercially available botanicals directly affect the primary brain enzymes responsible for gamma-aminobutyric acid (GABA) metabolism. Anxiolytic plants may interact with either glutamic acid decarboxylase (GAD) or GABA transaminase (GABA-T) and ultimately influence brain GABA levels and neurotransmission. Two in vitro rat brain homogenate assays were developed to determine the inhibitory concentrations (IC50) of aqueous and ethanolic plant extracts. Approximately 70% of all extracts that were tested showed little or no inhibitory effect (IC50 values greater than 1 mg/mL) and are therefore unlikely to affect GABA metabolism as tested. The aqueous extract of Melissa officinalis (lemon balm) exhibited the greatest inhibition of GABA-T activity (IC50 = 0.35 mg/mL). Extracts from Centella asiatica (gotu kola) and Valeriana officinalis (valerian) stimulated GAD activity by over 40% at a dose of 1 mg/mL. On the other hand, both Matricaria recutita (German chamomile) and Humulus lupulus (hops) showed significant inhibition of GAD activity (0.11-0.65 mg/mL). Several of these species may therefore warrant further pharmacological investigation. The relation between enzyme activity and possible in vivo mode of action is discussed.

Tiliacora triandra, an Anti-Intoxication Plant, Improves Memory Impairment, Neurodegeneration, Cholinergic Function, and Oxidative Stress in Hippocampus of Ethanol Dependence Rats.

PubMed

Phunchago, Nattaporn; Wattanathorn, Jintanaporn; Chaisiwamongkol, Kowit

2015-01-01

Oxidative stress plays an important role in brain dysfunctions induced by alcohol. Since less therapeutic agent against cognitive deficit and brain damage induced by chronic alcohol consumption is less available, we aimed to assess the effect of Tiliacora triandra extract, a plant possessing antioxidant activity, on memory impairment, neuron density, cholinergic function, and oxidative stress in hippocampus of alcoholic rats. Male Wistar rats were induced ethanol dependence condition by semivoluntary intake of alcohol for 15 weeks. Alcoholic rats were orally given T. triandra at doses of 100, 200, and 400 mg·kg(-1)BW for 14 days. Memory assessment was performed every 7 days while neuron density, activities of AChE, SOD, CAT, and GSH-Px and, MDA level in hippocampus were assessed at the end of study. Interestingly, the extract mitigated the increased escape latency, AChE and MDA level. The extract also mitigated the decreased retention time, SOD, CAT, and GSH-Px activities, and neurons density in hippocampus induced by alcohol. These data suggested that the extract improved memory deficit in alcoholic rats partly via the decreased oxidative stress and the suppression of AChE. Therefore, T. triandra is the potential reagent for treating brain dysfunction induced by alcohol. However, further researches are necessary to understand the detail mechanism and possible active ingredient.

Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges

PubMed Central

Mahmud, Mufti; Vassanelli, Stefano

2016-01-01

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data. PMID:27313507

The braingraph.org database of high resolution structural connectomes and the brain graph tools.

PubMed

Kerepesi, Csaba; Szalkai, Balázs; Varga, Bálint; Grolmusz, Vince

2017-10-01

Based on the data of the NIH-funded Human Connectome Project, we have computed structural connectomes of 426 human subjects in five different resolutions of 83, 129, 234, 463 and 1015 nodes and several edge weights. The graphs are given in anatomically annotated GraphML format that facilitates better further processing and visualization. For 96 subjects, the anatomically classified sub-graphs can also be accessed, formed from the vertices corresponding to distinct lobes or even smaller regions of interests of the brain. For example, one can easily download and study the connectomes, restricted to the frontal lobes or just to the left precuneus of 96 subjects using the data. Partially directed connectomes of 423 subjects are also available for download. We also present a GitHub-deposited set of tools, called the Brain Graph Tools, for several processing tasks of the connectomes on the site http://braingraph.org.

Brain Dynamics Sustaining Rapid Rule Extraction from Speech

ERIC Educational Resources Information Center

de Diego-Balaguer, Ruth; Fuentemilla, Lluis; Rodriguez-Fornells, Antoni

2011-01-01

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization…