Spontaneous brain activity predicts learning ability of foreign sounds.

PubMed

Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

2013-05-29

Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

Seizures, refractory status epilepticus, and depolarization block as endogenous brain activities

NASA Astrophysics Data System (ADS)

El Houssaini, Kenza; Ivanov, Anton I.; Bernard, Christophe; Jirsa, Viktor K.

2015-01-01

Epilepsy, refractory status epilepticus, and depolarization block are pathological brain activities whose mechanisms are poorly understood. Using a generic mathematical model of seizure activity, we show that these activities coexist under certain conditions spanning the range of possible brain activities. We perform a detailed bifurcation analysis and predict strategies to escape from some of the pathological states. Experimental results using rodent data provide support of the model, highlighting the concept that these pathological activities belong to the endogenous repertoire of brain activities.

Intrinsic resting-state activity predicts working memory brain activation and behavioral performance.

PubMed

Zou, Qihong; Ross, Thomas J; Gu, Hong; Geng, Xiujuan; Zuo, Xi-Nian; Hong, L Elliot; Gao, Jia-Hong; Stein, Elliot A; Zang, Yu-Feng; Yang, Yihong

2013-12-01

Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance. Copyright © 2012 Wiley Periodicals, Inc.

Top-Down Predictions in the Cognitive Brain

ERIC Educational Resources Information Center

Kveraga, Kestutis; Ghuman, Avniel S.; Bar, Moshe

2007-01-01

The human brain is not a passive organ simply waiting to be activated by external stimuli. Instead, we propose that the brain continuously employs memory of past experiences to interpret sensory information and predict the immediately relevant future. The basic elements of this proposal include analogical mapping, associative representations and…

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.

Anterior cingulate taste activation predicts ad libitum intake of sweet and savory drinks in healthy, normal-weight men.

PubMed

Spetter, Maartje S; de Graaf, Cees; Viergever, Max A; Smeets, Paul A M

2012-04-01

After food consumption, the motivation to eat (wanting) decreases and associated brain reward responses change. Wanting-related brain responses and how these are affected by consumption of specific foods are ill documented. Moreover, the predictive value of food-induced brain responses for subsequent consumption has not been assessed. We aimed to determine the effects of consumption of sweet and savory foods on taste activation in the brain and to assess how far taste activation can predict subsequent ad libitum intake. Fifteen healthy men (age: 27 ± 2 y, BMI: 22.0 ± 1.5 kg/m2) participated in a randomized crossover trial. After a >3-h fast, participants were scanned with the use of functional MRI before and after consumption of a sweet or savory preload (0.35 L fruit or tomato juice) on two occasions. After the scans, the preload juice was consumed ad libitum. During scanning, participants tasted the juices and rated their pleasantness. Striatal taste activation decreased after juice consumption, independent of pleasantness. Sweet and savory taste activation were not differentially affected by consumption. Anterior cingulate taste activation predicted subsequent ad libitum intake of sweet (r = -0.78; P < 0.001(uncorrected)) as well as savory juice (r = -0.70; P < 0.001(uncorrected)). In conclusion, we showed how taste activation of brain reward areas changes following food consumption. These changes may be associated with the food's physiological relevance. Further, the results suggest that anterior cingulate taste activation reflects food-specific satiety. This extends our understanding of the representation of food specific-appetite in the brain and shows that neuroimaging may provide objective and more accurate measures of food motivation than self-report measures.

From Vivaldi to Beatles and back: predicting lateralized brain responses to music.

PubMed

Alluri, Vinoo; Toiviainen, Petri; Lund, Torben E; Wallentin, Mikkel; Vuust, Peter; Nandi, Asoke K; Ristaniemi, Tapani; Brattico, Elvira

2013-12-01

We aimed at predicting the temporal evolution of brain activity in naturalistic music listening conditions using a combination of neuroimaging and acoustic feature extraction. Participants were scanned using functional Magnetic Resonance Imaging (fMRI) while listening to two musical medleys, including pieces from various genres with and without lyrics. Regression models were built to predict voxel-wise brain activations which were then tested in a cross-validation setting in order to evaluate the robustness of the hence created models across stimuli. To further assess the generalizability of the models we extended the cross-validation procedure by including another dataset, which comprised continuous fMRI responses of musically trained participants to an Argentinean tango. Individual models for the two musical medleys revealed that activations in several areas in the brain belonging to the auditory, limbic, and motor regions could be predicted. Notably, activations in the medial orbitofrontal region and the anterior cingulate cortex, relevant for self-referential appraisal and aesthetic judgments, could be predicted successfully. Cross-validation across musical stimuli and participant pools helped identify a region of the right superior temporal gyrus, encompassing the planum polare and the Heschl's gyrus, as the core structure that processed complex acoustic features of musical pieces from various genres, with or without lyrics. Models based on purely instrumental music were able to predict activation in the bilateral auditory cortices, parietal, somatosensory, and left hemispheric primary and supplementary motor areas. The presence of lyrics on the other hand weakened the prediction of activations in the left superior temporal gyrus. Our results suggest spontaneous emotion-related processing during naturalistic listening to music and provide supportive evidence for the hemispheric specialization for categorical sounds with realistic stimuli. We herewith introduce a powerful means to predict brain responses to music, speech, or soundscapes across a large variety of contexts. © 2013.

Predicting risky choices from brain activity patterns

PubMed Central

Helfinstein, Sarah M.; Schonberg, Tom; Congdon, Eliza; Karlsgodt, Katherine H.; Mumford, Jeanette A.; Sabb, Fred W.; Cannon, Tyrone D.; London, Edythe D.; Bilder, Robert M.; Poldrack, Russell A.

2014-01-01

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights. PMID:24550270

Individual differences in intrinsic brain connectivity predict decision strategy.

PubMed

Barnes, Kelly Anne; Anderson, Kevin M; Plitt, Mark; Martin, Alex

2014-10-15

When humans are provided with ample time to make a decision, individual differences in strategy emerge. Using an adaptation of a well-studied decision making paradigm, motion direction discrimination, we probed the neural basis of individual differences in strategy. We tested whether strategies emerged from moment-to-moment reconfiguration of functional brain networks involved in decision making with task-evoked functional MRI (fMRI) and whether intrinsic properties of functional brain networks, measured at rest with functional connectivity MRI (fcMRI), were associated with strategy use. We found that human participants reliably selected one of two strategies across 2 days of task performance, either continuously accumulating evidence or waiting for task difficulty to decrease. Individual differences in decision strategy were predicted both by the degree of task-evoked activation of decision-related brain regions and by the strength of pretask correlated spontaneous brain activity. These results suggest that spontaneous brain activity constrains strategy selection on perceptual decisions.

Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction.

PubMed

Ding, Zhongxiang; Zhang, Han; Lv, Xiao-Fei; Xie, Fei; Liu, Lizhi; Qiu, Shijun; Li, Li; Shen, Dinggang

2018-01-01

Radiation therapy, a major method of treatment for brain cancer, may cause severe brain injuries after many years. We used a rare and unique cohort of nasopharyngeal carcinoma patients with normal-appearing brains to study possible early irradiation injury in its presymptomatic phase before severe, irreversible necrosis happens. The aim is to detect any structural or functional imaging biomarker that is sensitive to early irradiation injury, and to understand the recovery and progression of irradiation injury that can shed light on outcome prediction for early clinical intervention. We found an acute increase in local brain activity that is followed by extensive reductions in such activity in the temporal lobe and significant loss of functional connectivity in a distributed, large-scale, high-level cognitive function-related brain network. Intriguingly, these radiosensitive functional alterations were found to be fully or partially recoverable. In contrast, progressive late disruptions to the integrity of the related far-end white matter structure began to be significant after one year. Importantly, early increased local brain functional activity was predictive of severe later temporal lobe necrosis. Based on these findings, we proposed a dynamic, multifactorial model for radiation injury and another preventive model for timely clinical intervention. Hum Brain Mapp 39:407-427, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

What does brain response to neutral faces tell us about major depression? evidence from machine learning and fMRI.

PubMed

Oliveira, Leticia; Ladouceur, Cecile D; Phillips, Mary L; Brammer, Michael; Mourao-Miranda, Janaina

2013-01-01

A considerable number of previous studies have shown abnormalities in the processing of emotional faces in major depression. Fewer studies, however, have focused specifically on abnormal processing of neutral faces despite evidence that depressed patients are slow and less accurate at recognizing neutral expressions in comparison with healthy controls. The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients. TWO INDEPENDENT DEPRESSED SAMPLES: (1) Nineteen medication-free patients with depression and 19 healthy volunteers and (2) Eighteen depressed individuals and 18 age and gender-ratio-matched healthy volunteers viewed emotional faces (sad/neutral; happy/neutral) during an fMRI experiment. We used a new pattern recognition framework: first, we trained the classifier to discriminate between two brain states (e.g. viewing happy faces vs. viewing neutral faces) using data only from healthy controls (HC). Second, we tested the classifier using patterns of brain activation of a patient and a healthy control for the same stimuli. Finally, we tested if the classifier's predictions (predictive probabilities) for emotional and neutral face classification were different for healthy controls and depressed patients. Predictive probabilities to patterns of brain activation to neutral faces in both groups of patients were significantly lower in comparison to the healthy controls. This difference was specific to neutral faces. There were no significant differences in predictive probabilities to patterns of brain activation to sad faces (sample 1) and happy faces (samples 2) between depressed patients and healthy controls. Our results suggest that the pattern of brain activation to neutral faces in depressed patients is not consistent with the pattern observed in healthy controls subject to the same stimuli. This difference in brain activation might underlie the behavioural misinterpretation of the neutral faces content by the depressed patients.

Prestimulus brain activity predicts primacy in list learning

PubMed Central

Galli, Giulia; Choy, Tsee Leng; Otten, Leun J.

2012-01-01

Brain activity immediately before an event can predict whether the event will later be remembered. This indicates that memory formation is influenced by anticipatory mechanisms engaged ahead of stimulus presentation. Here, we asked whether anticipatory processes affect the learning of short word lists, and whether such activity varies as a function of serial position. Participants memorized lists of intermixed visual and auditory words with either an elaborative or rote rehearsal strategy. At the end of each list, a distraction task was performed followed by free recall. Recall performance was better for words in initial list positions and following elaborative rehearsal. Electrical brain activity before auditory words predicted later recall in the elaborative rehearsal condition. Crucially, anticipatory activity only affected recall when words occurred in initial list positions. This indicates that anticipatory processes, possibly related to general semantic preparation, contribute to primacy effects. PMID:22888370

Brain State Is a Major Factor in Preseizure Hippocampal Network Activity and Influences Success of Seizure Intervention

PubMed Central

Ewell, Laura A.; Liang, Liang; Armstrong, Caren; Soltész, Ivan; Leutgeb, Stefan

2015-01-01

Neural dynamics preceding seizures are of interest because they may shed light on mechanisms of seizure generation and could be predictive. In healthy animals, hippocampal network activity is shaped by behavioral brain state and, in epilepsy, seizures selectively emerge during specific brain states. To determine the degree to which changes in network dynamics before seizure are pathological or reflect ongoing fluctuations in brain state, dorsal hippocampal neurons were recorded during spontaneous seizures in a rat model of temporal lobe epilepsy. Seizures emerged from all brain states, but with a greater likelihood after REM sleep, potentially due to an observed increase in baseline excitability during periods of REM compared with other brains states also characterized by sustained theta oscillations. When comparing the firing patterns of the same neurons across brain states associated with and without seizures, activity dynamics before seizures followed patterns typical of the ongoing brain state, or brain state transitions, and did not differ until the onset of the electrographic seizure. Next, we tested whether disparate activity patterns during distinct brain states would influence the effectiveness of optogenetic curtailment of hippocampal seizures in a mouse model of temporal lobe epilepsy. Optogenetic curtailment was significantly more effective for seizures preceded by non-theta states compared with seizures that emerged from theta states. Our results indicate that consideration of behavioral brain state preceding a seizure is important for the appropriate interpretation of network dynamics leading up to a seizure and for designing effective seizure intervention. SIGNIFICANCE STATEMENT Hippocampal single-unit activity is strongly shaped by behavioral brain state, yet this relationship has been largely ignored when studying activity dynamics before spontaneous seizures in medial temporal lobe epilepsy. In light of the increased attention on using single-unit activity for the prediction of seizure onset and closed-loop seizure intervention, we show a need for monitoring brain state to interpret correctly whether changes in neural activity before seizure onset is pathological or normal. Moreover, we also find that the brain state preceding a seizure determines the success of therapeutic interventions to curtail seizure duration. Together, these findings suggest that seizure prediction and intervention will be more successful if tailored for the specific brain states from which seizures emerge. PMID:26609157

Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals.

PubMed

Kauppi, Jukka-Pekka; Kandemir, Melih; Saarinen, Veli-Matti; Hirvenkari, Lotta; Parkkonen, Lauri; Klami, Arto; Hari, Riitta; Kaski, Samuel

2015-05-15

We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements. Copyright © 2015 Elsevier Inc. All rights reserved.

Distributed Patterns of Reactivation Predict Vividness of Recollection.

PubMed

St-Laurent, Marie; Abdi, Hervé; Buchsbaum, Bradley R

2015-10-01

According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection.

fMRI Reactivity to High-Calorie Food Pictures Predicts Short- and Long-Term Outcome in a Weight-Loss Program

PubMed Central

Murdaugh, Donna L.; Cox, James E.; Cook, Edwin W.; Weller, Rosalyn E.

2011-01-01

Behavioral studies have suggested that food cues have stronger motivating effects in obese than in normal-weight individuals, which may be a risk factor underlying obesity. Previous cross-sectional neuroimaging studies have suggested that this difference is mediated by increased reactivity to food cues in parts of the reward system in obese individuals. To date, however, only a few prospective neuroimaging studies have been conducted to examine whether individual differences in brain activation elicited by food cues can predict differences in weight change. We used functional magnetic resonance imaging (fMRI) to investigate activation in reward-system as well as other brain regions in response to viewing high-calorie food vs. control pictures in 25 obese individuals before and after a 12-week psychosocial weight-loss treatment and at 9-mo follow-up. In those obese individuals who were least successful in losing weight during the treatment, we found greater pre-treatment activation to high-calorie food vs. control pictures in brain regions implicated in reward-system processes, such as the nucleus accumbens, anterior cingulate, and insula. We found similar correlations with weight loss in brain regions implicated by other studies in vision and attention, such as superior occipital cortex, inferior and superior parietal lobule, and prefrontal cortex. Furthermore, less successful weight maintenance at 9-mo follow-up was predicted by greater post-treatment activation in such brain regions as insula, ventral tegmental area, putamen, and fusiform gyrus. In summary, we found that greater activation in brain regions mediating motivational and attentional salience of food cues in obese individuals at the start of a weight-loss program was predictive of less success in the program and that such activation following the program predicted poorer weight control over a 9-mo follow-up period. PMID:22332246

Resting lateralized activity predicts the cortical response and appraisal of emotions: an fNIRS study.

PubMed

Balconi, Michela; Grippa, Elisabetta; Vanutelli, Maria Elide

2015-12-01

This study explored the effect of lateralized left-right resting brain activity on prefrontal cortical responsiveness to emotional cues and on the explicit appraisal (stimulus evaluation) of emotions based on their valence. Indeed subjective responses to different emotional stimuli should be predicted by brain resting activity and should be lateralized and valence-related (positive vs negative valence). A hemodynamic measure was considered (functional near-infrared spectroscopy). Indeed hemodynamic resting activity and brain response to emotional cues were registered when subjects (N = 19) viewed emotional positive vs negative stimuli (IAPS). Lateralized index response during resting state, LI (lateralized index) during emotional processing and self-assessment manikin rating were considered. Regression analysis showed the significant predictive effect of resting activity (more left or right lateralized) on both brain response and appraisal of emotional cues based on stimuli valence. Moreover, significant effects were found as a function of valence (more right response to negative stimuli; more left response to positive stimuli) during emotion processing. Therefore, resting state may be considered a predictive marker of the successive cortical responsiveness to emotions. The significance of resting condition for emotional behavior was discussed. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving.

PubMed

Garcia, Javier O; Brooks, Justin; Kerick, Scott; Johnson, Tony; Mullen, Tim R; Vettel, Jean M

2017-04-15

Conventional neuroimaging analyses have ascribed function to particular brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity as measured with EEG and use a methodology to estimate directionality in brain-behavior interactions. After source reconstruction, activity within specific frequency bands (delta: 2-3Hz; theta: 4-7Hz; alpha: 8-12Hz; beta: 13-25Hz) in a priori regions of interest was linked to continuous behavioral measurements, and we used a predictive filtering scheme to estimate the asymmetry between brain-to-behavior and behavior-to-brain prediction using a variant of Granger causality. We applied this approach to a simulated driving task and examined directed relationships between brain activity and continuous driving performance (steering behavior or vehicle heading error). Our results indicated that two neuro-behavioral states may be explored with this methodology: a Proactive brain state that actively plans the response to the sensory information and is characterized by delta-beta activity, and a Reactive brain state that processes incoming information and reacts to environmental statistics primarily within the alpha band. Published by Elsevier Inc.

A balance of activity in brain control and reward systems predicts self-regulatory outcomes

PubMed Central

Chen, Pin-Hao A.; Huckins, Jeremy F.; Hofmann, Wilhelm; Kelley, William M.; Heatherton, Todd F.

2017-01-01

Abstract Previous neuroimaging work has shown that increased reward-related activity following exposure to food cues is predictive of self-control failure. The balance model suggests that self-regulation failures result from an imbalance in reward and executive control mechanisms. However, an open question is whether the relative balance of activity in brain systems associated with executive control (vs reward) supports self-regulatory outcomes when people encounter tempting cues in daily life. Sixty-nine chronic dieters, a population known for frequent lapses in self-control, completed a food cue-reactivity task during an fMRI scanning session, followed by a weeklong sampling of daily eating behaviors via ecological momentary assessment. We related participants’ food cue activity in brain systems associated with executive control and reward to real-world eating patterns. Specifically, a balance score representing the amount of activity in brain regions associated with self-regulatory control, relative to automatic reward-related activity, predicted dieters’ control over their eating behavior during the following week. This balance measure may reflect individual self-control capacity and be useful for examining self-regulation success in other domains and populations. PMID:28158874

A balance of activity in brain control and reward systems predicts self-regulatory outcomes.

PubMed

Lopez, Richard B; Chen, Pin-Hao A; Huckins, Jeremy F; Hofmann, Wilhelm; Kelley, William M; Heatherton, Todd F

2017-05-01

Previous neuroimaging work has shown that increased reward-related activity following exposure to food cues is predictive of self-control failure. The balance model suggests that self-regulation failures result from an imbalance in reward and executive control mechanisms. However, an open question is whether the relative balance of activity in brain systems associated with executive control (vs reward) supports self-regulatory outcomes when people encounter tempting cues in daily life. Sixty-nine chronic dieters, a population known for frequent lapses in self-control, completed a food cue-reactivity task during an fMRI scanning session, followed by a weeklong sampling of daily eating behaviors via ecological momentary assessment. We related participants' food cue activity in brain systems associated with executive control and reward to real-world eating patterns. Specifically, a balance score representing the amount of activity in brain regions associated with self-regulatory control, relative to automatic reward-related activity, predicted dieters' control over their eating behavior during the following week. This balance measure may reflect individual self-control capacity and be useful for examining self-regulation success in other domains and populations. © The Author (2017). Published by Oxford University Press.

Functional brain imaging predicts public health campaign success

PubMed Central

O’Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

2016-01-01

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a ‘self-localizer’ defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400 000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R2 up to 0.65) and (ii) this relationship depends on message content—self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns. PMID:26400858

Top-down predictions in the cognitive brain

PubMed Central

Kveraga, Kestutis; Ghuman, Avniel S.; Bar, Moshe

2007-01-01

The human brain is not a passive organ simply waiting to be activated by external stimuli. Instead, it is proposed tat the brain continuously employs memory of past experiences to interpret sensory information and predict the immediately relevant future. This review concentrates on visual recognition as the model system for developing and testing ideas about the role and mechanisms of top-down predictions in the brain. We cover relevant behavioral, computational and neural aspects. These ideas are then extended to other domains. The basic elements of this proposal include analogical mapping, associative representations and the generation of predictions. Connections to a host of cognitive processes will be made and implications to several mental disorders will be proposed. PMID:17923222

Imagine All the People: How the Brain Creates and Uses Personality Models to Predict Behavior

PubMed Central

Hassabis, Demis; Spreng, R. Nathan; Rusu, Andrei A.; Robbins, Clifford A.; Mar, Raymond A.; Schacter, Daniel L.

2014-01-01

The behaviors of other people are often central to envisioning the future. The ability to accurately predict the thoughts and actions of others is essential for successful social interactions, with far-reaching consequences. Despite its importance, little is known about how the brain represents people in order to predict behavior. In this functional magnetic resonance imaging study, participants learned the unique personality of 4 protagonists and imagined how each would behave in different scenarios. The protagonists' personalities were composed of 2 traits: Agreeableness and Extraversion. Which protagonist was being imagined was accurately inferred based solely on activity patterns in the medial prefrontal cortex using multivariate pattern classification, providing novel evidence that brain activity can reveal whom someone is thinking about. Lateral temporal and posterior cingulate cortex discriminated between different degrees of agreeableness and extraversion, respectively. Functional connectivity analysis confirmed that regions associated with trait-processing and individual identities were functionally coupled. Activity during the imagination task, and revealed by functional connectivity, was consistent with the default network. Our results suggest that distinct regions code for personality traits, and that the brain combines these traits to represent individuals. The brain then uses this “personality model” to predict the behavior of others in novel situations. PMID:23463340

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

PubMed

Just, Marcel Adam; Wang, Jing; Cherkassky, Vladimir L

2017-08-15

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy. The two core components implemented in the model that reflect the theory were the choice of intermediate semantic features and the brain regions associated with the neurosemantic dimensions. This approach also predicts the neural representation of object nouns across participants, studies, and sentence contexts. Moreover, we find that the neural representation of an agent-verb-object proto-sentence is more accurately characterized by the neural signatures of its components as they occur in a similar context than by the neural signatures of these components as they occur in isolation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

PubMed Central

Wager, Tor D.; Atlas, Lauren Y.; Leotti, Lauren A.; Rilling, James K.

2012-01-01

Recent studies have identified brain correlates of placebo analgesia, but none have assessed how accurately patterns of brain activity can predict individual differences in placebo responses. We reanalyzed data from two fMRI studies of placebo analgesia (N = 47), using patterns of fMRI activity during the anticipation and experience of pain to predict new subjects’ scores on placebo analgesia and placebo-induced changes in pain processing. We used a cross-validated regression procedure, LASSO-PCR, which provided both unbiased estimates of predictive accuracy and interpretable maps of which regions are most important for prediction. Increased anticipatory activity in a frontoparietal network and decreases in a posterior insular/temporal network predicted placebo analgesia. Patterns of anticipatory activity across the cortex predicted a moderate amount of variance in the placebo response (~12% overall, ~40% for study 2 alone), which is substantial considering the multiple likely contributing factors. The most predictive regions were those associated with emotional appraisal, rather than cognitive control or pain processing. During pain, decreases in limbic and paralimbic regions most strongly predicted placebo analgesia. Responses within canonical pain-processing regions explained significant variance in placebo analgesia, but the pattern of effects was inconsistent with widespread decreases in nociceptive processing. Together, the findings suggest that engagement of emotional appraisal circuits drives individual variation in placebo analgesia, rather than early suppression of nociceptive processing. This approach provides a framework that will allow prediction accuracy to increase as new studies provide more precise information for future predictive models. PMID:21228154

Correlation of tissue concentrations of the pyrethroid bifenthrin with neurotoxicity in the rat.

PubMed

Scollon, Edward J; Starr, James M; Crofton, Kevin M; Wolansky, Marcelo J; DeVito, Michael J; Hughes, Michael F

2011-11-28

The potential for human exposure to pyrethroid pesticides has prompted pharmacodynamic and pharmacokinetic research to better characterize risk. This work tested the hypothesis that blood and brain concentrations of the pyrethroid bifenthrin are predictive of neurotoxic effects. Adult male Long Evans rats received a single oral dose of bifenthrin dissolved in corn oil. Using figure-eight mazes, motor activity was measured for 1h at 4- and 7-h following exposure to bifenthrin (0-16mg/kg or 0-9mg/kg, respectively; n=4-8/group). Whole blood and brains were collected immediately following motor activity assays. Bifenthrin concentrations in blood and brain were quantified using HPLC/MS/MS. Bifenthrin exposure decreased motor activity from 20% to 70% in a dose-dependent manner at both time points. The relationship between motor activity data and administered dose, and blood and brain bifenthrin concentrations were described using a sigmoidal E(max) model. The relationships between motor activity and administered dose or blood concentrations were different between the 4- and 7-h time points. The relationship between motor activity and brain concentration was not significantly different between the two time points. These data suggest that momentary brain concentration of bifenthrin may be a more precise dose metric for predicting behavioral effects because the relationship between brain concentration and locomotor activity is independent of the time of exposure. Published by Elsevier Ireland Ltd.

Effects of age of acquisition on brain activation during Chinese character recognition.

PubMed

Weekes, Brendan Stuart; Chan, Alice H D; Tan, Li Hai

2008-01-01

The age of acquisition of a word (AoA) has a specific effect on brain activation during word identification in English and German. However, the neural locus of AoA effects differs across studies. According to Hernandez and Fiebach [Hernandez, A., & Fiebach, C. (2006). The brain bases of reading late-learned words: Evidence from functional MRI. Visual Cognition, 13(8), 1027-1043], the effects of AoA on brain activation depend on the predictability of the connections between input (orthography) and output (phonology) in a lexical network. We tested this hypothesis by examining AoA effects in a non-alphabetic script with relatively arbitrary mappings between orthography and phonology--Chinese. Our results showed that the effects of AoA in Chinese speakers are located in brain regions that are spatially distinctive including the bilateral middle temporal gyrus and the left inferior parietal cortex. An additional finding was that word frequency had an independent effect on brain activation in the right middle occipital gyrus only. We conclude that spatially distinctive effects of AoA on neural activity depend on the predictability of the mappings between orthography and phonology and reflect a division of labour towards greater lexical-semantic retrieval in non-alphabetic scripts.

Constructing realistic engrams: poststimulus activity of hippocampus and dorsal striatum predicts subsequent episodic memory.

PubMed

Ben-Yakov, Aya; Dudai, Yadin

2011-06-15

Encoding of real-life episodic memory commonly involves integration of information as the episode unfolds. Offline processing immediately following event offset is expected to play a role in encoding the episode into memory. In this study, we examined whether distinct human brain activity time-locked to the offset of short narrative audiovisual episodes could predict subsequent memory for the gist of the episodes. We found that a set of brain regions, most prominently the bilateral hippocampus and the bilateral caudate nucleus, exhibit memory-predictive activity time-locked to the stimulus offset. We propose that offline activity in these regions reflects registration to memory of integrated episodes.

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage

PubMed Central

Clark, Ian A.; Niehaus, Katherine E.; Duff, Eugene P.; Di Simplicio, Martina C.; Clifford, Gari D.; Smith, Stephen M.; Mackay, Clare E.; Woolrich, Mark W.; Holmes, Emily A.

2014-01-01

After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms. PMID:25151915

Appearance Matters: Neural Correlates of Food Choice and Packaging Aesthetics

PubMed Central

Van der Laan, Laura N.; De Ridder, Denise T. D.; Viergever, Max A.; Smeets, Paul A. M.

2012-01-01

Neuro-imaging holds great potential for predicting choice behavior from brain responses. In this study we used both traditional mass-univariate and state-of-the-art multivariate pattern analysis to establish which brain regions respond to preferred packages and to what extent neural activation patterns can predict realistic low-involvement consumer choices. More specifically, this was assessed in the context of package-induced binary food choices. Mass-univariate analyses showed that several regions, among which the bilateral striatum, were more strongly activated in response to preferred food packages. Food choices could be predicted with an accuracy of up to 61.2% by activation patterns in brain regions previously found to be involved in healthy food choices (superior frontal gyrus) and visual processing (middle occipital gyrus). In conclusion, this study shows that mass-univariate analysis can detect small package-induced differences in product preference and that MVPA can successfully predict realistic low-involvement consumer choices from functional MRI data. PMID:22848586

"Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach".

PubMed

Van de Putte, Eowyn; De Baene, Wouter; Price, Cathy J; Duyck, Wouter

2018-05-01

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Predictive Brain State: Timing Deficiency in Traumatic Brain Injury?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

Attention and memory deficits observed in traumatic brain injury (TBI) are postulated to result from the shearing of white matter connections between the prefrontal cortex, parietal lobe, and cerebellum that are critical in the generation, maintenance, and precise timing of anticipatory neural activity. These fiber tracts are part of a neural network that generates predictions of future states and events, processes that are required for optimal performance on attention and working memory tasks. The authors discuss the role of this anticipatory neural system for understanding the varied symptoms and potential rehabilitation interventions for TBI. Preparatory neural activity normally allows the efficient integration of sensory information with goal-based representations. It is postulated that an impairment in the generation of this activity in traumatic brain injury (TBI) leads to performance variability as the brain shifts from a predictive to reactive mode. This dysfunction may constitute a fundamental defect in TBI as well as other attention disorders, causing working memory deficits, distractibility, a loss of goal-oriented behavior, and decreased awareness. “The future is not what is coming to meet us, but what we are moving forward to meet.” —Jean-Marie Guyau1 PMID:18460693

Major depression in mothers predict reduced ventral striatum activation in adolescent female offspring with and without depression

USDA-ARS?s Scientific Manuscript database

Prior research has identified reduced reward-related brain activation as a promising endophenotype for the early identification of adolescents with major depressive disorder. However, it is unclear whether reduced reward-related brain activation constitutes a true vulnerability for major depressive ...

Functional brain imaging predicts public health campaign success.

PubMed

Falk, Emily B; O'Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

2016-02-01

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a 'self-localizer' defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400,000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R(2) up to 0.65) and (ii) this relationship depends on message content-self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Emotion regulation modulates anticipatory brain activity that predicts emotional memory encoding in women.

PubMed

Galli, Giulia; Griffiths, Victoria A; Otten, Leun J

2014-03-01

It has been shown that the effectiveness with which unpleasant events are encoded into memory is related to brain activity set in train before the events. Here, we assessed whether encoding-related activity before an aversive event can be modulated by emotion regulation. Electrical brain activity was recorded from the scalps of healthy women while they performed an incidental encoding task on randomly intermixed unpleasant and neutral visual scenes. A cue presented 1.5 s before each picture indicated the upcoming valence. In half of the blocks of trials, the instructions emphasized to let emotions arise in a natural way. In the other half, participants were asked to decrease their emotional response by adopting the perspective of a detached observer. Memory for the scenes was probed 1 day later with a recognition memory test. Brain activity before unpleasant scenes predicted later memory of the scenes, but only when participants felt their emotions and did not detach from them. The findings indicate that emotion regulation can eliminate the influence of anticipatory brain activity on memory encoding. This may be relevant for the understanding and treatment of psychiatric diseases with a memory component.

How the brain attunes to sentence processing: Relating behavior, structure, and function

PubMed Central

Fengler, Anja; Meyer, Lars; Friederici, Angela D.

2016-01-01

Unlike other aspects of language comprehension, the ability to process complex sentences develops rather late in life. Brain maturation as well as verbal working memory (vWM) expansion have been discussed as possible reasons. To determine the factors contributing to this functional development, we assessed three aspects in different age-groups (5–6 years, 7–8 years, and adults): first, functional brain activity during the processing of increasingly complex sentences; second, brain structure in language-related ROIs; and third, the behavioral comprehension performance on complex sentences and the performance on an independent vWM test. At the whole-brain level, brain functional data revealed a qualitatively similar neural network in children and adults including the left pars opercularis (PO), the left inferior parietal lobe together with the posterior superior temporal gyrus (IPL/pSTG), the supplementary motor area, and the cerebellum. While functional activation of the language-related ROIs PO and IPL/pSTG predicted sentence comprehension performance for all age-groups, only adults showed a functional selectivity in these brain regions with increased activation for more complex sentences. The attunement of both the PO and IPL/pSTG toward a functional selectivity for complex sentences is predicted by region-specific gray matter reduction while that of the IPL/pSTG is additionally predicted by vWM span. Thus, both structural brain maturation and vWM expansion provide the basis for the emergence of functional selectivity in language-related brain regions leading to more efficient sentence processing during development. PMID:26777477

Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

PubMed Central

Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

2016-01-01

Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037, 2016. © 2016 Wiley Periodicals, Inc. PMID:26757216

Oxytocin receptor gene and racial ingroup bias in empathy-related brain activity.

PubMed

Luo, Siyang; Li, Bingfeng; Ma, Yina; Zhang, Wenxia; Rao, Yi; Han, Shihui

2015-04-15

The human brain responds more strongly to racial ingroup than outgroup individuals' pain. This racial ingroup bias varies across individuals and has been attributed to social experiences. What remains unknown is whether the racial ingroup bias in brain activity is associated with a genetic polymorphism. We investigated genetic associations of racial ingroup bias in the brain activity to racial ingroup and outgroup faces that received painful or non-painful stimulations by scanning A/A and G/G homozygous of the oxytocin receptor gene polymorphism (OXTR rs53576) using functional MRI. We found that G/G compared to A/A individuals showed stronger activity in the anterior cingulate and supplementary motor area (ACC/SMA) in response to racial ingroup members' pain, whereas A/A relative to G/G individuals exhibited greater activity in the nucleus accumbens (NAcc) in response to racial outgroup members' pain. Moreover, the racial ingroup bias in ACC/SMA activity positively predicted participants' racial ingroup bias in implicit attitudes and NAcc activity to racial outgroup individuals' pain negatively predicted participants' motivations to reduce racial outgroup members' pain. Our results suggest that the two variants of OXTR rs53576 are associated with racial ingroup bias in brain activities that are linked to implicit attitude and altruistic motivation, respectively. Copyright © 2015 Elsevier Inc. All rights reserved.

Similar brain networks for detecting visuo-motor and visuo-proprioceptive synchrony.

PubMed

Balslev, Daniela; Nielsen, Finn A; Lund, Torben E; Law, Ian; Paulson, Olaf B

2006-05-15

The ability to recognize feedback from own movement as opposed to the movement of someone else is important for motor control and social interaction. The neural processes involved in feedback recognition are incompletely understood. Two competing hypotheses have been proposed: the stimulus is compared with either (a) the proprioceptive feedback or with (b) the motor command and if they match, then the external stimulus is identified as feedback. Hypothesis (a) predicts that the neural mechanisms or brain areas involved in distinguishing self from other during passive and active movement are similar, whereas hypothesis (b) predicts that they are different. In this fMRI study, healthy subjects saw visual cursor movement that was either synchronous or asynchronous with their active or passive finger movements. The aim was to identify the brain areas where the neural activity depended on whether the visual stimulus was feedback from own movement and to contrast the functional activation maps for active and passive movement. We found activity increases in the right temporoparietal cortex in the condition with asynchronous relative to synchronous visual feedback from both active and passive movements. However, no statistically significant difference was found between these sets of activated areas when the active and passive movement conditions were compared. With a posterior probability of 0.95, no brain voxel had a contrast effect above 0.11% of the whole-brain mean signal. These results do not support the hypothesis that recognition of visual feedback during active and passive movement relies on different brain areas.

Electroencephalographic identifiers of motor adaptation learning

NASA Astrophysics Data System (ADS)

Özdenizci, Ozan; Yalçın, Mustafa; Erdoğan, Ahmetcan; Patoğlu, Volkan; Grosse-Wentrup, Moritz; Çetin, Müjdat

2017-08-01

Objective. Recent brain-computer interface (BCI) assisted stroke rehabilitation protocols tend to focus on sensorimotor activity of the brain. Relying on evidence claiming that a variety of brain rhythms beyond sensorimotor areas are related to the extent of motor deficits, we propose to identify neural correlates of motor learning beyond sensorimotor areas spatially and spectrally for further use in novel BCI-assisted neurorehabilitation settings. Approach. Electroencephalographic (EEG) data were recorded from healthy subjects participating in a physical force-field adaptation task involving reaching movements through a robotic handle. EEG activity recorded during rest prior to the experiment and during pre-trial movement preparation was used as features to predict motor adaptation learning performance across subjects. Main results. Subjects learned to perform straight movements under the force-field at different adaptation rates. Both resting-state and pre-trial EEG features were predictive of individual adaptation rates with relevance of a broad network of beta activity. Beyond sensorimotor regions, a parieto-occipital cortical component observed across subjects was involved strongly in predictions and a fronto-parietal cortical component showed significant decrease in pre-trial beta-powers for users with higher adaptation rates and increase in pre-trial beta-powers for users with lower adaptation rates. Significance. Including sensorimotor areas, a large-scale network of beta activity is presented as predictive of motor learning. Strength of resting-state parieto-occipital beta activity or pre-trial fronto-parietal beta activity can be considered in BCI-assisted stroke rehabilitation protocols with neurofeedback training or volitional control of neural activity for brain-robot interfaces to induce plasticity.

Learning Temporal Statistics for Sensory Predictions in Aging.

PubMed

Luft, Caroline Di Bernardi; Baker, Rosalind; Goldstone, Aimee; Zhang, Yang; Kourtzi, Zoe

2016-03-01

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

c-Fos expression predicts long-term social memory retrieval in mice.

PubMed

Lüscher Dias, Thomaz; Fernandes Golino, Hudson; Moura de Oliveira, Vinícius Elias; Dutra Moraes, Márcio Flávio; Schenatto Pereira, Grace

2016-10-15

The way the rodent brain generally processes socially relevant information is rather well understood. How social information is stored into long-term social memory, however, is still under debate. Here, brain c-Fos expression was measured after adult mice were exposed to familiar or novel juveniles and expression was compared in several memory and socially relevant brain areas. Machine Learning algorithm Random Forest was then used to predict the social interaction category of adult mice based on c-Fos expression in these areas. Interaction with a familiar co-specific altered brain activation in the olfactory bulb, amygdala, hippocampus, lateral septum and medial prefrontal cortex. Remarkably, Random Forest was able to predict interaction with a familiar juvenile with 100% accuracy. Activity in the olfactory bulb, amygdala, hippocampus and the medial prefrontal cortex were crucial to this prediction. From our results, we suggest long-term social memory depends on initial social olfactory processing in the medial amygdala and its output connections synergistically with non-social contextual integration by the hippocampus and medial prefrontal cortex top-down modulation of primary olfactory structures. Copyright © 2016 Elsevier B.V. All rights reserved.

Regional brain activity during early-stage intense romantic love predicted relationship outcomes after 40 months: an fMRI assessment.

PubMed

Xu, Xiaomeng; Brown, Lucy; Aron, Arthur; Cao, Guikang; Feng, Tingyong; Acevedo, Bianca; Weng, Xuchu

2012-09-20

Early-stage romantic love is associated with activation in reward and motivation systems of the brain. Can these localized activations, or others, predict long-term relationship stability? We contacted participants from a previous fMRI study of early-stage love by Xu et al. [34] after 40 months from initial assessments. We compared brain activation during the initial assessment at early-stage love for those who were still together at 40 months and those who were apart, and surveyed those still together about their relationship happiness and commitment at 40 months. Six participants who were still with their partners at 40 months (compared to six who had broken up) showed less activation during early-stage love in the medial orbitofrontal cortex, right subcallosal cingulate and right accumbens, regions implicated in long-term love and relationship satisfaction [1,2]. These regions of deactivation at the early stage of love were also negatively correlated with relationship happiness scores collected at 40 months. Other areas involved were the caudate tail, and temporal and parietal lobes. These data are preliminary evidence that neural responses in the early stages of romantic love can predict relationship stability and quality up to 40 months later in the relationship. The brain regions involved suggest that forebrain reward functions may be predictive for relationship stability, as well as regions involved in social evaluation, emotional regulation, and mood. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Inferencing Processes After Right Hemisphere Brain Damage: Effects of Contextual Bias

PubMed Central

Blake, Margaret Lehman

2009-01-01

Purpose Comprehension deficits associated with right hemisphere brain damage (RHD) have been attributed to an inability to use context, but there is little direct evidence to support the claim. This study evaluated the effect of varying contextual bias on predictive inferencing by adults with RHD. Method Fourteen adults with no brain damage (NBD) and 14 with RHD read stories constructed with either high predictability or low predictability of a specific outcome. Reading time for a sentence that disconfirmed the target outcome was measured and compared with a control story context. Results Adults with RHD evidenced activation of predictive inferences only for highly predictive conditions, whereas NBD adults generated inferences in both high- and low-predictability stories. Adults with RHD were more likely than those with NBD to require additional time to integrate inferences in high-predictability conditions. The latter finding was related to working memory for the RHD group. Results are interpreted in light of previous findings obtained using the same stimuli. Conclusions RHD does not abolish the ability to use context. Evidence of predictive inferencing is influenced by task and strength of inference activation. Treatment considerations and cautions regarding interpreting results from one methodology are discussed. PMID:19252126

Predictions interact with missing sensory evidence in semantic processing areas.

PubMed

Scharinger, Mathias; Bendixen, Alexandra; Herrmann, Björn; Henry, Molly J; Mildner, Toralf; Obleser, Jonas

2016-02-01

Human brain function draws on predictive mechanisms that exploit higher-level context during lower-level perception. These mechanisms are particularly relevant for situations in which sensory information is compromised or incomplete, as for example in natural speech where speech segments may be omitted due to sluggish articulation. Here, we investigate which brain areas support the processing of incomplete words that were predictable from semantic context, compared with incomplete words that were unpredictable. During functional magnetic resonance imaging (fMRI), participants heard sentences that orthogonally varied in predictability (semantically predictable vs. unpredictable) and completeness (complete vs. incomplete, i.e. missing their final consonant cluster). The effects of predictability and completeness interacted in heteromodal semantic processing areas, including left angular gyrus and left precuneus, where activity did not differ between complete and incomplete words when they were predictable. The same regions showed stronger activity for incomplete than for complete words when they were unpredictable. The interaction pattern suggests that for highly predictable words, the speech signal does not need to be complete for neural processing in semantic processing areas. Hum Brain Mapp 37:704-716, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Predicting consumer behavior: using novel mind-reading approaches.

PubMed

Calvert, Gemma A; Brammer, Michael J

2012-01-01

Advances in machine learning as applied to functional magnetic resonance imaging (fMRI) data offer the possibility of pretesting and classifying marketing communications using unbiased pattern recognition algorithms. By using these algorithms to analyze brain responses to brands, products, or existing marketing communications that either failed or succeeded in the marketplace and identifying the patterns of brain activity that characterize success or failure, future planned campaigns or new products can now be pretested to determine how well the resulting brain responses match the desired (successful) pattern of brain activity without the need for verbal feedback. This major advance in signal processing is poised to revolutionize the application of these brain-imaging techniques in the marketing sector by offering greater accuracy of prediction in terms of consumer acceptance of new brands, products, and campaigns at a speed that makes them accessible as routine pretesting tools that will clearly demonstrate return on investment.

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

The change of the brain activation patterns as children learn algebra equation solving

NASA Astrophysics Data System (ADS)

Qin, Yulin; Carter, Cameron S.; Silk, Eli M.; Stenger, V. Andrew; Fissell, Kate; Goode, Adam; Anderson, John R.

2004-04-01

In a brain imaging study of children learning algebra, it is shown that the same regions are active in children solving equations as are active in experienced adults solving equations. As with adults, practice in symbol manipulation produces a reduced activation in prefrontal cortex area. However, unlike adults, practice seems also to produce a decrease in a parietal area that is holding an image of the equation. This finding suggests that adolescents' brain responses are more plastic and change more with practice. These results are integrated in a cognitive model that predicts both the behavioral and brain imaging results.

Modulation of thermal pain-related brain activity with virtual reality: evidence from fMRI.

PubMed

Hoffman, Hunter G; Richards, Todd L; Coda, Barbara; Bills, Aric R; Blough, David; Richards, Anne L; Sharar, Sam R

2004-06-07

This study investigated the neural correlates of virtual reality analgesia. Virtual reality significantly reduced subjective pain ratings (i.e. analgesia). Using fMRI, pain-related brain activity was measured for each participant during conditions of no virtual reality and during virtual reality (order randomized). As predicted, virtual reality significantly reduced pain-related brain activity in all five regions of interest; the anterior cingulate cortex, primary and secondary somatosensory cortex, insula, and thalamus (p<0.002, corrected). Results showed direct modulation of human brain pain responses by virtual reality distraction. Copyright 2004 Lippincott Williams and Wilkins

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging.

PubMed

Voss, Michelle W; Weng, Timothy B; Burzynska, Agnieszka Z; Wong, Chelsea N; Cooke, Gillian E; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P; Olson, Erin A; McAuley, Edward; Kramer, Arthur F

2016-05-01

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. Copyright © 2015 Elsevier Inc. All rights reserved.

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

PubMed Central

Voss, Michelle W.; Weng, Timothy B.; Burzynska, Agnieszka Z.; Wong, Chelsea N.; Cooke, Gillian E.; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P.; Olson, Erin A.; McAuley, Edward; Kramer, Arthur F.

2015-01-01

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the Default Mode Network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. PMID:26493108

Predicting Visual Consciousness Electrophysiologically from Intermittent Binocular Rivalry

PubMed Central

O’Shea, Robert P.; Kornmeier, Jürgen; Roeber, Urte

2013-01-01

Purpose We sought brain activity that predicts visual consciousness. Methods We used electroencephalography (EEG) to measure brain activity to a 1000-ms display of sine-wave gratings, oriented vertically in one eye and horizontally in the other. This display yields binocular rivalry: irregular alternations in visual consciousness between the images viewed by the eyes. We replaced both gratings with 200 ms of darkness, the gap, before showing a second display of the same rival gratings for another 1000 ms. We followed this by a 1000-ms mask then a 2000-ms inter-trial interval (ITI). Eleven participants pressed keys after the second display in numerous trials to say whether the orientation of the visible grating changed from before to after the gap or not. Each participant also responded to numerous non-rivalry trials in which the gratings had identical orientations for the two eyes and for which the orientation of both either changed physically after the gap or did not. Results We found that greater activity from lateral occipital-parietal-temporal areas about 180 ms after initial onset of rival stimuli predicted a change in visual consciousness more than 1000 ms later, on re-presentation of the rival stimuli. We also found that less activity from parietal, central, and frontal electrodes about 400 ms after initial onset of rival stimuli predicted a change in visual consciousness about 800 ms later, on re-presentation of the rival stimuli. There was no such predictive activity when the change in visual consciousness occurred because the stimuli changed physically. Conclusion We found early EEG activity that predicted later visual consciousness. Predictive activity 180 ms after onset of the first display may reflect adaption of the neurons mediating visual consciousness in our displays. Predictive activity 400 ms after onset of the first display may reflect a less-reliable brain state mediating visual consciousness. PMID:24124536

Neuroimaging explanations of age-related differences in task performance.

PubMed

Steffener, Jason; Barulli, Daniel; Habeck, Christian; Stern, Yaakov

2014-01-01

Advancing age affects both cognitive performance and functional brain activity and interpretation of these effects has led to a variety of conceptual research models without always explicitly linking the two effects. However, to best understand the multifaceted effects of advancing age, age differences in functional brain activity need to be explicitly tied to the cognitive task performance. This work hypothesized that age-related differences in task performance are partially explained by age-related differences in functional brain activity and formally tested these causal relationships. Functional MRI data was from groups of young and old adults engaged in an executive task-switching experiment. Analyses were voxel-wise testing of moderated-mediation and simple mediation statistical path models to determine whether age group, brain activity and their interaction explained task performance in regions demonstrating an effect of age group. Results identified brain regions whose age-related differences in functional brain activity significantly explained age-related differences in task performance. In all identified locations, significant moderated-mediation relationships resulted from increasing brain activity predicting worse (slower) task performance in older but not younger adults. Findings suggest that advancing age links task performance to the level of brain activity. The overall message of this work is that in order to understand the role of functional brain activity on cognitive performance, analysis methods should respect theoretical relationships. Namely, that age affects brain activity and brain activity is related to task performance.

Activity in the human brain predicting differential heart rate responses to emotional facial expressions.

PubMed

Critchley, Hugo D; Rotshtein, Pia; Nagai, Yoko; O'Doherty, John; Mathias, Christopher J; Dolan, Raymond J

2005-02-01

The James-Lange theory of emotion proposes that automatically generated bodily reactions not only color subjective emotional experience of stimuli, but also necessitate a mechanism by which these bodily reactions are differentially generated to reflect stimulus quality. To examine this putative mechanism, we simultaneously measured brain activity and heart rate to identify regions where neural activity predicted the magnitude of heart rate responses to emotional facial expressions. Using a forewarned reaction time task, we showed that orienting heart rate acceleration to emotional face stimuli was modulated as a function of the emotion depicted. The magnitude of evoked heart rate increase, both across the stimulus set and within each emotion category, was predicted by level of activity within a matrix of interconnected brain regions, including amygdala, insula, anterior cingulate, and brainstem. We suggest that these regions provide a substrate for translating visual perception of emotional facial expression into differential cardiac responses and thereby represent an interface for selective generation of visceral reactions that contribute to the embodied component of emotional reaction.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words "lick" or "pick") and between affirmative and negated sentence meanings. Copyright © 2017 Grisoni et al.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words “lick” or “pick”) and between affirmative and negated sentence meanings. PMID:28411271

Baseline Brain Activity Predicts Response to Neuromodulatory Pain Treatment

PubMed Central

Jensen, Mark P.; Sherlin, Leslie H.; Fregni, Felipe; Gianas, Ann; Howe, Jon D.; Hakimian, Shahin

2015-01-01

Objectives The objective of this study was to examine the associations between baseline electroencephalogram (EEG)-assessed brain oscillations and subsequent response to four neuromodulatory treatments. Based on available research, we hypothesized that baseline theta oscillations would prospectively predict response to hypnotic analgesia. Analyses involving other oscillations and the other treatments (meditation, neurofeedback, and both active and sham transcranial direct current stimulation) were viewed as exploratory, given the lack of previous research examining brain oscillations as predictors of response to these other treatments. Design Randomized controlled study of single sessions of four neuromodulatory pain treatments and a control procedure. Methods Thirty individuals with spinal cord injury and chronic pain had their EEG recorded before each session of four active treatments (hypnosis, meditation, EEG biofeedback, transcranial direct current stimulation) and a control procedure (sham transcranial direct stimulation). Results As hypothesized, more presession theta power was associated with greater response to hypnotic analgesia. In exploratory analyses, we found that less baseline alpha power predicted pain reduction with meditation. Conclusions The findings support the idea that different patients respond to different pain treatments and that between-person treatment response differences are related to brain states as measured by EEG. The results have implications for the possibility of enhancing pain treatment response by either 1) better patient/treatment matching or 2) influencing brain activity before treatment is initiated in order to prepare patients to respond. Research is needed to replicate and confirm the findings in additional samples of individuals with chronic pain. PMID:25287554

Virtual milgram: empathic concern or personal distress? Evidence from functional MRI and dispositional measures.

PubMed

Cheetham, Marcus; Pedroni, Andreas F; Antley, Angus; Slater, Mel; Jäncke, Lutz

2009-01-01

One motive for behaving as the agent of another's aggression appears to be anchored in as yet unelucidated mechanisms of obedience to authority. In a recent partial replication of Milgram's obedience paradigm within an immersive virtual environment, participants administered pain to a female virtual human and observed her suffering. Whether the participants' response to the latter was more akin to other-oriented empathic concern for her well-being or to a self-oriented aversive state of personal distress in response to her distress is unclear. Using the stimuli from that study, this event-related fMRI-based study analysed brain activity during observation of the victim in pain versus not in pain. This contrast revealed activation in pre-defined brain areas known to be involved in affective processing but not in those commonly associated with affect sharing (e.g., ACC and insula). We then examined whether different dimensions of dispositional empathy predict activity within the same pre-defined brain regions: While personal distress and fantasy (i.e., tendency to transpose oneself into fictional situations and characters) predicted brain activity, empathic concern and perspective taking predicted no change in neuronal response associated with pain observation. These exploratory findings suggest that there is a distinct pattern of brain activity associated with observing the pain-related behaviour of the victim within the context of this social dilemma, that this observation evoked a self-oriented aversive state of personal distress, and that the objective "reality" of pain is of secondary importance for this response. These findings provide a starting point for experimentally more rigorous investigation of obedience.

Predicting novel histopathological microlesions in human epileptic brain through transcriptional clustering.

PubMed

Dachet, Fabien; Bagla, Shruti; Keren-Aviram, Gal; Morton, Andrew; Balan, Karina; Saadat, Laleh; Valyi-Nagy, Tibor; Kupsky, William; Song, Fei; Dratz, Edward; Loeb, Jeffrey A

2015-02-01

Although epilepsy is associated with a variety of abnormalities, exactly why some brain regions produce seizures and others do not is not known. We developed a method to identify cellular changes in human epileptic neocortex using transcriptional clustering. A paired analysis of high and low spiking tissues recorded in vivo from 15 patients predicted 11 cell-specific changes together with their 'cellular interactome'. These predictions were validated histologically revealing millimetre-sized 'microlesions' together with a global increase in vascularity and microglia. Microlesions were easily identified in deeper cortical layers using the neuronal marker NeuN, showed a marked reduction in neuronal processes, and were associated with nearby activation of MAPK/CREB signalling, a marker of epileptic activity, in superficial layers. Microlesions constitute a common, undiscovered layer-specific abnormality of neuronal connectivity in human neocortex that may be responsible for many 'non-lesional' forms of epilepsy. The transcriptional clustering approach used here could be applied more broadly to predict cellular differences in other brain and complex tissue disorders. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Brain regions engaged by part- and whole-task performance in a video game: a model-based test of the decomposition hypothesis.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Anderson, Abraham R; Poole, Ben; Qin, Yulin

2011-12-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.

The associative memory deficit in aging is related to reduced selectivity of brain activity during encoding

PubMed Central

Saverino, Cristina; Fatima, Zainab; Sarraf, Saman; Oder, Anita; Strother, Stephen C.; Grady, Cheryl L.

2016-01-01

Human aging is characterized by reductions in the ability to remember associations between items, despite intact memory for single items. Older adults also show less selectivity in task-related brain activity, such that patterns of activation become less distinct across multiple experimental tasks. This reduced selectivity, or dedifferentiation, has been found for episodic memory, which is often reduced in older adults, but not for semantic memory, which is maintained with age. We used functional magnetic resonance imaging (fMRI) to investigate whether there is a specific reduction in selectivity of brain activity during associative encoding in older adults, but not during item encoding, and whether this reduction predicts associative memory performance. Healthy young and older adults were scanned while performing an incidental-encoding task for pictures of objects and houses under item or associative instructions. An old/new recognition test was administered outside the scanner. We used agnostic canonical variates analysis and split-half resampling to detect whole brain patterns of activation that predicted item vs. associative encoding for stimuli that were later correctly recognized. Older adults had poorer memory for associations than did younger adults, whereas item memory was comparable across groups. Associative encoding trials, but not item encoding trials, were predicted less successfully in older compared to young adults, indicating less distinct patterns of associative-related activity in the older group. Importantly, higher probability of predicting associative encoding trials was related to better associative memory after accounting for age and performance on a battery of neuropsychological tests. These results provide evidence that neural distinctiveness at encoding supports associative memory and that a specific reduction of selectivity in neural recruitment underlies age differences in associative memory. PMID:27082043

Method of empirical dependences in estimation and prediction of activity of creatine kinase isoenzymes in cerebral ischemia

NASA Astrophysics Data System (ADS)

Sergeeva, Tatiana F.; Moshkova, Albina N.; Erlykina, Elena I.; Khvatova, Elena M.

2016-04-01

Creatine kinase is a key enzyme of energy metabolism in the brain. There are known cytoplasmic and mitochondrial creatine kinase isoenzymes. Mitochondrial creatine kinase exists as a mixture of two oligomeric forms - dimer and octamer. The aim of investigation was to study catalytic properties of cytoplasmic and mitochondrial creatine kinase and using of the method of empirical dependences for the possible prediction of the activity of these enzymes in cerebral ischemia. Ischemia was revealed to be accompanied with the changes of the activity of creatine kinase isoenzymes and oligomeric state of mitochondrial isoform. There were made the models of multiple regression that permit to study the activity of creatine kinase system in cerebral ischemia using a calculating method. Therefore, the mathematical method of empirical dependences can be applied for estimation and prediction of the functional state of the brain by the activity of creatine kinase isoenzymes in cerebral ischemia.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Virtual reality and consciousness inference in dreaming.

PubMed

Hobson, J Allan; Hong, Charles C-H; Friston, Karl J

2014-01-01

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that - through experience-dependent plasticity - becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep - and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain's generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis - evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research.

The Physiochemistry of Capped Nanosilver Predicts Its Biological Activity in Rat Brain Endothelial Cells (REBEC4)

EPA Science Inventory

The “capping” or coating of nanosilver (nanoAg) extends its potency by limiting its oxidation and aggregation and stabilizing its size and shape. The ability of such coated nanoAg to alter the permeability and activate oxidative stress pathways in rat brain endothelia...

A probabilistic framework to infer brain functional connectivity from anatomical connections.

PubMed

Deligianni, Fani; Varoquaux, Gael; Thirion, Bertrand; Robinson, Emma; Sharp, David J; Edwards, A David; Rueckert, Daniel

2011-01-01

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

A PC-based system for predicting movement from deep brain signals in Parkinson's disease.

PubMed

Loukas, Constantinos; Brown, Peter

2012-07-01

There is much current interest in deep brain stimulation (DBS) of the subthalamic nucleus (STN) for the treatment of Parkinson's disease (PD). This type of surgery has enabled unprecedented access to deep brain signals in the awake human. In this paper we present an easy-to-use computer based system for recording, displaying, archiving, and processing electrophysiological signals from the STN. The system was developed for predicting self-paced hand-movements in real-time via the online processing of the electrophysiological activity of the STN. It is hoped that such a computerised system might have clinical and experimental applications. For example, those sites within the STN most relevant to the processing of voluntary movement could be identified through the predictive value of their activities with respect to the timing of future movement. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Bilinguals at the "cocktail party": dissociable neural activity in auditory-linguistic brain regions reveals neurobiological basis for nonnative listeners' speech-in-noise recognition deficits.

PubMed

Bidelman, Gavin M; Dexter, Lauren

2015-04-01

We examined a consistent deficit observed in bilinguals: poorer speech-in-noise (SIN) comprehension for their nonnative language. We recorded neuroelectric mismatch potentials in mono- and bi-lingual listeners in response to contrastive speech sounds in noise. Behaviorally, late bilinguals required ∼10dB more favorable signal-to-noise ratios to match monolinguals' SIN abilities. Source analysis of cortical activity demonstrated monotonic increase in response latency with noise in superior temporal gyrus (STG) for both groups, suggesting parallel degradation of speech representations in auditory cortex. Contrastively, we found differential speech encoding between groups within inferior frontal gyrus (IFG)-adjacent to Broca's area-where noise delays observed in nonnative listeners were offset in monolinguals. Notably, brain-behavior correspondences double dissociated between language groups: STG activation predicted bilinguals' SIN, whereas IFG activation predicted monolinguals' performance. We infer higher-order brain areas act compensatorily to enhance impoverished sensory representations but only when degraded speech recruits linguistic brain mechanisms downstream from initial auditory-sensory inputs. Copyright © 2015 Elsevier Inc. All rights reserved.

Inferring brain-computational mechanisms with models of activity measurements

PubMed Central

Diedrichsen, Jörn

2016-01-01

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574316

Spatial Attention, Motor Intention, and Bayesian Cue Predictability in the Human Brain.

PubMed

Kuhns, Anna B; Dombert, Pascasie L; Mengotti, Paola; Fink, Gereon R; Vossel, Simone

2017-05-24

Predictions about upcoming events influence how we perceive and respond to our environment. There is increasing evidence that predictions may be generated based upon previous observations following Bayesian principles, but little is known about the underlying cortical mechanisms and their specificity for different cognitive subsystems. The present study aimed at identifying common and distinct neural signatures of predictive processing in the spatial attentional and motor intentional system. Twenty-three female and male healthy human volunteers performed two probabilistic cueing tasks with either spatial or motor cues while lying in the fMRI scanner. In these tasks, the percentage of cue validity changed unpredictably over time. Trialwise estimates of cue predictability were derived from a Bayesian observer model of behavioral responses. These estimates were included as parametric regressors for analyzing the BOLD time series. Parametric effects of cue predictability in valid and invalid trials were considered to reflect belief updating by precision-weighted prediction errors. The brain areas exhibiting predictability-dependent effects dissociated between the spatial attention and motor intention task, with the right temporoparietal cortex being involved during spatial attention and the left angular gyrus and anterior cingulate cortex during motor intention. Connectivity analyses revealed that all three areas showed predictability-dependent coupling with the right hippocampus. These results suggest that precision-weighted prediction errors of stimulus locations and motor responses are encoded in distinct brain regions, but that crosstalk with the hippocampus may be necessary to integrate new trialwise outcomes in both cognitive systems. SIGNIFICANCE STATEMENT The brain is able to infer the environments' statistical structure and responds strongly to expectancy violations. In the spatial attentional domain, it has been shown that parts of the attentional networks are sensitive to the predictability of stimuli. It remains unknown, however, whether these effects are ubiquitous or if they are specific for different cognitive systems. The present study compared the influence of model-derived cue predictability on brain activity in the spatial attentional and motor intentional system. We identified areas with distinct predictability-dependent activation for spatial attention and motor intention, but also common connectivity changes of these regions with the hippocampus. These findings provide novel insights into the generality and specificity of predictive processing signatures in the human brain. Copyright © 2017 the authors 0270-6474/17/375334-11$15.00/0.

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

PubMed

Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

2018-01-01

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

Neural response to pictorial health warning labels can predict smoking behavioral change

PubMed Central

Riddle, Philip J.; Newman-Norlund, Roger D.; Baer, Jessica; Thrasher, James F.

2016-01-01

In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants’ self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals’ intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). PMID:27405615

Neural response to pictorial health warning labels can predict smoking behavioral change.

PubMed

Riddle, Philip J; Newman-Norlund, Roger D; Baer, Jessica; Thrasher, James F

2016-11-01

In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants' self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals' intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). © The Author (2016). Published by Oxford University Press.

Individual brain structure and modelling predict seizure propagation

PubMed Central

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K.

2017-01-01

Abstract See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. PMID:28364550

The sequential structure of brain activation predicts skill.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Brain activity in the right-frontal pole and lateral occipital cortex predicts successful post-operatory outcome after surgery for anterior glenoumeral instability.

PubMed

Zanchi, Davide; Cunningham, Gregory; Lädermann, Alexandre; Ozturk, Mehmet; Hoffmeyer, Pierre; Haller, Sven

2017-03-29

Shoulder apprehension is more complex than a pure mechanical problem of the shoulder, creating a scar at the brain level that prevents the performance of specific movements. Surgery corrects for shoulder instability at the physical level, but a re-dislocation within the first year is rather common. Predicting which patient will be likely to have re-dislocation is therefore crucial. We hypothesized that the assessment of neural activity at baseline and follow-up is the key factor to predict the post-operatory outcome. 13 patients with shoulder apprehension (30.03 ± 7.64 years) underwent clinical and fMRI examination before and one year after surgery for shoulder dislocation contrasting apprehension cue videos and control videos. Data analyses included task-related general linear model (GLM) and correlations imaging results with clinical scores. Clinical examination showed decreased pain and increased shoulder functions for post-op vs. pre-op. Coherently, GLM results show decreased activation of the left pre-motor cortex for post-surgery vs. pre-surgery. Right-frontal pole and right-occipital cortex activity predicts good recovery of shoulder function measured by STT. Our findings demonstrate that beside physical changes, changes at the brain level also occur one year after surgery. In particular, decreased activity in pre-motor and orbito-frontal cortex is key factor for a successful post-operatory outcome.

It's OK if 'my brain made me do it': people's intuitions about free will and neuroscientific prediction.

PubMed

Nahmias, Eddy; Shepard, Jason; Reuter, Shane

2014-11-01

In recent years, a number of prominent scientists have argued that free will is an illusion, appealing to evidence demonstrating that information about brain activity can be used to predict behavior before people are aware of having made a decision. These scientists claim that the possibility of perfect prediction based on neural information challenges the ordinary understanding of free will. In this paper we provide evidence suggesting that most people do not view the possibility of neuro-prediction as a threat to free will unless it also raises concerns about manipulation of the agent's behavior. In Experiment 1 two scenarios described future brain imaging technology that allows perfect prediction of decisions and actions based on earlier neural activity, and this possibility did not undermine most people's attributions of free will or responsibility, except in the scenario that also allowed manipulation. In Experiment 2 the scenarios increased the salience of the physicalist implications of neuro-prediction, while in Experiment 3 the scenarios suggested dualism, with perfect prediction by mindreaders. The patterns of results for these two experiments were similar to the results in Experiment 1, suggesting that participants do not understand free will to require specific metaphysical conditions regarding the mind-body relation. Most people seem to understand free will in a way that is not threatened by perfect prediction based on neural information, suggesting that they believe that just because "my brain made me do it," that does not mean that I didn't do it of my own free will. Copyright © 2014 Elsevier B.V. All rights reserved.

In silico predicted reproductive endocrine transcriptional regulatory networks during zebrafish (Danio rerio) development.

PubMed

Hala, D

2017-03-21

The interconnected topology of transcriptional regulatory networks (TRNs) readily lends to mathematical (or in silico) representation and analysis as a stoichiometric matrix. Such a matrix can be 'solved' using the mathematical method of extreme pathway (ExPa) analysis, which identifies uniquely activated genes subject to transcription factor (TF) availability. In this manuscript, in silico multi-tissue TRN models of brain, liver and gonad were used to study reproductive endocrine developmental programming in zebrafish (Danio rerio) from 0.25h post fertilization (hpf; zygote) to 90 days post fertilization (dpf; adult life stage). First, properties of TRN models were studied by sequentially activating all genes in multi-tissue models. This analysis showed the brain to exhibit lowest proportion of co-regulated genes (19%) relative to liver (23%) and gonad (32%). This was surprising given that the brain comprised 75% and 25% more TFs than liver and gonad respectively. Such 'hierarchy' of co-regulatory capability (brain

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images.

PubMed

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S; Lin, Weili; Shen, Dinggang

2015-09-01

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images

PubMed Central

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [18F]FDG PET image by low-dose brain [18F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [18F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [18F]FDG PET image and substantially enhanced image quality of low-dose brain [18F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [18F]FDG PET image using low-dose brain [18F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [18F]FDG PET can be well-predicted using MRI and low-dose brain [18F]FDG PET. PMID:26328979

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

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

Kang, Jiayin; Gao, Yaozong; Shi, Feng

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. Asmore » yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.« less

Dynamic Variation in Pleasure in Children Predicts Nonlinear Change in Lateral Frontal Brain Electrical Activity

ERIC Educational Resources Information Center

Light, Sharee N.; Coan, James A.; Frye, Corrina; Goldsmith, H. Hill; Davidson, Richard J.

2009-01-01

Individual variation in the experience and expression of pleasure may relate to differential patterns of lateral frontal activity. Brain electrical measures have been used to study the asymmetric involvement of lateral frontal cortex in positive emotion, but the excellent time resolution of these measures has not been used to capture…

Development of a Conceptual Model to Predict Physical Activity Participation in Adults with Brain Injuries

ERIC Educational Resources Information Center

Driver, Simon

2008-01-01

The purpose was to examine psychosocial factors that influence the physical activity behaviors of adults with brain injuries. Two differing models, based on Harter's model of self-worth, were proposed to examine the relationship between perceived competence, social support, physical self-worth, affect, and motivation. Adults numbering 384 with…

Audience preferences are predicted by temporal reliability of neural processing

PubMed Central

Dmochowski, Jacek P.; Bezdek, Matthew A.; Abelson, Brian P.; Johnson, John S.; Schumacher, Eric H.; Parra, Lucas C.

2014-01-01

Naturalistic stimuli evoke highly reliable brain activity across viewers. Here we record neural activity from a group of naive individuals while viewing popular, previously-broadcast television content for which the broad audience response is characterized by social media activity and audience ratings. We find that the level of inter-subject correlation in the evoked encephalographic responses predicts the expressions of interest and preference among thousands. Surprisingly, ratings of the larger audience are predicted with greater accuracy than those of the individuals from whom the neural data is obtained. An additional functional magnetic resonance imaging study employing a separate sample of subjects shows that the level of neural reliability evoked by these stimuli covaries with the amount of blood-oxygenation-level-dependent (BOLD) activation in higher-order visual and auditory regions. Our findings suggest that stimuli which we judge favourably may be those to which our brains respond in a stereotypical manner shared by our peers. PMID:25072833

Audience preferences are predicted by temporal reliability of neural processing.

PubMed

Dmochowski, Jacek P; Bezdek, Matthew A; Abelson, Brian P; Johnson, John S; Schumacher, Eric H; Parra, Lucas C

2014-07-29

Naturalistic stimuli evoke highly reliable brain activity across viewers. Here we record neural activity from a group of naive individuals while viewing popular, previously-broadcast television content for which the broad audience response is characterized by social media activity and audience ratings. We find that the level of inter-subject correlation in the evoked encephalographic responses predicts the expressions of interest and preference among thousands. Surprisingly, ratings of the larger audience are predicted with greater accuracy than those of the individuals from whom the neural data is obtained. An additional functional magnetic resonance imaging study employing a separate sample of subjects shows that the level of neural reliability evoked by these stimuli covaries with the amount of blood-oxygenation-level-dependent (BOLD) activation in higher-order visual and auditory regions. Our findings suggest that stimuli which we judge favourably may be those to which our brains respond in a stereotypical manner shared by our peers.

Virtual Milgram: Empathic Concern or Personal Distress? Evidence from Functional MRI and Dispositional Measures

PubMed Central

Cheetham, Marcus; Pedroni, Andreas F.; Antley, Angus; Slater, Mel; Jäncke, Lutz

2009-01-01

One motive for behaving as the agent of another's aggression appears to be anchored in as yet unelucidated mechanisms of obedience to authority. In a recent partial replication of Milgram's obedience paradigm within an immersive virtual environment, participants administered pain to a female virtual human and observed her suffering. Whether the participants’ response to the latter was more akin to other-oriented empathic concern for her well-being or to a self-oriented aversive state of personal distress in response to her distress is unclear. Using the stimuli from that study, this event-related fMRI-based study analysed brain activity during observation of the victim in pain versus not in pain. This contrast revealed activation in pre-defined brain areas known to be involved in affective processing but not in those commonly associated with affect sharing (e.g., ACC and insula). We then examined whether different dimensions of dispositional empathy predict activity within the same pre-defined brain regions: While personal distress and fantasy (i.e., tendency to transpose oneself into fictional situations and characters) predicted brain activity, empathic concern and perspective taking predicted no change in neuronal response associated with pain observation. These exploratory findings suggest that there is a distinct pattern of brain activity associated with observing the pain-related behaviour of the victim within the context of this social dilemma, that this observation evoked a self-oriented aversive state of personal distress, and that the objective “reality” of pain is of secondary importance for this response. These findings provide a starting point for experimentally more rigorous investigation of obedience. PMID:19876407

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Individual brain structure and modelling predict seizure propagation.

PubMed

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K

2017-03-01

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Unique semantic space in the brain of each beholder predicts perceived similarity

PubMed Central

Charest, Ian; Kievit, Rogier A.; Schmitz, Taylor W.; Deca, Diana; Kriegeskorte, Nikolaus

2014-01-01

The unique way in which each of us perceives the world must arise from our brain representations. If brain imaging could reveal an individual’s unique mental representation, it could help us understand the biological substrate of our individual experiential worlds in mental health and disease. However, imaging studies of object vision have focused on commonalities between individuals rather than individual differences and on category averages rather than representations of particular objects. Here we investigate the individually unique component of brain representations of particular objects with functional MRI (fMRI). Subjects were presented with unfamiliar and personally meaningful object images while we measured their brain activity on two separate days. We characterized the representational geometry by the dissimilarity matrix of activity patterns elicited by particular object images. The representational geometry remained stable across scanning days and was unique in each individual in early visual cortex and human inferior temporal cortex (hIT). The hIT representation predicted perceived similarity as reflected in dissimilarity judgments. Importantly, hIT predicted the individually unique component of the judgments when the objects were personally meaningful. Our results suggest that hIT brain representational idiosyncrasies accessible to fMRI are expressed in an individual's perceptual judgments. The unique way each of us perceives the world thus might reflect the individually unique representation in high-level visual areas. PMID:25246586

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

PubMed

Johnson, Martin; Kozielska, Magdalena; Pilla Reddy, Venkatesh; Vermeulen, An; Barton, Hugh A; Grimwood, Sarah; de Greef, Rik; Groothuis, Geny M M; Danhof, Meindert; Proost, Johannes H

2016-04-01

To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Learning-based prediction of gestational age from ultrasound images of the fetal brain.

PubMed

Namburete, Ana I L; Stebbing, Richard V; Kemp, Bryn; Yaqub, Mohammad; Papageorghiou, Aris T; Alison Noble, J

2015-04-01

We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance.

PubMed

He, Biyu J; Zempel, John M

2013-01-01

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.

Brain Regions Engaged by Part- and Whole-task Performance in a Video Game: A Model-based Test of the Decomposition Hypothesis

PubMed Central

Anderson, John R.; Bothell, Daniel; Fincham, Jon M.; Anderson, Abraham R.; Poole, Ben; Qin, Yulin

2013-01-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model’s predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits. PMID:21557648

Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action.

PubMed

Bissonette, Gregory B; Roesch, Matthew R

2016-01-01

Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum.

Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action

PubMed Central

Roesch, Matthew R.

2017-01-01

Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum. PMID:26276036

MR brain volumetric measurements are predictive of neurobehavioral impairment in the HIV-1 transgenic rat.

PubMed

Casas, Rafael; Muthusamy, Siva; Wakim, Paul G; Sinharay, Sanhita; Lentz, Margaret R; Reid, William C; Hammoud, Dima A

2018-01-01

HIV infection is known to be associated with brain volume loss, even in optimally treated patients. In this study, we assessed whether dynamic brain volume changes over time are predictive of neurobehavorial performance in the HIV-1 transgenic (Tg) rat, a model of treated HIV-positive patients. Cross-sectional brain MRI imaging was first performed comparing Tg and wild type (WT) rats at 3 and 19 months of age. Longitudinal MRI and neurobehavioral testing of another group of Tg and WT rats was then performed from 5 to 23 weeks of age. Whole brain and subregional image segmentation was used to assess the rate of brain growth over time. We used repeated-measures mixed models to assess differences in brain volumes and to establish how predictive the volume differences are of specific neurobehavioral deficits. Cross-sectional imaging showed smaller whole brain volumes in Tg compared to WT rats at 3 and at 19 months of age. Longitudinally, Tg brain volumes were smaller than age-matched WT rats at all time points, starting as early as 5 weeks of age. The Tg striatal growth rate delay between 5 and 9 weeks of age was greater than that of the whole brain. Striatal volume in combination with genotype was the most predictive of rota-rod scores and in combination with genotype and age was the most predictive of total exploratory activity scores in the Tg rats. The disproportionately delayed striatal growth compared to whole brain between 5 and 9 weeks of age and the role of striatal volume in predicting neurobehavioral deficits suggest an important role of the dopaminergic system in HIV associated neuropathology. This might explain problems with motor coordination and executive decisions in this animal model. Smaller brain and subregional volumes and neurobehavioral deficits were seen as early as 5 weeks of age, suggesting an early brain insult in the Tg rat. Neuroprotective therapy testing in this model should thus target this early stage of development, before brain damage becomes irreversible.

The proactive brain: memory for predictions

PubMed Central

Bar, Moshe

2009-01-01

It is proposed that the human brain is proactive in that it continuously generates predictions that anticipate the relevant future. In this proposal, analogies are derived from elementary information that is extracted rapidly from the input, to link that input with the representations that exist in memory. Finding an analogical link results in the generation of focused predictions via associative activation of representations that are relevant to this analogy, in the given context. Predictions in complex circumstances, such as social interactions, combine multiple analogies. Such predictions need not be created afresh in new situations, but rather rely on existing scripts in memory, which are the result of real as well as of previously imagined experiences. This cognitive neuroscience framework provides a new hypothesis with which to consider the purpose of memory, and can help explain a variety of phenomena, ranging from recognition to first impressions, and from the brain's ‘default mode’ to a host of mental disorders. PMID:19528004

Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat.

PubMed

Eisenbarth, Hedwig; Chang, Luke J; Wager, Tor D

2016-11-23

Psychosocial stressors induce autonomic nervous system (ANS) responses in multiple body systems that are linked to health risks. Much work has focused on the common effects of stress, but ANS responses in different body systems are dissociable and may result from distinct patterns of cortical-subcortical interactions. Here, we used machine learning to develop multivariate patterns of fMRI activity predictive of heart rate (HR) and skin conductance level (SCL) responses during social threat in humans (N = 18). Overall, brain patterns predicted both HR and SCL in cross-validated analyses successfully (r HR = 0.54, r SCL = 0.58, both p < 0.0001). These patterns partly reflected central stress mechanisms common to both responses because each pattern predicted the other signal to some degree (r HR→SCL = 0.21 and r SCL→HR = 0.22, both p < 0.01), but they were largely physiological response specific. Both patterns included positive predictive weights in dorsal anterior cingulate and cerebellum and negative weights in ventromedial PFC and local pattern similarity analyses within these regions suggested that they encode common central stress mechanisms. However, the predictive maps and searchlight analysis suggested that the patterns predictive of HR and SCL were substantially different across most of the brain, including significant differences in ventromedial PFC, insula, lateral PFC, pre-SMA, and dmPFC. Overall, the results indicate that specific patterns of cerebral activity track threat-induced autonomic responses in specific body systems. Physiological measures of threat are not interchangeable, but rather reflect specific interactions among brain systems. We show that threat-induced increases in heart rate and skin conductance share some common representations in the brain, located mainly in the vmPFC, temporal and parahippocampal cortices, thalamus, and brainstem. However, despite these similarities, the brain patterns that predict these two autonomic responses are largely distinct. This evidence for largely output-measure-specific regulation of autonomic responses argues against a common system hypothesis and provides evidence that different autonomic measures reflect distinct, measurable patterns of cortical-subcortical interactions. Copyright © 2016 the authors 0270-6474/16/3611987-12$15.00/0.

Developmental changes in category-specific brain responses to numbers and letters in a working memory task

PubMed Central

Libertus, Melissa E.; Brannon, Elizabeth M.; Pelphrey, Kevin A.

2009-01-01

Neuroimaging studies have identified a common network of brain regions involving the prefrontal and parietal cortices across a variety of working memory (WM) tasks. However, previous studies have also reported category-specific dissociations of activation within this network. In this study, we investigated the development of category-specific activation in a WM task with digits, letters, and faces. Eight-year-old children and adults performed a 2-back WM task while their brain activity was measured using functional magnetic resonance imaging (fMRI). Overall, children were significantly slower and less accurate than adults on all three WM conditions (digits, letters, and faces); however, within each age group, behavioral performance across the three conditions was very similar. FMRI results revealed category-specific activation in adults but not children in the intraparietal sulcus for the digit condition. Likewise, during the letter condition, category-specific activation was observed in adults but not children in the left occipital–temporal cortex. In contrast, children and adults showed highly similar brain-activity patterns in the lateral fusiform gyri when solving the 2-back WM task with face stimuli. Our results suggest that 8-year-old children do not yet engage the typical brain regions that have been associated with abstract or semantic processing of numerical symbols and letters when these processes are task-irrelevant and the primary task is demanding. Nevertheless, brain activity in letter-responsive areas predicted children’s spelling performance underscoring the relationship between abstract processing of letters and linguistic abilities. Lastly, behavioral performance on the WM task was predictive of math and language abilities highlighting the connection between WM and other cognitive abilities in development. PMID:19027079

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach

NASA Astrophysics Data System (ADS)

Rish, Irina; Bashivan, Pouya; Cecchi, Guillermo A.; Goldstein, Rita Z.

2016-03-01

The objective of this study is to investigate effects of methylphenidate on brain activity in individuals with cocaine use disorder (CUD) using functional MRI (fMRI). Methylphenidate hydrochloride (MPH) is an indirect dopamine agonist commonly used for treating attention deficit/hyperactivity disorders; it was also shown to have some positive effects on CUD subjects, such as improved stop signal reaction times associated with better control/inhibition,1 as well as normalized task-related brain activity2 and resting-state functional connectivity in specific areas.3 While prior fMRI studies of MPH in CUDs have focused on mass-univariate statistical hypothesis testing, this paper evaluates multivariate, whole-brain effects of MPH as captured by the generalization (prediction) accuracy of different classification techniques applied to features extracted from resting-state functional networks (e.g., node degrees). Our multivariate predictive results based on resting-state data from3 suggest that MPH tends to normalize network properties such as voxel degrees in CUD subjects, thus providing additional evidence for potential benefits of MPH in treating cocaine addiction.

Molecular determinants of blood-brain barrier permeation.

PubMed

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood-brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution.

Molecular determinants of blood–brain barrier permeation

PubMed Central

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood–brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution. PMID:26305616

Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

NASA Astrophysics Data System (ADS)

Moon, Joon-Young; Kim, Junhyeok; Ko, Tae-Wook; Kim, Minkyung; Iturria-Medina, Yasser; Choi, Jee-Hyun; Lee, Joseph; Mashour, George A.; Lee, Uncheol

2017-04-01

Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks. We first derive a mathematical principle that explains the emergence of directionality from the underlying brain network structure. We then apply the analytical methods to the anatomical brain networks of human, macaque, and mouse, successfully predicting simulation and empirical electroencephalographic data. The results demonstrate that the global directionality patterns in resting state brain networks can be predicted solely by their unique network structures. This study forms a foundation for a more comprehensive understanding of how neural information is directed and integrated in complex brain networks.

Brain activation by music in patients in a vegetative or minimally conscious state following diffuse brain injury.

PubMed

Okumura, Yuka; Asano, Yoshitaka; Takenaka, Shunsuke; Fukuyama, Seisuke; Yonezawa, Shingo; Kasuya, Yukinori; Shinoda, Jun

2014-01-01

The aim of this study was to objectively evaluate the brain activity potential of patients with impaired consciousness in a chronic stage of diffuse brain injury (DBI) using functional MRI (fMRI) following music stimulation (MS). Two patients in a minimally conscious state (MCS) and five patients in a vegetative state (VS) due to severe DBI were enrolled along with 21 healthy adults. This study examined the brain regions activated by music and assessed topographical differences of the MS-activated brain among healthy adults and these patients. MS was shown to activate the bilateral superior temporal gyri (STG) of both healthy adults and patients in an MCS. In four of five patients in a VS, however, no significant activation in STG could be induced by the same MS. The remaining patient in a VS displayed the same MS-induced brain activation in STG as healthy adults and patients in an MCS and this patient's status also improved to an MCS 4 months after the study. The presence of STG activation by MS may predict a possible improvement of patients in a VS to MCS and fMRI employing MS may be a useful modality to objectively evaluate consciousness in these patients.

Functional split brain in a driving/listening paradigm.

PubMed

Sasai, Shuntaro; Boly, Melanie; Mensen, Armand; Tononi, Giulio

2016-12-13

We often engage in two concurrent but unrelated activities, such as driving on a quiet road while listening to the radio. When we do so, does our brain split into functionally distinct entities? To address this question, we imaged brain activity with fMRI in experienced drivers engaged in a driving simulator while listening either to global positioning system instructions (integrated task) or to a radio show (split task). We found that, compared with the integrated task, the split task was characterized by reduced multivariate functional connectivity between the driving and listening networks. Furthermore, the integrated information content of the two networks, predicting their joint dynamics above and beyond their independent dynamics, was high in the integrated task and zero in the split task. Finally, individual subjects' ability to switch between high and low information integration predicted their driving performance across integrated and split tasks. This study raises the possibility that under certain conditions of daily life, a single brain may support two independent functional streams, a "functional split brain" similar to what is observed in patients with an anatomical split.

Active interoceptive inference and the emotional brain

PubMed Central

Friston, Karl J.

2016-01-01

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’. PMID:28080966

Multiple brain networks for visual self-recognition with different sensitivity for motion and body part.

PubMed

Sugiura, Motoaki; Sassa, Yuko; Jeong, Hyeonjeong; Miura, Naoki; Akitsuki, Yuko; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

2006-10-01

Multiple brain networks may support visual self-recognition. It has been hypothesized that the left ventral occipito-temporal cortex processes one's own face as a symbol, and the right parieto-frontal network processes self-image in association with motion-action contingency. Using functional magnetic resonance imaging, we first tested these hypotheses based on the prediction that these networks preferentially respond to a static self-face and to moving one's whole body, respectively. Brain activation specifically related to self-image during familiarity judgment was compared across four stimulus conditions comprising a two factorial design: factor Motion contrasted picture (Picture) and movie (Movie), and factor Body part a face (Face) and whole body (Body). Second, we attempted to segregate self-specific networks using a principal component analysis (PCA), assuming an independent pattern of inter-subject variability in activation over the four stimulus conditions in each network. The bilateral ventral occipito-temporal and the right parietal and frontal cortices exhibited self-specific activation. The left ventral occipito-temporal cortex exhibited greater self-specific activation for Face than for Body, in Picture, consistent with the prediction for this region. The activation profiles of the right parietal and frontal cortices did not show preference for Movie Body predicted by the assumed roles of these regions. The PCA extracted two cortical networks, one with its peaks in the right posterior, and another in frontal cortices; their possible roles in visuo-spatial and conceptual self-representations, respectively, were suggested by previous findings. The results thus supported and provided evidence of multiple brain networks for visual self-recognition.

Alter spontaneous activity in amygdala and vmPFC during fear consolidation following 24 h sleep deprivation.

PubMed

Feng, Pan; Becker, Benjamin; Feng, Tingyong; Zheng, Yong

2018-05-15

Sleep deprivation (SD) has been associated with cognitive and emotional disruptions, however its impact on the acquisition of fear and subsequent fear memory consolidation remain unknown. To address this question, we measured human brain activity before and after fear acquisition under conditions of 24 h sleep deprivation versus normal sleep using resting-state functional magnetic resonance imaging (rs-fMRI). Additionally, we explored whether the fear acquisition-induced change of brain activity during the fear memory consolidation window can be predicted by subjective fear ratings and autonomic fear response, assessed by skin conductance responses (SCR) during acquisition. Behaviorally, the SD group demonstrated increased subjective and autonomic fear responses compared to controls at the stage of fear acquisition. During the stage of fear consolidation, the SD group displayed decreased ventromedial prefrontal cortex (vmPFC) activity and concomitantly increased amygdala activity. Moreover, in the SD group fear acquisition-induced brain activity changes in amygdala were positively correlated with both, subjective and autonomic fear indices during acquisition, whereas in controls changes vmPFC activity were positively correlated with fear indices during acquisition. Together, the present findings suggested that SD may weaken the top-down ability of the vmPFC to regulate amygdala activity during fear memory consolidation. Moreover, subjective and objective fear at fear acquisition stage can predict the change of brain activity in amygdala in fear memory consolidation following SD. Copyright © 2018 Elsevier Inc. All rights reserved.

Activity flow over resting-state networks shapes cognitive task activations.

PubMed

Cole, Michael W; Ito, Takuya; Bassett, Danielle S; Schultz, Douglas H

2016-12-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Activity flow over resting-state networks shapes cognitive task activations

PubMed Central

Cole, Michael W.; Ito, Takuya; Bassett, Danielle S.; Schultz, Douglas H.

2016-01-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allows prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations. PMID:27723746

Virtual reality and consciousness inference in dreaming

PubMed Central

Hobson, J. Allan; Hong, Charles C.-H.; Friston, Karl J.

2014-01-01

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that – through experience-dependent plasticity – becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep – and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain’s generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis – evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research. PMID:25346710

Coherent activity between brain regions that code for value is linked to the malleability of human behavior

PubMed Central

Cooper, Nicole; Bassett, Danielle S.; Falk, Emily B.

2017-01-01

Brain activity in medial prefrontal cortex (MPFC) during exposure to persuasive messages can predict health behavior change. This brain-behavior relationship has been linked to areas of MPFC previously associated with self-related processing; however, the mechanism underlying this relationship is unclear. We explore two components of self-related processing – self-reflection and subjective valuation – and examine coherent activity between relevant networks of brain regions during exposure to health messages encouraging exercise and discouraging sedentary behaviors. We find that objectively logged reductions in sedentary behavior in the following month are linked to functional connectivity within brain regions associated with positive valuation, but not within regions associated with self-reflection on personality traits. Furthermore, functional connectivity between valuation regions contributes additional information compared to average brain activation within single brain regions. These data support an account in which MPFC integrates the value of messages to the self during persuasive health messaging and speak to broader questions of how humans make decisions about how to behave. PMID:28240271

Learning Computational Models of Video Memorability from fMRI Brain Imaging.

PubMed

Han, Junwei; Chen, Changyuan; Shao, Ling; Hu, Xintao; Han, Jungong; Liu, Tianming

2015-08-01

Generally, various visual media are unequally memorable by the human brain. This paper looks into a new direction of modeling the memorability of video clips and automatically predicting how memorable they are by learning from brain functional magnetic resonance imaging (fMRI). We propose a novel computational framework by integrating the power of low-level audiovisual features and brain activity decoding via fMRI. Initially, a user study experiment is performed to create a ground truth database for measuring video memorability and a set of effective low-level audiovisual features is examined in this database. Then, human subjects' brain fMRI data are obtained when they are watching the video clips. The fMRI-derived features that convey the brain activity of memorizing videos are extracted using a universal brain reference system. Finally, due to the fact that fMRI scanning is expensive and time-consuming, a computational model is learned on our benchmark dataset with the objective of maximizing the correlation between the low-level audiovisual features and the fMRI-derived features using joint subspace learning. The learned model can then automatically predict the memorability of videos without fMRI scans. Evaluations on publically available image and video databases demonstrate the effectiveness of the proposed framework.

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

PubMed Central

Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.

2013-01-01

Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient. PMID:22945462

Visual short term memory related brain activity predicts mathematical abilities.

PubMed

Boulet-Craig, Aubrée; Robaey, Philippe; Lacourse, Karine; Jerbi, Karim; Oswald, Victor; Krajinovic, Maja; Laverdière, Caroline; Sinnett, Daniel; Jolicoeur, Pierre; Lippé, Sarah

2017-07-01

Previous research suggests visual short-term memory (VSTM) capacity and mathematical abilities are significantly related. Moreover, both processes activate similar brain regions within the parietal cortex, in particular, the intraparietal sulcus; however, it is still unclear whether the neuronal underpinnings of VSTM directly correlate with mathematical operation and reasoning abilities. The main objective was to investigate the association between parieto-occipital brain activity during the retention period of a VSTM task and performance in mathematics. The authors measured mathematical abilities and VSTM capacity as well as brain activity during memory maintenance using magnetoencephalography (MEG) in 19 healthy adult participants. Event-related magnetic fields (ERFs) were computed on the MEG data. Linear regressions were used to estimate the strength of the relation between VSTM related brain activity and mathematical abilities. The amplitude of parieto-occipital cerebral activity during the retention of visual information was related to performance in 2 standardized mathematical tasks: mathematical reasoning and calculation fluency. The findings show that brain activity during retention period of a VSTM task is associated with mathematical abilities. Contributions of VSTM processes to numerical cognition should be considered in cognitive interventions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A new threshold of apparent diffusion coefficient values in white matter after successful tissue plasminogen activator treatment for acute brain ischemia.

PubMed

Sato, Atsushi; Shimizu, Yusaku; Koyama, Junichi; Hongo, Kazuhiro

2017-06-01

Tissue plasminogen activator (tPA) is effective for the treatment of acute brain ischemia, but may trigger fatal brain edema or hemorrhage if the brain ischemia results in a large infarct. Herein, we attempted to predict the extent of infarcts by determining the optimal threshold of ADC values on DWI that predictively distinguishes between infarct and reversible areas, and by reconstructing color-coded images based on this threshold. The study subjects consisted of 36 patients with acute brain ischemia in whom MRA had confirmed reopening of the occluded arteries in a short time (mean: 99min) after tPA treatment. We measured the apparetnt diffusion coefficient (ADC) values in several small regions of interest over the white matter within high-intensity areas on the initial diffusion weighted image (DWI); then, by comparing the findings to the follow-up images, we obtained the optimal threshold of ADC values using receiver-operating characteristic analysis. The threshold obtained (583×10 -6 m 2 /s) was lower than those previously reported; this threshold could distinguish between infarct and reversible areas with considerable accuracy (sensitivity: 0.87, specificity: 0.94). The threshold obtained and the reconstructed images were predictive of the final radiological result of tPA treatment, and this threshold may be helpful in determining the appropriate management of patients with acute brain ischemia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Using the brain's fight-or-flight response for predicting mental illness on the human space flight program

NASA Astrophysics Data System (ADS)

Losik, L.

A predictive medicine program allows disease and illness including mental illness to be predicted using tools created to identify the presence of accelerated aging (a.k.a. disease) in electrical and mechanical equipment. When illness and disease can be predicted, actions can be taken so that the illness and disease can be prevented and eliminated. A predictive medicine program uses the same tools and practices from a prognostic and health management program to process biological and engineering diagnostic data provided in analog telemetry during prelaunch readiness and space exploration missions. The biological and engineering diagnostic data necessary to predict illness and disease is collected from the pre-launch spaceflight readiness activities and during space flight for the ground crew to perform a prognostic analysis on the results from a diagnostic analysis. The diagnostic, biological data provided in telemetry is converted to prognostic (predictive) data using the predictive algorithms. Predictive algorithms demodulate telemetry behavior. They illustrate the presence of accelerated aging/disease in normal appearing systems that function normally. Mental illness can predicted using biological diagnostic measurements provided in CCSDS telemetry from a spacecraft such as the ISS or from a manned spacecraft in deep space. The measurements used to predict mental illness include biological and engineering data from an astronaut's circadian and ultranian rhythms. This data originates deep in the brain that is also damaged from the long-term exposure to cortisol and adrenaline anytime the body's fight or flight response is activated. This paper defines the brain's FOFR; the diagnostic, biological and engineering measurements needed to predict mental illness, identifies the predictive algorithms necessary to process the behavior in CCSDS analog telemetry to predict and thus prevent mental illness from occurring on human spaceflight missions.

Rate equation for creatine kinase predicts the in vivo reaction velocity: /sup 31/P NMR surface coil studies in brain, heart, and skeletal muscle of the living rat

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

Bittl, J.A.; DeLayre, J.; Ingwall, J.S.

1987-09-22

Brain, heart, and skeletal muscle contain four different creatine kinase isozymes and various concentrations of substrates for the creatine kinase reaction. To identify if the velocity of the creatine kinase reaction under cellular conditions is regulated by enzyme activity and substrate concentrations as predicted by the rate equation, the authors used /sup 31/P NMR and spectrophotometric techniques to measure reaction velocity, enzyme content, isozyme distribution, and concentrations of substrates in brain, heart, and skeletal muscle of living rat under basal or resting conditions. The total tissue activity of creatine kinase in the direction of MgATP synthesis provided an estimate formore » V/sub max/ and exceeded the NMR-determined in vivo reaction velocities by an order of magnitude. The isozyme composition varied among the three tissues: >99% BB for brain; 14% MB, 61% MM, and 25% mitochondrial for heart; and 98% MM and 2% mitochondrial for skeletal muscle. The NMR-determined reaction velocities agreed with predicted values from the creatine kinase rate equation. The concentrations of free creatine and cytosolic MgADP, being less than or equal to the dissociation constants for each isozyme, were dominant terms in the creatine kinase rate equation for predicting the in vivo reaction velocity. Thus, they observed that the velocity of the creatine kinase reaction is regulated by total tissue enzyme activity and by the concentrations of creatine and MgADP in a manner that is independent of isozyme distribution.« less

Recalling and forgetting dreams: theta and alpha oscillations during sleep predict subsequent dream recall.

PubMed

Marzano, Cristina; Ferrara, Michele; Mauro, Federica; Moroni, Fabio; Gorgoni, Maurizio; Tempesta, Daniela; Cipolli, Carlo; De Gennaro, Luigi

2011-05-04

Under the assumption that dream recall is a peculiar form of declarative memory, we have hypothesized that (1) the encoding of dream contents during sleep should share some electrophysiological mechanisms with the encoding of episodic memories of the awake brain and (2) recalling a dream(s) after awakening from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep should be associated with different brain oscillations. Here, we report that cortical brain oscillations of human sleep are predictive of successful dream recall. In particular, after morning awakening from REM sleep, a higher frontal 5-7 Hz (theta) activity was associated with successful dream recall. This finding mirrors the increase in frontal theta activity during successful encoding of episodic memories in wakefulness. Moreover, in keeping with the different EEG background, a different predictive relationship was found after awakening from stage 2 NREM sleep. Specifically, a lower 8-12 Hz (alpha) oscillatory activity of the right temporal area was associated with a successful dream recall. These findings provide the first evidence of univocal cortical electroencephalographic correlates of dream recall, suggesting that the neurophysiological mechanisms underlying the encoding and recall of episodic memories may remain the same across different states of consciousness.

Choice from non-choice: Predicting consumer preferences from BOLD signals obtained during passive viewing

PubMed Central

Levy, Ifat; Lazzaro, Stephanie C.; Rutledge, Robb B.; Glimcher, Paul W.

2011-01-01

Decision-making is often viewed as a two-stage process, where subjective values are first assigned to each option and then the option of the highest value is selected. Converging evidence suggests that these subjective values are represented in the striatum and medial prefrontal cortex (MPFC). A separate line of evidence suggests that activation in the same areas represents the values of rewards even when choice is not required, as in classical conditioning tasks. However, it is unclear whether the same neural mechanism is engaged in both cases. To address this question we measured brain activation with fMRI while human subjects passively viewed individual consumer goods. We then sampled activation from predefined regions of interest and used it to predict subsequent choices between the same items made outside of the scanner. Our results show that activation in the striatum and MPFC in the absence of choice predicts subsequent choices, suggesting that these brain areas represent value in a similar manner whether or not choice is required. PMID:21209196

Asymmetric frontal brain activity and parental rejection predict altruistic behavior: moderation of oxytocin effects.

PubMed

Huffmeijer, Renske; Alink, Lenneke R A; Tops, Mattie; Bakermans-Kranenburg, Marian J; van IJzendoorn, Marinus H

2012-06-01

Asymmetric frontal brain activity has been widely implicated in reactions to emotional stimuli and is thought to reflect individual differences in approach-withdrawal motivation. Here, we investigate whether asymmetric frontal activity, as a measure of approach-withdrawal motivation, also predicts charitable donations after a charity's (emotion-eliciting) promotional video showing a child in need is viewed, in a sample of 47 young adult women. In addition, we explore possibilities for mediation and moderation, by asymmetric frontal activity, of the effects of intranasally administered oxytocin and parental love withdrawal on charitable donations. Greater relative left frontal activity was related to larger donations. In addition, we found evidence of moderation: Low levels of parental love withdrawal predicted larger donations in the oxytocin condition for participants showing greater relative right frontal activity. We suggest that when approach motivation is high (reflected in greater relative left frontal activity), individuals are generally inclined to take action upon seeing someone in need and, thus, to donate money to actively help out. Only when approach motivation is low (reflected in less relative left/greater relative right activity) do empathic concerns affected by oxytocin and experiences of love withdrawal play an important part in deciding about donations.

Laminar fMRI and computational theories of brain function.

PubMed

Stephan, K E; Petzschner, F H; Kasper, L; Bayer, J; Wellstein, K V; Stefanics, G; Pruessmann, K P; Heinzle, J

2017-11-02

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback.

PubMed

Bagarinao, Epifanio; Yoshida, Akihiro; Ueno, Mika; Terabe, Kazunori; Kato, Shohei; Isoda, Haruo; Nakai, Toshiharu

2018-01-01

Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain-computer/brain-machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants' performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.

Pharmacological validation of in-silico guided novel nootropic potential of Achyranthes aspera L.

PubMed

Gawande, Dinesh Yugraj; Goel, Rajesh Kumar

2015-12-04

Achyranthes aspera (A. aspera) has been used as a brain tonic in folk medicine. Although, ethnic use of medicinal plant has been basis for drug discovery from medicinal plants, but the available in-silico tools can be useful to find novel pharmacological uses of medicinal plants beyond their ethnic use. To validate in-silico prediction for novel nootropic effect of A. aspera by employing battery of tests in mice. Phytoconstituents of A. aspera reported in Dictionary of Natural Product were subjected to in-silico prediction using PASS and Pharmaexpert. The nootropic activity predicted for A. aspera was assessed using radial arm maze, passive shock avoidance and novel object recognition tests in mice. After behavioral evaluation animals were decapitated and their brains were collected and stored for estimation of glutamate levels and acetylcholinesterase activity. In-silico activity spectrum for majority of A. aspera phytoconstituents exhibited excellent prediction score for nootropic activity of this plant. A. aspera extract treatment significantly improved the learning and memory as evident by decreased working memory errors, reference memory errors and latency time in radial arm maze, step through latency in passive shock avoidance and increased recognition index in novel object recognition were observed, moreover significantly enhanced glutamate levels and reduced acetylcholinesterase activity in hippocampus and cortex were observed as compared to the saline treated group. In-silico and in-vivo results suggest that A. aspera plant may improve the learning and memory by modulating the brain glutamatergic and cholinergic neurotransmission. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Altered predictive capability of the brain network EEG model in schizophrenia during cognition.

PubMed

Gomez-Pilar, Javier; Poza, Jesús; Gómez, Carlos; Northoff, Georg; Lubeiro, Alba; Cea-Cañas, Benjamín B; Molina, Vicente; Hornero, Roberto

2018-05-12

The study of the mechanisms involved in cognition is of paramount importance for the understanding of the neurobiological substrates in psychiatric disorders. Hence, this research is aimed at exploring the brain network dynamics during a cognitive task. Specifically, we analyze the predictive capability of the pre-stimulus theta activity to ascertain the functional brain dynamics during cognition in both healthy and schizophrenia subjects. Firstly, EEG recordings were acquired during a three-tone oddball task from fifty-one healthy subjects and thirty-five schizophrenia patients. Secondly, phase-based coupling measures were used to generate the time-varying functional network for each subject. Finally, pre-stimulus network connections were iteratively modified according to different models of network reorganization. This adjustment was applied by minimizing the prediction error through recurrent iterations, following the predictive coding approach. Both controls and schizophrenia patients follow a reinforcement of the secondary neural pathways (i.e., pathways between cortical brain regions weakly connected during pre-stimulus) for most of the subjects, though the ratio of controls that exhibited this behavior was statistically significant higher than for patients. These findings suggest that schizophrenia is associated with an impaired ability to modify brain network configuration during cognition. Furthermore, we provide direct evidence that the changes in phase-based brain network parameters from pre-stimulus to cognitive response in the theta band are closely related to the performance in important cognitive domains. Our findings not only contribute to the understanding of healthy brain dynamics, but also shed light on the altered predictive neuronal substrates in schizophrenia. Copyright © 2018 Elsevier B.V. All rights reserved.

Metabolic and hemodynamic events following changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings

PubMed Central

Mangia, Silvia; Giove, Federico; Tkáč, Ivan; Logothetis, Nikos K.; Henry, Pierre-Gilles; Olman, Cheryl A.; Maraviglia, Bruno; Di Salle, Francesco; Uğurbil, Kâmil

2009-01-01

Unraveling the energy metabolism and the hemodynamic outcomes of excitatory and inhibitory neuronal activity is critical not only for our basic understanding of overall brain function, but also for the understanding of many brain disorders. Methodologies of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) are powerful tools for the non-invasive investigation of brain metabolism and physiology. However, the temporal and spatial resolution of in vivo MRS and MRI is not suitable to provide direct evidence for hypotheses that involve metabolic compartmentalization between different cell types, or to untangle the complex neuronal micro-circuitry which results in changes of electrical activity. This review aims at describing how the current models of brain metabolism, mainly built on the basis of in vitro evidence, relate to experimental findings recently obtained in vivo by 1H MRS, 13C MRS and MRI. The hypotheses related to the role of different metabolic substrates, the metabolic neuron-glia interactions, along with the available theoretical predictions of the energy budget of neurotransmission, will be discussed. In addition, the cellular and network mechanisms that characterize different types of increased and suppressed neuronal activity will be considered within the sensitivity-constraints of MRS and MRI. PMID:19002199

Increases in brain activity during social competition predict decreases in working memory performance and later recall.

PubMed

DiMenichi, Brynne C; Tricomi, Elizabeth

2017-01-01

In our fMRI experiment, participants completed a learning task in both a noncompetitive and a socially competitive learning environment. Despite reporting a preference for completing the task while competing, participants remembered significantly more during the task and later recalled more from the noncompetitive learning environment. Furthermore, during working memory maintenance, there was performance-related deactivation in the medial prefrontal cortex (mPFC) and the precuneus/PCC. During feedback presentation, there was greater activation in the mPFC and the precuneus/PCC while competing. Differential activation in the precuneus/PCC predicted worse later recall for information learned competitively. Since previous research suggests that the mPFC is involved in social-referencing, while the precuneus/PCC is implicated in off-task thoughts, our results suggest that receiving feedback regarding competition produces more activation in brain regions implicated in social interaction, as well as task distraction. While competition may make a task more enjoyable, the goal of winning may distract from maximizing performance. Hum Brain Mapp 38:457-471, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Does Feedback-Related Brain Response during Reinforcement Learning Predict Socio-motivational (In-)dependence in Adolescence?

PubMed Central

Raufelder, Diana; Boehme, Rebecca; Romund, Lydia; Golde, Sabrina; Lorenz, Robert C.; Gleich, Tobias; Beck, Anne

2016-01-01

This multi-methodological study applied functional magnetic resonance imaging to investigate neural activation in a group of adolescent students (N = 88) during a probabilistic reinforcement learning task. We related patterns of emerging brain activity and individual learning rates to socio-motivational (in-)dependence manifested in four different motivation types (MTs): (1) peer-dependent MT, (2) teacher-dependent MT, (3) peer-and-teacher-dependent MT, (4) peer-and-teacher-independent MT. A multinomial regression analysis revealed that the individual learning rate predicts students’ membership to the independent MT, or the peer-and-teacher-dependent MT. Additionally, the striatum, a brain region associated with behavioral adaptation and flexibility, showed increased learning-related activation in students with motivational independence. Moreover, the prefrontal cortex, which is involved in behavioral control, was more active in students of the peer-and-teacher-dependent MT. Overall, this study offers new insights into the interplay of motivation and learning with (1) a focus on inter-individual differences in the role of peers and teachers as source of students’ individual motivation and (2) its potential neurobiological basis. PMID:27199873

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Modeling the toxicokinetics of 24-hour toluene exposure in rats, impact of activity patterns and enzyme induction

EPA Science Inventory

Toluene, a solvent used in numerous consumer and industrial applications, exerts its critical effects on the brain and nervous system following inhalation exposure. Our previously published PBPK model successfully predicted toluene concentrations in blood and brain over a range o...

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback

PubMed Central

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone. PMID:29342146

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

PubMed

Buckley, Christopher L; Toyoizumi, Taro

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone.

Prefrontal and parietal activity is modulated by the rule complexity of inductive reasoning and can be predicted by a cognitive model.

PubMed

Jia, Xiuqin; Liang, Peipeng; Shi, Lin; Wang, Defeng; Li, Kuncheng

2015-01-01

In neuroimaging studies, increased task complexity can lead to increased activation in task-specific regions or to activation of additional regions. How the brain adapts to increased rule complexity during inductive reasoning remains unclear. In the current study, three types of problems were created: simple rule induction (i.e., SI, with rule complexity of 1), complex rule induction (i.e., CI, with rule complexity of 2), and perceptual control. Our findings revealed that increased activations accompany increased rule complexity in the right dorsal lateral prefrontal cortex (DLPFC) and medial posterior parietal cortex (precuneus). A cognitive model predicted both the behavioral and brain imaging results. The current findings suggest that neural activity in frontal and parietal regions is modulated by rule complexity, which may shed light on the neural mechanisms of inductive reasoning. Copyright © 2014. Published by Elsevier Ltd.

Brain Organization and Psychodynamics

PubMed Central

Peled, Avi; Geva, Amir B.

1999-01-01

Any attempt to link brain neural activity and psychodynamic concepts requires a tremendous conceptual leap. Such a leap may be facilitated if a common language between brain and mind can be devised. System theory proposes formulations that may aid in reconceptualizing psychodynamic descriptions in terms of neural organizations in the brain. Once adopted, these formulations can help to generate testable predictions about brain–psychodynamic relations and thus significantly affect the future of psychotherapy. (The Journal of Psychotherapy Practice and Research 1999; 8:24–39) PMID:9888105

Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy

NASA Astrophysics Data System (ADS)

Quyen, Michel Le Van; Martinerie, Jacques; Adam, Claude; Varela, Francisco J.

1999-03-01

The degree of interdependence between intracranial electroencephalographic (EEG) channels was investigated in epileptic patients with temporal lobe seizures during interictal (between seizures) periods. With a novel method to characterize nonlinear cross-predictability, that is, the predictability of one channel using another channel as data base, we demonstrated here a possibility to extract information on the spatio-temporal organization of interactions between multichannel recording sites. This method determines whether two channels contain common activity, and often, whether one channel contains activity induced by the activity of the other channel. In particular, the technique and the comparison with surrogate data demonstrated that transient large-scale nonlinear entrainments by the epileptogenic region can be identified, this with or without epileptic activity. Furthermore, these recurrent activities related with the epileptic foci occurred in well-defined spatio-temporal patterns. This suggests that the epileptogenic region can exhibit very subtle influences on other brain regions during an interictal period and raises the possibility that the cross-predictability analysis of interictal data may be used as a significant aid in locating epileptogenic foci.

Lip movements entrain the observers’ low-frequency brain oscillations to facilitate speech intelligibility

PubMed Central

Park, Hyojin; Kayser, Christoph; Thut, Gregor; Gross, Joachim

2016-01-01

During continuous speech, lip movements provide visual temporal signals that facilitate speech processing. Here, using MEG we directly investigated how these visual signals interact with rhythmic brain activity in participants listening to and seeing the speaker. First, we investigated coherence between oscillatory brain activity and speaker’s lip movements and demonstrated significant entrainment in visual cortex. We then used partial coherence to remove contributions of the coherent auditory speech signal from the lip-brain coherence. Comparing this synchronization between different attention conditions revealed that attending visual speech enhances the coherence between activity in visual cortex and the speaker’s lips. Further, we identified a significant partial coherence between left motor cortex and lip movements and this partial coherence directly predicted comprehension accuracy. Our results emphasize the importance of visually entrained and attention-modulated rhythmic brain activity for the enhancement of audiovisual speech processing. DOI: http://dx.doi.org/10.7554/eLife.14521.001 PMID:27146891

Changes in Appetitive Associative Strength Modulates Nucleus Accumbens, But Not Orbitofrontal Cortex Neuronal Ensemble Excitability.

PubMed

Ziminski, Joseph J; Hessler, Sabine; Margetts-Smith, Gabriella; Sieburg, Meike C; Crombag, Hans S; Koya, Eisuke

2017-03-22

Cues that predict the availability of food rewards influence motivational states and elicit food-seeking behaviors. If a cue no longer predicts food availability, then animals may adapt accordingly by inhibiting food-seeking responses. Sparsely activated sets of neurons, coined "neuronal ensembles," have been shown to encode the strength of reward-cue associations. Although alterations in intrinsic excitability have been shown to underlie many learning and memory processes, little is known about these properties specifically on cue-activated neuronal ensembles. We examined the activation patterns of cue-activated orbitofrontal cortex (OFC) and nucleus accumbens (NAc) shell ensembles using wild-type and Fos-GFP mice, which express green fluorescent protein (GFP) in activated neurons, after appetitive conditioning with sucrose and extinction learning. We also investigated the neuronal excitability of recently activated, GFP+ neurons in these brain areas using whole-cell electrophysiology in brain slices. Exposure to a sucrose cue elicited activation of neurons in both the NAc shell and OFC. In the NAc shell, but not the OFC, these activated GFP+ neurons were more excitable than surrounding GFP- neurons. After extinction, the number of neurons activated in both areas was reduced and activated ensembles in neither area exhibited altered excitability. These data suggest that learning-induced alterations in the intrinsic excitability of neuronal ensembles is regulated dynamically across different brain areas. Furthermore, we show that changes in associative strength modulate the excitability profile of activated ensembles in the NAc shell. SIGNIFICANCE STATEMENT Sparsely distributed sets of neurons called "neuronal ensembles" encode learned associations about food and cues predictive of its availability. Widespread changes in neuronal excitability have been observed in limbic brain areas after associative learning, but little is known about the excitability changes that occur specifically on neuronal ensembles that encode appetitive associations. Here, we reveal that sucrose cue exposure recruited a more excitable ensemble in the nucleus accumbens, but not orbitofrontal cortex, compared with their surrounding neurons. This excitability difference was not observed when the cue's salience was diminished after extinction learning. These novel data provide evidence that the intrinsic excitability of appetitive memory-encoding ensembles is regulated differentially across brain areas and adapts dynamically to changes in associative strength. Copyright © 2017 the authors 0270-6474/17/373160-11$15.00/0.

Brain volumetry and self-regulation of brain activity relevant for neurofeedback.

PubMed

Ninaus, M; Kober, S E; Witte, M; Koschutnig, K; Neuper, C; Wood, G

2015-09-01

Neurofeedback is a technique to learn to control brain signals by means of real time feedback. In the present study, the individual ability to learn two EEG neurofeedback protocols - sensorimotor rhythm and gamma rhythm - was related to structural properties of the brain. The volumes in the anterior insula bilaterally, left thalamus, right frontal operculum, right putamen, right middle frontal gyrus, and right lingual gyrus predicted the outcomes of sensorimotor rhythm training. Gray matter volumes in the supplementary motor area and left middle frontal gyrus predicted the outcomes of gamma rhythm training. These findings combined with further evidence from the literature are compatible with the existence of a more general self-control network, which through self-referential and self-control processes regulates neurofeedback learning. Copyright © 2015 Elsevier B.V. All rights reserved.

Prefrontal Cortex Structure Predicts Training-Induced Improvements in Multitasking Performance.

PubMed

Verghese, Ashika; Garner, K G; Mattingley, Jason B; Dux, Paul E

2016-03-02

The ability to perform multiple, concurrent tasks efficiently is a much-desired cognitive skill, but one that remains elusive due to the brain's inherent information-processing limitations. Multitasking performance can, however, be greatly improved through cognitive training (Van Selst et al., 1999, Dux et al., 2009). Previous studies have examined how patterns of brain activity change following training (for review, see Kelly and Garavan, 2005). Here, in a large-scale human behavioral and imaging study of 100 healthy adults, we tested whether multitasking training benefits, assessed using a standard dual-task paradigm, are associated with variability in brain structure. We found that the volume of the rostral part of the left dorsolateral prefrontal cortex (DLPFC) predicted an individual's response to training. Critically, this association was observed exclusively in a task-specific training group, and not in an active-training control group. Our findings reveal a link between DLPFC structure and an individual's propensity to gain from training on a task that taps the limits of cognitive control. Cognitive "brain" training is a rapidly growing, multibillion dollar industry (Hayden, 2012) that has been touted as the panacea for a variety of disorders that result in cognitive decline. A key process targeted by such training is "cognitive control." Here, we combined an established cognitive control measure, multitasking ability, with structural brain imaging in a sample of 100 participants. Our goal was to determine whether individual differences in brain structure predict the extent to which people derive measurable benefits from a cognitive training regime. Ours is the first study to identify a structural brain marker-volume of left hemisphere dorsolateral prefrontal cortex-associated with the magnitude of multitasking performance benefits induced by training at an individual level. Copyright © 2016 the authors 0270-6474/16/362638-08$15.00/0.

Dissociating mental states related to doing nothing by means of fMRI pattern classification.

PubMed

Kühn, Simone; Bodammer, Nils Christian; Brass, Marcel

2010-12-01

Most juridical systems recognize intentional non-actions - the failure to render assistance - as intentional acts by regarding them as in principle culpable. This raises the fundamental question whether intentional non-actions can be distinguished from simply not doing anything. Classical GLM analysis on functional magnetic resonance imaging (fMRI) data reveals that not doing anything is associated with resting state brain areas whereas intentionally non-acting is associated with brain activity in left inferior parietal lobe and left dorsal premotor cortex. By means of pattern classification we quantify the accuracy with which we can distinguish these two mental states on the basis of brain activity. In order to identify brain regions that harbour a distributed, overlapping representation of voluntary non-actions and the decision not to act we performed pattern classification on brain areas that did not appear in the GLM contrasts. The prediction rate is not reduced and we show that the prediction relies mostly on brain areas that have been associated with action production and motor imagery as supplementary motor area, right inferior frontal gyrus and right middle temporal area (V5/MT). Hence our data support the implicit assumption of legal practice that voluntary non-action shares important features with overt voluntary action. Copyright © 2010 Elsevier Inc. All rights reserved.

Brain Monoamine Oxidase-A Activity Predicts Trait Aggression

PubMed Central

Alia-Klein, Nelly; Goldstein, Rita Z.; Kriplani, Aarti; Logan, Jean; Tomasi, Dardo; Williams, Benjamin; Telang, Frank; Shumay, Elena; Biegon, Anat; Craig, Ian W.; Henn, Fritz; Wang, Gene-Jack; Volkow, Nora D.; Fowler, Joanna S.

2008-01-01

The genetic deletion of monoamine oxidase A (MAO A, an enzyme which breaks down the monoamine neurotransmitters norepinephrine, serotonin and dopamine) produces aggressive phenotypes across species. Therefore, a common polymorphism in the MAO A gene (MAOA, MIM 309850, referred to as high or low based on transcription in non-neuronal cells) has been investigated in a number of externalizing behavioral and clinical phenotypes. These studies provide evidence linking the low MAOA genotype and violent behavior but only through interaction with severe environmental stressors during childhood. Here, we hypothesized that in healthy adult males the gene product of MAO A in the brain, rather than the gene per se, would be associated with regulating the concentration of brain amines involved in trait aggression. Brain MAO A activity was measured in-vivo in healthy non-smoking men with positron emission tomography using a radioligand specific for MAO A (clorgyline labeled with carbon 11). Trait aggression was measured with the Multidimensional Personality Questionnaire (MPQ). Here we report for the first time that brain MAO A correlates inversely with the MPQ trait measure of aggression (but not with other personality traits) such that the lower the MAO A activity in cortical and subcortical brain regions the higher the self-reported aggression (in both MAOA genotype groups) contributing to more than a third of the variability. Since trait aggression is a measure used to predict antisocial behavior, these results underscore the relevance of MAO A as a neurochemical substrate of aberrant aggression. PMID:18463263

Evidence that the methylation state of the monoamine oxidase A (MAOA) gene predicts brain activity of MAO A enzyme in healthy men.

PubMed

Shumay, Elena; Logan, Jean; Volkow, Nora D; Fowler, Joanna S

2012-10-01

Human brain function is mediated by biochemical processes, many of which can be visualized and quantified by positron emission tomography (PET). PET brain imaging of monoamine oxidase A (MAO A)-an enzyme metabolizing neurotransmitters-revealed that MAO A levels vary widely between healthy men and this variability was not explained by the common MAOA genotype (VNTR genotype), suggesting that environmental factors, through epigenetic modifications, may mediate it. Here, we analyzed MAOA methylation in white blood cells (by bisulphite conversion of genomic DNA and subsequent sequencing of cloned DNA products) and measured brain MAO A levels (using PET and [(11)C]clorgyline, a radiotracer with specificity for MAO A) in 34 healthy non-smoking male volunteers. We found significant interindividual differences in methylation status and methylation patterns of the core MAOA promoter. The VNTR genotype did not influence the methylation status of the gene or brain MAO A activity. In contrast, we found a robust association of the regional and CpG site-specific methylation of the core MAOA promoter with brain MAO A levels. These results suggest that the methylation status of the MAOA promoter (detected in white blood cells) can reliably predict the brain endophenotype. Therefore, the status of MAOA methylation observed in healthy males merits consideration as a variable contributing to interindividual differences in behavior.

Evidence that the methylation state of the monoamine oxidase A (MAOA) gene predicts brain activity of MAOA enzyme in healthy men

PubMed Central

Shumay, Elena; Logan, Jean; Volkow, Nora D.; Fowler, Joanna S.

2012-01-01

Human brain function is mediated by biochemical processes, many of which can be visualized and quantified by positron emission tomography (PET). PET brain imaging of monoamine oxidase A (MAOA)—an enzyme metabolizing neurotransmitters—revealed that MAOA levels vary widely between healthy men and this variability was not explained by the common MAOA genotype (VNTR genotype), suggesting that environmental factors, through epigenetic modifications, may mediate it. Here, we analyzed MAOA methylation in white blood cells (by bisulphite conversion of genomic DNA and subsequent sequencing of cloned DNA products) and measured brain MAOA levels (using PET and [11C]clorgyline, a radiotracer with specificity for MAOA) in 34 healthy non-smoking male volunteers. We found significant interindividual differences in methylation status and methylation patterns of the core MAOA promoter. The VNTR genotype did not influence the methylation status of the gene or brain MAOA activity. In contrast, we found a robust association of the regional and CpG site-specific methylation of the core MAOA promoter with brain MAOA levels. These results suggest that the methylation status of the MAOA promoter (detected in white blood cells) can reliably predict the brain endophenotype. Therefore, the status of MAOA methylation observed in healthy males merits consideration as a variable contributing to interindividual differences in behavior. PMID:22948232

Attention and prediction in human audition: a lesson from cognitive psychophysiology

PubMed Central

Schröger, Erich; Marzecová, Anna; SanMiguel, Iria

2015-01-01

Attention is a hypothetical mechanism in the service of perception that facilitates the processing of relevant information and inhibits the processing of irrelevant information. Prediction is a hypothetical mechanism in the service of perception that considers prior information when interpreting the sensorial input. Although both (attention and prediction) aid perception, they are rarely considered together. Auditory attention typically yields enhanced brain activity, whereas auditory prediction often results in attenuated brain responses. However, when strongly predicted sounds are omitted, brain responses to silence resemble those elicited by sounds. Studies jointly investigating attention and prediction revealed that these different mechanisms may interact, e.g. attention may magnify the processing differences between predicted and unpredicted sounds. Following the predictive coding theory, we suggest that prediction relates to predictions sent down from predictive models housed in higher levels of the processing hierarchy to lower levels and attention refers to gain modulation of the prediction error signal sent up to the higher level. As predictions encode contents and confidence in the sensory data, and as gain can be modulated by the intention of the listener and by the predictability of the input, various possibilities for interactions between attention and prediction can be unfolded. From this perspective, the traditional distinction between bottom-up/exogenous and top-down/endogenous driven attention can be revisited and the classic concepts of attentional gain and attentional trace can be integrated. PMID:25728182

Optical mapping of brain activation during the English to Chinese and Chinese to English sight translation

PubMed Central

He, Yan; Wang, Meng-Yun; Li, Defeng; Yuan, Zhen

2017-01-01

Translating from Chinese into another language or vice versa is becoming a widespread phenomenon. However, current neuroimaging studies are insufficient to reveal the neural mechanism underlying translation asymmetry during Chinese/English sight translation. In this study, functional near infrared spectroscopy (fNIRS) was used to extract the brain activation patterns associated with Chinese/English sight translation. Eleven unbalanced Chinese (L1)/English (L2) bilinguals participated in this study based on an intra-group experimental design, in which two translation and two reading aloud tasks were administered: forward translation (from L1 to L2), backward translation (from L2 to L1), L1 reading, and L2 reading. As predicted, our findings revealed that forward translation elicited more pronounced brain activation in Broca’s area, suggesting that neural correlates of translation vary according to the direction of translation. Additionally, significant brain activation in the left PFC was involved in backward translation, indicating the importance of this brain region during the translation process. The identical activation patterns could not be discovered in forward translation, indicating the cognitive processing of reading logographic languages (i.e. Chinese) might recruit incongruent brain regions. PMID:29296476

Dynamic causal modelling of brain-behaviour relationships.

PubMed

Rigoux, L; Daunizeau, J

2015-08-15

In this work, we expose a mathematical treatment of brain-behaviour relationships, which we coin behavioural Dynamic Causal Modelling or bDCM. This approach aims at decomposing the brain's transformation of stimuli into behavioural outcomes, in terms of the relative contribution of brain regions and their connections. In brief, bDCM places the brain at the interplay between stimulus and behaviour: behavioural outcomes arise from coordinated activity in (hidden) neural networks, whose dynamics are driven by experimental inputs. Estimating neural parameters that control network connectivity and plasticity effectively performs a neurobiologically-constrained approximation to the brain's input-outcome transform. In other words, neuroimaging data essentially serves to enforce the realism of bDCM's decomposition of input-output relationships. In addition, post-hoc artificial lesions analyses allow us to predict induced behavioural deficits and quantify the importance of network features for funnelling input-output relationships. This is important, because this enables one to bridge the gap with neuropsychological studies of brain-damaged patients. We demonstrate the face validity of the approach using Monte-Carlo simulations, and its predictive validity using empirical fMRI/behavioural data from an inhibitory control task. Lastly, we discuss promising applications of this work, including the assessment of functional degeneracy (in the healthy brain) and the prediction of functional recovery after lesions (in neurological patients). Copyright © 2015 Elsevier Inc. All rights reserved.

Available processing resources influence encoding-related brain activity before an event

PubMed Central

Galli, Giulia; Gebert, A. Dorothea; Otten, Leun J.

2013-01-01

Effective cognitive functioning not only relies on brain activity elicited by an event, but also on activity that precedes it. This has been demonstrated in a number of cognitive domains, including memory. Here, we show that brain activity that precedes the effective encoding of a word into long-term memory depends on the availability of sufficient processing resources. We recorded electrical brain activity from the scalps of healthy adult men and women while they memorized intermixed visual and auditory words for later recall. Each word was preceded by a cue that indicated the modality of the upcoming word. The degree to which processing resources were available before word onset was manipulated by asking participants to make an easy or difficult perceptual discrimination on the cue. Brain activity before word onset predicted later recall of the word, but only in the easy discrimination condition. These findings indicate that anticipatory influences on long-term memory are limited in capacity and sensitive to the degree to which attention is divided between tasks. Prestimulus activity that affects later encoding can only be engaged when the necessary cognitive resources can be allocated to the encoding process. PMID:23219383

Functional split brain in a driving/listening paradigm

PubMed Central

Boly, Melanie; Mensen, Armand; Tononi, Giulio

2016-01-01

We often engage in two concurrent but unrelated activities, such as driving on a quiet road while listening to the radio. When we do so, does our brain split into functionally distinct entities? To address this question, we imaged brain activity with fMRI in experienced drivers engaged in a driving simulator while listening either to global positioning system instructions (integrated task) or to a radio show (split task). We found that, compared with the integrated task, the split task was characterized by reduced multivariate functional connectivity between the driving and listening networks. Furthermore, the integrated information content of the two networks, predicting their joint dynamics above and beyond their independent dynamics, was high in the integrated task and zero in the split task. Finally, individual subjects’ ability to switch between high and low information integration predicted their driving performance across integrated and split tasks. This study raises the possibility that under certain conditions of daily life, a single brain may support two independent functional streams, a “functional split brain” similar to what is observed in patients with an anatomical split. PMID:27911805

Estimation of State Transition Probabilities: A Neural Network Model

NASA Astrophysics Data System (ADS)

Saito, Hiroshi; Takiyama, Ken; Okada, Masato

2015-12-01

Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.

Permutation auto-mutual information of electroencephalogram in anesthesia

NASA Astrophysics Data System (ADS)

Liang, Zhenhu; Wang, Yinghua; Ouyang, Gaoxiang; Voss, Logan J.; Sleigh, Jamie W.; Li, Xiaoli

2013-04-01

Objective. The dynamic change of brain activity in anesthesia is an interesting topic for clinical doctors and drug designers. To explore the dynamical features of brain activity in anesthesia, a permutation auto-mutual information (PAMI) method is proposed to measure the information coupling of electroencephalogram (EEG) time series obtained in anesthesia. Approach. The PAMI is developed and applied on EEG data collected from 19 patients under sevoflurane anesthesia. The results are compared with the traditional auto-mutual information (AMI), SynchFastSlow (SFS, derived from the BIS index), permutation entropy (PE), composite PE (CPE), response entropy (RE) and state entropy (SE). Performance of all indices is assessed by pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability. Main results. The PK/PD modeling and prediction probability analysis show that the PAMI index correlates closely with the anesthetic effect. The coefficient of determination R2 between PAMI values and the sevoflurane effect site concentrations, and the prediction probability Pk are higher in comparison with other indices. The information coupling in EEG series can be applied to indicate the effect of the anesthetic drug sevoflurane on the brain activity as well as other indices. The PAMI of the EEG signals is suggested as a new index to track drug concentration change. Significance. The PAMI is a useful index for analyzing the EEG dynamics during general anesthesia.

Structural and functional bases of inhibited temperament.

PubMed

Clauss, Jacqueline A; Seay, April L; VanDerKlok, Ross M; Avery, Suzanne N; Cao, Aize; Cowan, Ronald L; Benningfield, Margaret M; Blackford, Jennifer Urbano

2014-12-01

Children born with an inhibited temperament are at heightened risk for developing anxiety, depression and substance use. Inhibited temperament is believed to have a biological basis; however, little is known about the structural brain basis of this vulnerability trait. Structural MRI scans were obtained from 84 (44 inhibited, 40 uninhibited) young adults. Given previous findings of amygdala hyperactivity in inhibited individuals, groups were compared on three measures of amygdala structure. To identify novel substrates of inhibited temperament, a whole brain analysis was performed. Functional activation and connectivity were examined across both groups. Inhibited adults had larger amygdala and caudate volume and larger volume predicted greater activation to neutral faces. In addition, larger amygdala volume predicted greater connectivity with subcortical and higher order visual structures. Larger caudate volume predicted greater connectivity with the basal ganglia, and less connectivity with primary visual and auditory cortex. We propose that larger volume in these salience detection regions may result in increased activation and enhanced connectivity in response to social stimuli. Given the strong link between inhibited temperament and risk for psychiatric illness, novel therapeutics that target these brain regions and related neural circuits have the potential to reduce rates of illness in vulnerable individuals. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa.

PubMed

DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W

2017-06-01

Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa

PubMed Central

DeGuzman, Marisa; Shott, Megan E.; Yang, Tony T.; Riederer, Justin; Frank, Guido K.W.

2017-01-01

Objective Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Method Female adolescents with anorexia nervosa (N=21; mean age, 15.2 years [SD=2.4]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 16.4 years [SD=1.9]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Results Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Conclusions Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs. PMID:28231717

Mitochondrial Complex 1 Activity Measured by Spectrophotometry Is Reduced across All Brain Regions in Ageing and More Specifically in Neurodegeneration.

PubMed

Pollard, Amelia Kate; Craig, Emma Louise; Chakrabarti, Lisa

2016-01-01

Mitochondrial function, in particular complex 1 of the electron transport chain (ETC), has been shown to decrease during normal ageing and in neurodegenerative disease. However, there is some debate concerning which area of the brain has the greatest complex 1 activity. It is important to identify the pattern of activity in order to be able to gauge the effect of age or disease related changes. We determined complex 1 activity spectrophotometrically in the cortex, brainstem and cerebellum of middle aged mice (70-71 weeks), a cerebellar ataxic neurodegeneration model (pcd5J) and young wild type controls. We share our updated protocol on the measurements of complex1 activity and find that mitochondrial fractions isolated from frozen tissues can be measured for robust activity. We show that complex 1 activity is clearly highest in the cortex when compared with brainstem and cerebellum (p<0.003). Cerebellum and brainstem mitochondria exhibit similar levels of complex 1 activity in wild type brains. In the aged brain we see similar levels of complex 1 activity in all three-brain regions. The specific activity of complex 1 measured in the aged cortex is significantly decreased when compared with controls (p<0.0001). Both the cerebellum and brainstem mitochondria also show significantly reduced activity with ageing (p<0.05). The mouse model of ataxia predictably has a lower complex 1 activity in the cerebellum, and although reductions are measured in the cortex and brain stem, the remaining activity is higher than in the aged brains. We present clear evidence that complex 1 activity decreases across the brain with age and much more specifically in the cerebellum of the pcd5j mouse. Mitochondrial impairment can be a region specific phenomenon in disease, but in ageing appears to affect the entire brain, abolishing the pattern of higher activity in cortical regions.

Brain entropy and human intelligence: A resting-state fMRI study

PubMed Central

Calderone, Daniel; Morales, Leah J.

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns. PMID:29432427

Brain entropy and human intelligence: A resting-state fMRI study.

PubMed

Saxe, Glenn N; Calderone, Daniel; Morales, Leah J

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

Multi-scale integration and predictability in resting state brain activity

PubMed Central

Kolchinsky, Artemy; van den Heuvel, Martijn P.; Griffa, Alessandra; Hagmann, Patric; Rocha, Luis M.; Sporns, Olaf; Goñi, Joaquín

2014-01-01

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales. PMID:25104933

Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging.

PubMed

Iannaccone, Reto; Hauser, Tobias U; Ball, Juliane; Brandeis, Daniel; Walitza, Susanne; Brem, Silvia

2015-10-01

Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78% of the subjects with a sensitivity and specificity of 77.78% based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083-3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.

Choice from non-choice: predicting consumer preferences from blood oxygenation level-dependent signals obtained during passive viewing.

PubMed

Levy, Ifat; Lazzaro, Stephanie C; Rutledge, Robb B; Glimcher, Paul W

2011-01-05

Decision-making is often viewed as a two-stage process, where subjective values are first assigned to each option and then the option of the highest value is selected. Converging evidence suggests that these subjective values are represented in the striatum and medial prefrontal cortex (MPFC). A separate line of evidence suggests that activation in the same areas represents the values of rewards even when choice is not required, as in classical conditioning tasks. However, it is unclear whether the same neural mechanism is engaged in both cases. To address this question we measured brain activation with functional magnetic resonance imaging while human subjects passively viewed individual consumer goods. We then sampled activation from predefined regions of interest and used it to predict subsequent choices between the same items made outside of the scanner. Our results show that activation in the striatum and MPFC in the absence of choice predicts subsequent choices, suggesting that these brain areas represent value in a similar manner whether or not choice is required.

Prediction of Treatment Outcome in Patients with Obsessive-Compulsive Disorder with Low-Resolution Brain Electromagnetic Tomography: A Prospective EEG Study.

PubMed

Krause, Daniela; Folkerts, Malte; Karch, Susanne; Keeser, Daniel; Chrobok, Agnieszka I; Zaudig, Michael; Hegerl, Ulrich; Juckel, Georg; Pogarell, Oliver

2015-01-01

The issue of predicting treatment response and identifying, in advance, which patient will profit from treating obsessive-compulsive disorder (OCD) seems to be an elusive goal. This prospective study investigated brain electric activity [using Low-Resolution Brain Electromagnetic Tomography (LORETA)] for the purpose of predicting response to treatment. Forty-one unmedicated patients with a DSM-IV diagnosis of OCD were included. A resting 32-channel EEG was obtained from each participant before and after 10 weeks of standardized treatment with sertraline and behavioral therapy. LORETA was used to localize the sources of brain electrical activity. At week 10, patients were divided into responders and non-responders (according to a reduction of symptom severity >50% on the Y-BOCS). LORETA analysis revealed that at baseline responders showed compared to non-responders a significantly lower brain electric activity within the beta 1 (t = 2.86, p < 0.05), 2 (t = 2.81, p < 0.05), and 3 (t = 2.76, p < 0.05) frequency bands and ROI analysis confirmed a reduced activity in alpha 2 (t = 2.06, p < 0.05) in the anterior cingulate cortex (ACC). When baseline LORETA data were compared to follow-up data, the analysis showed in the responder group a significantly lower brain electrical resting activity in the beta 1 (t = 3.17. p < 0.05) and beta 3 (t = 3.11. p < 0.05) frequency bands and equally for the ROI analysis of the orbitofrontal cortex (OFC) in the alpha 2 (t = 2.15. p < 0.05) frequency band. In the group of non-responders the opposite results were found. In addition, a positive correlation between frequency alpha 2 (rho = 0.40, p = 0.010), beta 3 (rho = 0.42, p = 0.006), delta (rho = 0.33, p = 0.038), theta (rho = 0.34, p = 0.031), alpha 1 (rho = 0.38, p = 0.015), and beta1 (rho = 0.34, p = 0.028) of the OFC and the bands delta (rho = 0.33, p = 0.035), alpha 1 (rho = 0.36, p = 0.019), alpha 2 (rho = 0.34, p = 0.031), and beta 3 (rho = 0.38, p = 0.015) of the ACC with a reduction of the Y-BOCS scores was identified. Our results suggest that measuring brain activity with LORETA could be an efficient and applicable technique to prospectively identify treatment responders in OCD.

Prediction of Treatment Outcome in Patients with Obsessive-Compulsive Disorder with Low-Resolution Brain Electromagnetic Tomography: A Prospective EEG Study

PubMed Central

Krause, Daniela; Folkerts, Malte; Karch, Susanne; Keeser, Daniel; Chrobok, Agnieszka I.; Zaudig, Michael; Hegerl, Ulrich; Juckel, Georg; Pogarell, Oliver

2016-01-01

The issue of predicting treatment response and identifying, in advance, which patient will profit from treating obsessive-compulsive disorder (OCD) seems to be an elusive goal. This prospective study investigated brain electric activity [using Low-Resolution Brain Electromagnetic Tomography (LORETA)] for the purpose of predicting response to treatment. Forty-one unmedicated patients with a DSM-IV diagnosis of OCD were included. A resting 32-channel EEG was obtained from each participant before and after 10 weeks of standardized treatment with sertraline and behavioral therapy. LORETA was used to localize the sources of brain electrical activity. At week 10, patients were divided into responders and non-responders (according to a reduction of symptom severity >50% on the Y-BOCS). LORETA analysis revealed that at baseline responders showed compared to non-responders a significantly lower brain electric activity within the beta 1 (t = 2.86, p < 0.05), 2 (t = 2.81, p < 0.05), and 3 (t = 2.76, p < 0.05) frequency bands and ROI analysis confirmed a reduced activity in alpha 2 (t = 2.06, p < 0.05) in the anterior cingulate cortex (ACC). When baseline LORETA data were compared to follow-up data, the analysis showed in the responder group a significantly lower brain electrical resting activity in the beta 1 (t = 3.17. p < 0.05) and beta 3 (t = 3.11. p < 0.05) frequency bands and equally for the ROI analysis of the orbitofrontal cortex (OFC) in the alpha 2 (t = 2.15. p < 0.05) frequency band. In the group of non-responders the opposite results were found. In addition, a positive correlation between frequency alpha 2 (rho = 0.40, p = 0.010), beta 3 (rho = 0.42, p = 0.006), delta (rho = 0.33, p = 0.038), theta (rho = 0.34, p = 0.031), alpha 1 (rho = 0.38, p = 0.015), and beta1 (rho = 0.34, p = 0.028) of the OFC and the bands delta (rho = 0.33, p = 0.035), alpha 1 (rho = 0.36, p = 0.019), alpha 2 (rho = 0.34, p = 0.031), and beta 3 (rho = 0.38, p = 0.015) of the ACC with a reduction of the Y-BOCS scores was identified. Our results suggest that measuring brain activity with LORETA could be an efficient and applicable technique to prospectively identify treatment responders in OCD. PMID:26834658

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.

PubMed

Gowin, Joshua L; Ball, Tali M; Wittmann, Marc; Tapert, Susan F; Paulus, Martin P

2015-07-01

Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders. Published by Elsevier Ireland Ltd.

Separate neural mechanisms underlie choices and strategic preferences in risky decision making.

PubMed

Venkatraman, Vinod; Payne, John W; Bettman, James R; Luce, Mary Frances; Huettel, Scott A

2009-05-28

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using an economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual's preferred strategy. Choices that maximized gains or minimized losses were predicted by functional magnetic resonance imaging activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending on strategies, traits, and context.

Separate neural mechanisms underlie choices and strategic preferences in risky decision making

PubMed Central

Venkatraman, Vinod; Payne, John W.; Bettman, James R.; Luce, Mary Frances; Huettel, Scott A.

2011-01-01

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using a novel economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual’s preferred strategy. Choices that maximized gains or minimized losses were predicted by fMRI activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending upon strategies, traits, and context. PMID:19477159

Successful emotion regulation is predicted by amygdala activity and aspects of personality: A latent variable approach.

PubMed

Morawetz, Carmen; Alexandrowicz, Rainer W; Heekeren, Hauke R

2017-04-01

The experience of emotions and their cognitive control are based upon neural responses in prefrontal and subcortical regions and could be affected by personality and temperamental traits. Previous studies established an association between activity in reappraisal-related brain regions (e.g., inferior frontal gyrus and amygdala) and emotion regulation success. Given these relationships, we aimed to further elucidate how individual differences in emotion regulation skills relate to brain activity within the emotion regulation network on the one hand, and personality/temperamental traits on the other. We directly examined the relationship between personality and temperamental traits, emotion regulation success and its underlying neuronal network in a large sample (N = 82) using an explicit emotion regulation task and functional MRI (fMRI). We applied a multimethodological analysis approach, combing standard activation-based analyses with structural equation modeling. First, we found that successful downregulation is predicted by activity in key regions related to emotion processing. Second, the individual ability to successfully upregulate emotions is strongly associated with the ability to identify feelings, conscientiousness, and neuroticism. Third, the successful downregulation of emotion is modulated by openness to experience and habitual use of reappraisal. Fourth, the ability to regulate emotions is best predicted by a combination of brain activity and personality as well temperamental traits. Using a multimethodological analysis approach, we provide a first step toward a causal model of individual differences in emotion regulation ability by linking biological systems underlying emotion regulation with descriptive constructs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Neural responses to negative outcomes predict success in community-based substance use treatment

PubMed Central

Forster, Sarah E.; Finn, Peter R.; Brown, Joshua W.

2017-01-01

Background and aims Activation in some specific brain regions has demonstrated promise as prognostic indicators in substance dependent individuals (SDIs) but this issue has not yet been explored in SDIs attending typical of community-based treatment. We used a data-driven, exploratory approach to identify brain-based predictors of treatment outcome in a representative community sample of SDIs. The predictive utility of brain-based measures was evaluated against clinical indicators, cognitive-behavioral performance, and self-report assessments. Design Prospective clinical outcome design, evaluating baseline functional magnetic resonance imaging data from the Balloon Analogue Risk Task (BART) as a predictor of 3-month substance use treatment outcomes. Setting Community-based substance use programs in Bloomington, Indiana, USA. Participants Twenty-three SDIs (17 male, ages 18–43) in an intensive outpatient or residential treatment program; abstinent 1–4 weeks at baseline. Measurements Event-related brain response, BART performance, and self-report scores at treatment onset, substance use outcome measure (based on days of use) Findings Using voxel-level predictive modeling and leave-one-out cross-validation, an elevated response to unexpected negative feedback in bilateral amygdala and anterior hippocampus (Amyg/aHipp) at baseline successfully predicted greater substance use over the 3-month study interval (p ≤ 0.006, cluster-corrected). This effect was robust to inclusion of significant non-brain-based covariates. A larger response to negative feedback in bilateral Amyg/aHipp was also associated with faster reward-seeking responses after negative feedback (r(23) = −0.544, p = 0.007; r(23) = −0.588, p = 0.003). A model including Amyg/aHipp activation, faster reward-seeking after negative feedback, and significant self-report scores accounted for 45% of the variance in substance use outcomes in our sample. Conclusions An elevated response to unexpected negative feedback in bilateral amygdala and anterior hippocampus (Amyg/aHipp) appears to predict relapse to substance use in people attending community-based treatment. PMID:28029198

Preliminary results of BRAVO project: brain computer interfaces for Robotic enhanced Action in Visuo-motOr tasks.

PubMed

Bergamasco, Massimo; Frisoli, Antonio; Fontana, Marco; Loconsole, Claudio; Leonardis, Daniele; Troncossi, Marco; Foumashi, Mohammad Mozaffari; Parenti-Castelli, Vincenzo

2011-01-01

This paper presents the preliminary results of the project BRAVO (Brain computer interfaces for Robotic enhanced Action in Visuo-motOr tasks). The objective of this project is to define a new approach to the development of assistive and rehabilitative robots for motor impaired users to perform complex visuomotor tasks that require a sequence of reaches, grasps and manipulations of objects. BRAVO aims at developing new robotic interfaces and HW/SW architectures for rehabilitation and regain/restoration of motor function in patients with upper limb sensorimotor impairment through extensive rehabilitation therapy and active assistance in the execution of Activities of Daily Living. The final system developed within this project will include a robotic arm exoskeleton and a hand orthosis that will be integrated together for providing force assistance. The main novelty that BRAVO introduces is the control of the robotic assistive device through the active prediction of intention/action. The system will actually integrate the information about the movement carried out by the user with a prediction of the performed action through an interpretation of current gaze of the user (measured through eye-tracking), brain activation (measured through BCI) and force sensor measurements. © 2011 IEEE

Reading a Suspenseful Literary Text Activates Brain Areas Related to Social Cognition and Predictive Inference

PubMed Central

Lehne, Moritz; Engel, Philipp; Rohrmeier, Martin; Menninghaus, Winfried; Jacobs, Arthur M.; Koelsch, Stefan

2015-01-01

Stories can elicit powerful emotions. A key emotional response to narrative plots (e.g., novels, movies, etc.) is suspense. Suspense appears to build on basic aspects of human cognition such as processes of expectation, anticipation, and prediction. However, the neural processes underlying emotional experiences of suspense have not been previously investigated. We acquired functional magnetic resonance imaging (fMRI) data while participants read a suspenseful literary text (E.T.A. Hoffmann's “The Sandman”) subdivided into short text passages. Individual ratings of experienced suspense obtained after each text passage were found to be related to activation in the medial frontal cortex, bilateral frontal regions (along the inferior frontal sulcus), lateral premotor cortex, as well as posterior temporal and temporo-parietal areas. The results indicate that the emotional experience of suspense depends on brain areas associated with social cognition and predictive inference. PMID:25946306

Abstract Linguistic Structure Correlates with Temporal Activity during Naturalistic Comprehension

PubMed Central

Brennan, Jonathan R.; Stabler, Edward P.; Van Wagenen, Sarah E.; Luh, Wen-Ming; Hale, John T.

2016-01-01

Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations. PMID:27208858

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy

PubMed Central

Boyd, Roslyn N; Davies, Peter SW; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-01-01

Objectives Cerebral palsy (CP) remains the world’s most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8–12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). Methods and analyses This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006–2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. Ethics and dissemination The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5–5 then 8–12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation. Trial registration number ACTRN: 12616001488493 PMID:28706091

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

PubMed

Deligianni, Fani; Centeno, Maria; Carmichael, David W; Clayden, Jonathan D

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

PubMed Central

Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

Relative cortico-subcortical shift in brain activity but preserved training-induced neural modulation in older adults during bimanual motor learning.

PubMed

Santos Monteiro, Thiago; Beets, Iseult A M; Boisgontier, Matthieu P; Gooijers, Jolien; Pauwels, Lisa; Chalavi, Sima; King, Brad; Albouy, Geneviève; Swinnen, Stephan P

2017-10-01

To study age-related differences in neural activation during motor learning, functional magnetic resonance imaging scans were acquired from 25 young (mean 21.5-year old) and 18 older adults (mean 68.6-year old) while performing a bimanual coordination task before (pretest) and after (posttest) a 2-week training intervention on the task. We studied whether task-related brain activity and training-induced brain activation changes differed between age groups, particularly with respect to the hyperactivation typically observed in older adults. Findings revealed that older adults showed lower performance levels than younger adults but similar learning capability. At the cerebral level, the task-related hyperactivation in parietofrontal areas and underactivation in subcortical areas observed in older adults were not differentially modulated by the training intervention. However, brain activity related to task planning and execution decreased from pretest to posttest in temporo-parieto-frontal areas and subcortical areas in both age groups, suggesting similar processes of enhanced activation efficiency with advanced skill level. Furthermore, older adults who displayed higher activity in prefrontal regions at pretest demonstrated larger training-induced performance gains. In conclusion, in spite of prominent age-related brain activation differences during movement planning and execution, the mechanisms of learning-related reduction of brain activation appear to be similar in both groups. Importantly, cerebral activity during early learning can differentially predict the amplitude of the training-induced performance benefit between young and older adults. Copyright © 2017 Elsevier Inc. All rights reserved.

A Review of Transcranial Magnetic Stimulation and Multimodal Neuroimaging to Characterize Post-Stroke Neuroplasticity

PubMed Central

Auriat, Angela M.; Neva, Jason L.; Peters, Sue; Ferris, Jennifer K.; Boyd, Lara A.

2015-01-01

Following stroke, the brain undergoes various stages of recovery where the central nervous system can reorganize neural circuitry (neuroplasticity) both spontaneously and with the aid of behavioral rehabilitation and non-invasive brain stimulation. Multiple neuroimaging techniques can characterize common structural and functional stroke-related deficits, and importantly, help predict recovery of function. Diffusion tensor imaging (DTI) typically reveals increased overall diffusivity throughout the brain following stroke, and is capable of indexing the extent of white matter damage. Magnetic resonance spectroscopy (MRS) provides an index of metabolic changes in surviving neural tissue after stroke, serving as a marker of brain function. The neural correlates of altered brain activity after stroke have been demonstrated by abnormal activation of sensorimotor cortices during task performance, and at rest, using functional magnetic resonance imaging (fMRI). Electroencephalography (EEG) has been used to characterize motor dysfunction in terms of increased cortical amplitude in the sensorimotor regions when performing upper limb movement, indicating abnormally increased cognitive effort and planning in individuals with stroke. Transcranial magnetic stimulation (TMS) work reveals changes in ipsilesional and contralesional cortical excitability in the sensorimotor cortices. The severity of motor deficits indexed using TMS has been linked to the magnitude of activity imbalance between the sensorimotor cortices. In this paper, we will provide a narrative review of data from studies utilizing DTI, MRS, fMRI, EEG, and brain stimulation techniques focusing on TMS and its combination with uni- and multimodal neuroimaging methods to assess recovery after stroke. Approaches that delineate the best measures with which to predict or positively alter outcomes will be highlighted. PMID:26579069

Gait and Glasgow Coma Scale scores can predict functional recovery in patients with traumatic brain injury☆

PubMed Central

Bilgin, Sevil; Guclu-Gunduz, Arzu; Oruckaptan, Hakan; Kose, Nezire; Celik, Bülent

2012-01-01

Fifty-one patients with mild (n = 14), moderate (n = 10) and severe traumatic brain injury (n = 27) received early rehabilitation. Level of consciousness was evaluated using the Glasgow Coma Score. Functional level was determined using the Glasgow Outcome Score, whilst mobility was evaluated using the Mobility Scale for Acute Stroke. Activities of daily living were assessed using the Barthel Index. Following Bobath neurodevelopmental therapy, the level of consciousness was significantly improved in patients with moderate and severe traumatic brain injury, but was not greatly influenced in patients with mild traumatic brain injury. Mobility and functional level were significantly improved in patients with mild, moderate and severe traumatic brain injury. Gait recovery was more obvious in patients with mild traumatic brain injury than in patients with moderate and severe traumatic brain injury. Activities of daily living showed an improvement but this was insignificant except for patients with severe traumatic brain injury. Nevertheless, complete recovery was not acquired at discharge. Multiple regression analysis showed that gait and Glasgow Coma Scale scores can be considered predictors of functional outcomes following traumatic brain injury. PMID:25624828

The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics.

PubMed

Melozzi, Francesca; Woodman, Marmaduke M; Jirsa, Viktor K; Bernard, Christophe

2017-01-01

Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain's structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain (TVB) to whole mouse brain network modeling based on individual diffusion magnetic resonance imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain (TVMB) to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. TVMB enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.

Predictive Feedback and Conscious Visual Experience

PubMed Central

Panichello, Matthew F.; Cheung, Olivia S.; Bar, Moshe

2012-01-01

The human brain continuously generates predictions about the environment based on learned regularities in the world. These predictions actively and efficiently facilitate the interpretation of incoming sensory information. We review evidence that, as a result of this facilitation, predictions directly influence conscious experience. Specifically, we propose that predictions enable rapid generation of conscious percepts and bias the contents of awareness in situations of uncertainty. The possible neural mechanisms underlying this facilitation are discussed. PMID:23346068

Parental Rearing Behavior Prospectively Predicts Adolescents' Risky Decision-Making and Feedback-Related Electrical Brain Activity

ERIC Educational Resources Information Center

Euser, Anja S.; Evans, Brittany E.; Greaves-Lord, Kirstin; Huizink, Anja C.; Franken, Ingmar H. A.

2013-01-01

The present study examined the role of parental rearing behavior in adolescents' risky decision-making and the brain's feedback processing mechanisms. Healthy adolescent participants ("n" = 110) completed the EMBU-C, a self-report questionnaire on perceived parental rearing behaviors between 2006 and 2008 (T1). Subsequently, after an…

Predicting Clinical Outcome Using Brain Activation Associated with Set-Shifting and Central Coherence Skills in Anorexia Nervosa

PubMed Central

Garrett, Amy; Lock, James; Datta, Nandini; Beenhaker, Judy; Kesler, Shelli R.; Reiss, Allan L.

2014-01-01

Background Patients with Anorexia Nervosa (AN) have neuropsychological deficits in set shifting (SS) and central coherence (CC) consistent with an inflexible thinking style and overly detailed processing style, respectively. This study investigates brain activation during SS and CC tasks in patients with AN and tests whether this activation is a biomarker that predicts response to treatment. Methods : FMRI data were collected from 21 females with AN while performing a SS task (the Wisconsin Card Sort) and a CC task (embedded figures), and used to predict outcome following 16 weeks of treatment (either 16 weeks of cognitive behavioral therapy or 8 weeks cognitive remediation training followed by 8 weeks of cognitive behavioral therapy). Results Significant activation during the SS task included bilateral dorsolateral and ventrolateral prefrontal cortex and left anterior middle frontal gyrus. Higher scores on the neuropsychological test of SS (measured outside the scanner at baseline) were correlated with greater DLPFC and VLPFC activation. Improvements in SS following treatment were significantly predicted by a combination of low VLPFC and high anterior middle frontal activation (R squared = .68, p=.001). For the CC task, the visual and parietal areas were activated, but were not significantly correlated with neuropsychological measures of CC and did not predict outcome. Conclusion Cognitive flexibility requires the support of several prefrontal cortex resources. As previous studies suggest that the VLPFC is important for selecting responses, patients who demonstrate that deficit may benefit the most from cognitive therapy with or without cognitive remediation training. The ability to sustain inhibition of an unwanted response, subserved by the anterior middle frontal gyrus, is a cognitive feature that predicts favorable outcome to cognitive treatment. CC deficits may not be an effective predictor of clinical outcome. PMID:25027478

Oxytocin enhances inter-brain synchrony during social coordination in male adults

PubMed Central

Mu, Yan; Guo, Chunyan

2016-01-01

Recent brain imaging research has revealed oxytocin (OT) effects on an individual's brain activity during social interaction but tells little about whether and how OT modulates the coherence of inter-brain activity related to two individuals' coordination behavior. We developed a new real-time coordination game that required two individuals of a dyad to synchronize with a partner (coordination task) or with a computer (control task) by counting in mind rhythmically. Electroencephalography (EEG) was recorded simultaneously from a dyad to examine OT effects on inter-brain synchrony of neural activity during interpersonal coordination. Experiment 1 found that dyads showed smaller interpersonal time lags of counting and greater inter-brain synchrony of alpha-band neural oscillations during the coordination (vs control) task and these effects were reliably observed in female but not male dyads. Moreover, the increased alpha-band inter-brain synchrony predicted better interpersonal behavioral synchrony across all participants. Experiment 2, using a double blind, placebo-controlled between-subjects design, revealed that intranasal OT vs placebo administration in male dyads improved interpersonal behavioral synchrony in both the coordination and control tasks but specifically enhanced alpha-band inter-brain neural oscillations during the coordination task. Our findings provide first evidence that OT enhances inter-brain synchrony in male adults to facilitate social coordination. PMID:27510498

Identification of brain regions predicting epileptogenesis by serial [18F]GE-180 positron emission tomography imaging of neuroinflammation in a rat model of temporal lobe epilepsy.

PubMed

Russmann, Vera; Brendel, Matthias; Mille, Erik; Helm-Vicidomini, Angela; Beck, Roswitha; Günther, Lisa; Lindner, Simon; Rominger, Axel; Keck, Michael; Salvamoser, Josephine D; Albert, Nathalie L; Bartenstein, Peter; Potschka, Heidrun

2017-01-01

Excessive activation of inflammatory signaling pathways seems to be a hallmark of epileptogenesis. Positron emission tomography (PET) allows in vivo detection of brain inflammation with spatial information and opportunities for longitudinal follow-up scanning protocols. Here, we assessed whether molecular imaging of the 18 kDa translocator protein (TSPO) can serve as a biomarker for the development of epilepsy. Therefore, brain uptake of [ 18 F]GE-180, a highly selective radioligand of TSPO, was investigated in a longitudinal PET study in a chronic rat model of temporal lobe epilepsy. Analyses revealed that the influence of the epileptogenic insult on [ 18 F]GE-180 brain uptake was most pronounced in the earlier phase of epileptogenesis. Differences were evident in various brain regions during earlier phases of epileptogenesis with [ 18 F]GE-180 standardized uptake value enhanced by 2.1 to 2.7fold. In contrast, brain regions exhibiting differences seemed to be more restricted with less pronounced increases of tracer uptake by 1.8-2.5fold four weeks following status epilepticus and by 1.5-1.8fold in the chronic phase. Based on correlation analysis, we were able to identify regions with a predictive value showing a correlation with seizure development. These regions include the amygdala as well as a cluster of brain areas. This cluster comprises parts of different brain regions, e.g. the hippocampus, parietal cortex, thalamus, and somatosensory cortex. In conclusion, the data provide evidence that [ 18 F]GE-180 PET brain imaging can serve as a biomarker of epileptogenesis. The identification of brain regions with predictive value might facilitate the development of preventive concepts as well as the early assessment of the interventional success. Future studies are necessary to further confirm the predictivity of the approach.

Brain Mechanisms Supporting Violated Expectations of Pain

PubMed Central

Zeidan, Fadel; Lobanov, Oleg V.; Kraft, Robert A.; Coghill, Robert C.

2015-01-01

The subjective experience of pain is influenced by interactions between prior experiences, future predictions and incoming afferent information. Expectations of high pain can exacerbate pain while expectations of low pain during a consistently noxious stimulus can produce significant reductions in pain. However, the brain mechanisms associated with processing mismatches between expected and experienced pain are poorly understood, but are important for imparting salience to a sensory event in order to override erroneous top-down expectancy-mediated information. The present investigation examined pain-related brain activation when expectations of pain were abruptly violated. After conditioning participants to cues predicting low or high pain, ten incorrectly cued stimuli were administered across 56 stimulus trials to determine if expectations would be less influential on pain when there is a high discordance between pre-stimulus cues and corresponding thermal stimulation. Incorrectly cued stimuli produced pain ratings and pain-related brain activation consistent with placebo analgesia, nocebo hyperalgesia, and violated expectations. Violated expectations of pain were associated with activation in distinct regions of the inferior parietal lobe, including the supramarginal and angular gyrus, and intraparietal sulcus, the superior parietal lobe, cerebellum and occipital lobe. Thus, violated expectations of pain engage mechanisms supporting salience-driven sensory discrimination, working memory, and associative learning processes. By overriding the influence of expectations on pain, these brain mechanisms are likely engaged in clinical situations where patients’ unrealistic expectations for pain relief diminish the efficacy of pain treatments. Accordingly, these findings underscore the importance of maintaining realistic expectations to augment the effectiveness of pain management. PMID:26083664

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

PubMed Central

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-01-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of log BB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (log P), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental log BB data had been determined in vivo. In particular, since molecules with log BB > 0.3 cross the blood-brain barrier (BBB) readily while molecules with log BB < −1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the log BB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. PMID:20427217

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

PubMed

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-06-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. Published by Elsevier Inc.

The Neural Correlates of Hierarchical Predictions for Perceptual Decisions.

PubMed

Weilnhammer, Veith A; Stuke, Heiner; Sterzer, Philipp; Schmack, Katharina

2018-05-23

Sensory information is inherently noisy, sparse, and ambiguous. In contrast, visual experience is usually clear, detailed, and stable. Bayesian theories of perception resolve this discrepancy by assuming that prior knowledge about the causes underlying sensory stimulation actively shapes perceptual decisions. The CNS is believed to entertain a generative model aligned to dynamic changes in the hierarchical states of our volatile sensory environment. Here, we used model-based fMRI to study the neural correlates of the dynamic updating of hierarchically structured predictions in male and female human observers. We devised a crossmodal associative learning task with covertly interspersed ambiguous trials in which participants engaged in hierarchical learning based on changing contingencies between auditory cues and visual targets. By inverting a Bayesian model of perceptual inference, we estimated individual hierarchical predictions, which significantly biased perceptual decisions under ambiguity. Although "high-level" predictions about the cue-target contingency correlated with activity in supramodal regions such as orbitofrontal cortex and hippocampus, dynamic "low-level" predictions about the conditional target probabilities were associated with activity in retinotopic visual cortex. Our results suggest that our CNS updates distinct representations of hierarchical predictions that continuously affect perceptual decisions in a dynamically changing environment. SIGNIFICANCE STATEMENT Bayesian theories posit that our brain entertains a generative model to provide hierarchical predictions regarding the causes of sensory information. Here, we use behavioral modeling and fMRI to study the neural underpinnings of such hierarchical predictions. We show that "high-level" predictions about the strength of dynamic cue-target contingencies during crossmodal associative learning correlate with activity in orbitofrontal cortex and the hippocampus, whereas "low-level" conditional target probabilities were reflected in retinotopic visual cortex. Our findings empirically corroborate theorizations on the role of hierarchical predictions in visual perception and contribute substantially to a longstanding debate on the link between sensory predictions and orbitofrontal or hippocampal activity. Our work fundamentally advances the mechanistic understanding of perceptual inference in the human brain. Copyright © 2018 the authors 0270-6474/18/385008-14$15.00/0.

Monitoring and control of amygdala neurofeedback involves distributed information processing in the human brain.

PubMed

Paret, Christian; Zähringer, Jenny; Ruf, Matthias; Gerchen, Martin Fungisai; Mall, Stephanie; Hendler, Talma; Schmahl, Christian; Ende, Gabriele

2018-03-30

Brain-computer interfaces provide conscious access to neural activity by means of brain-derived feedback ("neurofeedback"). An individual's abilities to monitor and control feedback are two necessary processes for effective neurofeedback therapy, yet their underlying functional neuroanatomy is still being debated. In this study, healthy subjects received visual feedback from their amygdala response to negative pictures. Activation and functional connectivity were analyzed to disentangle the role of brain regions in different processes. Feedback monitoring was mapped to the thalamus, ventromedial prefrontal cortex (vmPFC), ventral striatum (VS), and rostral PFC. The VS responded to feedback corresponding to instructions while rPFC activity differentiated between conditions and predicted amygdala regulation. Control involved the lateral PFC, anterior cingulate, and insula. Monitoring and control activity overlapped in the VS and thalamus. Extending current neural models of neurofeedback, this study introduces monitoring and control of feedback as anatomically dissociated processes, and suggests their important role in voluntary neuromodulation. © 2018 Wiley Periodicals, Inc.

Monte Carlo simulation of near-infrared light propagation in realistic adult head models with hair follicles

NASA Astrophysics Data System (ADS)

Pan, Boan; Fang, Xiang; Liu, Weichao; Li, Nanxi; Zhao, Ke; Li, Ting

2018-02-01

Near infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) has been used to measure brain activation, which are clinically important. Monte Carlo simulation has been applied to the near infrared light propagation model in biological tissue, and has the function of predicting diffusion and brain activation. However, previous studies have rarely considered hair and hair follicles as a contributing factor. Here, we attempt to use MCVM (Monte Carlo simulation based on 3D voxelized media) to examine light transmission, absorption, fluence, spatial sensitivity distribution (SSD) and brain activation judgement in the presence or absence of the hair follicles. The data in this study is a series of high-resolution cryosectional color photograph of a standing Chinse male adult. We found that the number of photons transmitted under the scalp decreases dramatically and the photons exported to detector is also decreasing, as the density of hair follicles increases. If there is no hair follicle, the above data increase and has the maximum value. Meanwhile, the light distribution and brain activation have a stable change along with the change of hair follicles density. The findings indicated hair follicles make influence of NIRS in light distribution and brain activation judgement.

Regional Homogeneity Predicts Creative Insight: A Resting-State fMRI Study.

PubMed

Lin, Jiabao; Cui, Xuan; Dai, Xiaoying; Mo, Lei

2018-01-01

Creative insight plays an important role in our daily life. Previous studies have investigated the neural correlates of creative insight by functional magnetic resonance imaging (fMRI), however, the intrinsic resting-state brain activity associated with creative insight is still unclear. In the present study, we used regional homogeneity (ReHo) as an index in resting-state fMRI (rs-fMRI) to identify brain regions involved in individual differences in creative insight, which was compued by the response time (RT) of creative Chinese character chunk decomposition. The findings indicated that ReHo in the anterior cingulate cortex (ACC)/caudate nucleus (CN) and angular gyrus (AG)/superior temporal gyrus (STG)/inferior parietal lobe (IPL) negatively predicted creative insight. Furthermore, these findings suggested that spontaneous brain activity in multiple regions related to breaking and establishing mental sets, goal-directed solutions exploring, shifting attention, forming new associations and emotion experience contributes to creative insight. In conclusion, the present study provides new evidence to further understand the cognitive processing and neural correlates of creative insight.

When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients.

PubMed

Boly, M; Coleman, M R; Davis, M H; Hampshire, A; Bor, D; Moonen, G; Maquet, P A; Pickard, J D; Laureys, S; Owen, A M

2007-07-01

The assessment of voluntary behavior in non-communicative brain injured patients is often challenging due to the existence of profound motor impairment. In the absence of a full understanding of the neural correlates of consciousness, even a normal activation in response to passive sensory stimulation cannot be considered as proof of the presence of awareness in these patients. In contrast, predicted activation in response to the instruction to perform a mental imagery task would provide evidence of voluntary task-dependent brain activity, and hence of consciousness, in non-communicative patients. However, no data yet exist to indicate which imagery instructions would yield reliable single subject activation. The aim of the present study was to establish such a paradigm in healthy volunteers. Two exploratory experiments evaluated the reproducibility of individual brain activation elicited by four distinct mental imagery tasks. The two most robust mental imagery tasks were found to be spatial navigation and motor imagery. In a third experiment, where these two tasks were directly compared, differentiation of each task from one another and from rest periods was assessed blindly using a priori criteria and was correct for every volunteer. The spatial navigation and motor imagery tasks described here permit the identification of volitional brain activation at the single subject level, without a motor response. Volunteer as well as patient data [Owen, A.M., Coleman, M.R., Boly, M., Davis, M.H., Laureys, S., Pickard J.D., 2006. Detecting awareness in the vegetative state. Science 313, 1402] strongly suggest that this paradigm may provide a method for assessing the presence of volitional brain activity, and thus of consciousness, in non-communicative brain-injured patients.

Now or Later? An fMRI study of the effects of endogenous opioid blockade on a decision-making network

PubMed Central

Boettiger, Charlotte A.; Kelley, Elizabeth A.; Mitchell, Jennifer M.; D’Esposito, Mark; Fields, Howard L.

2009-01-01

Previously, we found that distinct brain areas predict individual selection bias in decisions between small immediate (“Now”) and larger delayed rewards (“Later”). Furthermore, such selection bias can be manipulated by endogenous opioid blockade. To test whether blocking endogenous opioids with Naltrexone (NTX) alters brain activity during decision-making in areas predicting individual bias, we compared fMRI BOLD signal correlated with Now versus Later decision-making after acute administration of NTX (50 mg) or placebo. We tested abstinent alcoholics and control subjects in a double-blind two-session design. We defined regions of interest (ROI) centered on activation peaks predicting Now versus Later selection bias. NTX administration significantly increased BOLD signal during decision-making in the right lateral orbital gyrus ROI, an area where enhanced activity during decision-making predicts Later bias. Exploratory analyses identified additional loci where BOLD signal during decision-making was enhanced (left orbitofrontal cortex, left inferior temporal gyrus, and cerebellum) or reduced (right superior temporal pole) by NTX. Additional analyses identified sites, including the right lateral orbital gyrus, in which NTX effects on BOLD signal predicted NTX effects on selection bias. These data agree with opioid receptor expression in human frontal and temporal cortices, and suggest possible mechanisms of NTX’s therapeutic effects. PMID:19258022

Brain mediators of predictive cue effects on perceived pain

PubMed Central

Atlas, Lauren Y.; Bolger, Niall; Lindquist, Martin A.; Wager, Tor D.

2010-01-01

Information about upcoming pain strongly influences pain experience in experimental and clinical settings, but little is known about the brain mechanisms that link expectation and experience. To identify the pathways by which informational cues influence perception, analyses must jointly consider both the effects of cues on brain responses and the relationship between brain responses and changes in reported experience. Our task and analysis strategy were designed to test these relationships. Auditory cues elicited expectations for low or high painful thermal stimulation, and we assessed how cues influenced human subjects’ pain reports and BOLD fMRI responses to matched levels of noxious heat. We used multi-level mediation analysis to identify brain regions that 1) are modulated by predictive cues, 2) predict trial-to-trial variations in pain reports, and 3) formally mediate the relationship between cues and reported pain. Cues influenced heat-evoked responses in most canonical pain-processing regions, including both medial and lateral pain pathways. Effects on several regions correlated with pre-task expectations, suggesting that expectancy plays a prominent role. A subset of pain-processing regions, including anterior cingulate cortex, anterior insula, and thalamus, formally mediated cue effects on pain. Effects on these regions were in turn mediated by cue-evoked anticipatory activity in the medial orbitofrontal cortex (OFC) and ventral striatum, areas not previously directly implicated in nociception. These results suggest that activity in pain-processing regions reflects a combination of nociceptive input and top-down information related to expectations, and that anticipatory processes in OFC and striatum may play a key role in modulating pain processing. PMID:20881115

Brain Regional Blood Flow and Working Memory Performance Predict Change in Blood Pressure Over 2 Years.

PubMed

Jennings, J Richard; Heim, Alicia F; Sheu, Lei K; Muldoon, Matthew F; Ryan, Christopher; Gach, H Michael; Schirda, Claudiu; Gianaros, Peter J

2017-12-01

Hypertension is a presumptive risk factor for premature cognitive decline. However, lowering blood pressure (BP) does not uniformly reverse cognitive decline, suggesting that high BP per se may not cause cognitive decline. We hypothesized that essential hypertension has initial effects on the brain that, over time, manifest as cognitive dysfunction in conjunction with both brain vascular abnormalities and systemic BP elevation. Accordingly, we tested whether neuropsychological function and brain blood flow responses to cognitive challenges among prehypertensive individuals would predict subsequent progression of BP. Midlife adults (n=154; mean age, 49; 45% men) with prehypertensive BP underwent neuropsychological testing and assessment of regional cerebral blood flow (rCBF) response to cognitive challenges. Neuropsychological performance measures were derived for verbal and logical memory (memory), executive function, working memory, mental efficiency, and attention. A pseudo-continuous arterial spin labeling magnetic resonance imaging sequence compared rCBF responses with control and active phases of cognitive challenges. Brain areas previously associated with BP were grouped into composites for frontoparietal, frontostriatal, and insular-subcortical rCBF areas. Multiple regression models tested whether BP after 2 years was predicted by initial BP, initial neuropsychological scores, and initial rCBF responses to cognitive challenge. The neuropsychological composite of working memory (standardized beta, -0.276; se=0.116; P =0.02) and the frontostriatal rCBF response to cognitive challenge (standardized beta, 0.234; se=0.108; P =0.03) significantly predicted follow-up BP. Initial BP failed to significantly predict subsequent cognitive performance or rCBF. Changes in brain function may precede or co-occur with progression of BP toward hypertensive levels in midlife. © 2017 American Heart Association, Inc.

Biomarkers of Traumatic Injury Are Transported from Brain to Blood via the Glymphatic System

PubMed Central

Plog, Benjamin A.; Dashnaw, Matthew L.; Hitomi, Emi; Peng, Weiguo; Liao, Yonghong; Lou, Nanhong; Deane, Rashid

2015-01-01

The nonspecific and variable presentation of traumatic brain injury (TBI) has motivated an intense search for blood-based biomarkers that can objectively predict the severity of injury. However, it is not known how cytosolic proteins released from traumatized brain tissue reach the peripheral blood. Here we show in a murine TBI model that CSF movement through the recently characterized glymphatic pathway transports biomarkers to blood via the cervical lymphatics. Clinically relevant manipulation of glymphatic activity, including sleep deprivation and cisternotomy, suppressed or eliminated TBI-induced increases in serum S100β, GFAP, and neuron specific enolase. We conclude that routine TBI patient management may limit the clinical utility of blood-based biomarkers because their brain-to-blood transport depends on glymphatic activity. PMID:25589747

The prestimulus default mode network state predicts cognitive task performance levels on a mental rotation task.

PubMed

Kamp, Tabea; Sorger, Bettina; Benjamins, Caroline; Hausfeld, Lars; Goebel, Rainer

2018-06-22

Linking individual task performance to preceding, regional brain activation is an ongoing goal of neuroscientific research. Recently, it could be shown that the activation and connectivity within large-scale brain networks prior to task onset influence performance levels. More specifically, prestimulus default mode network (DMN) effects have been linked to performance levels in sensory near-threshold tasks, as well as cognitive tasks. However, it still remains uncertain how the DMN state preceding cognitive tasks affects performance levels when the period between task trials is long and flexible, allowing participants to engage in different cognitive states. We here investigated whether the prestimulus activation and within-network connectivity of the DMN are predictive of the correctness and speed of task performance levels on a cognitive (match-to-sample) mental rotation task, employing a sparse event-related functional magnetic resonance imaging (fMRI) design. We found that prestimulus activation in the DMN predicted the speed of correct trials, with a higher amplitude preceding correct fast response trials compared to correct slow response trials. Moreover, we found higher connectivity within the DMN before incorrect trials compared to correct trials. These results indicate that pre-existing activation and connectivity states within the DMN influence task performance on cognitive tasks, both effecting the correctness and speed of task execution. The findings support existing theories and empirical work on relating mind-wandering and cognitive task performance to the DMN and expand these by establishing a relationship between the prestimulus DMN state and the speed of cognitive task performance. © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

Human brain networks function in connectome-specific harmonic waves.

PubMed

Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

2016-01-21

A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.

Working memory brain activity and capacity link MAOA polymorphism to aggressive behavior during development.

PubMed

Ziermans, T; Dumontheil, I; Roggeman, C; Peyrard-Janvid, M; Matsson, H; Kere, J; Klingberg, T

2012-02-28

A developmental increase in working memory capacity is an important part of cognitive development, and low working memory (WM) capacity is a risk factor for developing psychopathology. Brain activity represents a promising endophenotype for linking genes to behavior and for improving our understanding of the neurobiology of WM development. We investigated gene-brain-behavior relationships by focusing on 18 single-nucleotide polymorphisms (SNPs) located in six dopaminergic candidate genes (COMT, SLC6A3/DAT1, DBH, DRD4, DRD5, MAOA). Visuospatial WM (VSWM) brain activity, measured with functional magnetic resonance imaging, and VSWM capacity were assessed in a longitudinal study of typically developing children and adolescents. Behavioral problems were evaluated using the Child Behavior Checklist (CBCL). One SNP (rs6609257), located ~6.6 kb downstream of the monoamine oxidase A gene (MAOA) on human chromosome X, significantly affected brain activity in a network of frontal, parietal and occipital regions. Increased activity in this network, but not in caudate nucleus or anterior prefrontal regions, was correlated with VSWM capacity, which in turn predicted externalizing (aggressive/oppositional) symptoms, with higher WM capacity associated with fewer externalizing symptoms. There were no direct significant correlations between rs6609257 and behavioral symptoms. These results suggest a mediating role of WM brain activity and capacity in linking the MAOA gene to aggressive behavior during development.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

PubMed Central

Hagmann, Patric; Deco, Gustavo

2015-01-01

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information. PMID:26317432

Load-related brain activation predicts spatial working memory performance in youth aged 9–12 and is associated with executive function at earlier ages

PubMed Central

Huang, Anna S.; Klein, Daniel N.; Leung, Hoi-Chung

2015-01-01

Spatial working memory is a central cognitive process that matures through adolescence in conjunction with major changes in brain function and anatomy. Here we focused on late childhood and early adolescence to more closely examine the neural correlates of performance variability during this important transition period. Using a modified spatial 1-back task with two memory load conditions in an fMRI study, we examined the relationship between load-dependent neural responses and task performance in a sample of 39 youth aged 9–12 years. Our data revealed that between-subject differences in task performance was predicted by load-dependent deactivation in default network regions, including the ventral anterior cingulate cortex (vACC) and posterior cingulate cortex (PCC). Although load-dependent increases in activation in prefrontal and posterior parietal regions were only weakly correlated with performance, increased prefrontal-parietal coupling was associated with better performance. Furthermore, behavioral measures of executive function from as early as age 3 predicted current load-dependent deactivation in vACC and PCC. These findings suggest that both task positive and task negative brain activation during spatial working memory contributed to successful task performance in late childhood/early adolescence. This may serve as a good model for studying executive control deficits in developmental disorders. PMID:26562059

Load-related brain activation predicts spatial working memory performance in youth aged 9-12 and is associated with executive function at earlier ages.

PubMed

Huang, Anna S; Klein, Daniel N; Leung, Hoi-Chung

2016-02-01

Spatial working memory is a central cognitive process that matures through adolescence in conjunction with major changes in brain function and anatomy. Here we focused on late childhood and early adolescence to more closely examine the neural correlates of performance variability during this important transition period. Using a modified spatial 1-back task with two memory load conditions in an fMRI study, we examined the relationship between load-dependent neural responses and task performance in a sample of 39 youth aged 9-12 years. Our data revealed that between-subject differences in task performance was predicted by load-dependent deactivation in default network regions, including the ventral anterior cingulate cortex (vACC) and posterior cingulate cortex (PCC). Although load-dependent increases in activation in prefrontal and posterior parietal regions were only weakly correlated with performance, increased prefrontal-parietal coupling was associated with better performance. Furthermore, behavioral measures of executive function from as early as age 3 predicted current load-dependent deactivation in vACC and PCC. These findings suggest that both task positive and task negative brain activation during spatial working memory contributed to successful task performance in late childhood/early adolescence. This may serve as a good model for studying executive control deficits in developmental disorders. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Functional correlates of the therapeutic and adverse effects evoked by thalamic stimulation for essential tremor

PubMed Central

Gibson, William S.; Jo, Hang Joon; Testini, Paola; Cho, Shinho; Felmlee, Joel P.; Welker, Kirk M.; Klassen, Bryan T.; Min, Hoon-Ki

2016-01-01

Deep brain stimulation is an established neurosurgical therapy for movement disorders including essential tremor and Parkinson’s disease. While typically highly effective, deep brain stimulation can sometimes yield suboptimal therapeutic benefit and can cause adverse effects. In this study, we tested the hypothesis that intraoperative functional magnetic resonance imaging could be used to detect deep brain stimulation-evoked changes in functional and effective connectivity that would correlate with the therapeutic and adverse effects of stimulation. Ten patients receiving deep brain stimulation of the ventralis intermedius thalamic nucleus for essential tremor underwent functional magnetic resonance imaging during stimulation applied at a series of stimulation localizations, followed by evaluation of deep brain stimulation-evoked therapeutic and adverse effects. Correlations between the therapeutic effectiveness of deep brain stimulation (3 months postoperatively) and deep brain stimulation-evoked changes in functional and effective connectivity were assessed using region of interest-based correlation analysis and dynamic causal modelling, respectively. Further, we investigated whether brain regions might exist in which activation resulting from deep brain stimulation might correlate with the presence of paraesthesias, the most common deep brain stimulation-evoked adverse effect. Thalamic deep brain stimulation resulted in activation within established nodes of the tremor circuit: sensorimotor cortex, thalamus, contralateral cerebellar cortex and deep cerebellar nuclei (FDR q < 0.05). Stimulation-evoked activation in all these regions of interest, as well as activation within the supplementary motor area, brainstem, and inferior frontal gyrus, exhibited significant correlations with the long-term therapeutic effectiveness of deep brain stimulation (P < 0.05), with the strongest correlation (P < 0.001) observed within the contralateral cerebellum. Dynamic causal modelling revealed a correlation between therapeutic effectiveness and attenuated within-region inhibitory connectivity in cerebellum. Finally, specific subregions of sensorimotor cortex were identified in which deep brain stimulation-evoked activation correlated with the presence of unwanted paraesthesias. These results suggest that thalamic deep brain stimulation in tremor likely exerts its effects through modulation of both olivocerebellar and thalamocortical circuits. In addition, our findings indicate that deep brain stimulation-evoked functional activation maps obtained intraoperatively may contain predictive information pertaining to the therapeutic and adverse effects induced by deep brain stimulation. PMID:27329768

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction

PubMed Central

Chen, Ishita; Ong, Rowena E.; Simpson, Amber L.; Sun, Kay; Thompson, Reid C.

2015-01-01

In recent work, an atlas-based statistical model for brain shift prediction, which accounts for uncertainty in the intraoperative environment, has been proposed. Previous work reported in the literature using this technique did not account for local deformation caused by surgical retraction. It is challenging to precisely localize the retractor location prior to surgery and the retractor is often moved in the course of the procedure. This paper proposes a technique that involves computing the retractor-induced brain deformation in the operating room through an active model solve and linearly superposing the solution with the precomputed deformation atlas. As a result, the new method takes advantage of the atlas-based framework’s accounting for uncertainties while also incorporating the effects of retraction with minimal intraoperative computing. This new approach was tested using simulation and phantom experiments. The results showed an improvement in average shift correction from 50% (ranging from 14 to 81%) for gravity atlas alone to 80% using the active solve retraction component (ranging from 73 to 85%). This paper presents a novel yet simple way to integrate retraction into the atlas-based brain shift computation framework. PMID:23864146

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

PubMed Central

Unni, Anirudh; Ihme, Klas; Jipp, Meike; Rieger, Jochem W.

2017-01-01

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous ‘n’ speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing. PMID:28424602

Assessing the Driver's Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study.

PubMed

Unni, Anirudh; Ihme, Klas; Jipp, Meike; Rieger, Jochem W

2017-01-01

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver's cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous 'n' speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

Time delay between cardiac and brain activity during sleep transitions

NASA Astrophysics Data System (ADS)

Long, Xi; Arends, Johan B.; Aarts, Ronald M.; Haakma, Reinder; Fonseca, Pedro; Rolink, Jérôme

2015-04-01

Human sleep consists of wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep that includes light and deep sleep stages. This work investigated the time delay between changes of cardiac and brain activity for sleep transitions. Here, the brain activity was quantified by electroencephalographic (EEG) mean frequency and the cardiac parameters included heart rate, standard deviation of heartbeat intervals, and their low- and high-frequency spectral powers. Using a cross-correlation analysis, we found that the cardiac variations during wake-sleep and NREM sleep transitions preceded the EEG changes by 1-3 min but this was not the case for REM sleep transitions. These important findings can be further used to predict the onset and ending of some sleep stages in an early manner.

MEG Can Map Short and Long-Term Changes in Brain Activity following Deep Brain Stimulation for Chronic Pain

PubMed Central

Mohseni, Hamid R.; Smith, Penny P.; Parsons, Christine E.; Young, Katherine S.; Hyam, Jonathan A.; Stein, Alan; Stein, John F.; Green, Alexander L.; Aziz, Tipu Z.; Kringelbach, Morten L.

2012-01-01

Deep brain stimulation (DBS) has been shown to be clinically effective for some forms of treatment-resistant chronic pain, but the precise mechanisms of action are not well understood. Here, we present an analysis of magnetoencephalography (MEG) data from a patient with whole-body chronic pain, in order to investigate changes in neural activity induced by DBS for pain relief over both short- and long-term. This patient is one of the few cases treated using DBS of the anterior cingulate cortex (ACC). We demonstrate that a novel method, null-beamforming, can be used to localise accurately brain activity despite the artefacts caused by the presence of DBS electrodes and stimulus pulses. The accuracy of our source localisation was verified by correlating the predicted DBS electrode positions with their actual positions. Using this beamforming method, we examined changes in whole-brain activity comparing pain relief achieved with deep brain stimulation (DBS ON) and compared with pain experienced with no stimulation (DBS OFF). We found significant changes in activity in pain-related regions including the pre-supplementary motor area, brainstem (periaqueductal gray) and dissociable parts of caudal and rostral ACC. In particular, when the patient reported experiencing pain, there was increased activity in different regions of ACC compared to when he experienced pain relief. We were also able to demonstrate long-term functional brain changes as a result of continuous DBS over one year, leading to specific changes in the activity in dissociable regions of caudal and rostral ACC. These results broaden our understanding of the underlying mechanisms of DBS in the human brain. PMID:22675503

Acute Brain Dysfunction: Development and Validation of a Daily Prediction Model.

PubMed

Marra, Annachiara; Pandharipande, Pratik P; Shotwell, Matthew S; Chandrasekhar, Rameela; Girard, Timothy D; Shintani, Ayumi K; Peelen, Linda M; Moons, Karl G M; Dittus, Robert S; Ely, E Wesley; Vasilevskis, Eduard E

2018-03-24

The goal of this study was to develop and validate a dynamic risk model to predict daily changes in acute brain dysfunction (ie, delirium and coma), discharge, and mortality in ICU patients. Using data from a multicenter prospective ICU cohort, a daily acute brain dysfunction-prediction model (ABD-pm) was developed by using multinomial logistic regression that estimated 15 transition probabilities (from one of three brain function states [normal, delirious, or comatose] to one of five possible outcomes [normal, delirious, comatose, ICU discharge, or died]) using baseline and daily risk factors. Model discrimination was assessed by using predictive characteristics such as negative predictive value (NPV). Calibration was assessed by plotting empirical vs model-estimated probabilities. Internal validation was performed by using a bootstrap procedure. Data were analyzed from 810 patients (6,711 daily transitions). The ABD-pm included individual risk factors: mental status, age, preexisting cognitive impairment, baseline and daily severity of illness, and daily administration of sedatives. The model yielded very high NPVs for "next day" delirium (NPV: 0.823), coma (NPV: 0.892), normal cognitive state (NPV: 0.875), ICU discharge (NPV: 0.905), and mortality (NPV: 0.981). The model demonstrated outstanding calibration when predicting the total number of patients expected to be in any given state across predicted risk. We developed and internally validated a dynamic risk model that predicts the daily risk for one of three cognitive states, ICU discharge, or mortality. The ABD-pm may be useful for predicting the proportion of patients for each outcome state across entire ICU populations to guide quality, safety, and care delivery activities. Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Visual cortex activity predicts subjective experience after reading books with colored letters.

PubMed

Colizoli, Olympia; Murre, Jaap M J; Scholte, H Steven; van Es, Daniel M; Knapen, Tomas; Rouw, Romke

2016-07-29

One of the most astonishing properties of synesthesia is that the evoked concurrent experiences are perceptual. Is it possible to acquire similar effects after learning cross-modal associations that resemble synesthetic mappings? In this study, we examine whether brain activation in early visual areas can be directly related to letter-color associations acquired by training. Non-synesthetes read specially prepared books with colored letters for several weeks and were scanned using functional magnetic resonance imaging. If the acquired letter-color associations were visual in nature, then brain activation in visual cortex while viewing the trained black letters (compared to untrained black letters) should predict the strength of the associations, the quality of the color experience, or the vividness of visual mental imagery. Results showed that training-related activation of area V4 was correlated with differences in reported subjective color experience. Trainees who were classified as having stronger 'associator' types of color experiences also had more negative activation for trained compared to untrained achromatic letters in area V4. In contrast, the strength of the acquired associations (measured as the Stroop effect) was not reliably reflected in visual cortex activity. The reported vividness of visual mental imagery was related to veridical color activation in early visual cortex, but not to the acquired color associations. We show for the first time that subjective experience related to a synesthesia-training paradigm was reflected in visual brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Decoding negative affect personality trait from patterns of brain activation to threat stimuli.

PubMed

Fernandes, Orlando; Portugal, Liana C L; Alves, Rita de Cássia S; Arruda-Sanchez, Tiago; Rao, Anil; Volchan, Eliane; Pereira, Mirtes; Oliveira, Letícia; Mourao-Miranda, Janaina

2017-01-15

Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Reduced Cortical Activity Impairs Development and Plasticity after Neonatal Hypoxia Ischemia

PubMed Central

Ranasinghe, Sumudu; Or, Grace; Wang, Eric Y.; Ievins, Aiva; McLean, Merritt A.; Niell, Cristopher M.; Chau, Vann; Wong, Peter K. H.; Glass, Hannah C.; Sullivan, Joseph

2015-01-01

Survivors of preterm birth are at high risk of pervasive cognitive and learning impairments, suggesting disrupted early brain development. The limits of viability for preterm birth encompass the third trimester of pregnancy, a “precritical period” of activity-dependent development characterized by the onset of spontaneous and evoked patterned electrical activity that drives neuronal maturation and formation of cortical circuits. Reduced background activity on electroencephalogram (EEG) is a sensitive marker of brain injury in human preterm infants that predicts poor neurodevelopmental outcome. We studied a rodent model of very early hypoxic–ischemic brain injury to investigate effects of injury on both general background and specific patterns of cortical activity measured with EEG. EEG background activity is depressed transiently after moderate hypoxia–ischemia with associated loss of spindle bursts. Depressed activity, in turn, is associated with delayed expression of glutamate receptor subunits and transporters. Cortical pyramidal neurons show reduced dendrite development and spine formation. Complementing previous observations in this model of impaired visual cortical plasticity, we find reduced somatosensory whisker barrel plasticity. Finally, EEG recordings from human premature newborns with brain injury demonstrate similar depressed background activity and loss of bursts in the spindle frequency band. Together, these findings suggest that abnormal development after early brain injury may result in part from disruption of specific forms of brain activity necessary for activity-dependent circuit development. SIGNIFICANCE STATEMENT Preterm birth and term birth asphyxia result in brain injury from inadequate oxygen delivery and constitute a major and growing worldwide health problem. Poor outcomes are noted in a majority of very premature (<25 weeks gestation) newborns, resulting in death or life-long morbidity with motor, sensory, learning, behavioral, and language disabilities that limit academic achievement and well-being. Limited progress has been made to develop therapies that improve neurologic outcomes. The overall objective of this study is to understand the effect of early brain injury on activity-dependent brain development and cortical plasticity to develop new treatments that will optimize repair and recovery after brain injury. PMID:26311776

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

The time course of activity in dorsolateral prefrontal cortex and anterior cingulate cortex during top-down attentional control.

PubMed

Silton, Rebecca Levin; Heller, Wendy; Towers, David N; Engels, Anna S; Spielberg, Jeffrey M; Edgar, J Christopher; Sass, Sarah M; Stewart, Jennifer L; Sutton, Bradley P; Banich, Marie T; Miller, Gregory A

2010-04-15

A network of brain regions has been implicated in top-down attentional control, including left dorsolateral prefrontal cortex (LDLPFC) and dorsal anterior cingulate cortex (dACC). The present experiment evaluated predictions of the cascade-of-control model (Banich, 2009), which predicts that during attentionally-demanding tasks, LDLPFC imposes a top-down attentional set which precedes late-stage selection performed by dACC. Furthermore, the cascade-of-control model argues that dACC must increase its activity to compensate when top-down control by LDLPFC is poor. The present study tested these hypotheses using fMRI and dense-array ERP data collected from the same 80 participants in separate sessions. fMRI results guided ERP source modeling to characterize the time course of activity in LDLPFC and dACC. As predicted, dACC activity subsequent to LDLPFC activity distinguished congruent and incongruent conditions on the Stroop task. Furthermore, when LDLPFC activity was low, the level of dACC activity was related to performance outcome. These results demonstrate that dACC responds to attentional demand in a flexible manner that is dependent on the level of LDLPFC activity earlier in a trial. Overall, results were consistent with the temporal course of regional brain function proposed by the cascade-of-control model. Copyright 2009 Elsevier Inc. All rights reserved.

Spontaneous activity in default-mode network predicts ascription of self-relatedness to stimuli.

PubMed

Qin, Pengmin; Grimm, Simone; Duncan, Niall W; Fan, Yan; Huang, Zirui; Lane, Timothy; Weng, Xuchu; Bajbouj, Malek; Northoff, Georg

2016-04-01

Spontaneous activity levels prior to stimulus presentation can determine how that stimulus will be perceived. It has also been proposed that such spontaneous activity, particularly in the default-mode network (DMN), is involved in self-related processing. We therefore hypothesised that pre-stimulus activity levels in the DMN predict whether a stimulus is judged as self-related or not. Participants were presented in the MRI scanner with a white noise stimulus that they were instructed contained their name or another. They then had to respond with which name they thought they heard. Regions where there was an activity level difference between self and other response trials 2 s prior to the stimulus being presented were identified. Pre-stimulus activity levels were higher in the right temporoparietal junction, the right temporal pole and the left superior temporal gyrus in trials where the participant responded that they heard their own name than trials where they responded that they heard another. Pre-stimulus spontaneous activity levels in particular brain regions, largely overlapping with the DMN, predict the subsequent judgement of stimuli as self-related. This extends our current knowledge of self-related processing and its apparent relationship with intrinsic brain activity in what can be termed a rest-self overlap. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Sleep and Predicted Cognitive Performance of New Cadets during Cadet Basic Training at the United States Military Academy

DTIC Science & Technology

2005-09-01

7 B. SLEEP ARCHITECTURE..................................7 1. Circadian Rhythm and Human Sleep Drive...body temperature. Van Dongen & Dinges, 2000 ....10 Figure 2. EEG of Human Brain Activity During Sleep. http://ist-socrates.berkeley.edu/~jmp...the predicted levels of human performance based on circadian rhythms , amount and quality of sleep, and combines cognitive performance 5 predictions

Neuroprotective effects of a brain permeant 6-aminoquinoxaline derivative in cell culture conditions that model the loss of dopaminergic neurons in Parkinson disease.

PubMed

Le Douaron, Gael; Schmidt, Fanny; Amar, Majid; Kadar, Hanane; Debortoli, Lucila; Latini, Alexandra; Séon-Méniel, Blandine; Ferrié, Laurent; Michel, Patrick Pierre; Touboul, David; Brunelle, Alain; Raisman-Vozari, Rita; Figadère, Bruno

2015-01-07

Parkinson disease is a neurodegenerative disorder of aging, characterized by disabling motor symptoms resulting from the loss of midbrain dopaminergic neurons and the decrease of dopamine in the striatum. Current therapies are directed at treating the symptoms but there is presently no cure for the disease. In order to discover neuroprotective compounds with a therapeutical potential, our research team has established original and highly regioselective methods for the synthesis of 2,3-disubstituted 6-aminoquinoxalines. To evaluate the neuroprotective activity of these molecules, we used midbrain cultures and various experimental conditions that promote dopaminergic cell loss. Among a series of 11 molecules, only compound MPAQ (2-methyl-3-phenyl-6-aminoquinoxaline) afforded substantial protection in a paradigm where dopaminergic neurons die spontaneously and progressively as they mature. Prediction of blood-brain barrier permeation by Quantitative Structure-Activity Relationship studies (QSARs) suggested that MPAQ was able to reach the brain parenchyma with sufficient efficacy. HPLC-MS/MS quantification in brain homogenates and MALDI-TOF mass spectrometry imaging on brain tissue sections performed in MPAQ-treated mice allowed us to confirm this prediction and to demonstrate, by MALDI-TOF mass spectrometry imaging, that MPAQ was localized in areas containing vulnerable neurons and/or their terminals. Of interest, MPAQ also rescued dopaminergic neurons, which (i) acquired dependency on the trophic peptide GDNF for their survival or (ii) underwent oxidative stress-mediated insults mediated by catalytically active iron. In summary, MPAQ possesses an interesting pharmacological profile as it penetrates the brain parenchyma and counteracts mechanisms possibly contributive to dopaminergic cell death in Parkinson disease. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

PubMed Central

Ritchie, J. Brendan; Tovar, David A.; Carlson, Thomas A.

2015-01-01

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition. PMID:26107634

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

PubMed Central

Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

2015-01-01

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. PMID:26714277

LSD-induced entropic brain activity predicts subsequent personality change.

PubMed

Lebedev, A V; Kaelen, M; Lövdén, M; Nilsson, J; Feilding, A; Nutt, D J; Carhart-Harris, R L

2016-09-01

Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Global brain dynamics during social exclusion predict subsequent behavioral conformity

PubMed Central

Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O’Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

2018-01-01

Abstract Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. PMID:29529310

Neural decoding of collective wisdom with multi-brain computing.

PubMed

Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

2012-01-02

Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally, our methods highlight the potential of multi-brain computing as a technique to rapidly and in parallel gather increased information about the environment as well as to access collective perceptual/cognitive choices and mental states. Copyright © 2011 Elsevier Inc. All rights reserved.

Perception of social synchrony induces mother-child gamma coupling in the social brain.

PubMed

Levy, Jonathan; Goldstein, Abraham; Feldman, Ruth

2017-07-01

The recent call to move from focus on one brain's functioning to two-brain communication initiated a search for mechanisms that enable two humans to coordinate brain response during social interactions. Here, we utilized the mother-child context as a developmentally salient setting to study two-brain coupling. Mothers and their 9-year-old children were videotaped at home in positive and conflictual interactions. Positive interactions were microcoded for social synchrony and conflicts for overall dialogical style. Following, mother and child underwent magnetoencephalography while observing the positive vignettes. Episodes of behavioral synchrony, compared to non-synchrony, increased gamma-band power in the superior temporal sulcus (STS), hub of social cognition, mirroring and mentalizing. This neural pattern was coupled between mother and child. Brain-to-brain coordination was anchored in behavioral synchrony; only during episodes of behavioral synchrony, but not during non-synchronous moments, mother's and child's STS gamma power was coupled. Importantly, neural synchrony was not found during observation of unfamiliar mother-child interaction Maternal empathic/dialogical conflict style predicted mothers' STS activations whereas child withdrawal predicted attenuated STS response in both partners. Results define a novel neural marker for brain-to-brain synchrony, highlight the role of rapid bottom-up oscillatory mechanisms for neural coupling and indicate that behavior-based processes may drive synchrony between two brains during social interactions. © The Author (2017). Published by Oxford University Press.

Brain indices of disagreement with one’s social values predict EU referendum voting behavior

PubMed Central

Sirota, Miroslav; Materassi, Maurizio; Zaninotto, Francesca; Terry, Philip

2017-01-01

Abstract Pre-electoral surveys typically attempt, and sometimes fail, to predict voting behavior on the basis of explicit measures of agreement or disagreement with a candidate or political position. Here, we assessed whether a specific brain signature of disagreement with one’s social values, the event-related potential component N400, could be predictive of voting behavior. We examined this possibility in the context of the EU referendum in the UK. In the 5 weeks preceding the referendum, we recorded the N400 while participants with different vote intentions expressed their agreement or disagreement with pro- and against-EU statements. We showed that the N400 responded to statements incongruent with one’s view regarding the EU. Crucially, this effect predicted actual voting behavior in decided as well as undecided voters. The N400 was a better predictor of voting choice than an explicit index of preference based on the behavioral responses. Our findings demonstrate that well-defined patterns of brain activity can forecast future voting behavior. PMID:28981799

Anticipatory activity in the human thalamus is predictive of reaction times.

PubMed

Nikulin, V V; Marzinzik, F; Wahl, M; Schneider, G-H; Kupsch, A; Curio, G; Klostermann, F

2008-09-09

Responding to environmental stimuli in a fast manner is a fundamental behavioral capacity. The pace at which one responds is known to be predetermined by cortical areas, but it remains to be shown if subcortical structures also take part in defining motor swiftness. As the thalamus has previously been implicated in behavioral control, we tested if neuronal activity at this level could also predict the reaction time of upcoming movements. To this end we simultaneously recorded electrical brain activity from the scalp and the ventral intermediate nucleus (VIM) of the thalamus in patients undergoing thalamic deep brain stimulation. Based on trial-to-trial analysis of a Go/NoGo task, we demonstrate that both cortical and thalamic neuronal activity prior to the delivery of upcoming Go stimulus correlates with the reaction time. This result goes beyond the demonstration of thalamic activity being associated with but potentially staying invariant to motor performance. In contrast, it indicates that the latencies at which we respond to environmental stimuli are not exclusively related to cortical pre-movement states but are also correlated with anticipatory thalamic activity.

Oxytocin enhances inter-brain synchrony during social coordination in male adults.

PubMed

Mu, Yan; Guo, Chunyan; Han, Shihui

2016-12-01

Recent brain imaging research has revealed oxytocin (OT) effects on an individual's brain activity during social interaction but tells little about whether and how OT modulates the coherence of inter-brain activity related to two individuals' coordination behavior. We developed a new real-time coordination game that required two individuals of a dyad to synchronize with a partner (coordination task) or with a computer (control task) by counting in mind rhythmically. Electroencephalography (EEG) was recorded simultaneously from a dyad to examine OT effects on inter-brain synchrony of neural activity during interpersonal coordination. Experiment 1 found that dyads showed smaller interpersonal time lags of counting and greater inter-brain synchrony of alpha-band neural oscillations during the coordination (vs control) task and these effects were reliably observed in female but not male dyads. Moreover, the increased alpha-band inter-brain synchrony predicted better interpersonal behavioral synchrony across all participants. Experiment 2, using a double blind, placebo-controlled between-subjects design, revealed that intranasal OT vs placebo administration in male dyads improved interpersonal behavioral synchrony in both the coordination and control tasks but specifically enhanced alpha-band inter-brain neural oscillations during the coordination task. Our findings provide first evidence that OT enhances inter-brain synchrony in male adults to facilitate social coordination. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Movement initiation-locked activity of the anterior putamen predicts future movement instability in periodic bimanual movement.

PubMed

Aramaki, Yu; Haruno, Masahiko; Osu, Rieko; Sadato, Norihiro

2011-07-06

In periodic bimanual movements, anti-phase-coordinated patterns often change into in-phase patterns suddenly and involuntarily. Because behavior in the initial period of a sequence of cycles often does not show any obvious errors, it is difficult to predict subsequent movement errors in the later period of the cyclical sequence. Here, we evaluated performance in the later period of the cyclical sequence of bimanual periodic movements using human brain activity measured with functional magnetic resonance imaging as well as using initial movement features. Eighteen subjects performed a 30 s bimanual finger-tapping task. We calculated differences in initiation-locked transient brain activity between antiphase and in-phase tapping conditions. Correlation analysis revealed that the difference in the anterior putamen activity during antiphase compared within-phase tapping conditions was strongly correlated with future instability as measured by the mean absolute deviation of the left-hand intertap interval during antiphase movements relative to in-phase movements (r = 0.81). Among the initial movement features we measured, only the number of taps to establish the antiphase movement pattern exhibited a significant correlation. However, the correlation efficient of 0.60 was not high enough to predict the characteristics of subsequent movement. There was no significant correlation between putamen activity and initial movement features. It is likely that initiating unskilled difficult movements requires increased anterior putamen activity, and this activity increase may facilitate the initiation of movement via the basal ganglia-thalamocortical circuit. Our results suggest that initiation-locked transient activity of the anterior putamen can be used to predict future motor performance.

Mice with reduced brain-derived neurotrophic factor expression show decreased choline acetyltransferase activity, but regular brain monoamine levels and unaltered emotional behavior.

PubMed

Chourbaji, Sabine; Hellweg, Rainer; Brandis, Dorothee; Zörner, Björn; Zacher, Christiane; Lang, Undine E; Henn, Fritz A; Hörtnagl, Heide; Gass, Peter

2004-02-05

The "neurotrophin hypothesis" of depression predicts that depressive disorders in humans coincide with a decreased activity and/or expression of brain-derived neurotrophic factor (BDNF) in the brain. Therefore, we investigated whether mice with a reduced BDNF expression due to heterozygous gene disruption demonstrate depression-like neurochemical changes or behavioral symptoms. BNDF protein levels of adult BDNF(+/-) mice were reduced to about 60% in several brain areas investigated, including the hippocampus, frontal cortex, striatum, and hypothalamus. The content of monoamines (serotonin, norepinephrine, and dopamine) as well as of serotonin and dopamine degradation products was unchanged in these brain regions. By contrast, choline acetyltransferase activity was significantly reduced by 19% in the hippocampus of BDNF(+/-) mice, indicating that the cholinergic system of the basal forebrain is critically dependent on sufficient endogenous BDNF levels in adulthood. Moreover, BDNF(+/-) mice exhibited normal corticosterone and adrenocorticotropic hormone (ACTH) serum levels under baseline conditions and following immobilization stress. In a panel of behavioral tests investigating locomotor activity, exploration, anxiety, fear-associated learning, and behavioral despair, BDNF(+/-) mice were indistinguishable from wild-type littermates. Thus, a chronic reduction of BDNF protein content in adult mice is not sufficient to induce neurochemical or behavioral alterations that are reminiscent of depressive symptoms in humans.

Resting-state functional connectivity and motor imagery brain activation

PubMed Central

Saiote, Catarina; Tacchino, Andrea; Brichetto, Giampaolo; Roccatagliata, Luca; Bommarito, Giulia; Cordano, Christian; Battaglia, Mario; Mancardi, Giovanni Luigi; Inglese, Matilde

2016-01-01

Motor imagery (MI) relies on the mental simulation of an action without any overt motor execution (ME), and can facilitate motor learning and enhance the effect of rehabilitation in patients with neurological conditions. While functional magnetic resonance imaging (fMRI) during MI and ME reveals shared cortical representations, the role and functional relevance of the resting-state functional connectivity (RSFC) of brain regions involved in MI is yet unknown. Here, we performed resting-state fMRI followed by fMRI during ME and MI with the dominant hand. We used a behavioral chronometry test to measure ME and MI movement duration and compute an index of performance (IP). Then, we analyzed the voxel-matched correlation between the individual MI parameter estimates and seed-based RSFC maps in the MI network to measure the correspondence between RSFC and MI fMRI activation. We found that inter-individual differences in intrinsic connectivity in the MI network predicted several clusters of activation. Taken together, present findings provide first evidence that RSFC within the MI network is predictive of the activation of MI brain regions, including those associated with behavioral performance, thus suggesting a role for RSFC in obtaining a deeper understanding of neural substrates of MI and of MI ability. PMID:27273577

Habit strength is predicted by activity dynamics in goal-directed brain systems during training.

PubMed

Zwosta, Katharina; Ruge, Hannes; Goschke, Thomas; Wolfensteller, Uta

2018-01-15

Previous neuroscientific research revealed insights into the brain networks supporting goal-directed and habitual behavior, respectively. However, it remains unclear how these contribute to inter-individual differences in habit strength which is relevant for understanding not only normal behavior but also more severe dysregulations between these types of action control, such as in addiction. In the present fMRI study, we trained subjects on approach and avoidance behavior for an extended period of time before testing the habit strength of the acquired stimulus-response associations. We found that stronger habits were associated with a stronger decrease in inferior parietal lobule activity for approach and avoidance behavior and weaker vmPFC activity at the end of training for avoidance behavior, areas associated with the anticipation of outcome identity and value. VmPFC in particular showed markedly different activity dynamics during the training of approach and avoidance behavior. Furthermore, while ongoing training was accompanied by increasing functional connectivity between posterior putamen and premotor cortex, consistent with previous assumptions about the neural basis of increasing habitualization, this was not predictive of later habit strength. Together, our findings suggest that inter-individual differences in habitual behavior are driven by differences in the persistent involvement of brain areas supporting goal-directed behavior during training. Copyright © 2017. Published by Elsevier Inc.

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text.

PubMed

Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro; Poesio, Massimo; Baroni, Marco

2015-10-15

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations. Copyright © 2015 Elsevier Inc. All rights reserved.

Implementations of clinical functional magnetic resonance imaging using character-based paradigms for the prediction of Chinese language dominance.

PubMed

Liu, Ho-Ling; Wu, Chien-Te; Chen, Jian-Chuan; Hsu, Yuan-Yu; Wai, Yau-Yau; Wan, Yung-Liang

2003-01-01

Recently, functional MRI (fMRI) using word generation (WG) tasks has been shown to be effective for mapping the Chinese language-related brain areas. In clinical applications, however, patients' performance cannot be easily monitored during WG tasks. In this study, we evaluated the feasibility of a word choice (WC) paradigm in the clinical setting and compared the results with those from WG tasks. Intrasubject comparisons of fMRI with both WG and WC paradigms were performed on six normal human subjects and two tumor patients. Subject responses in the WC paradigm, based on semantic judgments, were recorded. Activation strength, extent, and laterality were evaluated and compared. Our results showed that fMRI with the WC paradigm evoked weaker neuronal activation than that with the WG paradigm in Chinese language-related brain areas. It was sufficient to reveal language laterality for clinical use, however. In addition, it resulted in less nonlanguage-specific brain activation. Results from the patient data demonstrated strong evidence for the necessity of incorporating response monitoring during fMRI studies, which suggested that fMRI with the WC paradigm is more appropriate to be implemented for the prediction of Chinese language dominance in clinical environments.

Synchronous brain activity across individuals underlies shared psychological perspectives

PubMed Central

Lahnakoski, Juha M.; Glerean, Enrico; Jääskeläinen, Iiro P.; Hyönä, Jukka; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

2014-01-01

For successful communication, we need to understand the external world consistently with others. This task requires sufficiently similar cognitive schemas or psychological perspectives that act as filters to guide the selection, interpretation and storage of sensory information, perceptual objects and events. Here we show that when individuals adopt a similar psychological perspective during natural viewing, their brain activity becomes synchronized in specific brain regions. We measured brain activity with functional magnetic resonance imaging (fMRI) from 33 healthy participants who viewed a 10-min movie twice, assuming once a ‘social’ (detective) and once a ‘non-social’ (interior decorator) perspective to the movie events. Pearson's correlation coefficient was used to derive multisubject voxelwise similarity measures (inter-subject correlations; ISCs) of functional MRI data. We used k-nearest-neighbor and support vector machine classifiers as well as a Mantel test on the ISC matrices to reveal brain areas wherein ISC predicted the participants' current perspective. ISC was stronger in several brain regions—most robustly in the parahippocampal gyrus, posterior parietal cortex and lateral occipital cortex—when the participants viewed the movie with similar rather than different perspectives. Synchronization was not explained by differences in visual sampling of the movies, as estimated by eye gaze. We propose that synchronous brain activity across individuals adopting similar psychological perspectives could be an important neural mechanism supporting shared understanding of the environment. PMID:24936687

PTSD Psychotherapy Outcome Predicted by Brain Activation During Emotional Reactivity and Regulation.

PubMed

Fonzo, Gregory A; Goodkind, Madeleine S; Oathes, Desmond J; Zaiko, Yevgeniya V; Harvey, Meredith; Peng, Kathy K; Weiss, M Elizabeth; Thompson, Allison L; Zack, Sanno E; Lindley, Steven E; Arnow, Bruce A; Jo, Booil; Gross, James J; Rothbaum, Barbara O; Etkin, Amit

2017-12-01

Exposure therapy is an effective treatment for posttraumatic stress disorder (PTSD), but many patients do not respond. Brain functions governing treatment outcome are not well characterized. The authors examined brain systems relevant to emotional reactivity and regulation, constructs that are thought to be central to PTSD and exposure therapy effects, to identify the functional traits of individuals most likely to benefit from treatment. Individuals with PTSD underwent functional MRI (fMRI) while completing three tasks assessing emotional reactivity and regulation. Participants were then randomly assigned to immediate prolonged exposure treatment (N=36) or a waiting list condition (N=30). A random subset of the prolonged exposure group (N=17) underwent single-pulse transcranial magnetic stimulation (TMS) concurrent with fMRI to examine whether predictive activation patterns reflect causal influence within circuits. Linear mixed-effects modeling in line with the intent-to-treat principle was used to examine how baseline brain function moderated the effect of treatment on PTSD symptoms. At baseline, individuals with larger treatment-related symptom reductions (compared with the waiting list condition) demonstrated 1) greater dorsal prefrontal activation and 2) less left amygdala activation, both during emotion reactivity; 3) better inhibition of the left amygdala induced by single TMS pulses to the right dorsolateral prefrontal cortex; and 4) greater ventromedial prefrontal/ventral striatal activation during emotional conflict regulation. Reappraisal-related activation was not a significant moderator of the treatment effect. Capacity to benefit from prolonged exposure in PTSD is gated by the degree to which prefrontal resources are spontaneously engaged when superficially processing threat and adaptively mitigating emotional interference, but not when deliberately reducing negative emotionality.

Brain activity elicited by viewing pictures of the own virtually amputated body predicts xenomelia.

PubMed

Oddo-Sommerfeld, Silvia; Hänggi, Jürgen; Coletta, Ludovico; Skoruppa, Silke; Thiel, Aylin; Stirn, Aglaja V

2018-01-08

Xenomelia is a rare condition characterized by the persistent desire for the amputation of physically healthy limbs. Prior studies highlighted the importance of superior and inferior parietal lobuli (SPL/IPL) and other sensorimotor regions as key brain structures associated with xenomelia. We expected activity differences in these areas in response to pictures showing the desired body state, i.e. that of an amputee in xenomelia. Functional magnetic resonance images were acquired in 12 xenomelia individuals and 11 controls while they viewed pictures of their own real and virtually amputated body. Pictures were rated on several dimensions. Multivariate statistics using machine learning was performed on imaging data. Brain activity when viewing pictures of one's own virtually amputated body predicted group membership accurately with a balanced accuracy of 82.58% (p = 0.002), sensitivity of 83.33% (p = 0.018), specificity of 81.82% (p = 0.015) and an area under the ROC curve of 0.77. Among the highest predictive brain regions were bilateral SPL, IPL, and caudate nucleus, other limb representing areas, but also occipital regions. Pleasantness and attractiveness ratings were higher for amputated bodies in xenomelia. Findings show that neuronal processing in response to pictures of one's own desired body state is different in xenomelia compared with controls and might represent a neuronal substrate of the xenomelia complaints that become behaviourally relevant, at least when rating the pleasantness and attractiveness of one's own body. Our findings converge with structural peculiarities reported in xenomelia and partially overlap in task and results with that of anorexia and transgender research. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biomarkers of traumatic injury are transported from brain to blood via the glymphatic system.

PubMed

Plog, Benjamin A; Dashnaw, Matthew L; Hitomi, Emi; Peng, Weiguo; Liao, Yonghong; Lou, Nanhong; Deane, Rashid; Nedergaard, Maiken

2015-01-14

The nonspecific and variable presentation of traumatic brain injury (TBI) has motivated an intense search for blood-based biomarkers that can objectively predict the severity of injury. However, it is not known how cytosolic proteins released from traumatized brain tissue reach the peripheral blood. Here we show in a murine TBI model that CSF movement through the recently characterized glymphatic pathway transports biomarkers to blood via the cervical lymphatics. Clinically relevant manipulation of glymphatic activity, including sleep deprivation and cisternotomy, suppressed or eliminated TBI-induced increases in serum S100β, GFAP, and neuron specific enolase. We conclude that routine TBI patient management may limit the clinical utility of blood-based biomarkers because their brain-to-blood transport depends on glymphatic activity. Copyright © 2015 the authors 0270-6474/15/350518-09$15.00/0.

Ongoing activity in temporally coherent networks predicts intra-subject fluctuation of response time to sporadic executive control demands.

PubMed

Nozawa, Takayuki; Sugiura, Motoaki; Yokoyama, Ryoichi; Ihara, Mizuki; Kotozaki, Yuka; Miyauchi, Carlos Makoto; Kanno, Akitake; Kawashima, Ryuta

2014-01-01

Can ongoing fMRI BOLD signals predict fluctuations in swiftness of a person's response to sporadic cognitive demands? This is an important issue because it clarifies whether intrinsic brain dynamics, for which spatio-temporal patterns are expressed as temporally coherent networks (TCNs), have effects not only on sensory or motor processes, but also on cognitive processes. Predictivity has been affirmed, although to a limited extent. Expecting a predictive effect on executive performance for a wider range of TCNs constituting the cingulo-opercular, fronto-parietal, and default mode networks, we conducted an fMRI study using a version of the color-word Stroop task that was specifically designed to put a higher load on executive control, with the aim of making its fluctuations more detectable. We explored the relationships between the fluctuations in ongoing pre-trial activity in TCNs and the task response time (RT). The results revealed the existence of TCNs in which fluctuations in activity several seconds before the onset of the trial predicted RT fluctuations for the subsequent trial. These TCNs were distributed in the cingulo-opercular and fronto-parietal networks, as well as in perceptual and motor networks. Our results suggest that intrinsic brain dynamics in these networks constitute "cognitive readiness," which plays an active role especially in situations where information for anticipatory attention control is unavailable. Fluctuations in these networks lead to fluctuations in executive control performance.

Dissecting and Targeting Latent Metastasis

DTIC Science & Technology

2014-09-01

metastasis of breast cancer (LMBC). These cells retain the potential to form overt metastasis for years. Targeting LMBC with new drugs offers an...cancer cell extravasation through the BBB in experimental models and predict brain metastasis in the clinic (16). Once inside the brain parenchyma... drugs that perturb gap junction activity (Chen et al submitted for publication). In our experiments, both drugs as single agents were effective

Prenatal Maternal Stress Predicts Methylation of Genes Regulating the Hypothalamic-Pituitary-Adrenocortical System in Mothers and Newborns in the Democratic Republic of Congo

ERIC Educational Resources Information Center

Kertes, Darlene A.; Kamin, Hayley S.; Hughes, David A.; Rodney, Nicole C.; Bhatt, Samarth; Mulligan, Connie J.

2016-01-01

Exposure to stress early in life permanently shapes activity of the hypothalamic-pituitary-adrenocortical (HPA) axis and the brain. Prenatally, glucocorticoids pass through the placenta to the fetus with postnatal impacts on brain development, birth weight (BW), and HPA axis functioning. Little is known about the biological mechanisms by which…

An EEG Finger-Print of fMRI deep regional activation.

PubMed

Meir-Hasson, Yehudit; Kinreich, Sivan; Podlipsky, Ilana; Hendler, Talma; Intrator, Nathan

2014-11-15

This work introduces a general framework for producing an EEG Finger-Print (EFP) which can be used to predict specific brain activity as measured by fMRI at a given deep region. This new approach allows for improved EEG spatial resolution based on simultaneous fMRI activity measurements. Advanced signal processing and machine learning methods were applied on EEG data acquired simultaneously with fMRI during relaxation training guided by on-line continuous feedback on changing alpha/theta EEG measure. We focused on demonstrating improved EEG prediction of activation in sub-cortical regions such as the amygdala. Our analysis shows that a ridge regression model that is based on time/frequency representation of EEG data from a single electrode, can predict the amygdala related activity significantly better than a traditional theta/alpha activity sampled from the best electrode and about 1/3 of the times, significantly better than a linear combination of frequencies with a pre-defined delay. The far-reaching goal of our approach is to be able to reduce the need for fMRI scanning for probing specific sub-cortical regions such as the amygdala as the basis for brain-training procedures. On the other hand, activity in those regions can be characterized with higher temporal resolution than is obtained by fMRI alone thus revealing additional information about their processing mode. Copyright © 2013 Elsevier Inc. All rights reserved.

Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

PubMed

Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

2015-12-01

It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01). Copyright © 2015 Elsevier Inc. All rights reserved.

Vicarious reinforcement learning signals when instructing others.

PubMed

Apps, Matthew A J; Lesage, Elise; Ramnani, Narender

2015-02-18

Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

Experimental and theoretical characterization of the voltage distribution generated by deep brain stimulation.

PubMed

Miocinovic, Svjetlana; Lempka, Scott F; Russo, Gary S; Maks, Christopher B; Butson, Christopher R; Sakaie, Ken E; Vitek, Jerrold L; McIntyre, Cameron C

2009-03-01

Deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease and shows great promise for numerous other disorders. While the fundamental purpose of DBS is to modulate neural activity with electric fields, little is known about the actual voltage distribution generated in the brain by DBS electrodes and as a result it is difficult to accurately predict which brain areas are directly affected by the stimulation. The goal of this study was to characterize the spatial and temporal characteristics of the voltage distribution generated by DBS electrodes. We experimentally recorded voltages around active DBS electrodes in either a saline bath or implanted in the brain of a non-human primate. Recordings were made during voltage-controlled and current-controlled stimulation. The experimental findings were compared to volume conductor electric field models of DBS parameterized to match the different experiments. Three factors directly affected the experimental and theoretical voltage measurements: 1) DBS electrode impedance, primarily dictated by a voltage drop at the electrode-electrolyte interface and the conductivity of the tissue medium, 2) capacitive modulation of the stimulus waveform, and 3) inhomogeneity and anisotropy of the tissue medium. While the voltage distribution does not directly predict the neural response to DBS, the results of this study do provide foundational building blocks for understanding the electrical parameters of DBS and characterizing its effects on the nervous system.

Excessive coupling of the salience network with intrinsic neurocognitive brain networks during rectal distension in adolescents with irritable bowel syndrome: a preliminary report

PubMed Central

Liu, Xiaolin; Silverman, Alan; Kern, Mark; Ward, B. Douglas; Li, Shi-Jiang; Shaker, Reza; Sood, Manu R.

2015-01-01

Background The neural network mechanisms underlying visceral hypersensitivity in irritable bowel syndrome (IBS) are incompletely understood. It has been proposed that an intrinsic salience network plays an important role in chronic pain and IBS symptoms. Using neuroimaging, we examined brain responses to rectal distension in adolescent IBS patients, focusing on determining the alteration of salience network integrity in IBS and its functional implications in current theoretical frameworks. We hypothesized that (1) brain responses to visceral stimulation in adolescents are similar to those in adults, and (2) IBS is associated with an altered salience network interaction with other neurocognitive networks, particularly the default mode network (DMN) and executive control network (ECN), as predicted by the theoretical models. Methods IBS patients and controls received subliminal and liminal rectal distension during imaging. Stimulus-induced brain activations were determined. Salience network integrity was evaluated by functional connectivity of its seed regions activated by rectal distension in the insular and cingulate cortices. Key Results Compared with controls, IBS patients demonstrated greater activation to rectal distension in neural structures of the homeostatic afferent and emotional arousal networks, especially the anterior cingulate and insular cortices. Greater brain responses to liminal vs. subliminal distension were observed in both groups. Particularly, IBS is uniquely associated with an excessive coupling of the salience network with the DMN and ECN in their key frontal and parietal node areas. Conclusions & Inferences Our study provided consistent evidence supporting the theoretical predictions of altered salience network functioning as a neuropathological mechanism of IBS symptoms. PMID:26467966

Salience network integrity predicts default mode network function after traumatic brain injury

PubMed Central

Bonnelle, Valerie; Ham, Timothy E.; Leech, Robert; Kinnunen, Kirsi M.; Mehta, Mitul A.; Greenwood, Richard J.; Sharp, David J.

2012-01-01

Efficient behavior involves the coordinated activity of large-scale brain networks, but the way in which these networks interact is uncertain. One theory is that the salience network (SN)—which includes the anterior cingulate cortex, presupplementary motor area, and anterior insulae—regulates dynamic changes in other networks. If this is the case, then damage to the structural connectivity of the SN should disrupt the regulation of associated networks. To investigate this hypothesis, we studied a group of 57 patients with cognitive impairments following traumatic brain injury (TBI) and 25 control subjects using the stop-signal task. The pattern of brain activity associated with stop-signal task performance was studied by using functional MRI, and the structural integrity of network connections was quantified by using diffusion tensor imaging. Efficient inhibitory control was associated with rapid deactivation within parts of the default mode network (DMN), including the precuneus and posterior cingulate cortex. TBI patients showed a failure of DMN deactivation, which was associated with an impairment of inhibitory control. TBI frequently results in traumatic axonal injury, which can disconnect brain networks by damaging white matter tracts. The abnormality of DMN function was specifically predicted by the amount of white matter damage in the SN tract connecting the right anterior insulae to the presupplementary motor area and dorsal anterior cingulate cortex. The results provide evidence that structural integrity of the SN is necessary for the efficient regulation of activity in the DMN, and that a failure of this regulation leads to inefficient cognitive control. PMID:22393019

Brain organization underlying superior mathematical abilities in children with autism.

PubMed

Iuculano, Teresa; Rosenberg-Lee, Miriam; Supekar, Kaustubh; Lynch, Charles J; Khouzam, Amirah; Phillips, Jennifer; Uddin, Lucina Q; Menon, Vinod

2014-02-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Medial-Frontal Stimulation Enhances Learning in Schizophrenia by Restoring Prediction Error Signaling.

PubMed

Reinhart, Robert M G; Zhu, Julia; Park, Sohee; Woodman, Geoffrey F

2015-09-02

Posterror learning, associated with medial-frontal cortical recruitment in healthy subjects, is compromised in neuropsychiatric disorders. Here we report novel evidence for the mechanisms underlying learning dysfunctions in schizophrenia. We show that, by noninvasively passing direct current through human medial-frontal cortex, we could enhance the event-related potential related to learning from mistakes (i.e., the error-related negativity), a putative index of prediction error signaling in the brain. Following this causal manipulation of brain activity, the patients learned a new task at a rate that was indistinguishable from healthy individuals. Moreover, the severity of delusions interacted with the efficacy of the stimulation to improve learning. Our results demonstrate a causal link between disrupted prediction error signaling and inefficient learning in schizophrenia. These findings also demonstrate the feasibility of nonpharmacological interventions to address cognitive deficits in neuropsychiatric disorders. When there is a difference between what we expect to happen and what we actually experience, our brains generate a prediction error signal, so that we can map stimuli to responses and predict outcomes accurately. Theories of schizophrenia implicate abnormal prediction error signaling in the cognitive deficits of the disorder. Here, we combine noninvasive brain stimulation with large-scale electrophysiological recordings to establish a causal link between faulty prediction error signaling and learning deficits in schizophrenia. We show that it is possible to improve learning rate, as well as the neural signature of prediction error signaling, in patients to a level quantitatively indistinguishable from that of healthy subjects. The results provide mechanistic insight into schizophrenia pathophysiology and suggest a future therapy for this condition. Copyright © 2015 the authors 0270-6474/15/3512232-09$15.00/0.

Sticking with the nice guy: trait warmth information impairs learning and modulates person perception brain network activity.

PubMed

Lee, Victoria K; Harris, Lasana T

2014-12-01

Social learning requires inferring social information about another person, as well as evaluating outcomes. Previous research shows that prior social information biases decision making and reduces reliance on striatal activity during learning (Delgado, Frank, & Phelps, Nature Neuroscience 8 (11): 1611-1618, 2005). A rich literature in social psychology on person perception demonstrates that people spontaneously infer social information when viewing another person (Fiske & Taylor, 2013) and engage a network of brain regions, including the medial prefrontal cortex, temporal parietal junction, superior temporal sulcus, and precuneus (Amodio & Frith, Nature Reviews Neuroscience, 7(4), 268-277, 2006; Haxby, Gobbini, & Montgomery, 2004; van Overwalle Human Brain Mapping, 30, 829-858, 2009). We investigate the role of these brain regions during social learning about well-established dimensions of person perception-trait warmth and trait competence. We test the hypothesis that activity in person perception brain regions interacts with learning structures during social learning. Participants play an investment game where they must choose an agent to invest on their behalf. This choice is guided by cues signaling trait warmth or trait competence based on framing of monetary returns. Trait warmth information impairs learning about human but not computer agents, while trait competence information produces similar learning rates for human and computer agents. We see increased activation to warmth information about human agents in person perception brain regions. Interestingly, activity in person perception brain regions during the decision phase negatively predicts activity in the striatum during feedback for trait competence inferences about humans. These results suggest that social learning may engage additional processing within person perception brain regions that hampers learning in economic contexts.

Interoceptive inference: From computational neuroscience to clinic.

PubMed

Owens, Andrew P; Allen, Micah; Ondobaka, Sasha; Friston, Karl J

2018-04-22

The central and autonomic nervous systems can be defined by their anatomical, functional and neurochemical characteristics, but neither functions in isolation. For example, fundamental components of autonomically mediated homeostatic processes are afferent interoceptive signals reporting the internal state of the body and efferent signals acting on interoceptive feedback assimilated by the brain. Recent predictive coding (interoceptive inference) models formulate interoception in terms of embodied predictive processes that support emotion and selfhood. We propose interoception may serve as a way to investigate holistic nervous system function and dysfunction in disorders of brain, body and behaviour. We appeal to predictive coding and (active) interoceptive inference, to describe the homeostatic functions of the central and autonomic nervous systems. We do so by (i) reviewing the active inference formulation of interoceptive and autonomic function, (ii) survey clinical applications of this formulation and (iii) describe how it offers an integrative approach to human physiology; particularly, interactions between the central and peripheral nervous systems in health and disease. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Frontal responses during learning predict vulnerability to the psychotogenic effects of ketamine: linking cognition, brain activity, and psychosis.

PubMed

Corlett, Philip R; Honey, Garry D; Aitken, Michael R F; Dickinson, Anthony; Shanks, David R; Absalom, Anthony R; Lee, Michael; Pomarol-Clotet, Edith; Murray, Graham K; McKenna, Peter J; Robbins, Trevor W; Bullmore, Edward T; Fletcher, Paul C

2006-06-01

Establishing a neurobiological account of delusion formation that links cognitive processes, brain activity, and symptoms is important to furthering our understanding of psychosis. To explore a theoretical model of delusion formation that implicates prediction error-dependent associative learning processes in a pharmacological functional magnetic resonance imaging study using the psychotomimetic drug ketamine. Within-subject, randomized, placebo-controlled study. Hospital-based clinical research facility, Addenbrooke's Hospital, Cambridge, England. The work was completed within the Wellcome Trust and Medical Research Council Behavioral and Clinical Neuroscience Institute, Cambridge. Fifteen healthy, right-handed volunteers (8 of whom were male) with a mean +/- SD age of 29 +/- 7 years and a mean +/- SD predicted full-scale IQ of 113 +/- 4 were recruited from within the local community by advertisement. Subjects were given low-dose ketamine (100 ng/mL of plasma) or placebo while performing a causal associative learning task during functional magnetic resonance imaging. In a separate session outside the scanner, the dose was increased (to 200 ng/mL of plasma) and subjects underwent a structured clinical interview. Brain activation, blood plasma levels of ketamine, and scores from psychiatric ratings scales (Brief Psychiatric Ratings Scale, Present State Examination, and Clinician-Administered Dissociative States Scale). Low-dose ketamine perturbs error-dependent learning activity in the right frontal cortex (P = .03). High-dose ketamine produces perceptual aberrations (P = .01) and delusion-like beliefs (P = .007). Critically, subjects showing the highest degree of frontal activation with placebo show the greatest occurrence of drug-induced perceptual aberrations (P = .03) and ideas or delusions of reference (P = .04). These findings relate aberrant prediction error-dependent associative learning to referential ideas and delusions via a perturbation of frontal cortical function. They are consistent with a model of delusion formation positing disruptions in error-dependent learning.

Network dysfunction predicts speech production after left hemisphere stroke.

PubMed

Geranmayeh, Fatemeh; Leech, Robert; Wise, Richard J S

2016-03-09

To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke. We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses. Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production. Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior. © 2016 American Academy of Neurology.

Network dysfunction predicts speech production after left hemisphere stroke

PubMed Central

Leech, Robert; Wise, Richard J.S.

2016-01-01

Objective: To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke. Methods: We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses. Results: Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production. Conclusions: Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior. PMID:26962070

The Predictive Brain State: Asynchrony in Disorders of Attention?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

It is postulated that a key function of attention in goal-oriented behavior is to reduce performance variability by generating anticipatory neural activity that can be synchronized with expected sensory information. A network encompassing the prefrontal cortex, parietal lobe, and cerebellum may be critical in the maintenance and timing of such predictive neural activity. Dysfunction of this temporal process may constitute a fundamental defect in attention, causing working memory problems, distractibility, and decreased awareness. PMID:19074688

Emotional arousal amplifies the effects of biased competition in the brain

PubMed Central

Lee, Tae-Ho; Sakaki, Michiko; Cheng, Ruth; Velasco, Ricardo

2014-01-01

The arousal-biased competition model predicts that arousal increases the gain on neural competition between stimuli representations. Thus, the model predicts that arousal simultaneously enhances processing of salient stimuli and impairs processing of relatively less-salient stimuli. We tested this model with a simple dot-probe task. On each trial, participants were simultaneously exposed to one face image as a salient cue stimulus and one place image as a non-salient stimulus. A border around the face cue location further increased its bottom-up saliency. Before these visual stimuli were shown, one of two tones played: one that predicted a shock (increasing arousal) or one that did not. An arousal-by-saliency interaction in category-specific brain regions (fusiform face area for salient faces and parahippocampal place area for non-salient places) indicated that brain activation associated with processing the salient stimulus was enhanced under arousal whereas activation associated with processing the non-salient stimulus was suppressed under arousal. This is the first functional magnetic resonance imaging study to demonstrate that arousal can enhance information processing for prioritized stimuli while simultaneously impairing processing of non-prioritized stimuli. Thus, it goes beyond previous research to show that arousal does not uniformly enhance perceptual processing, but instead does so selectively in ways that optimizes attention to highly salient stimuli. PMID:24532703

Outcome Uncertainty and Brain Activity Aberrance in the Insula and Anterior Cingulate Cortex Are Associated with Dysfunctional Impulsivity in Borderline Personality Disorder

PubMed Central

Mortensen, Jørgen Assar; Evensmoen, Hallvard Røe; Klensmeden, Gunilla; Håberg, Asta Kristine

2016-01-01

Uncertainty is recognized as an important component in distress, which may elicit impulsive behavior in patients with borderline personality disorder (BPD). These patients are known to be both impulsive and distress intolerant. The present study explored the connection between outcome uncertainty and impulsivity in BPD. The prediction was that cue primes, which provide incomplete information of subsequent target stimuli, led BPD patients to overrate the predictive value of these cues in order to reduce distress related to outcome uncertainty. This would yield dysfunctional impulsive behavior detected as commission errors to incorrectly primed targets. We hypothesized that dysfunctional impulsivity would be accompanied by aberrant brain activity in the right insula and anterior cingulate cortex (ACC), previously described to be involved in uncertainty processing, attention-/cognitive control and BPD pathology. 14 female BPD patients and 14 healthy matched controls (HCs) for comparison completed a Posner task during fMRI at 3T. The task was modified to limit the effect of spatial orientation and enhance the effect of conscious expectations. Brain activity was monitored in the priming phase where the effects of cue primes and neutral primes were compared. As predicted, the BPD group made significantly more commission errors to incorrectly primed targets than HCs. Also, the patients had faster reaction times to correctly primed targets relative to targets preceded by neutral primes. The BPD group had decreased activity in the right mid insula and increased activity in bilateral dorsal ACC during cue primes. The results indicate that strong expectations induced by cue primes led to reduced uncertainty, increased response readiness, and ultimately, dysfunctional impulsivity in BPD patients. We suggest that outcome uncertainty may be an important component in distress related impulsivity in BPD. PMID:27199724

Prediction of primary somatosensory neuron activity during active tactile exploration

PubMed Central

Campagner, Dario; Evans, Mathew Hywel; Bale, Michael Ross; Erskine, Andrew; Petersen, Rasmus Strange

2016-01-01

Primary sensory neurons form the interface between world and brain. Their function is well-understood during passive stimulation but, under natural behaving conditions, sense organs are under active, motor control. In an attempt to predict primary neuron firing under natural conditions of sensorimotor integration, we recorded from primary mechanosensory neurons of awake, head-fixed mice as they explored a pole with their whiskers, and simultaneously measured both whisker motion and forces with high-speed videography. Using Generalised Linear Models, we found that primary neuron responses were poorly predicted by whisker angle, but well-predicted by rotational forces acting on the whisker: both during touch and free-air whisker motion. These results are in apparent contrast to previous studies of passive stimulation, but could be reconciled by differences in the kinematics-force relationship between active and passive conditions. Thus, simple statistical models can predict rich neural activity elicited by natural, exploratory behaviour involving active movement of sense organs. DOI: http://dx.doi.org/10.7554/eLife.10696.001 PMID:26880559

Perception of social synchrony induces mother–child gamma coupling in the social brain

PubMed Central

Levy, Jonathan; Goldstein, Abraham

2017-01-01

Abstract The recent call to move from focus on one brain’s functioning to two-brain communication initiated a search for mechanisms that enable two humans to coordinate brain response during social interactions. Here, we utilized the mother–child context as a developmentally salient setting to study two-brain coupling. Mothers and their 9-year-old children were videotaped at home in positive and conflictual interactions. Positive interactions were microcoded for social synchrony and conflicts for overall dialogical style. Following, mother and child underwent magnetoencephalography while observing the positive vignettes. Episodes of behavioral synchrony, compared to non-synchrony, increased gamma-band power in the superior temporal sulcus (STS), hub of social cognition, mirroring and mentalizing. This neural pattern was coupled between mother and child. Brain-to-brain coordination was anchored in behavioral synchrony; only during episodes of behavioral synchrony, but not during non-synchronous moments, mother’s and child's STS gamma power was coupled. Importantly, neural synchrony was not found during observation of unfamiliar mother-child interaction Maternal empathic/dialogical conflict style predicted mothers’ STS activations whereas child withdrawal predicted attenuated STS response in both partners. Results define a novel neural marker for brain-to-brain synchrony, highlight the role of rapid bottom-up oscillatory mechanisms for neural coupling and indicate that behavior-based processes may drive synchrony between two brains during social interactions. PMID:28402479

Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics.

PubMed

Karuppiah Ramachandran, Vignesh Raja; Alblas, Huibert J; Le, Duc V; Meratnia, Nirvana

2018-05-24

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy.

PubMed

Boyd, Roslyn N; Davies, Peter Sw; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Sakzewski, Leanne; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-07-12

Cerebral palsy (CP) remains the world's most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8-12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006-2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5-5 then 8-12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation. ACTRN: 12616001488493. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior.

PubMed

Demos, Kathryn E; Heatherton, Todd F; Kelley, William M

2012-04-18

Failures of self-regulation are common, leading to many of the most vexing problems facing contemporary society, from overeating and obesity to impulsive sexual behavior and STDs. One reason that people may be prone to engaging in unwanted behaviors is heightened sensitivity to cues related to those behaviors; people may overeat because of hyperresponsiveness to food cues, addicts may relapse following exposure to their drug of choice, and some people might engage in impulsive sexual activity because they are easily aroused by erotic stimuli. An open question is the extent to which individual differences in neural cue reactivity relate to actual behavioral outcomes. Here we show that individual differences in human reward-related brain activity in the nucleus accumbens to food and sexual images predict subsequent weight gain and sexual activity 6 months later. These findings suggest that heightened reward responsivity in the brain to food and sexual cues is associated with indulgence in overeating and sexual activity, respectively, and provide evidence for a common neural mechanism associated with appetitive behaviors.

Pre-stimulus thalamic theta power predicts human memory formation.

PubMed

Sweeney-Reed, Catherine M; Zaehle, Tino; Voges, Jürgen; Schmitt, Friedhelm C; Buentjen, Lars; Kopitzki, Klaus; Richardson-Klavehn, Alan; Hinrichs, Hermann; Heinze, Hans-Jochen; Knight, Robert T; Rugg, Michael D

2016-09-01

Pre-stimulus theta (4-8Hz) power in the hippocampus and neocortex predicts whether a memory for a subsequent event will be formed. Anatomical studies reveal thalamus-hippocampal connectivity, and lesion, neuroimaging, and electrophysiological studies show that memory processing involves the dorsomedial (DMTN) and anterior thalamic nuclei (ATN). The small size and deep location of these nuclei have limited real-time study of their activity, however, and it is unknown whether pre-stimulus theta power predictive of successful memory formation is also found in these subcortical structures. We recorded human electrophysiological data from the DMTN and ATN of 7 patients receiving deep brain stimulation for refractory epilepsy. We found that greater pre-stimulus theta power in the right DMTN was associated with successful memory encoding, predicting both behavioral outcome and post-stimulus correlates of successful memory formation. In particular, significant correlations were observed between right DMTN theta power and both frontal theta and right ATN gamma (32-50Hz) phase alignment, and frontal-ATN theta-gamma cross-frequency coupling. We draw the following primary conclusions. Our results provide direct electrophysiological evidence in humans of a role for the DMTN as well as the ATN in memory formation. Furthermore, prediction of subsequent memory performance by pre-stimulus thalamic oscillations provides evidence that post-stimulus differences in thalamic activity that index successful and unsuccessful encoding reflect brain processes specifically underpinning memory formation. Finally, the findings broaden the understanding of brain states that facilitate memory encoding to include subcortical as well as cortical structures. Copyright © 2016 Elsevier Inc. All rights reserved.

Selective Deletion of the Brain-Specific Isoform of Renin Causes Neurogenic Hypertension.

PubMed

Shinohara, Keisuke; Liu, Xuebo; Morgan, Donald A; Davis, Deborah R; Sequeira-Lopez, Maria Luisa S; Cassell, Martin D; Grobe, Justin L; Rahmouni, Kamal; Sigmund, Curt D

2016-12-01

The renin-angiotensin system (RAS) in the brain is a critical determinant of blood pressure, but the mechanisms regulating RAS activity in the brain remain unclear. Expression of brain renin (renin-b) occurs from an alternative promoter-first exon. The predicted translation product is a nonsecreted enzymatically active renin whose function is unknown. We generated a unique mouse model by selectively ablating the brain-specific isoform of renin (renin-b) while preserving the expression and function of the classical isoform expressed in the kidney (renin-a). Preservation of renal renin was confirmed by measurements of renin gene expression and immunohistochemistry. Surprisingly, renin-b-deficient mice exhibited hypertension, increased sympathetic nerve activity to the kidney and heart, and impaired baroreflex sensitivity. Whereas these mice displayed decreased circulating RAS activity, there was a paradoxical increase in brain RAS activity. Physiologically, renin-b-deficient mice exhibited an exaggerated depressor response to intracerebroventricular administration of losartan, captopril, or aliskiren. At the molecular level, renin-b-deficient mice exhibited increased expression of angiotensin-II type 1 receptor in the paraventricular nucleus, which correlated with an increased renal sympathetic nerve response to leptin, which was dependent on angiotensin-II type 1 receptor activity. Interestingly, despite an ablation of renin-b expression, expression of renin-a was significantly increased in rostral ventrolateral medulla. These data support a new paradigm for the genetic control of RAS activity in the brain by a coordinated regulation of the renin isoforms, with expression of renin-b tonically inhibiting expression of renin-a under baseline conditions. Impairment of this control mechanism causes neurogenic hypertension. © 2016 American Heart Association, Inc.

Information dynamics of brain-heart physiological networks during sleep

NASA Astrophysics Data System (ADS)

Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

2014-10-01

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

Neural Correlates of Encoding Predict Infants' Memory in the Paired-Comparison Procedure

ERIC Educational Resources Information Center

Snyder, Kelly A.

2010-01-01

The present study used event-related potentials (ERPs) to monitor infant brain activity during the initial encoding of a previously novel visual stimulus, and examined whether ERP measures of encoding predicted infants' subsequent performance on a visual memory task (i.e., the paired-comparison task). A late slow wave component of the ERP measured…

Neural evidence of motivational conflict between social values.

PubMed

Leszkowicz, Emilia; Linden, David E J; Maio, Gregory R; Ihssen, Niklas

2017-10-01

Motivational interdependence is an organizing principle in Schwartz's circumplex model of social values, which has received abundant cross-cultural support. We used fMRI to test whether motivational relations between social values predict different brain responses in a situation of choice between values. We hypothesized that differences in brain responses would become evident when the more important value had to be selected in pairs of congruent (e.g., wealth and success) as opposed to incongruent (e.g., curiosity and stability) values as they are described in Schwartz's model, because the former serve mutually facilitating motives, whereas the latter serve mutually inhibiting motives. Consistent with the model, choosing between congruent values led to longer response times and more activation in conflict-related brain regions (e.g., the supplementary motor area, dorsolateral prefrontal cortex) than selecting between incongruent values. These results provide novel neural evidence supporting the circumplex model's predictions about motivational interdependence between social values. In particular, our results show that the neural networks underlying social values are organized in a way that allows activation patterns related to motivational similarity between congruent values to be dissociated from those related to incongruent values.

Visually cued motor synchronization: modulation of fMRI activation patterns by baseline condition.

PubMed

Cerasa, Antonio; Hagberg, Gisela E; Bianciardi, Marta; Sabatini, Umberto

2005-01-03

A well-known issue in functional neuroimaging studies, regarding motor synchronization, is to design suitable control tasks able to discriminate between the brain structures involved in primary time-keeper functions and those related to other processes such as attentional effort. The aim of this work was to investigate how the predictability of stimulus onsets in the baseline condition modulates the activity in brain structures related to processes involved in time-keeper functions during the performance of a visually cued motor synchronization task (VM). The rational behind this choice derives from the notion that using different stimulus predictability can vary the subject's attention and the consequently neural activity. For this purpose, baseline levels of BOLD activity were obtained from 12 subjects during a conventional-baseline condition: maintained fixation of the visual rhythmic stimuli presented in the VM task, and a random-baseline condition: maintained fixation of visual stimuli occurring randomly. fMRI analysis demonstrated that while brain areas with a documented role in basic time processing are detected independent of the baseline condition (right cerebellum, bilateral putamen, left thalamus, left superior temporal gyrus, left sensorimotor cortex, left dorsal premotor cortex and supplementary motor area), the ventral premotor cortex, caudate nucleus, insula and inferior frontal gyrus exhibited a baseline-dependent activation. We conclude that maintained fixation of unpredictable visual stimuli can be employed in order to reduce or eliminate neural activity related to attentional components present in the synchronization task.

The relationship between spatial configuration and functional connectivity of brain regions

PubMed Central

Woolrich, Mark W; Glasser, Matthew F; Robinson, Emma C; Beckmann, Christian F; Van Essen, David C

2018-01-01

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behaviour. For example, studies have used ‘functional connectivity fingerprints’ to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits. PMID:29451491

Working memory brain activity and capacity link MAOA polymorphism to aggressive behavior during development

PubMed Central

Ziermans, T; Dumontheil, I; Roggeman, C; Peyrard-Janvid, M; Matsson, H; Kere, J; Klingberg, T

2012-01-01

A developmental increase in working memory capacity is an important part of cognitive development, and low working memory (WM) capacity is a risk factor for developing psychopathology. Brain activity represents a promising endophenotype for linking genes to behavior and for improving our understanding of the neurobiology of WM development. We investigated gene–brain–behavior relationships by focusing on 18 single-nucleotide polymorphisms (SNPs) located in six dopaminergic candidate genes (COMT, SLC6A3/DAT1, DBH, DRD4, DRD5, MAOA). Visuospatial WM (VSWM) brain activity, measured with functional magnetic resonance imaging, and VSWM capacity were assessed in a longitudinal study of typically developing children and adolescents. Behavioral problems were evaluated using the Child Behavior Checklist (CBCL). One SNP (rs6609257), located ∼6.6 kb downstream of the monoamine oxidase A gene (MAOA) on human chromosome X, significantly affected brain activity in a network of frontal, parietal and occipital regions. Increased activity in this network, but not in caudate nucleus or anterior prefrontal regions, was correlated with VSWM capacity, which in turn predicted externalizing (aggressive/oppositional) symptoms, with higher WM capacity associated with fewer externalizing symptoms. There were no direct significant correlations between rs6609257 and behavioral symptoms. These results suggest a mediating role of WM brain activity and capacity in linking the MAOA gene to aggressive behavior during development. PMID:22832821

EKG-based detection of deep brain stimulation in fMRI studies.

PubMed

Fiveland, Eric; Madhavan, Radhika; Prusik, Julia; Linton, Renee; Dimarzio, Marisa; Ashe, Jeffrey; Pilitsis, Julie; Hancu, Ileana

2018-04-01

To assess the impact of synchronization errors between the assumed functional MRI paradigm timing and the deep brain stimulation (DBS) on/off cycling using a custom electrocardiogram-based triggering system METHODS: A detector for measuring and predicting the on/off state of cycling deep brain stimulation was developed and tested in six patients in office visits. Three-electrode electrocardiogram measurements, amplified by a commercial bio-amplifier, were used as input for a custom electronics box (e-box). The e-box transformed the deep brain stimulation waveforms into transistor-transistor logic pulses, recorded their timing, and propagated it in time. The e-box was used to trigger task-based deep brain stimulation functional MRI scans in 5 additional subjects; the impact of timing accuracy on t-test values was investigated in a simulation study using the functional MRI data. Following locking to each patient's individual waveform, the e-box was shown to predict stimulation onset with an average absolute error of 112 ± 148 ms, 30 min after disconnecting from the patients. The subsecond accuracy of the e-box in predicting timing onset is more than adequate for our slow varying, 30-/30-s on/off stimulation paradigm. Conversely, the experimental deep brain stimulation onset prediction accuracy in the absence of the e-box, which could be off by as much as 4 to 6 s, could significantly decrease activation strength. Using this detector, stimulation can be accurately synchronized to functional MRI acquisitions, without adding any additional hardware in the MRI environment. Magn Reson Med 79:2432-2439, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

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.

Influence of dorsolateral prefrontal cortex and ventral striatum on risk avoidance in addiction: a mediation analysis.

PubMed

Yamamoto, Dorothy J; Woo, Choong-Wan; Wager, Tor D; Regner, Michael F; Tanabe, Jody

2015-04-01

Alterations in frontal and striatal function are hypothesized to underlie risky decision making in drug users, but how these regions interact to affect behavior is incompletely understood. We used mediation analysis to investigate how prefrontal cortex and ventral striatum together influence risk avoidance in abstinent drug users. Thirty-seven abstinent substance-dependent individuals (SDI) and 43 controls underwent fMRI while performing a decision-making task involving risk and reward. Analyses of a priori regions-of-interest tested whether activity in dorsolateral prefrontal cortex (DLPFC) and ventral striatum (VST) explained group differences in risk avoidance. Whole-brain analysis was conducted to identify brain regions influencing the negative VST-risk avoidance relationship. Right DLPFC (RDLPFC) positively mediated the group-risk avoidance relationship (p < 0.05); RDLPFC activity was higher in SDI and predicted higher risk avoidance across groups, controlling for SDI vs. Conversely, VST activity negatively influenced risk avoidance (p < 0.05); it was higher in SDI, and predicted lower risk avoidance. Whole-brain analysis revealed that, across group, RDLPFC and left temporal-parietal junction positively (p ≤ 0.001) while right thalamus and left middle frontal gyrus negatively (p < 0.005) mediated the VST activity-risk avoidance relationship. RDLPFC activity mediated less risky decision making while VST mediated more risky decision making across drug users and controls. These results suggest a dual pathway underlying decision making, which, if imbalanced, may adversely influence choices involving risk. Modeling contributions of multiple brain systems to behavior through mediation analysis could lead to a better understanding of mechanisms of behavior and suggest neuromodulatory treatments for addiction. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Influence of dorsolateral prefrontal cortex and ventral striatum on risk avoidance in addiction: a mediation analysis*

PubMed Central

Yamamoto, Dorothy J.; Woo, Choong-Wan; Wager, Tor D.; Regner, Michael F.; Tanabe, Jody

2015-01-01

Background Alterations in frontal and striatal function are hypothesized to underlie risky decision-making in drug users, but how these regions interact to affect behavior is incompletely understood. We used mediation analysis to investigate how prefrontal cortex and ventral striatum together influence risk avoidance in abstinent drug users. Method Thirty-seven abstinent substance-dependent individuals (SDI) and 43 controls underwent fMRI while performing a decision-making task involving risk and reward. Analyses of a priori regions-of-interest tested whether activity in dorsolateral prefrontal cortex (DLPFC) and ventral striatum (VST) explained group differences in risk avoidance. Whole-brain analysis was conducted to identify brain regions influencing the negative VST-risk avoidance relationship. Results Right DLPFC (RDLPFC) positively mediated the group-risk avoidance relationship (p < 0.05); RDLPFC activity was higher in SDI and predicted higher risk avoidance across groups, controlling for SDI vs. controls. Conversely, VST activity negatively influenced risk avoidance (p < 0.05); it was higher in SDI, and predicted lower risk avoidance. Whole-brain analysis revealed that, across group, RDLPFC and left temporal-parietal junction positively (p ≤ 0.001) while right thalamus and left middle frontal gyrus negatively (p < 0.005) mediated the VST activity-risk avoidance relationship. Conclusion RDLPFC activity mediated less risky decision-making while VST mediated more risky decision-making across drug users and controls. These results suggest a dual pathway underlying decision-making, which, if imbalanced, may adversely influence choices involving risk. Modeling contributions of multiple brain systems to behavior through mediation analysis could lead to a better understanding of mechanisms of behavior and suggest neuromodulatory treatments for addiction. PMID:25736619

Neural correlates of the happy life: the amplitude of spontaneous low frequency fluctuations predicts subjective well-being.

PubMed

Kong, Feng; Hu, Siyuan; Wang, Xu; Song, Yiying; Liu, Jia

2015-02-15

Subjective well-being is assumed to be distributed in the hedonic hotspots of subcortical and cortical structures. However, the precise neural correlates underlying this construct, especially how it is maintained during the resting state, are still largely unknown. Here, we explored the neural basis of subjective well-being by correlating the regional fractional amplitude of low frequency fluctuations (fALFF) with the self-reported subjective well-being of healthy individuals. Behaviorally, we demonstrated that subjective well-being contained two related but distinct components: cognitive and affective well-being. Neurally, we showed that the fALFF in the bilateral posterior superior temporal gyrus (pSTG), right posterior mid-cingulate cortex (pMCC), right thalamus, left postcentral gyrus (PCG), right lingual gyrus, and left planum temporale (PT) positively predicted cognitive well-being, whereas the fALFF in the bilateral superior frontal gyrus (SFG), right orbitofrontal cortex (OFC), and left inferior temporal gyrus (ITG) negatively predicted cognitive well-being. In contrast, only the fALFF in the right amygdala reliably predicted affective well-being. Furthermore, emotional intelligence partially mediated the effects of the right pSTG and thalamus on cognitive well-being, as well as the effect of the right amygdala on affective well-being. In summary, we provide the first evidence that spontaneous brain activity in multiple regions associated with sensation, social perception, cognition, and emotion contributes to cognitive well-being, whereas the spontaneous brain activity in only one emotion-related region contributes to affective well-being, suggesting that the spontaneous activity of the human brain reflect the efficiency of subjective well-being. Copyright © 2014 Elsevier Inc. All rights reserved.

Prediction error and somatosensory insula activation in women recovered from anorexia nervosa

PubMed Central

Frank, Guido K.W.; Collier, Shaleise; Shott, Megan E.; O’Reilly, Randall C.

2016-01-01

Background Previous research in patients with anorexia nervosa showed heightened brain response during a taste reward conditioning task and heightened sensitivity to rewarding and punishing stimuli. Here we tested the hypothesis that individuals recovered from anorexia nervosa would also experience greater brain activation during this task as well as higher sensitivity to salient stimuli than controls. Methods Women recovered from restricting-type anorexia nervosa and healthy control women underwent fMRI during application of a prediction error taste reward learning paradigm. Results Twenty-four women recovered from anorexia nervosa (mean age 30.3 ± 8.1 yr) and 24 control women (mean age 27.4 ± 6.3 yr) took part in this study. The recovered anorexia nervosa group showed greater left posterior insula activation for the prediction error model analysis than the control group (family-wise error– and small volume–corrected p < 0.05). A group × condition analysis found greater posterior insula response in women recovered from anorexia nervosa than controls for unexpected stimulus omission, but not for unexpected receipt. Sensitivity to punishment was elevated in women recovered from anorexia nervosa. Limitations This was a cross-sectional study, and the sample size was modest. Conclusion Anorexia nervosa after recovery is associated with heightened prediction error–related brain response in the posterior insula as well as greater response to unexpected reward stimulus omission. This finding, together with behaviourally increased sensitivity to punishment, could indicate that individuals recovered from anorexia nervosa are particularly responsive to punishment. The posterior insula processes somatosensory stimuli, including unexpected bodily states, and greater response could indicate altered perception or integration of unexpected or maybe unwanted bodily feelings. Whether those findings develop during the ill state or whether they are biological traits requires further study. PMID:26836623

Prediction error and somatosensory insula activation in women recovered from anorexia nervosa.

PubMed

Frank, Guido K W; Collier, Shaleise; Shott, Megan E; O'Reilly, Randall C

2016-08-01

Previous research in patients with anorexia nervosa showed heightened brain response during a taste reward conditioning task and heightened sensitivity to rewarding and punishing stimuli. Here we tested the hypothesis that individuals recovered from anorexia nervosa would also experience greater brain activation during this task as well as higher sensitivity to salient stimuli than controls. Women recovered from restricting-type anorexia nervosa and healthy control women underwent fMRI during application of a prediction error taste reward learning paradigm. Twenty-four women recovered from anorexia nervosa (mean age 30.3 ± 8.1 yr) and 24 control women (mean age 27.4 ± 6.3 yr) took part in this study. The recovered anorexia nervosa group showed greater left posterior insula activation for the prediction error model analysis than the control group (family-wise error- and small volume-corrected p < 0.05). A group × condition analysis found greater posterior insula response in women recovered from anorexia nervosa than controls for unexpected stimulus omission, but not for unexpected receipt. Sensitivity to punishment was elevated in women recovered from anorexia nervosa. This was a cross-sectional study, and the sample size was modest. Anorexia nervosa after recovery is associated with heightened prediction error-related brain response in the posterior insula as well as greater response to unexpected reward stimulus omission. This finding, together with behaviourally increased sensitivity to punishment, could indicate that individuals recovered from anorexia nervosa are particularly responsive to punishment. The posterior insula processes somatosensory stimuli, including unexpected bodily states, and greater response could indicate altered perception or integration of unexpected or maybe unwanted bodily feelings. Whether those findings develop during the ill state or whether they are biological traits requires further study.

Specific regions of the brain are capable of fructose metabolism.

PubMed

Oppelt, Sarah A; Zhang, Wanming; Tolan, Dean R

2017-02-15

High fructose consumption in the Western diet correlates with disease states such as obesity and metabolic syndrome complications, including type II diabetes, chronic kidney disease, and non-alcoholic fatty acid liver disease. Liver and kidneys are responsible for metabolism of 40-60% of ingested fructose, while the physiological fate of the remaining fructose remains poorly understood. The primary metabolic pathway for fructose includes the fructose-transporting solute-like carrier transport proteins 2a (SLC2a or GLUT), including GLUT5 and GLUT9, ketohexokinase (KHK), and aldolase. Bioinformatic analysis of gene expression encoding these proteins (glut5, glut9, khk, and aldoC, respectively) identifies other organs capable of this fructose metabolism. This analysis predicts brain, lymphoreticular tissue, placenta, and reproductive tissues as possible additional organs for fructose metabolism. While expression of these genes is highest in liver, the brain is predicted to have expression levels of these genes similar to kidney. RNA in situ hybridization of coronal slices of adult mouse brains validate the in silico expression of glut5, glut9, khk, and aldoC, and show expression across many regions of the brain, with the most notable expression in the cerebellum, hippocampus, cortex, and olfactory bulb. Dissected samples of these brain regions show KHK and aldolase enzyme activity 5-10 times the concentration of that in liver. Furthermore, rates of fructose oxidation in these brain regions are 15-150 times that of liver slices, confirming the bioinformatics prediction and in situ hybridization data. This suggests that previously unappreciated regions across the brain can use fructose, in addition to glucose, for energy production. Copyright © 2016 Elsevier B.V. All rights reserved.

Specific regions of the brain are capable of fructose metabolism

PubMed Central

Oppelt, Sarah A.; Zhang, Wanming; Tolan, Dean R.

2017-01-01

High fructose consumption in the Western diet correlates with disease states such as obesity and metabolic syndrome complications, including type II diabetes, chronic kidney disease, and nonalcoholic fatty acid liver disease. Liver and kidneys are responsible for metabolism of 40–60% of ingested fructose, while the physiological fate of the remaining fructose remains poorly understood. The primary metabolic pathway for fructose includes the fructose-transporting solute-like carrier transport proteins 2a (SLC2a or GLUT), including GLUT5 and GLUT9, ketohexokinase (KHK), and aldolase. Bioinformatic analysis of gene expression encoding these proteins (glut5, glut9, khk, and aldoC, respectively) identifies other organs capable of this fructose metabolism. This analysis predicts brain, lymphoreticular tissue, placenta, and reproductive tissues as possible additional organs for fructose metabolism. While expression of these genes is highest in liver, the brain is predicted to have expression levels of these genes similar to kidney. RNA in situ hybridization of coronal slices of adult mouse brains validate the in silico expression of glut5, glut9, khk, and aldoC, and show expression across many regions of the brain, with the most notable expression in the cerebellum, hippocampus, cortex, and olfactory bulb. Dissected samples of these brain regions show KHK and aldolase enzyme activity 5–10 times the concentration of that in liver. Furthermore, rates of fructose oxidation in these brain regions are 15–150 times that of liver slices, confirming the bioinformatics prediction and in situ hybridization data. This suggests that previously unappreciated regions across the brain can use fructose, in addition to glucose, for energy production. PMID:28034722

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

NASA Astrophysics Data System (ADS)

Gollas, Frank; Tetzlaff, Ronald

2009-05-01

Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal autoregressive filter models are considered, for a prediction of EEG signal values. Thus Signal features values for successive, short, quasi stationary segments of brain electrical activity can be obtained, with the objective of detecting distinct changes prior to impending epileptic seizures. Furthermore long term recordings gained during presurgical diagnostics in temporal lobe epilepsy are analyzed and the predictive performance of the extracted features is evaluated statistically. Therefore a Receiver Operating Characteristic analysis is considered, assessing the distinguishability between distributions of supposed preictal and interictal periods.

Hemorrhagic transformation after ischemic stroke in animals and humans

PubMed Central

Jickling, Glen C; Liu, DaZhi; Stamova, Boryana; Ander, Bradley P; Zhan, Xinhua; Lu, Aigang; Sharp, Frank R

2014-01-01

Hemorrhagic transformation (HT) is a common complication of ischemic stroke that is exacerbated by thrombolytic therapy. Methods to better prevent, predict, and treat HT are needed. In this review, we summarize studies of HT in both animals and humans. We propose that early HT (<18 to 24 hours after stroke onset) relates to leukocyte-derived matrix metalloproteinase-9 (MMP-9) and brain-derived MMP-2 that damage the neurovascular unit and promote blood–brain barrier (BBB) disruption. This contrasts to delayed HT (>18 to 24 hours after stroke) that relates to ischemia activation of brain proteases (MMP-2, MMP-3, MMP-9, and endogenous tissue plasminogen activator), neuroinflammation, and factors that promote vascular remodeling (vascular endothelial growth factor and high-moblity-group-box-1). Processes that mediate BBB repair and reduce HT risk are discussed, including transforming growth factor beta signaling in monocytes, Src kinase signaling, MMP inhibitors, and inhibitors of reactive oxygen species. Finally, clinical features associated with HT in patients with stroke are reviewed, including approaches to predict HT by clinical factors, brain imaging, and blood biomarkers. Though remarkable advances in our understanding of HT have been made, additional efforts are needed to translate these discoveries to the clinic and reduce the impact of HT on patients with ischemic stroke. PMID:24281743

Persistent Physical Symptoms as Perceptual Dysregulation: A Neuropsychobehavioral Model and Its Clinical Implications.

PubMed

Henningsen, Peter; Gündel, Harald; Kop, Willem J; Löwe, Bernd; Martin, Alexandra; Rief, Winfried; Rosmalen, Judith G M; Schröder, Andreas; van der Feltz-Cornelis, Christina; Van den Bergh, Omer

2018-06-01

The mechanisms underlying the perception and experience of persistent physical symptoms are not well understood, and in the models, the specific relevance of peripheral input versus central processing, or of neurobiological versus psychosocial factors in general, is not clear. In this article, we proposed a model for this clinical phenomenon that is designed to be coherent with an underlying, relatively new model of the normal brain functions involved in the experience of bodily signals. Based on a review of recent literature, we describe central elements of this model and its clinical implications. In the model, the brain is seen as an active predictive processing or inferential device rather than one that is passively waiting for sensory input. A central aspect of the model is the attempt of the brain to minimize prediction errors that result from constant comparisons of predictions and sensory input. Two possibilities exist: adaptation of the generative model underlying the predictions or alteration of the sensory input via autonomic nervous activation (in the case of interoception). Following this model, persistent physical symptoms can be described as "failures of inference" and clinically well-known factors such as expectation are assigned a role, not only in the later amplification of bodily signals but also in the very basis of symptom perception. We discuss therapeutic implications of such a model including new interpretations for established treatments as well as new options such as virtual reality techniques combining exteroceptive and interoceptive information.

Effects of a GABA-ergic medication combination and initial alcohol withdrawal severity on cue-elicited brain activation among treatment-seeking alcoholics.

PubMed

Schacht, Joseph P; Anton, Raymond F; Randall, Patrick K; Li, Xingbao; Henderson, Scott; Myrick, Hugh

2013-06-01

Many studies have reported medication effects on alcohol cue-elicited brain activation or associations between such activation and subsequent drinking. However, few have combined the methodological rigor of a randomized clinical trial (RCT) with follow-up assessments to determine whether cue-elicited activation predicts relapse during treatment, the crux of alcoholism. This study analyzed functional magnetic resonance imaging (fMRI) data from 48 alcohol-dependent subjects enrolled in a 6-week RCT of an investigational pharmacotherapy. Subjects were randomized, based on their level of alcohol withdrawal (AW) at study entry, to receive either a combination of gabapentin (GBP; up to 1,200 mg for 39 days) and flumazenil (FMZ) infusions (2 days) or two placebos. Midway through the RCT, subjects were administered an fMRI alcohol cue reactivity task. There were no main effects of medication or initial AW status on cue-elicited activation, but these factors interacted, such that the GBP/FMZ/higher AW and placebo/lower AW groups, which had previously been shown to have relatively reduced drinking, demonstrated greater dorsal anterior cingulate cortex (dACC) activation to alcohol cues. Further analysis suggested that this finding represented differences in task-related deactivation and was associated with greater control over alcohol-related thoughts. Among study completers, regardless of medication or AW status, greater left dorsolateral prefrontal cortex (DLPFC) activation predicted more post-scan heavy drinking. These data suggest that alterations in task-related deactivation of dACC, a component of the default mode network, may predict better alcohol treatment response, while activation of DLPFC, an area associated with selective attention, may predict relapse drinking.

Use of brain electrical activity for the identification of hematomas in mild traumatic brain injury.

PubMed

Hanley, Daniel F; Chabot, Robert; Mould, W Andrew; Morgan, Timothy; Naunheim, Rosanne; Sheth, Kevin N; Chiang, William; Prichep, Leslie S

2013-12-15

This study investigates the potential clinical utility in the emergency department (ED) of an index of brain electrical activity to identify intracranial hematomas. The relationship between this index and depth, size, and type of hematoma was explored. Ten minutes of brain electrical activity was recorded from a limited montage in 38 adult patients with traumatic hematomas (CT scan positive) and 38 mild head injured controls (CT scan negative) in the ED. The volume of blood and distance from recording electrodes were measured by blinded independent experts. Brain electrical activity data were submitted to a classification algorithm independently developed traumatic brain injury (TBI) index to identify the probability of a CT+traumatic event. There was no significant relationship between the TBI-Index and type of hematoma, or distance of the bleed from recording sites. A significant correlation was found between TBI-Index and blood volume. The sensitivity to hematomas was 100%, positive predictive value was 74.5%, and positive likelihood ratio was 2.92. The TBI-Index, derived from brain electrical activity, demonstrates high accuracy for identification of traumatic hematomas. Further, this was not influenced by distance of the bleed from the recording electrodes, blood volume, or type of hematoma. Distance and volume limitations noted with other methods, (such as that based on near-infrared spectroscopy) were not found, thus suggesting the TBI-Index to be a potentially important adjunct to acute assessment of head injury. Because of the life-threatening risk of undetected hematomas (false negatives), specificity was permitted to be lower, 66%, in exchange for extremely high sensitivity.

A preliminary study of the effects of working memory training on brain function.

PubMed

Stevens, Michael C; Gaynor, Alexandra; Bessette, Katie L; Pearlson, Godfrey D

2016-06-01

Working memory (WM) training improves WM ability in Attention-Deficit/Hyperactivity Disorder (ADHD), but its efficacy for non-cognitive ADHD impairments ADHD has been sharply debated. The purpose of this preliminary study was to characterize WM training-related changes in ADHD brain function and see if they were linked to clinical improvement. We examined 18 adolescents diagnosed with DSM-IV Combined-subtype ADHD before and after 25 sessions of WM training using a frequently employed approach (Cogmed™) using a nonverbal Sternberg WM fMRI task, neuropsychological tests, and participant- and parent-reports of ADHD symptom severity and associated functional impairment. Whole brain SPM8 analyses identified ADHD activation deficits compared to 18 non-ADHD control participants, then tested whether impaired ADHD frontoparietal brain activation would increase following WM training. Post hoc tests examined the relationships between neural changes and neurocognitive or clinical improvements. As predicted, WM training increased WM performance, ADHD clinical functioning, and WM-related ADHD brain activity in several frontal, parietal and temporal lobe regions. Increased left inferior frontal sulcus region activity was seen in all Encoding, Maintenance, and Retrieval Sternberg task phases. ADHD symptom severity improvements were most often positively correlated with activation gains in brain regions known to be engaged for WM-related executive processing; improvement of different symptom types had different neural correlates. The responsiveness of both amodal WM frontoparietal circuits and executive process-specific WM brain regions was altered by WM training. The latter might represent a promising, relatively unexplored treatment target for researchers seeking to optimize clinical response in ongoing ADHD WM training development efforts.

Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity.

PubMed

Li, Zhengjun; Zang, Yu-Feng; Ding, Jianping; Wang, Ze

2017-04-01

The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test-retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

PubMed

Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-01-01

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

Definitions of state variables and state space for brain-computer interface : Part 2. Extraction and classification of feature vectors.

PubMed

Freeman, Walter J

2007-06-01

The hypothesis is proposed that the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action-perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain.

Modeling of light distribution in the brain for topographical imaging

NASA Astrophysics Data System (ADS)

Okada, Eiji; Hayashi, Toshiyuki; Kawaguchi, Hiroshi

2004-07-01

Multi-channel optical imaging system can obtain a topographical distribution of the activated region in the brain cortex by a simple mapping algorithm. Near-infrared light is strongly scattered in the head and the volume of tissue that contributes to the change in the optical signal detected with source-detector pair on the head surface is broadly distributed in the brain. This scattering effect results in poor resolution and contrast in the topographic image of the brain activity. We report theoretical investigations on the spatial resolution of the topographic imaging of the brain activity. The head model for the theoretical study consists of five layers that imitate the scalp, skull, subarachnoid space, gray matter and white matter. The light propagation in the head model is predicted by Monte Carlo simulation to obtain the spatial sensitivity profile for a source-detector pair. The source-detector pairs are one dimensionally arranged on the surface of the model and the distance between the adjoining source-detector pairs are varied from 4 mm to 32 mm. The change in detected intensity caused by the absorption change is obtained by Monte Carlo simulation. The position of absorption change is reconstructed by the conventional mapping algorithm and the reconstruction algorithm using the spatial sensitivity profiles. We discuss the effective interval between the source-detector pairs and the choice of reconstruction algorithms to improve the topographic images of brain activity.

Decoding the neural signatures of emotions expressed through sound.

PubMed

Sachs, Matthew E; Habibi, Assal; Damasio, Antonio; Kaplan, Jonas T

2018-07-01

Effective social functioning relies in part on the ability to identify emotions from auditory stimuli and respond appropriately. Previous studies have uncovered brain regions engaged by the affective information conveyed by sound. But some of the acoustical properties of sounds that express certain emotions vary remarkably with the instrument used to produce them, for example the human voice or a violin. Do these brain regions respond in the same way to different emotions regardless of the sound source? To address this question, we had participants (N = 38, 20 females) listen to brief audio excerpts produced by the violin, clarinet, and human voice, each conveying one of three target emotions-happiness, sadness, and fear-while brain activity was measured with fMRI. We used multivoxel pattern analysis to test whether emotion-specific neural responses to the voice could predict emotion-specific neural responses to musical instruments and vice-versa. A whole-brain searchlight analysis revealed that patterns of activity within the primary and secondary auditory cortex, posterior insula, and parietal operculum were predictive of the affective content of sound both within and across instruments. Furthermore, classification accuracy within the anterior insula was correlated with behavioral measures of empathy. The findings suggest that these brain regions carry emotion-specific patterns that generalize across sounds with different acoustical properties. Also, individuals with greater empathic ability have more distinct neural patterns related to perceiving emotions. These results extend previous knowledge regarding how the human brain extracts emotional meaning from auditory stimuli and enables us to understand and connect with others effectively. Copyright © 2018 Elsevier Inc. All rights reserved.

Words in Context: The Effects of Length, Frequency, and Predictability on Brain Responses During Natural Reading

PubMed Central

Schuster, Sarah; Hawelka, Stefan; Hutzler, Florian; Kronbichler, Martin; Richlan, Fabio

2016-01-01

Word length, frequency, and predictability count among the most influential variables during reading. Their effects are well-documented in eye movement studies, but pertinent evidence from neuroimaging primarily stem from single-word presentations. We investigated the effects of these variables during reading of whole sentences with simultaneous eye-tracking and functional magnetic resonance imaging (fixation-related fMRI). Increasing word length was associated with increasing activation in occipital areas linked to visual analysis. Additionally, length elicited a U-shaped modulation (i.e., least activation for medium-length words) within a brain stem region presumably linked to eye movement control. These effects, however, were diminished when accounting for multiple fixation cases. Increasing frequency was associated with decreasing activation within left inferior frontal, superior parietal, and occipito-temporal regions. The function of the latter region—hosting the putative visual word form area—was originally considered as limited to sublexical processing. An exploratory analysis revealed that increasing predictability was associated with decreasing activation within middle temporal and inferior frontal regions previously implicated in memory access and unification. The findings are discussed with regard to their correspondence with findings from single-word presentations and with regard to neurocognitive models of visual word recognition, semantic processing, and eye movement control during reading. PMID:27365297

Proteopathic tau seeding predicts tauopathy in vivo

PubMed Central

Holmes, Brandon B.; Furman, Jennifer L.; Mahan, Thomas E.; Yamasaki, Tritia R.; Mirbaha, Hilda; Eades, William C.; Belaygorod, Larisa; Cairns, Nigel J.; Holtzman, David M.; Diamond, Marc I.

2014-01-01

Transcellular propagation of protein aggregates, or proteopathic seeds, may drive the progression of neurodegenerative diseases in a prion-like manner. In tauopathies such as Alzheimer’s disease, this model predicts that tau seeds propagate pathology through the brain via cell–cell transfer in neural networks. The critical role of tau seeding activity is untested, however. It is unknown whether seeding anticipates and correlates with subsequent development of pathology as predicted for a causal agent. One major limitation has been the lack of a robust assay to measure proteopathic seeding activity in biological specimens. We engineered an ultrasensitive, specific, and facile FRET-based flow cytometry biosensor assay based on expression of tau or synuclein fusions to CFP and YFP, and confirmed its sensitivity and specificity to tau (∼300 fM) and synuclein (∼300 pM) fibrils. This assay readily discriminates Alzheimer’s disease vs. Huntington's disease and aged control brains. We then carried out a detailed time-course study in P301S tauopathy mice, comparing seeding activity versus histological markers of tau pathology, including MC1, AT8, PG5, and Thioflavin S. We detected robust seeding activity at 1.5 mo, >1 mo before the earliest histopathological stain. Proteopathic tau seeding is thus an early and robust marker of tauopathy, suggesting a proximal role for tau seeds in neurodegeneration. PMID:25261551

In sync: gamma oscillations and emotional memory

PubMed Central

Headley, Drew B.; Paré, Denis

2013-01-01

Emotional experiences leave vivid memories that can last a lifetime. The emotional facilitation of memory has been attributed to the engagement of diffusely projecting neuromodulatory systems that enhance the consolidation of synaptic plasticity in regions activated by the experience. This process requires the propagation of signals between brain regions, and for those signals to induce long-lasting synaptic plasticity. Both of these demands are met by gamma oscillations, which reflect synchronous population activity on a fast timescale (35–120 Hz). Regions known to participate in the formation of emotional memories, such as the basolateral amygdala, also promote gamma-band activation throughout cortical and subcortical circuits. Recent studies have demonstrated that gamma oscillations are enhanced during emotional situations, coherent between regions engaged by salient stimuli, and predict subsequent memory for cues associated with aversive stimuli. Furthermore, neutral stimuli that come to predict emotional events develop enhanced gamma oscillations, reflecting altered processing in the brain, which may underpin how past emotional experiences color future learning and memory. PMID:24319416

Uncovering Camouflage: Amygdala Activation Predicts Long-Term Memory of Induced Perceptual Insight

PubMed Central

Ludmer, Rachel; Dudai, Yadin; Rubin, Nava

2012-01-01

What brain mechanisms underlie learning of new knowledge from single events? We studied encoding in long-term memory of a unique type of one-shot experience, induced perceptual insight. While undergoing an fMRI brain scan, participants viewed degraded images of real-world pictures where the underlying objects were hard to recognize (‘camouflage’), followed by brief exposures to the original images (‘solution’), which led to induced insight (“Aha!”). A week later, participants’ memory was tested; a solution image was classified as ‘remembered’ if detailed perceptual knowledge was elicited from the camouflage image alone. During encoding, subsequently remembered images enjoyed higher activity in mid-level visual cortex and medial frontal cortex, but most pronouncedly in the amygdala, whose activity could be used to predict which solutions will remain in long-term memory. Our findings extend the known roles of amygdala in memory to include promoting of long-term memory of the sudden reorganization of internal representations. PMID:21382558

In sync: gamma oscillations and emotional memory.

PubMed

Headley, Drew B; Paré, Denis

2013-11-21

Emotional experiences leave vivid memories that can last a lifetime. The emotional facilitation of memory has been attributed to the engagement of diffusely projecting neuromodulatory systems that enhance the consolidation of synaptic plasticity in regions activated by the experience. This process requires the propagation of signals between brain regions, and for those signals to induce long-lasting synaptic plasticity. Both of these demands are met by gamma oscillations, which reflect synchronous population activity on a fast timescale (35-120 Hz). Regions known to participate in the formation of emotional memories, such as the basolateral amygdala, also promote gamma-band activation throughout cortical and subcortical circuits. Recent studies have demonstrated that gamma oscillations are enhanced during emotional situations, coherent between regions engaged by salient stimuli, and predict subsequent memory for cues associated with aversive stimuli. Furthermore, neutral stimuli that come to predict emotional events develop enhanced gamma oscillations, reflecting altered processing in the brain, which may underpin how past emotional experiences color future learning and memory.

COMT Val(108/158)Met polymorphism effects on emotional brain function and negativity bias.

PubMed

Williams, Leanne M; Gatt, Justine M; Grieve, Stuart M; Dobson-Stone, Carol; Paul, Robert H; Gordon, Evian; Schofield, Peter R

2010-11-15

Biases toward processing negative versus positive information vary as a function of level of awareness, and are modulated by monoamines. Excessive biases are associated with individual differences in mood and emotional stability, and emotional disorder. Here, we examined the impact of the catechol-O-methyltransferase (COMT) Val(108/158)Met polymorphism, involved in dopamine and norepinephrine catabolism, on both emotional brain function and self-reported negativity bias. COMT genotyping and self-reported level of negativity bias were completed for 46 healthy participants taking part in the Brain Resource International Database. Functional MRI was undertaken during perception of facial expressions of fear and happiness presented under unmasked (consciously identified) and masked (to prevent conscious detection) conditions. Structural MR images were also acquired. A greater number of COMT Met alleles predicted increased activation in brainstem, amygdala, basal ganglia and medial prefrontal regions for conscious fear, but decreased activation for conscious happiness. This pattern was also apparent for brainstem activation for the masked condition. Effects were most apparent for females. These differences could not be explained by gray matter variations. The Met-related profile of activation, particularly prefrontally, predicted greater negativity bias associated with risk for emotional disorder. The findings suggest that the COMT Met allele modulates neural substrates of negative versus positive emotion processing. This effect may contribute to negativity biases, which confer susceptibility for emotional disorders. Copyright 2010 Elsevier Inc. All rights reserved.

Development of brain systems for nonsymbolic numerosity and the relationship to formal math academic achievement.

PubMed

Haist, Frank; Wazny, Jarnet H; Toomarian, Elizabeth; Adamo, Maha

2015-02-01

A central question in cognitive and educational neuroscience is whether brain operations supporting nonlinguistic intuitive number sense (numerosity) predict individual acquisition and academic achievement for symbolic or "formal" math knowledge. Here, we conducted a developmental functional magnetic resonance imaging (MRI) study of nonsymbolic numerosity task performance in 44 participants including 14 school age children (6-12 years old), 14 adolescents (13-17 years old), and 16 adults and compared a brain activity measure of numerosity precision to scores from the Woodcock-Johnson III Broad Math index of math academic achievement. Accuracy and reaction time from the numerosity task did not reliably predict formal math achievement. We found a significant positive developmental trend for improved numerosity precision in the parietal cortex and intraparietal sulcus specifically. Controlling for age and overall cognitive ability, we found a reliable positive relationship between individual math achievement scores and parietal lobe activity only in children. In addition, children showed robust positive relationships between math achievement and numerosity precision within ventral stream processing areas bilaterally. The pattern of results suggests a dynamic developmental trajectory for visual discrimination strategies that predict the acquisition of formal math knowledge. In adults, the efficiency of visual discrimination marked by numerosity acuity in ventral occipital-temporal cortex and hippocampus differentiated individuals with better or worse formal math achievement, respectively. Overall, these results suggest that two different brain systems for nonsymbolic numerosity acuity may contribute to individual differences in math achievement and that the contribution of these systems differs across development. © 2014 Wiley Periodicals, Inc.

Development of brain systems for nonsymbolic numerosity and the relationship to formal math academic achievement

PubMed Central

Haist, Frank; Wazny, Jarnet H.; Toomarian, Elizabeth; Adamo, Maha

2015-01-01

A central question in cognitive and educational neuroscience is whether brain operations supporting non-linguistic intuitive number sense (numerosity) predict individual acquisition and academic achievement for symbolic or “formal” math knowledge. Here, we conducted a developmental functional MRI study of nonsymbolic numerosity task performance in 44 participants including 14 school age children (6–12 years-old), 14 adolescents (13–17 years-old), and 16 adults and compared a brain activity measure of numerosity precision to scores from the Woodcock-Johnson III Broad Math index of math academic achievement. Accuracy and reaction time from the numerosity task did not reliably predict formal math achievement. We found a significant positive developmental trend for improved numerosity precision in the parietal cortex and intraparietal sulcus (IPS) specifically. Controlling for age and overall cognitive ability, we found a reliable positive relationship between individual math achievement scores and parietal lobe activity only in children. In addition, children showed robust positive relationships between math achievement and numerosity precision within ventral stream processing areas bilaterally. The pattern of results suggests a dynamic developmental trajectory for visual discrimination strategies that predict the acquisition of formal math knowledge. In adults, the efficiency of visual discrimination marked by numerosity acuity in ventral occipital-temporal cortex and hippocampus differentiated individuals with better or worse formal math achievement, respectively. Overall, these results suggest that two different brain systems for nonsymbolic numerosity acuity may contribute to individual differences in math achievement and that the contribution of these systems differs across development. PMID:25327879

Neural correlates of trust.

PubMed

Krueger, Frank; McCabe, Kevin; Moll, Jorge; Kriegeskorte, Nikolaus; Zahn, Roland; Strenziok, Maren; Heinecke, Armin; Grafman, Jordan

2007-12-11

Trust is a critical social process that helps us to cooperate with others and is present to some degree in all human interaction. However, the underlying brain mechanisms of conditional and unconditional trust in social reciprocal exchange are still obscure. Here, we used hyperfunctional magnetic resonance imaging, in which two strangers interacted online with one another in a sequential reciprocal trust game while their brains were simultaneously scanned. By designing a nonanonymous, alternating multiround game, trust became bidirectional, and we were able to quantify partnership building and maintenance. Using within- and between-brain analyses, an examination of functional brain activity supports the hypothesis that the preferential activation of different neuronal systems implements these two trust strategies. We show that the paracingulate cortex is critically involved in building a trust relationship by inferring another person's intentions to predict subsequent behavior. This more recently evolved brain region can be differently engaged to interact with more primitive neural systems in maintaining conditional and unconditional trust in a partnership. Conditional trust selectively activated the ventral tegmental area, a region linked to the evaluation of expected and realized reward, whereas unconditional trust selectively activated the septal area, a region linked to social attachment behavior. The interplay of these neural systems supports reciprocal exchange that operates beyond the immediate spheres of kinship, one of the distinguishing features of the human species.

Neural correlates of trust

PubMed Central

Krueger, Frank; McCabe, Kevin; Moll, Jorge; Kriegeskorte, Nikolaus; Zahn, Roland; Strenziok, Maren; Heinecke, Armin; Grafman, Jordan

2007-01-01

Trust is a critical social process that helps us to cooperate with others and is present to some degree in all human interaction. However, the underlying brain mechanisms of conditional and unconditional trust in social reciprocal exchange are still obscure. Here, we used hyperfunctional magnetic resonance imaging, in which two strangers interacted online with one another in a sequential reciprocal trust game while their brains were simultaneously scanned. By designing a nonanonymous, alternating multiround game, trust became bidirectional, and we were able to quantify partnership building and maintenance. Using within- and between-brain analyses, an examination of functional brain activity supports the hypothesis that the preferential activation of different neuronal systems implements these two trust strategies. We show that the paracingulate cortex is critically involved in building a trust relationship by inferring another person's intentions to predict subsequent behavior. This more recently evolved brain region can be differently engaged to interact with more primitive neural systems in maintaining conditional and unconditional trust in a partnership. Conditional trust selectively activated the ventral tegmental area, a region linked to the evaluation of expected and realized reward, whereas unconditional trust selectively activated the septal area, a region linked to social attachment behavior. The interplay of these neural systems supports reciprocal exchange that operates beyond the immediate spheres of kinship, one of the distinguishing features of the human species. PMID:18056800

Abnormal Frontostriatal Activity During Unexpected Reward Receipt in Depression and Schizophrenia: Relationship to Anhedonia.

PubMed

Segarra, Nuria; Metastasio, Antonio; Ziauddeen, Hisham; Spencer, Jennifer; Reinders, Niels R; Dudas, Robert B; Arrondo, Gonzalo; Robbins, Trevor W; Clark, Luke; Fletcher, Paul C; Murray, Graham K

2016-07-01

Alterations in reward processes may underlie motivational and anhedonic symptoms in depression and schizophrenia. However it remains unclear whether these alterations are disorder-specific or shared, and whether they clearly relate to symptom generation or not. We studied brain responses to unexpected rewards during a simulated slot-machine game in 24 patients with depression, 21 patients with schizophrenia, and 21 healthy controls using functional magnetic resonance imaging. We investigated relationships between brain activation, task-related motivation, and questionnaire rated anhedonia. There was reduced activation in the orbitofrontal cortex, ventral striatum, inferior temporal gyrus, and occipital cortex in both depression and schizophrenia in comparison with healthy participants during receipt of unexpected reward. In the medial prefrontal cortex both patient groups showed reduced activation, with activation significantly more abnormal in schizophrenia than depression. Anterior cingulate and medial frontal cortical activation predicted task-related motivation, which in turn predicted anhedonia severity in schizophrenia. Our findings provide evidence for overlapping hypofunction in ventral striatal and orbitofrontal regions in depression and schizophrenia during unexpected reward receipt, and for a relationship between unexpected reward processing in the medial prefrontal cortex and the generation of motivational states.

Contextual Processing of Abstract Concepts Reveals Neural Representations of Non-Linguistic Semantic Content

PubMed Central

Wilson-Mendenhall, Christine D.; Simmons, W. Kyle; Martin, Alex; Barsalou, Lawrence W.

2014-01-01

Concepts develop for many aspects of experience, including abstract internal states and abstract social activities that do not refer to concrete entities in the world. The current study assessed the hypothesis that, like concrete concepts, distributed neural patterns of relevant, non-linguistic semantic content represent the meanings of abstract concepts. In a novel neuroimaging paradigm, participants processed two abstract concepts (convince, arithmetic) and two concrete concepts (rolling, red) deeply and repeatedly during a concept-scene matching task that grounded each concept in typical contexts. Using a catch trial design, neural activity associated with each concept word was separated from neural activity associated with subsequent visual scenes to assess activations underlying the detailed semantics of each concept. We predicted that brain regions underlying mentalizing and social cognition (e.g., medial prefrontal cortex, superior temporal sulcus) would become active to represent semantic content central to convince, whereas brain regions underlying numerical cognition (e.g., bilateral intraparietal sulcus) would become active to represent semantic content central to arithmetic. The results supported these predictions, suggesting that the meanings of abstract concepts arise from distributed neural systems that represent concept-specific content. PMID:23363408

Abnormal Frontostriatal Activity During Unexpected Reward Receipt in Depression and Schizophrenia: Relationship to Anhedonia

PubMed Central

Segarra, Nuria; Metastasio, Antonio; Ziauddeen, Hisham; Spencer, Jennifer; Reinders, Niels R; Dudas, Robert B; Arrondo, Gonzalo; Robbins, Trevor W; Clark, Luke; Fletcher, Paul C; Murray, Graham K

2016-01-01

Alterations in reward processes may underlie motivational and anhedonic symptoms in depression and schizophrenia. However it remains unclear whether these alterations are disorder-specific or shared, and whether they clearly relate to symptom generation or not. We studied brain responses to unexpected rewards during a simulated slot-machine game in 24 patients with depression, 21 patients with schizophrenia, and 21 healthy controls using functional magnetic resonance imaging. We investigated relationships between brain activation, task-related motivation, and questionnaire rated anhedonia. There was reduced activation in the orbitofrontal cortex, ventral striatum, inferior temporal gyrus, and occipital cortex in both depression and schizophrenia in comparison with healthy participants during receipt of unexpected reward. In the medial prefrontal cortex both patient groups showed reduced activation, with activation significantly more abnormal in schizophrenia than depression. Anterior cingulate and medial frontal cortical activation predicted task-related motivation, which in turn predicted anhedonia severity in schizophrenia. Our findings provide evidence for overlapping hypofunction in ventral striatal and orbitofrontal regions in depression and schizophrenia during unexpected reward receipt, and for a relationship between unexpected reward processing in the medial prefrontal cortex and the generation of motivational states. PMID:26708106

Chaos in the brain: imaging via chaoticity of EEG/MEG signals

NASA Astrophysics Data System (ADS)

Kowalik, Zbigniew J.; Elbert, Thomas; Rockstroh, Brigitte; Hoke, Manfried

1995-03-01

Brain electro- (EEG) or magnetoencephalogram (MEG) can be analyzed by using methods of the nonlinear system theory. We show that even for very short and nonstationary time series it is possible to functionally differentiate various brain activities. Usually the analysis assumes that the analyzed signals are both long and stationary, so that the classic spectral methods can be used. Even more convincing results can be obtained under these circumstances when the dimensional analysis or estimation of the Kolmogorov entropy or the Lyapunov exponent are performed. When measuring the spontaneous activity of a human brain the assumption of stationarity is questionable and `static' methods (correlation dimension, entropy, etc.) are then not adequate. In this case `dynamic' methods like pointwise-D2 dimension or chaoticity measures should be applied. Predictability measures in the form of local Lyapunov exponents are capable of revealing directly the chaoticity of a given process, and can practically be applied for functional differentiation of brain activity. We exemplify these in cases of apallic syndrome, tinnitus and schizophrenia. We show that: the average chaoticity in apallic syndrome differentiates brain states both in space and time, chaoticity changes temporally in case of schizophrenia (critical jumps of chaoticity), chaoticity changes locally in space, i.e., in the cortex plane in case of tinnitus.

Evidence for hubs in human functional brain networks

PubMed Central

Power, Jonathan D; Schlaggar, Bradley L; Lessov-Schlaggar, Christina N; Petersen, Steven E

2013-01-01

Summary Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: 1) finding network nodes that participate in multiple sub-networks of the brain, and 2) finding spatial locations where several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity. PMID:23972601

Different Brain Activities Predict Retrieval Success during Emotional and Semantic Encoding

ERIC Educational Resources Information Center

Padovani, Tullia; Koenig, Thomas; Brandeis, Daniel; Perrig, Walter J.

2011-01-01

There is an increasing line of evidence supporting the idea that the formation of lasting memories involves neural activity preceding stimulus presentation. Following this line, we presented words in an incidental learning setting and manipulated the prestimulus state by asking the participants to perform either an emotional (neutral or emotional)…

Variability in functional brain networks predicts expertise during action observation.

PubMed

Amoruso, Lucía; Ibáñez, Agustín; Fonseca, Bruno; Gadea, Sebastián; Sedeño, Lucas; Sigman, Mariano; García, Adolfo M; Fraiman, Ricardo; Fraiman, Daniel

2017-02-01

Observing an action performed by another individual activates, in the observer, similar circuits as those involved in the actual execution of that action. This activation is modulated by prior experience; indeed, sustained training in a particular motor domain leads to structural and functional changes in critical brain areas. Here, we capitalized on a novel graph-theory approach to electroencephalographic data (Fraiman et al., 2016) to test whether variability in functional brain networks implicated in Tango observation can discriminate between groups differing in their level of expertise. We found that experts and beginners significantly differed in the functional organization of task-relevant networks. Specifically, networks in expert Tango dancers exhibited less variability and a more robust functional architecture. Notably, these expertise-dependent effects were captured within networks derived from electrophysiological brain activity recorded in a very short time window (2s). In brief, variability in the organization of task-related networks seems to be a highly sensitive indicator of long-lasting training effects. This finding opens new methodological and theoretical windows to explore the impact of domain-specific expertise on brain plasticity, while highlighting variability as a fruitful measure in neuroimaging research. Copyright © 2016 Elsevier Inc. All rights reserved.

A method to predict different mechanisms for blood-brain barrier permeability of CNS activity compounds in Chinese herbs using support vector machine.

PubMed

Jiang, Ludi; Chen, Jiahua; He, Yusu; Zhang, Yanling; Li, Gongyu

2016-02-01

The blood-brain barrier (BBB), a highly selective barrier between central nervous system (CNS) and the blood stream, restricts and regulates the penetration of compounds from the blood into the brain. Drugs that affect the CNS interact with the BBB prior to their target site, so the prediction research on BBB permeability is a fundamental and significant research direction in neuropharmacology. In this study, we combed through the available data and then with the help of support vector machine (SVM), we established an experiment process for discovering potential CNS compounds and investigating the mechanisms of BBB permeability of them to advance the research in this field four types of prediction models, referring to CNS activity, BBB permeability, passive diffusion and efflux transport, were obtained in the experiment process. The first two models were used to discover compounds which may have CNS activity and also cross the BBB at the same time; the latter two were used to elucidate the mechanism of BBB permeability of those compounds. Three optimization parameter methods, Grid Search, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), were used to optimize the SVM models. Then, four optimal models were selected with excellent evaluation indexes (the accuracy, sensitivity and specificity of each model were all above 85%). Furthermore, discrimination models were utilized to study the BBB properties of the known CNS activity compounds in Chinese herbs and this may guide the CNS drug development. With the relatively systematic and quick approach, the application rationality of traditional Chinese medicines for treating nervous system disease in the clinical practice will be improved.

Decoding the future from past experience: learning shapes predictions in early visual cortex.

PubMed

Luft, Caroline D B; Meeson, Alan; Welchman, Andrew E; Kourtzi, Zoe

2015-05-01

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex. Copyright © 2015 the American Physiological Society.

Neural response to working memory demand predicts neurocognitive deficits in HIV.

PubMed

Cohen, Ronald A; Siegel, S; Gullett, J M; Porges, E; Woods, A J; Huang, H; Zhu, Y; Tashima, K; Ding, M-Z

2018-06-01

Human immunodeficiency virus (HIV) continues to have adverse effects on cognition and the brain in many infected people, despite a reduced incidence of HIV-associated dementia with combined antiretroviral therapy (cART). Working memory is often affected, along with attention, executive control, and cognitive processing speed. Verbal working memory (VWM) requires the interaction of each of the cognitive component processes along with a phonological loop for verbal repetition and rehearsal. HIV-related functional brain response abnormalities during VWM are evident in functional MRI (fMRI), though the neural substrate underlying these neurocognitive deficits is not well understood. The current study addressed this by comparing 24 HIV+ to 27 demographically matched HIV-seronegative (HIV-) adults with respect to fMRI activation on a VWM paradigm (n-back) relative to performance on two standardized tests of executive control, attention and processing speed (Stroop and Trail Making A-B). As expected, the HIV+ group had deficits on these neurocognitive tests compared to HIV- controls, and also differed in neural response on fMRI relative to neuropsychological performance. Reduced activation in VWM task-related brain regions on the 2-back was associated with Stroop interference deficits in HIV+ but not with either Trail Making A or B performance. Activation of the posterior cingulate cortex (PCC) of the default mode network during rest was associated with Hopkins Verbal Learning Test-2 (HVLT-2) learning in HIV+. These effects were not observed in the HIV- controls. Reduced dynamic range of neural response was also evident in HIV+ adults when activation on the 2-back condition was compared to the extent of activation of the default mode network during periods of rest. Neural dynamic range was associated with both Stroop and HVLT-2 performance. These findings provide evidence that HIV-associated alterations in neural activation induced by VWM demands and during rest differentially predict executive-attention and verbal learning deficits. That the Stroop, but not Trail Making was associated with VWM activation suggests that attentional regulation difficulties in suppressing interference and/or conflict regulation are a component of working memory deficits in HIV+ adults. Alterations in neural dynamic range may be a useful index of the impact of HIV on functional brain response and as a fMRI metric in predicting cognitive outcomes.

Social in, social out: How the brain responds to social language with more social language.

PubMed

O'Donnell, Matthew Brook; Falk, Emily B; Lieberman, Matthew D

Social connection is a fundamental human need. As such, people's brains are sensitized to social cues, such as those carried by language, and to promoting social communication. The neural mechanisms of certain key building blocks in this process, such as receptivity to and reproduction of social language, however, are not known. We combined quantitative linguistic analysis and neuroimaging to connect neural activity in brain regions used to simulate the mental states of others with exposure to, and re-transmission of, social language. Our results link findings on successful idea transmission from communication science, sociolinguistics and cognitive neuroscience to prospectively predict the degree of social language that participants utilize when re-transmitting ideas as a function of 1) initial language inputs and 2) neural activity during idea exposure.

Neural coding of basic reward terms of animal learning theory, game theory, microeconomics and behavioural ecology.

PubMed

Schultz, Wolfram

2004-04-01

Neurons in a small number of brain structures detect rewards and reward-predicting stimuli and are active during the expectation of predictable food and liquid rewards. These neurons code the reward information according to basic terms of various behavioural theories that seek to explain reward-directed learning, approach behaviour and decision-making. The involved brain structures include groups of dopamine neurons, the striatum including the nucleus accumbens, the orbitofrontal cortex and the amygdala. The reward information is fed to brain structures involved in decision-making and organisation of behaviour, such as the dorsolateral prefrontal cortex and possibly the parietal cortex. The neural coding of basic reward terms derived from formal theories puts the neurophysiological investigation of reward mechanisms on firm conceptual grounds and provides neural correlates for the function of rewards in learning, approach behaviour and decision-making.

The application of SHERPA (Systematic Human Error Reduction and Prediction Approach) in the development of compensatory cognitive rehabilitation strategies for stroke patients with left and right brain damage.

PubMed

Hughes, Charmayne M L; Baber, Chris; Bienkiewicz, Marta; Worthington, Andrew; Hazell, Alexa; Hermsdörfer, Joachim

2015-01-01

Approximately 33% of stroke patients have difficulty performing activities of daily living, often committing errors during the planning and execution of such activities. The objective of this study was to evaluate the ability of the human error identification (HEI) technique SHERPA (Systematic Human Error Reduction and Prediction Approach) to predict errors during the performance of daily activities in stroke patients with left and right hemisphere lesions. Using SHERPA we successfully predicted 36 of the 38 observed errors, with analysis indicating that the proportion of predicted and observed errors was similar for all sub-tasks and severity levels. HEI results were used to develop compensatory cognitive strategies that clinicians could employ to reduce or prevent errors from occurring. This study provides evidence for the reliability and validity of SHERPA in the design of cognitive rehabilitation strategies in stroke populations.

Whole-central nervous system functional imaging in larval Drosophila

PubMed Central

Lemon, William C.; Pulver, Stefan R.; Höckendorf, Burkhard; McDole, Katie; Branson, Kristin; Freeman, Jeremy; Keller, Philipp J.

2015-01-01

Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord. PMID:26263051

Resting-State fMRI Activity Predicts Unsupervised Learning and Memory in an Immersive Virtual Reality Environment

PubMed Central

Wong, Chi Wah; Olafsson, Valur; Plank, Markus; Snider, Joseph; Halgren, Eric; Poizner, Howard; Liu, Thomas T.

2014-01-01

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment. PMID:25286145

Predictive Suppression of Cortical Excitability and Its Deficit in Schizophrenia

PubMed Central

Schroeder, Charles E.; Leitman, David I.

2013-01-01

Recent neuroscience advances suggest that when interacting with our environment, along with previous experience, we use contextual cues and regularities to form predictions that guide our perceptions and actions. The goal of such active “predictive sensing” is to selectively enhance the processing and representation of behaviorally relevant information in an efficient manner. Since a hallmark of schizophrenia is impaired information selection, we tested whether this deficiency stems from dysfunctional predictive sensing by measuring the degree to which neuronal activity predicts relevant events. In healthy subjects, we established that these mechanisms are engaged in an effort-dependent manner and that, based on a correspondence between human scalp and intracranial nonhuman primate recordings, their main role is a predictive suppression of excitability in task-irrelevant regions. In contrast, schizophrenia patients displayed a reduced alignment of neuronal activity to attended stimuli, which correlated with their behavioral performance deficits and clinical symptoms. These results support the relevance of predictive sensing for normal and aberrant brain function, and highlight the importance of neuronal mechanisms that mold internal ongoing neuronal activity to model key features of the external environment. PMID:23843536

The role of the posterior cingulate cortex in cognition and disease

PubMed Central

Sharp, David J.

2014-01-01

The posterior cingulate cortex is a highly connected and metabolically active brain region. Recent studies suggest it has an important cognitive role, although there is no consensus about what this is. The region is typically discussed as having a unitary function because of a common pattern of relative deactivation observed during attentionally demanding tasks. One influential hypothesis is that the posterior cingulate cortex has a central role in supporting internally-directed cognition. It is a key node in the default mode network and shows increased activity when individuals retrieve autobiographical memories or plan for the future, as well as during unconstrained ‘rest’ when activity in the brain is ‘free-wheeling’. However, other evidence suggests that the region is highly heterogeneous and may play a direct role in regulating the focus of attention. In addition, its activity varies with arousal state and its interactions with other brain networks may be important for conscious awareness. Understanding posterior cingulate cortex function is likely to be of clinical importance. It is well protected against ischaemic stroke, and so there is relatively little neuropsychological data about the consequences of focal lesions. However, in other conditions abnormalities in the region are clearly linked to disease. For example, amyloid deposition and reduced metabolism is seen early in Alzheimer’s disease. Functional neuroimaging studies show abnormalities in a range of neurological and psychiatric disorders including Alzheimer’s disease, schizophrenia, autism, depression and attention deficit hyperactivity disorder, as well as ageing. Our own work has consistently shown abnormal posterior cingulate cortex function following traumatic brain injury, which predicts attentional impairments. Here we review the anatomy and physiology of the region and how it is affected in a range of clinical conditions, before discussing its proposed functions. We synthesize key findings into a novel model of the region’s function (the ‘Arousal, Balance and Breadth of Attention’ model). Dorsal and ventral subcomponents are functionally separated and differences in regional activity are explained by considering: (i) arousal state; (ii) whether attention is focused internally or externally; and (iii) the breadth of attentional focus. The predictions of the model can be tested within the framework of complex dynamic systems theory, and we propose that the dorsal posterior cingulate cortex influences attentional focus by ‘tuning’ whole-brain metastability and so adjusts how stable brain network activity is over time. PMID:23869106

Motor skill failure or flow-experience? Functional brain asymmetry and brain connectivity in elite and amateur table tennis players.

PubMed

Wolf, Sebastian; Brölz, Ellen; Keune, Philipp M; Wesa, Benjamin; Hautzinger, Martin; Birbaumer, Niels; Strehl, Ute

2015-02-01

Functional hemispheric asymmetry is assumed to constitute one underlying neurophysiological mechanism of flow-experience and skilled psycho-motor performance in table tennis athletes. We hypothesized that when initiating motor execution during motor imagery, elite table tennis players show higher right- than left-hemispheric temporal activity and stronger right temporal-premotor than left temporal-premotor theta coherence compared to amateurs. We additionally investigated, whether less pronounced left temporal cortical activity is associated with more world rank points and more flow-experience. To this aim, electroencephalographic data were recorded in 14 experts and 15 amateur table tennis players. Subjects watched videos of an opponent serving a ball and were instructed to imagine themselves responding with a specific table tennis stroke. Alpha asymmetry scores were calculated by subtracting left from right hemispheric 8-13 Hz alpha power. 4-7 Hz theta coherence was calculated between temporal (T3/T4) and premotor (Fz) cortex. Experts showed a significantly stronger shift towards lower relative left-temporal brain activity compared to amateurs and a significantly stronger right temporal-premotor coherence than amateurs. The shift towards lower relative left-temporal brain activity in experts was associated with more flow-experience and lower relative left temporal activity was correlated with more world rank points. The present findings suggest that skilled psycho-motor performance in elite table tennis players reflect less desynchronized brain activity at the left hemisphere and more coherent brain activity between fronto-temporal and premotor oscillations at the right hemisphere. This pattern probably reflect less interference of irrelevant communication of verbal-analytical with motor-control mechanisms which implies flow-experience and predict world rank in experts. Copyright © 2015 Elsevier B.V. All rights reserved.

Increased brain-predicted aging in treated HIV disease

PubMed Central

Underwood, Jonathan; Caan, Matthan W.A.; De Francesco, Davide; van Zoest, Rosan A.; Leech, Robert; Wit, Ferdinand W.N.M.; Portegies, Peter; Geurtsen, Gert J.; Schmand, Ben A.; Schim van der Loeff, Maarten F.; Franceschi, Claudio; Sabin, Caroline A.; Majoie, Charles B.L.M.; Winston, Alan; Reiss, Peter; Sharp, David J.

2017-01-01

Objective: To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. Methods: A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. Results: HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Conclusion: Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. PMID:28258081

Increased brain-predicted aging in treated HIV disease.

PubMed

Cole, James H; Underwood, Jonathan; Caan, Matthan W A; De Francesco, Davide; van Zoest, Rosan A; Leech, Robert; Wit, Ferdinand W N M; Portegies, Peter; Geurtsen, Gert J; Schmand, Ben A; Schim van der Loeff, Maarten F; Franceschi, Claudio; Sabin, Caroline A; Majoie, Charles B L M; Winston, Alan; Reiss, Peter; Sharp, David J

2017-04-04

To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. A large sample of virologically suppressed HIV-positive adults (n = 162, age 45-82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

PubMed

Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

2017-02-01

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning.

PubMed

Reindl, Vanessa; Gerloff, Christian; Scharke, Wolfgang; Konrad, Kerstin

2018-05-26

Parent-child synchrony, the coupling of behavioral and biological signals during social contact, may fine-tune the child's brain circuitries associated with emotional bond formation and the child's development of emotion regulation. Here, we examined the neurobiological underpinnings of these processes by measuring parent's and child's prefrontal neural activity concurrently with functional near-infrared spectroscopy hyperscanning. Each child played both a cooperative and a competitive game with the parent, mostly the mother, as well as an adult stranger. During cooperation, parent's and child's brain activities synchronized in the dorsolateral prefrontal and frontopolar cortex (FPC), which was predictive for their cooperative performance in subsequent trials. No significant brain-to-brain synchrony was observed in the conditions parent-child competition, stranger-child cooperation and stranger-child competition. Furthermore, parent-child compared to stranger-child brain-to-brain synchrony during cooperation in the FPC mediated the association between the parent's and the child's emotion regulation, as assessed by questionnaires. Thus, we conclude that brain-to-brain synchrony may represent an underlying neural mechanism of the emotional connection between parent and child, which is linked to the child's development of adaptive emotion regulation. Future studies may uncover whether brain-to-brain synchrony can serve as a neurobiological marker of the dyad's socio-emotional interaction, which is sensitive to risk conditions, and can be modified by interventions. Copyright © 2018 Elsevier Inc. All rights reserved.

Discerning measures of conscious brain processes associated with superior early motor performance: Capacity, coactivation, and character.

PubMed

van Duijn, Tina; Buszard, Tim; Hoskens, Merel C J; Masters, Rich S W

2017-01-01

This study explored the relationship between working memory (WM) capacity, corticocortical communication (EEG coherence), and propensity for conscious control of movement during the performance of a complex far-aiming task. We were specifically interested in the role of these variables in predicting motor performance by novices. Forty-eight participants completed (a) an assessment of WM capacity (an adapted Rotation Span task), (b) a questionnaire that assessed the propensity to consciously control movement (the Movement Specific Reinvestment Scale), and (c) a hockey push-pass task. The hockey push-pass task was performed in a single task (movement only) condition and a combined task (movement plus decision) condition. Electroencephalography (EEG) was used to examine brain activity during the single task. WM capacity best predicted single task performance. WM capacity in combination with T8-Fz coherence (between the visuospatial and motor regions of the brain) best predicted combined task performance. We discuss the implied roles of visuospatial information processing capacity, neural coactivation, and propensity for conscious processing during performance of complex motor tasks. © 2017 Elsevier B.V. All rights reserved.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

PubMed

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

Altered prefrontal brain activity in persons at risk for Alzheimer's disease: an fMRI study.

PubMed

Elgh, Eva; Larsson, Anne; Eriksson, Sture; Nyberg, Lars

2003-06-01

Early diagnosis of Alzheimer's disease (AD) is critical for adequate treatment and care. Recently it has been shown that functional magnetic resonance imaging (fMRI) can be important in preclinical detection of AD. The purpose of this study was to examine possible differences in memory-related brain activation between persons with high versus low risk for AD. This was achieved by combining a validated neurocognitive screening battery (the 7-minutes test) with memory assessment and fMRI. One hundred two healthy community-living persons with subjective memory complaints were recruited through advertisement and tested with the 7-minutes test. Based on their test performance they were classified as having either high (n = 8) or low risk (n = 94) for AD. Six high-risk individuals and six age-, sex-, and education-matched low-risk individuals were investigated with fMRI while engaged in episodic memory tasks. The high-risk individuals performed worse than low-risk individuals on tests of episodic memory. Patterns of brain activity during episodic encoding and retrieval showed significant group differences (p < .05 corrected). During both encoding and retrieval, the low-risk persons showed increased activity relative to a baseline condition in prefrontal brain regions that previously have been implicated in episodic memory. By contrast, the high-risk persons did not significantly activate any prefrontal regions, but instead showed increased activity in visual occipito-temporal regions. Patterns of prefrontal brain activity related to episodic memory differ between persons with high versus low risk for AD, and lowered prefrontal activity may predict subsequent disease.

Hippocampal Context Processing during Acquisition of a Predictive Learning Task Is Associated with Renewal in Extinction Recall.

PubMed

Lissek, Silke; Glaubitz, Benjamin; Schmidt-Wilcke, Tobias; Tegenthoff, Martin

2016-05-01

Renewal is defined as the recovery of an extinguished response if extinction and retrieval contexts differ. The context dependency of extinction, as demonstrated by renewal, has important implications for extinction-based therapies. Persons showing renewal (REN) exhibit higher hippocampal activation during extinction in associative learning than those without renewal (NOREN), demonstrating hippocampal context processing, and recruit ventromedial pFC in retrieval. Apart from these findings, brain processes generating renewal remain largely unknown. Conceivably, processing differences in task-relevant brain regions that ultimately lead to renewal may occur already in initial acquisition of associations. Therefore, in two fMRI studies, we investigated overall brain activation and hippocampal activation in REN and NOREN during acquisition of an associative learning task in response to presentation of a context alone or combined with a cue. Results of two studies demonstrated significant activation differences between the groups: In Study 1, a support vector machine classifier correctly assigned participants' brain activation patterns to REN and NOREN groups, respectively. In Study 2, REN and NOREN showed similar hippocampal involvement during context-only presentation, suggesting processing of novelty, whereas overall hippocampal activation to the context-cue compound, suggesting compound encoding, was higher in REN. Positive correlations between hippocampal activation and renewal level indicated more prominent hippocampal processing in REN. Results suggest that hippocampal processing of the context-cue compound rather than of context only during initial learning is related to a subsequent renewal effect. Presumably, REN participants use distinct encoding strategies during acquisition of context-related tasks, which reflect in their brain activation patterns and contribute to a renewal effect.

Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200×

PubMed Central

Papalexakis, Evangelos E.; Faloutsos, Christos; Mitchell, Tom M.; Talukdar, Partha Pratim; Sidiropoulos, Nicholas D.; Murphy, Brian

2015-01-01

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like ‘edible’, ‘fits in hand’)? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem. Can we accelerate any CMTF solver, so that it runs within a few minutes instead of tens of hours to a day, while maintaining good accuracy? We introduce TURBO-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, by up to 200×, along with an up to 65 fold increase in sparsity, with comparable accuracy to the baseline. We apply TURBO-SMT to BRAINQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. TURBO-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy. PMID:26473087

Stuck in default mode: inefficient cross-frequency synchronization may lead to age-related short-term memory decline.

PubMed

Pinal, Diego; Zurrón, Montserrat; Díaz, Fernando; Sauseng, Paul

2015-04-01

Aging-related decline in short-term memory capacity seems to be caused by deficient balancing of task-related and resting state brain networks activity; however, the exact neural mechanism underlying this deficit remains elusive. Here, we studied brain oscillatory activity in healthy young and old adults during visual information maintenance in a delayed match-to-sample task. Particular emphasis was on long range phase:amplitude coupling of frontal alpha (8-12 Hz) and posterior fast oscillatory activity (>30 Hz). It is argued that through posterior fast oscillatory activity nesting into the excitatory or the inhibitory phase of frontal alpha wave, long-range networks can be efficiently coupled or decoupled, respectively. On the basis of this mechanism, we show that healthy, elderly participants exhibit a lack of synchronization in task-relevant networks while maintaining synchronized regions of the resting state network. Lacking disconnection of this resting state network is predictive of aging-related short-term memory decline. These results support the idea of inefficient orchestration of competing brain networks in the aging human brain and identify the neural mechanism responsible for this control breakdown. Copyright © 2015 Elsevier Inc. All rights reserved.

Brain-derived neurotrophic factor and its receptors in Bergmann glia cells.

PubMed

Poblete-Naredo, Irais; Guillem, Alain M; Juárez, Claudia; Zepeda, Rossana C; Ramírez, Leticia; Caba, Mario; Hernández-Kelly, Luisa C; Aguilera, José; López-Bayghen, Esther; Ortega, Arturo

2011-12-01

Brain-derived neurotrophic factor is an abundant and widely distributed neurotrophin expressed in the Central Nervous System. It is critically involved in neuronal differentiation and survival. The expression of brain-derived neurotrophic factor and that of its catalytic active cognate receptor (TrkB) has been extensively studied in neuronal cells but their expression and function in glial cells is still controversial. Despite of this fact, brain-derived neurotrophic factor is released from astrocytes upon glutamate stimulation. A suitable model to study glia/neuronal interactions, in the context of glutamatergic synapses, is the well-characterized culture of chick cerebellar Bergmann glia cells. Using, this system, we show here that BDNF and its functional receptor are present in Bergmann glia and that BDNF stimulation is linked to the activation of the phosphatidyl-inositol 3 kinase/protein kinase C/mitogen-activated protein kinase/Activator Protein-1 signaling pathway. Accordingly, reverse transcription-polymerase chain reaction (RT-PCR) experiments predicted the expression of full-length and truncated TrkB isoforms. Our results suggest that Bergmann glia cells are able to express and respond to BDNF stimulation favoring the notion of their pivotal role in neuroprotection. Copyright © 2011 Elsevier B.V. All rights reserved.

Performance on an episodic encoding task yields further insight into functional brain development.

PubMed

McAuley, Tara; Brahmbhatt, Shefali; Barch, Deanna M

2007-01-15

To further characterize changes in functional brain development that are associated with the emergence of cognitive control, participants 14 to 28 years of age were scanned while performing an episodic encoding task with a levels-of-processing manipulation. Using data from the 12 youngest and oldest participants (endpoint groups), 18 regions were identified that showed group differences in task-related activity as a function of processing depth. One region, located in left inferior frontal gyrus, showed enhanced activity in deep relative to shallow encoding that was larger in magnitude for the older group. Seventeen regions showed enhanced activity in shallow relative to deep encoding that was larger in magnitude for the youngest group. These regions were distributed across a broad network that included both cortical and subcortical areas. Regression analyses using the entire sample showed that age made a significant contribution to the difference in beta weights between deep and shallow encoding for 17 of the 18 identified regions in the direction predicted by the endpoint analysis. We conclude that the patterns of brain activation associated with deep and shallow encoding differ between adolescents and young adults in a manner that is consistent with the interactive specialization account of functional brain development.

Language and reading development in the brain today: neuromarkers and the case for prediction.

PubMed

Buchweitz, Augusto

2016-01-01

The goal of this article is to provide an account of language development in the brain using the new information about brain function gleaned from cognitive neuroscience. This account goes beyond describing the association between language and specific brain areas to advocate the possibility of predicting language outcomes using brain-imaging data. The goal is to address the current evidence about language development in the brain and prediction of language outcomes. Recent studies will be discussed in the light of the evidence generated for predicting language outcomes and using new methods of analysis of brain data. The present account of brain behavior will address: (1) the development of a hardwired brain circuit for spoken language; (2) the neural adaptation that follows reading instruction and fosters the "grafting" of visual processing areas of the brain onto the hardwired circuit of spoken language; and (3) the prediction of language development and the possibility of translational neuroscience. Brain imaging has allowed for the identification of neural indices (neuromarkers) that reflect typical and atypical language development; the possibility of predicting risk for language disorders has emerged. A mandate to develop a bridge between neuroscience and health and cognition-related outcomes may pave the way for translational neuroscience. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Spectral Variability in the Aged Brain during Fine Motor Control

PubMed Central

Quandt, Fanny; Bönstrup, Marlene; Schulz, Robert; Timmermann, Jan E.; Zimerman, Maximo; Nolte, Guido; Hummel, Friedhelm C.

2016-01-01

Physiological aging is paralleled by a decline of fine motor skills accompanied by structural and functional alterations of the underlying brain network. Here, we aim to investigate age-related changes in the spectral distribution of neuronal oscillations during fine skilled motor function. We employ the concept of spectral entropy in order to describe the flatness and peaked-ness of a frequency spectrum to quantify changes in the spectral distribution of the oscillatory motor response in the aged brain. Electroencephalogram was recorded in elderly (n = 32) and young (n = 34) participants who performed either a cued finger movement or a pinch or a whole hand grip task with their dominant right hand. Whereas young participant showed distinct, well-defined movement-related power decreases in the alpha and upper beta band, elderly participants exhibited a flat broadband, frequency-unspecific power desynchronization. This broadband response was reflected by an increase of spectral entropy over sensorimotor and frontal areas in the aged brain. Neuronal activation patterns differed between motor tasks in the young brain, while the aged brain showed a similar activation pattern in all tasks. Moreover, we found a wider recruitment of the cortical motor network in the aged brain. The present study adds to the understanding of age-related changes of neural coding during skilled motor behavior, revealing a less predictable signal with great variability across frequencies in a wide cortical motor network in the aged brain. The increase in entropy in the aged brain could be a reflection of random noise-like activity or could represent a compensatory mechanism that serves a functional role. PMID:28066231

Thalamocortical Dysrhythmia: A Theoretical Update in Tinnitus

PubMed Central

De Ridder, Dirk; Vanneste, Sven; Langguth, Berthold; Llinas, Rodolfo

2015-01-01

Tinnitus is the perception of a sound in the absence of a corresponding external sound source. Pathophysiologically it has been attributed to bottom-up deafferentation and/or top-down noise-cancelling deficit. Both mechanisms are proposed to alter auditory ­thalamocortical signal transmission, resulting in thalamocortical dysrhythmia (TCD). In deafferentation, TCD is characterized by a slowing down of resting state alpha to theta activity associated with an increase in surrounding gamma activity, resulting in persisting cross-frequency coupling between theta and gamma activity. Theta burst-firing increases network synchrony and recruitment, a mechanism, which might enable long-range synchrony, which in turn could represent a means for finding the missing thalamocortical information and for gaining access to consciousness. Theta oscillations could function as a carrier wave to integrate the tinnitus-related focal auditory gamma activity in a consciousness enabling network, as envisioned by the global workspace model. This model suggests that focal activity in the brain does not reach consciousness, except if the focal activity becomes functionally coupled to a consciousness enabling network, aka the global workspace. In limited deafferentation, the missing information can be retrieved from the auditory cortical neighborhood, decreasing surround inhibition, resulting in TCD. When the deafferentation is too wide in bandwidth, it is hypothesized that the missing information is retrieved from theta-mediated parahippocampal auditory memory. This suggests that based on the amount of deafferentation TCD might change to parahippocampocortical persisting and thus pathological theta–gamma rhythm. From a Bayesian point of view, in which the brain is conceived as a prediction machine that updates its memory-based predictions through sensory updating, tinnitus is the result of a prediction error between the predicted and sensed auditory input. The decrease in sensory updating is reflected by decreased alpha activity and the prediction error results in theta–gamma and beta–gamma coupling. Thus, TCD can be considered as an adaptive mechanism to retrieve missing auditory input in tinnitus. PMID:26106362

Pharmacokinetics, brain distribution and plasma protein binding of carbamazepine and nine derivatives: new set of data for predictive in silico ADME models.

PubMed

Fortuna, Ana; Alves, Gilberto; Soares-da-Silva, Patrício; Falcão, Amílcar

2013-11-01

In silico approaches to predict absorption, distribution, metabolism and excretion (ADME) of new drug candidates are gaining a relevant importance in drug discovery programmes. When considering particularly the pharmacokinetics during the development of oral antiepileptic drugs (AEDs), one of the most prominent goals is designing compounds with good bioavailability and brain penetration. Thus, it is expected that in silico models able to predict these features may be applied during the early stages of AEDs discovery. The present investigation was mainly carried out in order to generate in vivo pharmacokinetic data that can be utilized for development and validation of in silico models. For this purpose, a single dose of each compound (1.4mmol/kg) was orally administered to male CD-1 mice. After quantifying the parent compound and main metabolites in plasma and brain up to 12h post-dosing, a non-compartmental pharmacokinetic analysis was performed and the corresponding brain/plasma ratios were calculated. Moreover the plasma protein binding was estimated in vitro applying the ultrafiltration procedure. The present in vivo pharmacokinetic characterization of the test compounds and corresponding metabolites demonstrated that the metabolism extensively compromised the in vivo activity of CBZ derivatives and their toxicity. Furthermore, it was clearly evidenced that the time to reach maximum peak concentration, bioavailability (given by the area under the curve) and metabolic stability (given by the AUC0-12h ratio of the parent compound and total systemic drug) influenced the in vivo pharmacological activities and must be considered as primary parameters to be investigated. All the test compounds presented brain/plasma ratios lower than 1.0, suggesting that the blood-brain barrier restricts drug entry into the brain. In agreement with in vitro studies already performed within our research group, CBZ, CBZ-10,11-epoxide and oxcarbazepine exhibited the highest brain/plasma ratios (>0.50), followed by eslicarbazepine, R-licarbazepine, trans-diol and BIA 2-024 (ratios within 0.05-0.50). BIA 2-265 was not found in the biophase, probably due to its high plasma-protein bound fraction (>90%) herein revealed for the first time. The comparative in vivo pharmacokinetic data obtained in the present work might be usefully applied in the context of discovery of new antiepileptic drugs that are derivatives of CBZ. Copyright © 2013 Elsevier B.V. All rights reserved.

A preliminary study of the effects of working memory training on brain function in Attention-Deficit/Hyperactivity Disorder

PubMed Central

Stevens, Michael C.; Gaynor, Alexandra; Bessette, Katie L.; Pearlson, Godfrey D.

2015-01-01

Working memory (WM) training improves WM ability in Attention-Deficit/Hyperactivity Disorder (ADHD), but its efficacy for non-cognitive ADHD impairments ADHD has been sharply debated. The purpose of this preliminary study was to characterize WM training-related changes in ADHD brain function and see if they were linked to clinical improvement. We examined 18 adolescents diagnosed with DSM-IV Combined-subtype ADHD before and after 25 sessions of WM training using a frequently employed approach (CogmedTM) using a nonverbal Sternberg WM fMRI task, neuropsychological tests, and participant- and parent-reports of ADHD symptom severity and associated functional impairment. Whole brain SPM8 analyses identified ADHD activation deficits compared to 18 non-ADHD control participants, then tested whether impaired ADHD frontoparietal brain activation would increase following WM training. Post hoc tests examined the relationships between neural changes and neurocognitive or clinical improvements. As predicted, WM training increased WM performance, ADHD clinical functioning, and WM-related ADHD brain activity in several frontal, parietal and temporal lobe regions. Increased left inferior frontal sulcus region activity was seen in all Encoding, Maintenance, and Retrieval Sternberg task phases. ADHD symptom severity improvements were most often positively correlated with activation gains in brain regions known to be engaged for WM-related executive processing; improvement of different symptom types had different neural correlates. The responsiveness of both amodal WM frontoparietal circuits and executive process-specific WM brain regions was altered by WM training. The latter might represent a promising, relatively unexplored treatment target for researchers seeking to optimize clinical response in ongoing ADHD WM training development efforts. PMID:26138580

Effects of aging on functional connectivity of the amygdala for subsequent memory of negative pictures: a network analysis of functional magnetic resonance imaging data.

PubMed

St Jacques, Peggy L; Dolcos, Florin; Cabeza, Roberto

2009-01-01

Aging is associated with preserved enhancement of emotional memory, as well as with age-related reductions in memory for negative stimuli, but the neural networks underlying such alterations are not clear. We used a subsequent-memory paradigm to identify brain activity predicting enhanced emotional memory in young and older adults. Activity in the amygdala predicted enhanced emotional memory, with subsequent-memory activity greater for negative stimuli than for neutral stimuli, across age groups, a finding consistent with an overall enhancement of emotional memory. However, older adults recruited greater activity in anterior regions and less activity in posterior regions in general for negative stimuli that were subsequently remembered. Functional connectivity of the amygdala with the rest of the brain was consistent with age-related reductions in memory for negative stimuli: Older adults showed decreased functional connectivity between the amygdala and the hippocampus, but increased functional connectivity between the amygdala and dorsolateral prefrontal cortices. These findings suggest that age-related differences in the enhancement of emotional memory might reflect decreased connectivity between the amygdala and typical subsequent-memory regions, as well as the engagement of regulatory processes that inhibit emotional responses.

Does Aerobic Exercise Influence Intrinsic Brain Activity? An Aerobic Exercise Intervention among Healthy Old Adults

PubMed Central

Flodin, Pär; Jonasson, Lars S.; Riklund, Katrin; Nyberg, Lars; Boraxbekk, C. J.

2017-01-01

Previous studies have indicated that aerobic exercise could reduce age related decline in cognition and brain functioning. Here we investigated the effects of aerobic exercise on intrinsic brain activity. Sixty sedentary healthy males and females (64–78 years) were randomized into either an aerobic exercise group or an active control group. Both groups recieved supervised training, 3 days a week for 6 months. Multimodal brain imaging data was acquired before and after the intervention, including 10 min of resting state brain functional magnetic resonance imaging (rs-fMRI) and arterial spin labeling (ASL). Additionally, a comprehensive battery of cognitive tasks assessing, e.g., executive function and episodic memory was administered. Both the aerobic and the control group improved in aerobic capacity (VO2-peak) over 6 months, but a significant group by time interaction confirmed that the aerobic group improved more. Contrary to our hypothesis, we did not observe any significant group by time interactions with regard to any measure of intrinsic activity. To further probe putative relationships between fitness and brain activity, we performed post hoc analyses disregarding group belongings. At baseline, VO2-peak was negativly related to BOLD-signal fluctuations (BOLDSTD) in mid temporal areas. Over 6 months, improvements in aerobic capacity were associated with decreased connectivity between left hippocampus and contralateral precentral gyrus, and positively to connectivity between right mid-temporal areas and frontal and parietal regions. Independent component analysis identified a VO2-related increase in coupling between the default mode network and left orbitofrontal cortex, as well as a decreased connectivity between the sensorimotor network and thalamus. Extensive exploratory data analyses of global efficiency, connectome wide multivariate pattern analysis (connectome-MVPA), as well as ASL, did not reveal any relationships between aerobic fitness and intrinsic brain activity. Moreover, fitness-predicted changes in functional connectivity did not relate to changes in cognition, which is likely due to absent cross-sectional or longitudinal relationships between VO2-peak and cognition. We conclude that the aerobic exercise intervention had limited influence on patterns of intrinsic brain activity, although post hoc analyses indicated that individual changes in aerobic capacity preferentially influenced mid-temporal brain areas. PMID:28848424

Does Aerobic Exercise Influence Intrinsic Brain Activity? An Aerobic Exercise Intervention among Healthy Old Adults.

PubMed

Flodin, Pär; Jonasson, Lars S; Riklund, Katrin; Nyberg, Lars; Boraxbekk, C J

2017-01-01

Previous studies have indicated that aerobic exercise could reduce age related decline in cognition and brain functioning. Here we investigated the effects of aerobic exercise on intrinsic brain activity. Sixty sedentary healthy males and females (64-78 years) were randomized into either an aerobic exercise group or an active control group. Both groups recieved supervised training, 3 days a week for 6 months. Multimodal brain imaging data was acquired before and after the intervention, including 10 min of resting state brain functional magnetic resonance imaging (rs-fMRI) and arterial spin labeling (ASL). Additionally, a comprehensive battery of cognitive tasks assessing, e.g., executive function and episodic memory was administered. Both the aerobic and the control group improved in aerobic capacity (VO 2 -peak) over 6 months, but a significant group by time interaction confirmed that the aerobic group improved more. Contrary to our hypothesis, we did not observe any significant group by time interactions with regard to any measure of intrinsic activity. To further probe putative relationships between fitness and brain activity, we performed post hoc analyses disregarding group belongings. At baseline, VO 2 -peak was negativly related to BOLD-signal fluctuations (BOLD STD ) in mid temporal areas. Over 6 months, improvements in aerobic capacity were associated with decreased connectivity between left hippocampus and contralateral precentral gyrus, and positively to connectivity between right mid-temporal areas and frontal and parietal regions. Independent component analysis identified a VO 2 -related increase in coupling between the default mode network and left orbitofrontal cortex, as well as a decreased connectivity between the sensorimotor network and thalamus. Extensive exploratory data analyses of global efficiency, connectome wide multivariate pattern analysis (connectome-MVPA), as well as ASL, did not reveal any relationships between aerobic fitness and intrinsic brain activity. Moreover, fitness-predicted changes in functional connectivity did not relate to changes in cognition, which is likely due to absent cross-sectional or longitudinal relationships between VO 2 -peak and cognition. We conclude that the aerobic exercise intervention had limited influence on patterns of intrinsic brain activity, although post hoc analyses indicated that individual changes in aerobic capacity preferentially influenced mid-temporal brain areas.

Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom.

PubMed

Dikker, Suzanne; Wan, Lu; Davidesco, Ido; Kaggen, Lisa; Oostrik, Matthias; McClintock, James; Rowland, Jess; Michalareas, Georgios; Van Bavel, Jay J; Ding, Mingzhou; Poeppel, David

2017-05-08

The human brain has evolved for group living [1]. Yet we know so little about how it supports dynamic group interactions that the study of real-world social exchanges has been dubbed the "dark matter of social neuroscience" [2]. Recently, various studies have begun to approach this question by comparing brain responses of multiple individuals during a variety of (semi-naturalistic) tasks [3-15]. These experiments reveal how stimulus properties [13], individual differences [14], and contextual factors [15] may underpin similarities and differences in neural activity across people. However, most studies to date suffer from various limitations: they often lack direct face-to-face interaction between participants, are typically limited to dyads, do not investigate social dynamics across time, and, crucially, they rarely study social behavior under naturalistic circumstances. Here we extend such experimentation drastically, beyond dyads and beyond laboratory walls, to identify neural markers of group engagement during dynamic real-world group interactions. We used portable electroencephalogram (EEG) to simultaneously record brain activity from a class of 12 high school students over the course of a semester (11 classes) during regular classroom activities (Figures 1A-1C; Supplemental Experimental Procedures, section S1). A novel analysis technique to assess group-based neural coherence demonstrates that the extent to which brain activity is synchronized across students predicts both student class engagement and social dynamics. This suggests that brain-to-brain synchrony is a possible neural marker for dynamic social interactions, likely driven by shared attention mechanisms. This study validates a promising new method to investigate the neuroscience of group interactions in ecologically natural settings. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Interoceptive predictions in the brain

PubMed Central

Barrett, Lisa Feldman; Simmons, W. Kyle

2016-01-01

Intuition suggests that perception follows sensation and therefore bodily feelings originate in the body. However, recent evidence goes against this logic: interoceptive experience may largely reflect limbic predictions about the expected state of the body that are constrained by ascending visceral sensations. In this Opinion article, we introduce the Embodied Predictive Interoception Coding model, which integrates an anatomical model of corticocortical connections with Bayesian active inference principles, to propose that agranular visceromotor cortices contribute to interoception by issuing interoceptive predictions. We then discuss how disruptions in interoceptive predictions could function as a common vulnerability for mental and physical illness. PMID:26016744

Ankyrin-binding activity of nervous system cell adhesion molecules expressed in adult brain.

PubMed

Davis, J Q; Bennett, V

1993-01-01

A family of ankyrin-binding glycoproteins have been identified in adult rat brain that include alternatively spliced products of the same pre-mRNA. A composite sequence of ankyrin-binding glycoprotein (ABGP) shares 72% amino acid sequence identity with chicken neurofascin, a membrane-spanning neural cell adhesion molecule in the Ig super-family expressed in embryonic brain. ABGP polypeptides and ankyrin associate as pure proteins in a 1:1 molar stoichiometry at a site located in the predicted cytoplasmic domain. ABGP polypeptides are expressed late in postnatal development to approximately the same levels as ankyrin, and comprise a significant fraction of brain membrane proteins. Immunofluorescence studies have shown that ABGP polypeptides are co-localized with ankyrinB. Major differences in developmental expression have been reported for neurofascin in embryos compared with the late postnatal expression of ABGP, suggesting that ABGP and neurofascin represent products of gene duplication events that have subsequently evolved in parallel with distinct roles. Predicted cytoplasmic domains of rat ABGP and chicken neurofascin are nearly identical to each other and closely related to a group of nervous system cell adhesion molecules with variable extracellular domains, including L1, Nr-CAM and Ng-CAM of vertebrates, and neuroglian of Drosophila. A hypothesis to be evaluated is that ankyrin-binding activity is shared by all of these proteins.

The perimenopausal aging transition in the female rat brain: decline in bioenergetic systems and synaptic plasticity.

PubMed

Yin, Fei; Yao, Jia; Sancheti, Harsh; Feng, Tao; Melcangi, Roberto C; Morgan, Todd E; Finch, Caleb E; Pike, Christian J; Mack, Wendy J; Cadenas, Enrique; Brinton, Roberta D

2015-07-01

The perimenopause is an aging transition unique to the female that leads to reproductive senescence which can be characterized by multiple neurological symptoms. To better understand potential underlying mechanisms of neurological symptoms of perimenopause, the present study determined genomic, biochemical, brain metabolic, and electrophysiological transformations that occur during this transition using a rat model recapitulating fundamental characteristics of the human perimenopause. Gene expression analyses indicated two distinct aging programs: chronological and endocrine. A critical period emerged during the endocrine transition from regular to irregular cycling characterized by decline in bioenergetic gene expression, confirmed by deficits in fluorodeoxyglucose-positron emission tomography (FDG-PET) brain metabolism, mitochondrial function, and long-term potentiation. Bioinformatic analysis predicted insulin/insulin-like growth factor 1 and adenosine monophosphate-activated protein kinase/peroxisome proliferator-activated receptor gamma coactivator 1 alpha (AMPK/PGC1α) signaling pathways as upstream regulators. Onset of acyclicity was accompanied by a rise in genes required for fatty acid metabolism, inflammation, and mitochondrial function. Subsequent chronological aging resulted in decline of genes required for mitochondrial function and β-amyloid degradation. Emergence of glucose hypometabolism and impaired synaptic function in brain provide plausible mechanisms of neurological symptoms of perimenopause and may be predictive of later-life vulnerability to hypometabolic conditions such as Alzheimer's. Copyright © 2015 Elsevier Inc. All rights reserved.

Cumulative stress in childhood is associated with blunted reward-related brain activity in adulthood

PubMed Central

Albert, Dustin; Iselin, Anne-Marie R.; Carré, Justin M.; Dodge, Kenneth A.; Hariri, Ahmad R.

2016-01-01

Early life stress (ELS) is strongly associated with negative outcomes in adulthood, including reduced motivation and increased negative mood. The mechanisms mediating these relations, however, are poorly understood. We examined the relation between exposure to ELS and reward-related brain activity, which is known to predict motivation and mood, at age 26, in a sample followed since kindergarten with annual assessments. Using functional neuroimaging, we assayed individual differences in the activity of the ventral striatum (VS) during the processing of monetary rewards associated with a simple card-guessing task, in a sample of 72 male participants. We examined associations between a cumulative measure of ELS exposure and VS activity in adulthood. We found that greater levels of cumulative stress during childhood and adolescence predicted lower reward-related VS activity in adulthood. Extending this general developmental pattern, we found that exposure to stress early in development (between kindergarten and grade 3) was significantly associated with variability in adult VS activity. Our results provide an important demonstration that cumulative life stress, especially during this childhood period, is associated with blunted reward-related VS activity in adulthood. These differences suggest neurobiological pathways through which a history of ELS may contribute to reduced motivation and increased negative mood. PMID:26443679

The Radical Plasticity Thesis: How the Brain Learns to be Conscious

PubMed Central

Cleeremans, Axel

2011-01-01

In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the “Radical Plasticity Thesis.” In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves “signal detection on the mind”; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks. PMID:21687455

The Radical Plasticity Thesis: How the Brain Learns to be Conscious.

PubMed

Cleeremans, Axel

2011-01-01

In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the "Radical Plasticity Thesis." In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves "signal detection on the mind"; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks.

The relationship between spatial configuration and functional connectivity of brain regions.

PubMed

Bijsterbosch, Janine Diane; Woolrich, Mark W; Glasser, Matthew F; Robinson, Emma C; Beckmann, Christian F; Van Essen, David C; Harrison, Samuel J; Smith, Stephen M

2018-02-16

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behaviour. For example, studies have used 'functional connectivity fingerprints' to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits. © 2018, Bijsterbosch et al.

Predictive modeling of neuroanatomic structures for brain atrophy detection

NASA Astrophysics Data System (ADS)

Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

2010-03-01

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

White matter changes linked to visual recovery after nerve decompression

PubMed Central

Paul, David A.; Gaffin-Cahn, Elon; Hintz, Eric B.; Adeclat, Giscard J.; Zhu, Tong; Williams, Zoë R.; Vates, G. Edward; Mahon, Bradford Z.

2015-01-01

The relationship between the integrity of white matter tracts and cortical function in the human brain remains poorly understood. Here we use a model of reversible white matter injury, compression of the optic chiasm by tumors of the pituitary gland, to study the structural and functional changes that attend spontaneous recovery of cortical function and visual abilities after surgical tumor removal and subsequent decompression of the nerves. We show that compression of the optic chiasm leads to demyelination of the optic tracts, which reverses as quickly as 4 weeks after nerve decompression. Furthermore, variability across patients in the severity of demyelination in the optic tracts predicts visual ability and functional activity in early cortical visual areas, and pre-operative measurements of myelination in the optic tracts predicts the magnitude of visual recovery after surgery. These data indicate that rapid regeneration of myelin in the human brain is a significant component of the normalization of cortical activity, and ultimately the recovery of sensory and cognitive function, after nerve decompression. More generally, our findings demonstrate the utility of diffusion tensor imaging as an in vivo measure of myelination in the human brain. PMID:25504884

Plasma copeptin level predicts acute traumatic coagulopathy and progressive hemorrhagic injury after traumatic brain injury.

PubMed

Yang, Ding-Bo; Yu, Wen-Hua; Dong, Xiao-Qiao; Du, Quan; Shen, Yong-Feng; Zhang, Zu-Yong; Zhu, Qiang; Che, Zhi-Hao; Liu, Qun-Jie; Wang, Hao; Jiang, Li; Du, Yuan-Feng

2014-08-01

Higher plasma copeptin levels correlate with poor clinical outcomes after traumatic brain injury. Nevertheless, their links with acute traumatic coagulopathy and progressive hemorrhagic injury are unknown. Therefore, we aimed to investigate the relationship between plasma copeptin levels, acute traumatic coagulopathy and progressive hemorrhagic injury in patients with severe traumatic brain injury. We prospectively studied 100 consecutive patients presenting within 6h from head trauma. Progressive hemorrhagic injury was present when the follow-up computerized tomography scan reported any increase in size or number of the hemorrhagic lesion, including newly developed ones. Acute traumatic coagulopathy was defined as an activated partial thromboplastic time greater than 40s and/or international normalized ratio greater than 1.2 and/or a platelet count less than 120×10(9)/L. We measured plasma copeptin levels on admission using an enzyme-linked immunosorbent assay in a blinded fashion. In multivariate logistic regression analysis, plasma copeptin level emerged as an independent predictor of progressive hemorrhagic injury and acute traumatic coagulopathy. Using receiver operating characteristic curves, we calculated areas under the curve for progressive hemorrhagic injury and acute traumatic coagulopathy. The predictive performance of copeptin was similar to that of Glasgow Coma Scale score. However, copeptin did not obviously improve the predictive value of Glasgow Coma Scale score. Thus, copeptin may help in the prediction of progressive hemorrhagic injury and acute traumatic coagulopathy after traumatic brain injury. Copyright © 2014 Elsevier Inc. All rights reserved.

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

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

NASA Astrophysics Data System (ADS)

Yin, Xi; Yang, Jing; Xiao, Feng; Yang, Yang; Shen, Hong-Bin

2018-03-01

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue-residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/. [Figure not available: see fulltext.

Utility of the Croatian translation of the community integration questionnaire-revised in a sample of adults with moderate to severe traumatic brain injury.

PubMed

Tršinski, Dubravko; Tadinac, Meri; Bakran, Žarko; Klepo, Ivana

2018-02-23

To examine the utility of the Community Integration Questionnaire-Revised, translated into Croatian, in a sample of adults with moderate to severe traumatic brain injury. The Community Integration Questionnaire-Revised was administered to a sample of 88 adults with traumatic brain injury and to a control sample matched by gender, age and education. Participants with traumatic brain injury were divided into four subgroups according to injury severity. The internal consistency of the Community Integration Questionnaire-Revised was satisfactory. The differences between the group with traumatic brain injury and the control group were statistically significant for the overall Community Integration Questionnaire-Revised score, as well as for all the subscales apart from the Home Integration subscale. The community Integration Questionnaire-Revised score varied significantly for subgroups with different severity of traumatic brain injury. The results show that the Croatian translation of the Community Integration Questionnaire-Revised is useful in assessing participation in adults with traumatic brain injury and confirm previous findings that severity of injury predicts community integration. Results of the new Electronic Social Networking scale indicate that persons who are more active on electronic social networks report better results for other domains of community integration, especially social activities. Implications for rehabilitation The Croatian translation of the Community Integration Questionnaire-Revised is a valid tool for long-term assessment of participation in various domains in persons with moderate to severe traumatic brain injury Persons with traumatic brain injury who are more active in the use of electronic social networking are also more integrated into social and productivity domains. Targeted training in the use of new technologies could enhance participation after traumatic brain injury.

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post-Bed Rest and Space Flight

NASA Technical Reports Server (NTRS)

Mulavara, A. P.; Peters, B.; De Dios, Y. E.; Gadd, N. E.; Caldwell, E. E.; Batson, C. D.; Goel, R.; Oddsson, L.; Kreutzberg, G.; Zanello, S.;

Dopamine reward prediction-error signalling: a two-component response

PubMed Central

Schultz, Wolfram

2017-01-01

Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy. PMID:26865020

Understanding in an instant: neurophysiological evidence for mechanistic language circuits in the brain.

PubMed

Pulvermüller, Friedemann; Shtyrov, Yury; Hauk, Olaf

2009-08-01

How long does it take the human mind to grasp the idea when hearing or reading a sentence? Neurophysiological methods looking directly at the time course of brain activity indexes of comprehension are critical for finding the answer to this question. As the dominant cognitive approaches, models of serial/cascaded and parallel processing, make conflicting predictions on the time course of psycholinguistic information access, they can be tested using neurophysiological brain activation recorded in MEG and EEG experiments. Seriality and cascading of lexical, semantic and syntactic processes receives support from late (latency approximately 1/2s) sequential neurophysiological responses, especially N400 and P600. However, parallelism is substantiated by early near-simultaneous brain indexes of a range of psycholinguistic processes, up to the level of semantic access and context integration, emerging already 100-250ms after critical stimulus information is present. Crucially, however, there are reliable latency differences of 20-50ms between early cortical area activations reflecting lexical, semantic and syntactic processes, which are left unexplained by current serial and parallel brain models of language. We here offer a mechanistic model grounded in cortical nerve cell circuits that builds upon neuroanatomical and neurophysiological knowledge and explains both near-simultaneous activations and fine-grained delays. A key concept is that of discrete distributed cortical circuits with specific inter-area topographies. The full activation, or ignition, of specifically distributed binding circuits explains the near-simultaneity of early neurophysiological indexes of lexical, syntactic and semantic processing. Activity spreading within circuits determined by between-area conduction delays accounts for comprehension-related regional activation differences in the millisecond range.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

Brain network connectivity in individuals with schizophrenia and their siblings.

PubMed

Repovs, Grega; Csernansky, John G; Barch, Deanna M

2011-05-15

Research on brain activity in schizophrenia has shown that changes in the function of any single region cannot explain the range of cognitive and affective impairments in this illness. Rather, neural circuits that support sensory, cognitive, and emotional processes are now being investigated as substrates for cognitive and affective impairments in schizophrenia, a shift in focus consistent with long-standing hypotheses about schizophrenia as a disconnection syndrome. Our goal was to further examine alterations in functional connectivity within and between the default mode network and three cognitive control networks (frontal-parietal, cingulo-opercular, and cerebellar) as a basis for such impairments. Resting state functional magnetic resonance imaging was collected from 40 individuals with DSM-IV-TR schizophrenia, 31 siblings of individuals with schizophrenia, 15 healthy control subjects, and 18 siblings of healthy control subjects while they rested quietly with their eyes closed. Connectivity metrics were compared between patients and control subjects for both within- and between-network connections and were used to predict clinical symptoms and cognitive function. Individuals with schizophrenia showed reduced distal and somewhat enhanced local connectivity between the cognitive control networks compared with control subjects. Additionally, greater connectivity between the frontal-parietal and cerebellar regions was robustly predictive of better cognitive performance across groups and predictive of fewer disorganization symptoms among patients. These results are consistent with the hypothesis that impairments of executive function and cognitive control result from disruption in the coordination of activity across brain networks and additionally suggest that these might reflect impairments in normal pattern of brain connectivity development. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

The Neurodynamics of Affect in the Laboratory Predicts Persistence of Real-World Emotional Responses.

PubMed

Heller, Aaron S; Fox, Andrew S; Wing, Erik K; McQuisition, Kaitlyn M; Vack, Nathan J; Davidson, Richard J

2015-07-22

Failure to sustain positive affect over time is a hallmark of depression and other psychopathologies, but the mechanisms supporting the ability to sustain positive emotional responses are poorly understood. Here, we investigated the neural correlates associated with the persistence of positive affect in the real world by conducting two experiments in humans: an fMRI task of reward responses and an experience-sampling task measuring emotional responses to a reward obtained in the field. The magnitude of DLPFC engagement to rewards administered in the laboratory predicted reactivity of real-world positive emotion following a reward administered in the field. Sustained ventral striatum engagement in the laboratory positively predicted the duration of real-world positive emotional responses. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. Significance statement: How real-world emotion, experienced over seconds, minutes, and hours, is instantiated in the brain over the course of milliseconds and seconds is unknown. We combined a novel, real-world experience-sampling task with fMRI to examine how individual differences in real-world emotion, experienced over minutes and hours, is subserved by affective neurodynamics of brain activity over the course of seconds. When winning money in the real world, individuals sustaining positive emotion the longest were those with the most prolonged ventral striatal activity. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. Copyright © 2015 the authors 0270-6474/15/3510503-07$15.00/0.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

Predicting hemispheric dominance for language production in healthy individuals using support vector machine.

PubMed

Zago, Laure; Hervé, Pierre-Yves; Genuer, Robin; Laurent, Alexandre; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Joliot, Marc

2017-12-01

We used a Support Vector Machine (SVM) classifier to assess hemispheric pattern of language dominance of 47 individuals categorized as non-typical for language from their hemispheric functional laterality index (HFLI) measured on a sentence minus word-list production fMRI-BOLD contrast map. The SVM classifier was trained at discriminating between Dominant and Non-Dominant hemispheric language production activation pattern on a group of 250 participants previously identified as Typicals (HFLI strongly leftward). Then, SVM was applied to each hemispheric language activation pattern of 47 non-typical individuals. The results showed that at least one hemisphere (left or right) was found to be Dominant in every, except 3 individuals, indicating that the "dominant" type of functional organization is the most frequent in non-typicals. Specifically, left hemisphere dominance was predicted in all non-typical right-handers (RH) and in 57.4% of non-typical left-handers (LH). When both hemisphere classifications were jointly considered, four types of brain patterns were observed. The most often predicted pattern (51%) was left-dominant (Dominant left-hemisphere and Non-Dominant right-hemisphere), followed by right-dominant (23%, Dominant right-hemisphere and Non-Dominant left-hemisphere) and co-dominant (19%, 2 Dominant hemispheres) patterns. Co-non-dominant was rare (6%, 2 Non-Dominant hemispheres), but was normal variants of hemispheric specialization. In RH, only left-dominant (72%) and co-dominant patterns were detected, while for LH, all types were found, although with different occurrences. Among the 10 LH with a strong rightward HFLI, 8 had a right-dominant brain pattern. Whole-brain analysis of the right-dominant pattern group confirmed that it exhibited a functional organization strictly mirroring that of left-dominant pattern group. Hum Brain Mapp 38:5871-5889, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Encoding and Decoding Models in Cognitive Electrophysiology

PubMed Central

Holdgraf, Christopher R.; Rieger, Jochem W.; Micheli, Cristiano; Martin, Stephanie; Knight, Robert T.; Theunissen, Frederic E.

2017-01-01

Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses. PMID:29018336

Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble.

PubMed

Jolivet, Renaud; Coggan, Jay S; Allaman, Igor; Magistretti, Pierre J

2015-02-01

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain's metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

Frames, biases, and rational decision-making in the human brain.

PubMed

De Martino, Benedetto; Kumaran, Dharshan; Seymour, Ben; Dolan, Raymond J

2006-08-04

Human choices are remarkably susceptible to the manner in which options are presented. This so-called "framing effect" represents a striking violation of standard economic accounts of human rationality, although its underlying neurobiology is not understood. We found that the framing effect was specifically associated with amygdala activity, suggesting a key role for an emotional system in mediating decision biases. Moreover, across individuals, orbital and medial prefrontal cortex activity predicted a reduced susceptibility to the framing effect. This finding highlights the importance of incorporating emotional processes within models of human choice and suggests how the brain may modulate the effect of these biasing influences to approximate rationality.

Effect of brain structure, brain function, and brain connectivity on relapse in alcohol-dependent patients.

PubMed

Beck, Anne; Wüstenberg, Torsten; Genauck, Alexander; Wrase, Jana; Schlagenhauf, Florian; Smolka, Michael N; Mann, Karl; Heinz, Andreas

2012-08-01

In alcohol-dependent patients, brain atrophy and functional brain activation elicited by alcohol-associated stimuli may predict relapse. However, to date, the interaction between both factors has not been studied. To determine whether results from structural and functional magnetic resonance imaging are associated with relapse in detoxified alcohol-dependent patients. A cue-reactivity functional magnetic resonance experiment with alcohol-associated and neutral stimuli. After a follow-up period of 3 months, the group of 46 detoxified alcohol-dependent patients was subdivided into 16 abstainers and 30 relapsers. Faculty for Clinical Medicine Mannheim at the University of Heidelberg, Germany. A total of 46 detoxified alcohol-dependent patients and 46 age- and sex-matched healthy control subjects Local gray matter volume, local stimulus-related functional magnetic resonance imaging activation, joint analyses of structural and functional data with Biological Parametric Mapping, and connectivity analyses adopting the psychophysiological interaction approach. Subsequent relapsers showed pronounced atrophy in the bilateral orbitofrontal cortex and in the right medial prefrontal and anterior cingulate cortex, compared with healthy controls and patients who remained abstinent. The local gray matter volume-corrected brain response elicited by alcohol-associated vs neutral stimuli in the left medial prefrontal cortex was enhanced for subsequent relapsers, whereas abstainers displayed an increased neural response in the midbrain (the ventral tegmental area extending into the subthalamic nucleus) and ventral striatum. For alcohol-associated vs neutral stimuli in abstainers compared with relapsers, the analyses of the psychophysiological interaction showed a stronger functional connectivity between the midbrain and the left amygdala and between the midbrain and the left orbitofrontal cortex. Subsequent relapsers displayed increased brain atrophy in brain areas associated with error monitoring and behavioral control. Correcting for gray matter reductions, we found that, in these patients, alcohol-related cues elicited increased activation in brain areas associated with attentional bias toward these cues and that, in patients who remained abstinent, increased activation and connectivity were observed in brain areas associated with processing of salient or aversive stimuli.

Neuroinflammation and its relationship to changes in brain volume and white matter lesions in multiple sclerosis.

PubMed

Datta, Gourab; Colasanti, Alessandro; Rabiner, Eugenii A; Gunn, Roger N; Malik, Omar; Ciccarelli, Olga; Nicholas, Richard; Van Vlierberghe, Eline; Van Hecke, Wim; Searle, Graham; Santos-Ribeiro, Andre; Matthews, Paul M

2017-11-01

Brain magnetic resonance imaging is an important tool in the diagnosis and monitoring of multiple sclerosis patients. However, magnetic resonance imaging alone provides limited information for predicting an individual patient's disability progression. In part, this is because magnetic resonance imaging lacks sensitivity and specificity for detecting chronic diffuse and multi-focal inflammation mediated by activated microglia/macrophages. The aim of this study was to test for an association between 18 kDa translocator protein brain positron emission tomography signal, which arises largely from microglial activation, and measures of subsequent disease progression in multiple sclerosis patients. Twenty-one patients with multiple sclerosis (seven with secondary progressive disease and 14 with a relapsing remitting disease course) underwent T1- and T2-weighted and magnetization transfer magnetic resonance imaging at baseline and after 1 year. Positron emission tomography scanning with the translocator protein radioligand 11C-PBR28 was performed at baseline. Brain tissue and lesion volumes were segmented from the T1- and T2-weighted magnetic resonance imaging and relative 11C-PBR28 uptake in the normal-appearing white matter was estimated as a distribution volume ratio with respect to a caudate pseudo-reference region. Normal-appearing white matter distribution volume ratio at baseline was correlated with enlarging T2-hyperintense lesion volumes over the subsequent year (ρ = 0.59, P = 0.01). A post hoc analysis showed that this association reflected behaviour in the subgroup of relapsing remitting patients (ρ = 0.74, P = 0.008). By contrast, in the subgroup of secondary progressive patients, microglial activation at baseline was correlated with later progression of brain atrophy (ρ = 0.86, P = 0.04). A regression model including the baseline normal-appearing white matter distribution volume ratio, T2 lesion volume and normal-appearing white matter magnetization transfer ratio for all of the patients combined explained over 90% of the variance in enlarging lesion volume over the subsequent 1 year. Glial activation in white matter assessed by translocator protein PET significantly improves predictions of white matter lesion enlargement in relapsing remitting patients and is associated with greater brain atrophy in secondary progressive disease over a period of short term follow-up. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Association Between Brain Activation and Functional Connectivity.

PubMed

Tomasi, Dardo; Volkow, Nora D

2018-04-13

The origin of the "resting-state" brain activity recorded with functional magnetic resonance imaging (fMRI) is still uncertain. Here we provide evidence for the neurovascular origins of the amplitude of the low-frequency fluctuations (ALFF) and the local functional connectivity density (lFCD) by comparing them with task-induced blood-oxygen level dependent (BOLD) responses, which are considered a proxy for neuronal activation. Using fMRI data for 2 different tasks (Relational and Social) collected by the Human Connectome Project in 426 healthy adults, we show that ALFF and lFCD have linear associations with the BOLD response. This association was significantly attenuated by a novel task signal regression (TSR) procedure, indicating that task performance enhances lFCD and ALFF in activated regions. We also show that lFCD predicts BOLD activation patterns, as was recently shown for other functional connectivity metrics, which corroborates that resting functional connectivity architecture impacts brain activation responses. Thus, our findings indicate a common source for BOLD responses, ALFF and lFCD, which is consistent with the neurovascular origin of local hemodynamic synchrony presumably reflecting coordinated fluctuations in neuronal activity. This study also supports the development of task-evoked functional connectivity density mapping.

Unique and Persistent Individual Patterns of Brain Activity Across Different Memory Retrieval Tasks

PubMed Central

Miller, Michael B.; Donovan, Christa-Lynn; Van Horn, John D.; German, Elaine; Sokol-Hessner, Peter; Wolford, George L.

2009-01-01

Fourteen subjects were scanned in two fMRI sessions separated by several months. During each session, subjects performed an episodic retrieval task, a semantic retrieval task, and a working memory task. We found that 1) despite extensive intersubject variability in the pattern of activity across the whole brain, individual activity patterns were stable over time, 2) activity patterns of the same individual performing different tasks were more similar than activity patterns of different individuals performing the same task, and 3) that individual differences in decision criterion on a recognition test predicted the degree of similarity between any two individuals’ patterns of brain activity, but individual differences in memory accuracy or similarity in structural anatomy did not. These results imply that the exclusive use of group maps may be ineffective in profiling the pattern of activations for a given task. This may be particularly true for a task like episodic retrieval, which is relatively strategic and can involve widely-distributed specialized processes that are peripheral to the actual retrieval of stored information. Further, these processes may be differentially engaged depending on individual differences in cognitive processing and/or physiology. PMID:19540922

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Brain age predicts mortality

PubMed Central

Cole, J H; Ritchie, S J; Bastin, M E; Valdés Hernández, M C; Muñoz Maniega, S; Royle, N; Corley, J; Pattie, A; Harris, S E; Zhang, Q; Wray, N R; Redmond, P; Marioni, R E; Starr, J M; Cox, S R; Wardlaw, J M; Sharp, D J; Deary, I J

2018-01-01

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death. PMID:28439103

Brain regions concerned with the identification of deceptive soccer moves by higher-skilled and lower-skilled players

PubMed Central

Wright, Michael J.; Bishop, Daniel T.; Jackson, Robin C.; Abernethy, Bruce

2013-01-01

Expert soccer players are able to utilize their opponents' early body kinematics to predict the direction in which the opponent will move. We have previously demonstrated enhanced fMRI activation in experts in the motor components of an action observation network (AON) during sports anticipation tasks. Soccer players often need to prevent opponents from successfully predicting their line of attack, and consequently may try to deceive them; for example, by performing a step-over. We examined how AON activations and expertise effects are modified by the presence of deception. Three groups of participants; higher-skilled males, lower-skilled males, and lower-skilled females, viewed video clips in point-light format, from a defender's perspective, of a player approaching and turning with the ball. The observer's task in the scanner was to determine whether the move was normal or deceptive (involving a step-over), while whole-brain functional images were acquired. In a second counterbalanced block with identical stimuli the task was to predict the direction of the ball. Activations of AON for identification of deception overlapped with activations from the direction identification task. Higher-skilled players showed significantly greater activation than lower-skilled players in a subset of AON areas; and lower-skilled males in turn showed greater activation than lower-skilled females, but females showed more activation in visual cortex. Activation was greater for deception identification than for direction identification in dorsolateral prefrontal cortex, medial frontal cortex, anterior insula, cingulate gyrus, and premotor cortex. Conversely, greater activation for direction than deception identification was found in anterior cingulate cortex and caudate nucleus. Results are consistent with the view that explicit identification of deceptive moves entails cognitive effort and also activates limbic structures associated with social cognition and affective responses. PMID:24381549

Social in, social out: How the brain responds to social language with more social language

PubMed Central

O’Donnell, Matthew Brook; Falk, Emily B.; Lieberman, Matthew D.

2014-01-01

Social connection is a fundamental human need. As such, people’s brains are sensitized to social cues, such as those carried by language, and to promoting social communication. The neural mechanisms of certain key building blocks in this process, such as receptivity to and reproduction of social language, however, are not known. We combined quantitative linguistic analysis and neuroimaging to connect neural activity in brain regions used to simulate the mental states of others with exposure to, and re-transmission of, social language. Our results link findings on successful idea transmission from communication science, sociolinguistics and cognitive neuroscience to prospectively predict the degree of social language that participants utilize when re-transmitting ideas as a function of 1) initial language inputs and 2) neural activity during idea exposure. PMID:27642220

The neural bases of cognitive conflict and control in moral judgment.

PubMed

Greene, Joshua D; Nystrom, Leigh E; Engell, Andrew D; Darley, John M; Cohen, Jonathan D

2004-10-14

Traditional theories of moral psychology emphasize reasoning and "higher cognition," while more recent work emphasizes the role of emotion. The present fMRI data support a theory of moral judgment according to which both "cognitive" and emotional processes play crucial and sometimes mutually competitive roles. The present results indicate that brain regions associated with abstract reasoning and cognitive control (including dorsolateral prefrontal cortex and anterior cingulate cortex) are recruited to resolve difficult personal moral dilemmas in which utilitarian values require "personal" moral violations, violations that have previously been associated with increased activity in emotion-related brain regions. Several regions of frontal and parietal cortex predict intertrial differences in moral judgment behavior, exhibiting greater activity for utilitarian judgments. We speculate that the controversy surrounding utilitarian moral philosophy reflects an underlying tension between competing subsystems in the brain.

Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging

PubMed Central

Vidal-Piñeiro, Dídac; Martin-Trias, Pablo; Arenaza-Urquijo, Eider M.; Sala-Llonch, Roser; Mena-Sánchez, Isaias; Bargalló, Núria; Falcón, Carles; Pascual-Leone, Álvaro; Bartrés-Faz, David

2015-01-01

Background Transcranial Magnetic Stimulation (TMS) can affect episodic memory, one of the main cognitive hallmarks of aging, but the mechanisms of action remain unclear. Objectives To evaluate the behavioral and functional impact of excitatory TMS in a group of healthy elders. Methods We applied a paradigm of repetitive TMS -intermittent theta-burst stimulation- over left inferior frontal gyrus in healthy elders (n=24) and evaluated its impact on the performance of an episodic memory task with two levels of processing and the associated brain activity as captured by a pre and post fMRI scans. Results In the post-TMS fMRI we found TMS-related activity increases in left prefrontal and cerebellum-occipital areas specifically during deep encoding but not during shallow encoding or at rest. Furthermore, we found a task-dependent change in connectivity during the encoding task between cerebellum-occipital areas and the TMS-targeted left inferior frontal region. This connectivity change correlated with the TMS effects over brain networks. Conclusions The results suggest that the aged brain responds to brain stimulation in a state-dependent manner as engaged by different tasks components and that TMS effect is related to inter-individual connectivity changes measures. These findings reveal fundamental insights into brain network dynamics in aging and the capacity to probe them with combined behavioral and stimulation approaches. PMID:24485466

Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging.

PubMed

Vidal-Piñeiro, Dídac; Martin-Trias, Pablo; Arenaza-Urquijo, Eider M; Sala-Llonch, Roser; Clemente, Imma C; Mena-Sánchez, Isaias; Bargalló, Núria; Falcón, Carles; Pascual-Leone, Álvaro; Bartrés-Faz, David

2014-01-01

Transcranial magnetic stimulation (TMS) can affect episodic memory, one of the main cognitive hallmarks of aging, but the mechanisms of action remain unclear. To evaluate the behavioral and functional impact of excitatory TMS in a group of healthy elders. We applied a paradigm of repetitive TMS - intermittent theta-burst stimulation - over left inferior frontal gyrus in healthy elders (n = 24) and evaluated its impact on the performance of an episodic memory task with two levels of processing and the associated brain activity as captured by a pre and post fMRI scans. In the post-TMS fMRI we found TMS-related activity increases in left prefrontal and cerebellum-occipital areas specifically during deep encoding but not during shallow encoding or at rest. Furthermore, we found a task-dependent change in connectivity during the encoding task between cerebellum-occipital areas and the TMS-targeted left inferior frontal region. This connectivity change correlated with the TMS effects over brain networks. The results suggest that the aged brain responds to brain stimulation in a state-dependent manner as engaged by different tasks components and that TMS effect is related to inter-individual connectivity changes measures. These findings reveal fundamental insights into brain network dynamics in aging and the capacity to probe them with combined behavioral and stimulation approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Task-Based Core-Periphery Organization of Human Brain Dynamics

PubMed Central

Bassett, Danielle S.; Wymbs, Nicholas F.; Rombach, M. Puck; Porter, Mason A.; Mucha, Peter J.; Grafton, Scott T.

2013-01-01

As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior. PMID:24086116

Real-time position reconstruction with hippocampal place cells.

PubMed

Guger, Christoph; Gener, Thomas; Pennartz, Cyriel M A; Brotons-Mas, Jorge R; Edlinger, Günter; Bermúdez I Badia, S; Verschure, Paul; Schaffelhofer, Stefan; Sanchez-Vives, Maria V

2011-01-01

Brain-computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.

From "Where" to "What": Distributed Representations of Brand Associations in the Human Brain.

PubMed

Chen, Yu-Ping; Nelson, Leif D; Hsu, Ming

2015-08-01

Considerable attention has been given to the notion that there exists a set of human-like characteristics associated with brands, referred to as brand personality. Here we combine newly available machine learning techniques with functional neuroimaging data to characterize the set of processes that give rise to these associations. We show that brand personality traits can be captured by the weighted activity across a widely distributed set of brain regions previously implicated in reasoning, imagery, and affective processing. That is, as opposed to being constructed via reflective processes, brand personality traits appear to exist a priori inside the minds of consumers, such that we were able to predict what brand a person is thinking about based solely on the relationship between brand personality associations and brain activity. These findings represent an important advance in the application of neuroscientific methods to consumer research, moving from work focused on cataloguing brain regions associated with marketing stimuli to testing and refining mental constructs central to theories of consumer behavior.

Anxiety type modulates immediate versus delayed engagement of attention-related brain regions.

PubMed

Spielberg, Jeffrey M; De Leon, Angeline A; Bredemeier, Keith; Heller, Wendy; Engels, Anna S; Warren, Stacie L; Crocker, Laura D; Sutton, Bradley P; Miller, Gregory A

2013-09-01

Background Habituation of the fear response, critical for the treatment of anxiety, is inconsistently observed during exposure to threatening stimuli. One potential explanation for this inconsistency is differential attentional engagement with negatively valenced stimuli as a function of anxiety type. Methods The present study tested this hypothesis by examining patterns of neural habituation associated with anxious arousal, characterized by panic symptoms and immediate engagement with negatively valenced stimuli, versus anxious apprehension, characterized by engagement in worry to distract from negatively valenced stimuli. Results As predicted, the two anxiety types evidenced distinct patterns of attentional engagement. Anxious arousal was associated with immediate activation in attention-related brain regions that habituated over time, whereas anxious apprehension was associated with delayed activation in attention-related brain regions that occurred only after habituation in a worry-related brain region. Conclusions Results further elucidate mechanisms involved in attention to negatively valenced stimuli and indicate that anxiety is a heterogeneous construct with regard to attention to such stimuli.

Anxiety type modulates immediate versus delayed engagement of attention-related brain regions

PubMed Central

Spielberg, Jeffrey M; De Leon, Angeline A; Bredemeier, Keith; Heller, Wendy; Engels, Anna S; Warren, Stacie L; Crocker, Laura D; Sutton, Bradley P; Miller, Gregory A

2013-01-01

Background Habituation of the fear response, critical for the treatment of anxiety, is inconsistently observed during exposure to threatening stimuli. One potential explanation for this inconsistency is differential attentional engagement with negatively valenced stimuli as a function of anxiety type. Methods The present study tested this hypothesis by examining patterns of neural habituation associated with anxious arousal, characterized by panic symptoms and immediate engagement with negatively valenced stimuli, versus anxious apprehension, characterized by engagement in worry to distract from negatively valenced stimuli. Results As predicted, the two anxiety types evidenced distinct patterns of attentional engagement. Anxious arousal was associated with immediate activation in attention-related brain regions that habituated over time, whereas anxious apprehension was associated with delayed activation in attention-related brain regions that occurred only after habituation in a worry-related brain region. Conclusions Results further elucidate mechanisms involved in attention to negatively valenced stimuli and indicate that anxiety is a heterogeneous construct with regard to attention to such stimuli. PMID:24392275

Reward sensitivity is associated with brain activity during erotic stimulus processing.

PubMed

Costumero, Victor; Barrós-Loscertales, Alfonso; Bustamante, Juan Carlos; Ventura-Campos, Noelia; Fuentes, Paola; Rosell-Negre, Patricia; Ávila, César

2013-01-01

The behavioral approach system (BAS) from Gray's reinforcement sensitivity theory is a neurobehavioral system involved in the processing of rewarding stimuli that has been related to dopaminergic brain areas. Gray's theory hypothesizes that the functioning of reward brain areas is modulated by BAS-related traits. To test this hypothesis, we performed an fMRI study where participants viewed erotic and neutral pictures, and cues that predicted their appearance. Forty-five heterosexual men completed the Sensitivity to Reward scale (from the Sensitivity to Punishment and Sensitivity to Reward Questionnaire) to measure BAS-related traits. Results showed that Sensitivity to Reward scores correlated positively with brain activity during reactivity to erotic pictures in the left orbitofrontal cortex, left insula, and right ventral striatum. These results demonstrated a relationship between the BAS and reward sensitivity during the processing of erotic stimuli, filling the gap of previous reports that identified the dopaminergic system as a neural substrate for the BAS during the processing of other rewarding stimuli such as money and food.

Identification of substance P precursor forms in human brain tissue.

PubMed Central

Nyberg, F; le Grevés, P; Terenius, L

1985-01-01

Substance P prohormones were identified in the caudate nucleus, hypothalamus, and substantia nigra of human brain. A polypeptide fraction of acidic brain extracts was fractionated on Sephadex G-50. The lyophilized fractions were sequentially treated with trypsin and a substance P-degrading enzyme with strong preference toward the Phe7-Phe8 and Phe8-Gly9 bonds. The released substance P(1-7) fragment was isolated by ion-exchange chromatography and quantitated by a specific radioimmunoassay. Confirmation of the structure of the isolated radioimmunoassay-active fragment was achieved by electrophoresis and HPLC. By using this enzymatic/radioimmunoassay procedure, two polypeptide fractions of apparent Mr 5000 and 15,000, respectively, were identified. The latter component was the major one of the two but was estimated to account for only about 5% of total substance P radioimmunoassay activity. Because it is of the size predicted from the nucleotide sequences of cDNA for substance P prohormones in bovine brain, the Mr 15,000 component may represent the full-length prohormone. PMID:2408270

Functional Interplay between Small Non-Coding RNAs and RNA Modification in the Brain.

PubMed

Leighton, Laura J; Bredy, Timothy W

2018-06-07

Small non-coding RNAs are essential for transcription, translation and gene regulation in all cell types, but are particularly important in neurons, with known roles in neurodevelopment, neuroplasticity and neurological disease. Many small non-coding RNAs are directly involved in the post-transcriptional modification of other RNA species, while others are themselves substrates for modification, or are functionally modulated by modification of their target RNAs. In this review, we explore the known and potential functions of several distinct classes of small non-coding RNAs in the mammalian brain, focusing on the newly recognised interplay between the epitranscriptome and the activity of small RNAs. We discuss the potential for this relationship to influence the spatial and temporal dynamics of gene activation in the brain, and predict that further research in the field of epitranscriptomics will identify interactions between small RNAs and RNA modifications which are essential for higher order brain functions such as learning and memory.

Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks.

PubMed

Ni, Zhaoheng; Yuksel, Ahmet Cem; Ni, Xiuyan; Mandel, Michael I; Xie, Lei

2017-08-01

Brain fog, also known as confusion, is one of the main reasons for low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in a human's mind in real time is a challenging and important task that can be applied to online education, driver fatigue detection and so on. In this paper, we apply Bidirectional LSTM Recurrent Neural Networks to classify students' confusion in watching online course videos from EEG data. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is the gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activity.

From “Where” to “What”: Distributed Representations of Brand Associations in the Human Brain

PubMed Central

Chen, Yu-Ping; Nelson, Leif D.; Hsu, Ming

2015-01-01

Considerable attention has been given to the notion that there exists a set of human-like characteristics associated with brands, referred to as brand personality. Here we combine newly available machine learning techniques with functional neuroimaging data to characterize the set of processes that give rise to these associations. We show that brand personality traits can be captured by the weighted activity across a widely distributed set of brain regions previously implicated in reasoning, imagery, and affective processing. That is, as opposed to being constructed via reflective processes, brand personality traits appear to exist a priori inside the minds of consumers, such that we were able to predict what brand a person is thinking about based solely on the relationship between brand personality associations and brain activity. These findings represent an important advance in the application of neuroscientific methods to consumer research, moving from work focused on cataloguing brain regions associated with marketing stimuli to testing and refining mental constructs central to theories of consumer behavior. PMID:27065490

Reward Sensitivity Is Associated with Brain Activity during Erotic Stimulus Processing

PubMed Central

Costumero, Victor; Barrós-Loscertales, Alfonso; Bustamante, Juan Carlos; Ventura-Campos, Noelia; Fuentes, Paola; Rosell-Negre, Patricia; Ávila, César

2013-01-01

The behavioral approach system (BAS) from Gray’s reinforcement sensitivity theory is a neurobehavioral system involved in the processing of rewarding stimuli that has been related to dopaminergic brain areas. Gray’s theory hypothesizes that the functioning of reward brain areas is modulated by BAS-related traits. To test this hypothesis, we performed an fMRI study where participants viewed erotic and neutral pictures, and cues that predicted their appearance. Forty-five heterosexual men completed the Sensitivity to Reward scale (from the Sensitivity to Punishment and Sensitivity to Reward Questionnaire) to measure BAS-related traits. Results showed that Sensitivity to Reward scores correlated positively with brain activity during reactivity to erotic pictures in the left orbitofrontal cortex, left insula, and right ventral striatum. These results demonstrated a relationship between the BAS and reward sensitivity during the processing of erotic stimuli, filling the gap of previous reports that identified the dopaminergic system as a neural substrate for the BAS during the processing of other rewarding stimuli such as money and food. PMID:23840558

The representation of order information in auditory-verbal short-term memory.

PubMed

Kalm, Kristjan; Norris, Dennis

2014-05-14

Here we investigate how order information is represented in auditory-verbal short-term memory (STM). We used fMRI and a serial recall task to dissociate neural activity patterns representing the phonological properties of the items stored in STM from the patterns representing their order. For this purpose, we analyzed fMRI activity patterns elicited by different item sets and different orderings of those items. These fMRI activity patterns were compared with the predictions made by positional and chaining models of serial order. The positional models encode associations between items and their positions in a sequence, whereas the chaining models encode associations between successive items and retain no position information. We show that a set of brain areas in the postero-dorsal stream of auditory processing store associations between items and order as predicted by a positional model. The chaining model of order representation generates a different pattern similarity prediction, which was shown to be inconsistent with the fMRI data. Our results thus favor a neural model of order representation that stores item codes, position codes, and the mapping between them. This study provides the first fMRI evidence for a specific model of order representation in the human brain. Copyright © 2014 the authors 0270-6474/14/346879-08$15.00/0.

Characterizing Focused-Ultrasound Mediated Drug Delivery to the Heterogeneous Primate Brain In Vivo with Acoustic Monitoring

NASA Astrophysics Data System (ADS)

Wu, Shih-Ying; Sanchez, Carlos Sierra; Samiotaki, Gesthimani; Buch, Amanda; Ferrera, Vincent P.; Konofagou, Elisa E.

2016-11-01

Focused ultrasound with microbubbles has been used to noninvasively and selectively deliver pharmacological agents across the blood-brain barrier (BBB) for treating brain diseases. Acoustic cavitation monitoring could serve as an on-line tool to assess and control the treatment. While it demonstrated a strong correlation in small animals, its translation to primates remains in question due to the anatomically different and highly heterogeneous brain structures with gray and white matteras well as dense vasculature. In addition, the drug delivery efficiency and the BBB opening volume have never been shown to be predictable through cavitation monitoring in primates. This study aimed at determining how cavitation activity is correlated with the amount and concentration of gadolinium delivered through the BBB and its associated delivery efficiency as well as the BBB opening volume in non-human primates. Another important finding entails the effect of heterogeneous brain anatomy and vasculature of a primate brain, i.e., presence of large cerebral vessels, gray and white matter that will also affect the cavitation activity associated with variation of BBB opening in different tissue types, which is not typically observed in small animals. Both these new findings are critical in the primate brain and provide essential information for clinical applications.

Characterizing Focused-Ultrasound Mediated Drug Delivery to the Heterogeneous Primate Brain In Vivo with Acoustic Monitoring

PubMed Central

Wu, Shih-Ying; Sanchez, Carlos Sierra; Samiotaki, Gesthimani; Buch, Amanda; Ferrera, Vincent P.; Konofagou, Elisa E.

2016-01-01

Focused ultrasound with microbubbles has been used to noninvasively and selectively deliver pharmacological agents across the blood-brain barrier (BBB) for treating brain diseases. Acoustic cavitation monitoring could serve as an on-line tool to assess and control the treatment. While it demonstrated a strong correlation in small animals, its translation to primates remains in question due to the anatomically different and highly heterogeneous brain structures with gray and white matteras well as dense vasculature. In addition, the drug delivery efficiency and the BBB opening volume have never been shown to be predictable through cavitation monitoring in primates. This study aimed at determining how cavitation activity is correlated with the amount and concentration of gadolinium delivered through the BBB and its associated delivery efficiency as well as the BBB opening volume in non-human primates. Another important finding entails the effect of heterogeneous brain anatomy and vasculature of a primate brain, i.e., presence of large cerebral vessels, gray and white matter that will also affect the cavitation activity associated with variation of BBB opening in different tissue types, which is not typically observed in small animals. Both these new findings are critical in the primate brain and provide essential information for clinical applications. PMID:27853267

MicroRNA profiling reveals new aspects of HIV neurodegeneration: caspase-6 regulates astrocyte survival.

PubMed

Noorbakhsh, Farshid; Ramachandran, Rithwik; Barsby, Nicola; Ellestad, Kristofor K; LeBlanc, Andrea; Dickie, Peter; Baker, Glen; Hollenberg, Morley D; Cohen, Eric A; Power, Christopher

2010-06-01

MicroRNAs (miRNAs) are small noncoding RNA molecules, which are known to regulate gene expression in physiological and pathological conditions. miRNA profiling was performed using brain tissue from patients with HIV encephalitis (HIVE), a neuroinflammatory/degenerative disorder caused by HIV infection of the brain. Microarray analysis showed differential expression of multiple miRNAs in HIVE compared to control brains. Target prediction and gene ontology enrichment analysis disclosed targeting of several gene families/biological processes by differentially expressed miRNAs (DEMs), with cell death-related genes, including caspase-6, showing a bias toward down-regulated DEMs. Consistent with the miRNA data, HIVE brains exhibited higher levels of caspase-6 transcripts compared with control patients. Immunohistochemical analysis showed localization of the cleaved form of caspase-6 in astrocytes in HIVE brain sections. Exposure of cultured human primary astrocytes to HIV viral protein R (Vpr) induced p53 up-regulation, loss of mitochondrial membrane potential, and caspase-6 activation followed by cell injury. Transgenic mice, expressing Vpr in microglial cells, demonstrated astrocyte apoptosis in brain, which was associated with caspase-6 activation and neurobehavioral abnormalities. Overall, these data point to previously unrecognized alterations in miRNA profile in the brain during HIV infection, which contribute to cell death through dysregulation of cell death machinery.

Neuroelectronics and modeling of electrical signals for monitoring and control of Parkinson's disease

NASA Astrophysics Data System (ADS)

Chintakuntla, Ritesh R.; Abraham, Jose K.; Varadan, Vijay K.

2009-03-01

The brain and the human nervous system are perhaps the most researched but least understood components of the human body. This is so because of the complex nature of its working and the high density of functions. The monitoring of neural signals could help one better understand the working of the brain and newer recording and monitoring methods have been developed ever since it was discovered that the brain communicates internally by means of electrical pulses. Neuroelectronics is the field which deals with the interface between electronics or semiconductors to living neurons. This includes monitoring of electrical activity from the brain as well as the development of feedback devices for stimulation of parts of the brain for treatment of disorders. In this paper these electrical signals are modeled through a nano/microelectrode arrays based on the electronic equivalent model using Cadence PSD 15.0. The results were compared with those previously published models such as Kupfmuller and Jenik's model, McGrogan's Neuron Model which are based on the Hodgkin and Huxley model. We have developed and equivalent circuit model using discrete passive components to simulate the electrical activity of the neurons. The simulated circuit can be easily be modified by adding some more ionic channels and the results can be used to predict necessary external stimulus needed for stimulation of neurons affected by the Parkinson's disease (PD). Implementing such a model in PD patients could predict the necessary voltages required for the electrical stimulation of the sub-thalamus region for the control tremor motion.

Computational modeling of an endovascular approach to deep brain stimulation

NASA Astrophysics Data System (ADS)

Teplitzky, Benjamin A.; Connolly, Allison T.; Bajwa, Jawad A.; Johnson, Matthew D.

2014-04-01

Objective. Deep brain stimulation (DBS) therapy currently relies on a transcranial neurosurgical technique to implant one or more electrode leads into the brain parenchyma. In this study, we used computational modeling to investigate the feasibility of using an endovascular approach to target DBS therapy. Approach. Image-based anatomical reconstructions of the human brain and vasculature were used to identify 17 established and hypothesized anatomical targets of DBS, of which five were found adjacent to a vein or artery with intraluminal diameter ≥1 mm. Two of these targets, the fornix and subgenual cingulate white matter (SgCwm) tracts, were further investigated using a computational modeling framework that combined segmented volumes of the vascularized brain, finite element models of the tissue voltage during DBS, and multi-compartment axon models to predict the direct electrophysiological effects of endovascular DBS. Main results. The models showed that: (1) a ring-electrode conforming to the vessel wall was more efficient at neural activation than a guidewire design, (2) increasing the length of a ring-electrode had minimal effect on neural activation thresholds, (3) large variability in neural activation occurred with suboptimal placement of a ring-electrode along the targeted vessel, and (4) activation thresholds for the fornix and SgCwm tracts were comparable for endovascular and stereotactic DBS, though endovascular DBS was able to produce significantly larger contralateral activation for a unilateral implantation. Significance. Together, these results suggest that endovascular DBS can serve as a complementary approach to stereotactic DBS in select cases.

Pupil size signals mental effort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus.

PubMed

Alnæs, Dag; Sneve, Markus Handal; Espeseth, Thomas; Endestad, Tor; van de Pavert, Steven Harry Pieter; Laeng, Bruno

2014-04-01

Attentional effort relates to the allocation of limited-capacity attentional resources to meet current task demands and involves the activation of top-down attentional systems in the brain. Pupillometry is a sensitive measure of this intensity aspect of top-down attentional control. Studies relate pupillary changes in response to cognitive processing to activity in the locus coeruleus (LC), which is the main hub of the brain's noradrenergic system and it is thought to modulate the operations of the brain's attentional systems. In the present study, participants performed a visual divided attention task known as multiple object tracking (MOT) while their pupil sizes were recorded by use of an infrared eye tracker and then were tested again with the same paradigm while brain activity was recorded using fMRI. We hypothesized that the individual pupil dilations, as an index of individual differences in mental effort, as originally proposed by Kahneman (1973), would be a better predictor of LC activity than the number of tracked objects during MOT. The current results support our hypothesis, since we observed pupil-related activity in the LC. Moreover, the changes in the pupil correlated with activity in the superior colliculus and the right thalamus, as well as cortical activity in the dorsal attention network, which previous studies have shown to be strongly activated during visual tracking of multiple targets. Follow-up pupillometric analyses of the MOT task in the same individuals also revealed that individual differences to cognitive load can be remarkably stable over a lag of several years. To our knowledge this is the first study using pupil dilations as an index of attentional effort in the MOT task and also relating these to functional changes in the brain that directly implicate the LC-NE system in the allocation of processing resources.

The role of prediction in social neuroscience

PubMed Central

Brown, Elliot C.; Brüne, Martin

2012-01-01

Research has shown that the brain is constantly making predictions about future events. Theories of prediction in perception, action and learning suggest that the brain serves to reduce the discrepancies between expectation and actual experience, i.e., by reducing the prediction error. Forward models of action and perception propose the generation of a predictive internal representation of the expected sensory outcome, which is matched to the actual sensory feedback. Shared neural representations have been found when experiencing one's own and observing other's actions, rewards, errors, and emotions such as fear and pain. These general principles of the “predictive brain” are well established and have already begun to be applied to social aspects of cognition. The application and relevance of these predictive principles to social cognition are discussed in this article. Evidence is presented to argue that simple non-social cognitive processes can be extended to explain complex cognitive processes required for social interaction, with common neural activity seen for both social and non-social cognitions. A number of studies are included which demonstrate that bottom-up sensory input and top-down expectancies can be modulated by social information. The concept of competing social forward models and a partially distinct category of social prediction errors are introduced. The evolutionary implications of a “social predictive brain” are also mentioned, along with the implications on psychopathology. The review presents a number of testable hypotheses and novel comparisons that aim to stimulate further discussion and integration between currently disparate fields of research, with regard to computational models, behavioral and neurophysiological data. This promotes a relatively new platform for inquiry in social neuroscience with implications in social learning, theory of mind, empathy, the evolution of the social brain, and potential strategies for treating social cognitive deficits. PMID:22654749

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example.

PubMed

Gunalan, Kabilar; Chaturvedi, Ashutosh; Howell, Bryan; Duchin, Yuval; Lempka, Scott F; Patriat, Remi; Sapiro, Guillermo; Harel, Noam; McIntyre, Cameron C

2017-01-01

Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

Neural mechanisms underlying spatial realignment during adaptation to optical wedge prisms.

PubMed

Chapman, Heidi L; Eramudugolla, Ranmalee; Gavrilescu, Maria; Strudwick, Mark W; Loftus, Andrea; Cunnington, Ross; Mattingley, Jason B

2010-07-01

Visuomotor adaptation to a shift in visual input produced by prismatic lenses is an example of dynamic sensory-motor plasticity within the brain. Prism adaptation is readily induced in healthy individuals, and is thought to reflect the brain's ability to compensate for drifts in spatial calibration between different sensory systems. The neural correlate of this form of functional plasticity is largely unknown, although current models predict the involvement of parieto-cerebellar circuits. Recent studies that have employed event-related functional magnetic resonance imaging (fMRI) to identify brain regions associated with prism adaptation have discovered patterns of parietal and cerebellar modulation as participants corrected their visuomotor errors during the early part of adaptation. However, the role of these regions in the later stage of adaptation, when 'spatial realignment' or true adaptation is predicted to occur, remains unclear. Here, we used fMRI to quantify the distinctive patterns of parieto-cerebellar activity as visuomotor adaptation develops. We directly contrasted activation patterns during the initial error correction phase of visuomotor adaptation with that during the later spatial realignment phase, and found significant recruitment of the parieto-cerebellar network--with activations in the right inferior parietal lobe and the right posterior cerebellum. These findings provide the first evidence of both cerebellar and parietal involvement during the spatial realignment phase of prism adaptation. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Predictive top-down integration of prior knowledge during speech perception.

PubMed

Sohoglu, Ediz; Peelle, Jonathan E; Carlyon, Robert P; Davis, Matthew H

2012-06-20

A striking feature of human perception is that our subjective experience depends not only on sensory information from the environment but also on our prior knowledge or expectations. The precise mechanisms by which sensory information and prior knowledge are integrated remain unclear, with longstanding disagreement concerning whether integration is strictly feedforward or whether higher-level knowledge influences sensory processing through feedback connections. Here we used concurrent EEG and MEG recordings to determine how sensory information and prior knowledge are integrated in the brain during speech perception. We manipulated listeners' prior knowledge of speech content by presenting matching, mismatching, or neutral written text before a degraded (noise-vocoded) spoken word. When speech conformed to prior knowledge, subjective perceptual clarity was enhanced. This enhancement in clarity was associated with a spatiotemporal profile of brain activity uniquely consistent with a feedback process: activity in the inferior frontal gyrus was modulated by prior knowledge before activity in lower-level sensory regions of the superior temporal gyrus. In parallel, we parametrically varied the level of speech degradation, and therefore the amount of sensory detail, so that changes in neural responses attributable to sensory information and prior knowledge could be directly compared. Although sensory detail and prior knowledge both enhanced speech clarity, they had an opposite influence on the evoked response in the superior temporal gyrus. We argue that these data are best explained within the framework of predictive coding in which sensory activity is compared with top-down predictions and only unexplained activity propagated through the cortical hierarchy.

Fast learning of simple perceptual discriminations reduces brain activation in working memory and in high-level auditory regions.

PubMed

Daikhin, Luba; Ahissar, Merav

2015-07-01

Introducing simple stimulus regularities facilitates learning of both simple and complex tasks. This facilitation may reflect an implicit change in the strategies used to solve the task when successful predictions regarding incoming stimuli can be formed. We studied the modifications in brain activity associated with fast perceptual learning based on regularity detection. We administered a two-tone frequency discrimination task and measured brain activation (fMRI) under two conditions: with and without a repeated reference tone. Although participants could not explicitly tell the difference between these two conditions, the introduced regularity affected both performance and the pattern of brain activation. The "No-Reference" condition induced a larger activation in frontoparietal areas known to be part of the working memory network. However, only the condition with a reference showed fast learning, which was accompanied by a reduction of activity in two regions: the left intraparietal area, involved in stimulus retention, and the posterior superior-temporal area, involved in representing auditory regularities. We propose that this joint reduction reflects a reduction in the need for online storage of the compared tones. We further suggest that this change reflects an implicit strategic shift "backwards" from reliance mainly on working memory networks in the "No-Reference" condition to increased reliance on detected regularities stored in high-level auditory networks.

The maturation of cortical sleep rhythms and networks over early development.

PubMed

Chu, C J; Leahy, J; Pathmanathan, J; Kramer, M A; Cash, S S

2014-07-01

Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The maturation of cortical sleep rhythms and networks over early development

PubMed Central

Chu, CJ; Leahy, J; Pathmanathan, J; Kramer, MA; Cash, SS

2014-01-01

Objective Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. Methods We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. Results We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Conclusion Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. Significance This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. PMID:24418219

Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation.

PubMed

Dmochowski, Jacek P; Koessler, Laurent; Norcia, Anthony M; Bikson, Marom; Parra, Lucas C

2017-08-15

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4-7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Quantitative modeling of multiscale neural activity

NASA Astrophysics Data System (ADS)

Robinson, Peter A.; Rennie, Christopher J.

2007-01-01

The electrical activity of the brain has been observed for over a century and is widely used to probe brain function and disorders, chiefly through the electroencephalogram (EEG) recorded by electrodes on the scalp. However, the connections between physiology and EEGs have been chiefly qualitative until recently, and most uses of the EEG have been based on phenomenological correlations. A quantitative mean-field model of brain electrical activity is described that spans the range of physiological and anatomical scales from microscopic synapses to the whole brain. Its parameters measure quantities such as synaptic strengths, signal delays, cellular time constants, and neural ranges, and are all constrained by independent physiological measurements. Application of standard techniques from wave physics allows successful predictions to be made of a wide range of EEG phenomena, including time series and spectra, evoked responses to stimuli, dependence on arousal state, seizure dynamics, and relationships to functional magnetic resonance imaging (fMRI). Fitting to experimental data also enables physiological parameters to be infered, giving a new noninvasive window into brain function, especially when referenced to a standardized database of subjects. Modifications of the core model to treat mm-scale patchy interconnections in the visual cortex are also described, and it is shown that resulting waves obey the Schroedinger equation. This opens the possibility of classical cortical analogs of quantum phenomena.

Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation

PubMed Central

Dmochowski, Jacek P.; Koessler, Laurent; Norcia, Anthony M.; Bikson, Marom; Parra, Lucas C.

2018-01-01

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4–7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. PMID:28578130

How the brain predicts people's behavior in relation to rules and desires. Evidence of a medio-prefrontal dissociation.

PubMed

Corradi-Dell'Acqua, Corrado; Turri, Francesco; Kaufmann, Laurence; Clément, Fabrice; Schwartz, Sophie

2015-09-01

Forming and updating impressions about others is critical in everyday life and engages portions of the dorsomedial prefrontal cortex (dMPFC), the posterior cingulate cortex (PCC) and the amygdala. Some of these activations are attributed to "mentalizing" functions necessary to represent people's mental states, such as beliefs or desires. Evolutionary psychology and developmental studies, however, suggest that interpersonal inferences can also be obtained through the aid of deontic heuristics, which dictate what must (or must not) be done in given circumstances. We used fMRI and asked 18 participants to predict whether unknown characters would follow their desires or obey external rules. Participants had no means, at the beginning, to make accurate predictions, but slowly learned (throughout the experiment) each character's behavioral profile. We isolated brain regions whose activity changed during the experiment, as a neural signature of impression updating: whereas dMPFC was progressively more involved in predicting characters' behavior in relation to their desires, the medial orbitofrontal cortex and the amygdala were progressively more recruited in predicting rule-based behavior. Our data provide evidence of a neural dissociation between deontic inference and theory-of-mind (ToM), and support a differentiation of orbital and dorsal prefrontal cortex in terms of low- and high-level social cognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

Changes of mind in an attractor network of decision-making.

PubMed

Albantakis, Larissa; Deco, Gustavo

2011-06-01

Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks. Especially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes them from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we demonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the choice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task. Based on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the network predicts neural activity during changes of mind and accurately simulates reaction times, performance and percentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming activity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation, which up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of attractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor from linear diffusion models.

Surface acoustic wave probe implant for predicting epileptic seizures

DOEpatents

Gopalsami, Nachappa [Naperville, IL; Kulikov, Stanislav [Sarov, RU; Osorio, Ivan [Leawood, KS; Raptis, Apostolos C [Downers Grove, IL

2012-04-24

A system and method for predicting and avoiding a seizure in a patient. The system and method includes use of an implanted surface acoustic wave probe and coupled RF antenna to monitor temperature of the patient's brain, critical changes in the temperature characteristic of a precursor to the seizure. The system can activate an implanted cooling unit which can avoid or minimize a seizure in the patient.

Individual differences in brainstem and basal ganglia structure predict postural control and balance loss in young and older adults.

PubMed

Boisgontier, Matthieu P; Cheval, Boris; Chalavi, Sima; van Ruitenbeek, Peter; Leunissen, Inge; Levin, Oron; Nieuwboer, Alice; Swinnen, Stephan P

2017-02-01

It remains unclear which specific brain regions are the most critical for human postural control and balance, and whether they mediate the effect of age. Here, associations between postural performance and corticosubcortical brain regions were examined in young and older adults using multiple structural imaging and linear mixed models. Results showed that of the regions involved in posture, the brainstem was the strongest predictor of postural control and balance: lower brainstem volume predicted larger center of pressure deviation and higher odds of balance loss. Analyses of white and gray matter in the brainstem showed that the pedunculopontine nucleus area appeared to be critical for postural control in both young and older adults. In addition, the brainstem mediated the effect of age on postural control, underscoring the brainstem's fundamental role in aging. Conversely, lower basal ganglia volume predicted better postural performance, suggesting an association between greater neural resources in the basal ganglia and greater movement vigor, resulting in exaggerated postural adjustments. Finally, results showed that practice, shorter height and heavier weight (i.e., higher body mass index), higher total physical activity, and larger ankle active (but not passive) range of motion were predictive of more stable posture, irrespective of age. Copyright © 2016 Elsevier Inc. All rights reserved.

Explanatory pluralism: An unrewarding prediction error for free energy theorists.

PubMed

Colombo, Matteo; Wright, Cory

2017-03-01

Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity-the anhedonia, incentive salience, and reward prediction error hypotheses-we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More generally, we suggest that the form of scientific progress in the cognitive sciences is unlikely to be a single overarching grand unifying theory. Copyright © 2016 Elsevier Inc. All rights reserved.

Role of EEG background activity, seizure burden and MRI in predicting neurodevelopmental outcome in full-term infants with hypoxic-ischaemic encephalopathy in the era of therapeutic hypothermia.

PubMed

Weeke, Lauren C; Boylan, Geraldine B; Pressler, Ronit M; Hallberg, Boubou; Blennow, Mats; Toet, Mona C; Groenendaal, Floris; de Vries, Linda S

2016-11-01

To investigate the role of EEG background activity, electrographic seizure burden, and MRI in predicting neurodevelopmental outcome in infants with hypoxic-ischaemic encephalopathy (HIE) in the era of therapeutic hypothermia. Twenty-six full-term infants with HIE (September 2011-September 2012), who had video-EEG monitoring during the first 72 h, an MRI performed within the first two weeks and neurodevelopmental assessment at two years were evaluated. EEG background activity at age 24, 36 and 48 h, seizure burden, and severity of brain injury on MRI, were compared and related to neurodevelopmental outcome. EEG background activity was significantly associated with neurodevelopmental outcome at 36 h (p = 0.009) and 48 h after birth (p = 0.029) and with severity of brain injury on MRI at 36 h (p = 0.002) and 48 h (p = 0.018). All infants with a high seizure burden and moderate-severe injury on MRI had an abnormal outcome. The positive predictive value (PPV) of EEG for abnormal outcome was 100% at 36 h and 48 h and the negative predictive value (NPV) was 75% at 36 h and 69% at 48 h. The PPV of MRI was 100% and the NPV 85%. The PPV of seizure burden was 78% and the NPV 71%. Severely abnormal EEG background activity at 36 h and 48 h after birth was associated with severe injury on MRI and abnormal neurodevelopmental outcome. High seizure burden was only associated with abnormal outcome in combination with moderate-severe injury on MRI. Copyright © 2016 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Changes in brain entropy are related to abstract temporal topology. Comment on "Topodynamics of metastable brains" by Arturo Tozzi et al.

NASA Astrophysics Data System (ADS)

Çankaya, Mehmet Niyazi; Déli, Eva

2017-07-01

It is a great aspiration to consider biological systems, especially the notoriously unpredictable brain, with mathematical tools, because of their reliable predictive power. The classic idea that the brain can be compartmentalized into operational modules, such as vision, movement, emotions or consciousness has come up empty. A new surge of publications sets out the mathematical analysis of this highly integrated, complex and self-regulating system. For example, the resting brain's recurring electromagnetic activities form a highly reproducible harmonic function [1], which permits the use of matrix formulation, borrowed from quantum mechanics, to assess the probabilities of measurable properties, or ;observables;. It has also been suggested that resting state electric activities might take the form of a hypersphere [2], and even the particle-like formalism of the self-regulatory nature of consciousness has even been proposed [3]. The 'TOPODYNAMICS OF METASTABLE BRAINS' by Tozzi et al. [4] is part of the growing wave of publications that seeks to explain the brain's global dynamics within a physical framework. This fast growing literature has uncovered that the oscillatory networks of local electromagnetic potential differences, which are highly responsive to the environment, formulate according to non-classical principles and can be best modeled by the mathematical framework of dynamic and complex physical systems. However, the ability to connect the oscillatory dynamics of the brain to the global cognitive processes of the mind has been difficult. The TOPODYNAMICS OF METASTABLE BRAINS is a pioneering attempt to approach these seemingly disparate areas and bridge their conceptual, methodological divide. Specifically, topodynamics examines how the changing electric signals of the brain form an abstract topology during evoked and resting potential, and give rise to cognitive processes. For example, rapid transitional periods intercept the stable, operational modules of the brain's electric activities that parallel changes in thoughts or evolution of concepts. Within the framework of operational architectonics, Tozzi et al. applied the methods of the Bursuk-Ulam theorem (BUT) to uncover the detailed dynamics of brain activities, such as dimensionality, entropy changes, and information accumulation. The authors find that ripples of rapid transitional periods, with sudden changes and reorganization of the information and entropy, parallels shifts both in dimensionality of temporal dynamics, as well as in cognitive processes. The method therefore can uncover how entropic and dimensionality changes are interconnected with emerging mental concepts. It also highlights the differences between lower conceptual processes, such as sensory processing, and higher cognitive synthesis, such as semantics, for example. In physical systems, information, dimensionality and entropy are related according to well-established formulas. In this direction, the entropy values of the volume and surface area are added into the evaluation of brain functionings [5-7]. If the same relationship is true in the wet and constantly changing biological complexity of the brain, then it would give us predictive capability toward the understanding of cognition, aid the treatment of mental problems and diseases in psychiatry and psychology, and facilitate the design of a new generation of artificial intelligent machines.

Individual differences in human brain development.

PubMed

Brown, Timothy T

2017-01-01

This article discusses recent scientific advances in the study of individual differences in human brain development. Focusing on structural neuroimaging measures of brain morphology and tissue properties, two kinds of variability are related and explored: differences across individuals of the same age and differences across age as a result of development. A recent multidimensional modeling study is explained, which was able to use brain measures to predict an individual's chronological age within about one year on average, in children, adolescents, and young adults between 3 and 20 years old. These findings reveal great regularity in the sequence of the aggregate brain state across different ages and phases of development, despite the pronounced individual differences people show on any single brain measure at any given age. Future research is suggested, incorporating additional measures of brain activity and function. WIREs Cogn Sci 2017, 8:e1389. doi: 10.1002/wcs.1389 For further resources related to this article, please visit the WIREs website. © 2016 The Authors. WIREs Cognitive Science published by Wiley Periodicals, Inc.

Limitations on the developing preterm brain: impact of periventricular white matter lesions on brain connectivity and cognition.

PubMed

Pavlova, Marina A; Krägeloh-Mann, Ingeborg

2013-04-01

Brain lesions to the white matter in peritrigonal regions, periventricular leukomalacia, in children who were born prematurely represent an important model for studying limitations on brain development. The lesional pattern is of early origin and bilateral, that constrains the compensatory potential of the brain. We suggest that (i) topography and severity of periventricular lesions may have a long-term predictive value for cognitive and social capabilities in preterm birth survivors; and (ii) periventricular lesions may impact cognitive and social functions by affecting brain connectivity, and thereby, the dissociable neural networks underpinning these functions. A further pathway to explore is the relationship between cerebral palsy and cognitive outcome. Restrictions caused by motor disability may affect active exploration of surrounding and social participation that may in turn differentially impinge on cognitive development and social cognition. As an outline for future research, we underscore sex differences, as the sex of a preterm newborn may shape the mechanisms by which the developing brain is affected.

Diminished Medial Prefrontal Activity behind Autistic Social Judgments of Incongruent Information

PubMed Central

Watanabe, Takamitsu; Yahata, Noriaki; Abe, Osamu; Kuwabara, Hitoshi; Inoue, Hideyuki; Takano, Yosuke; Iwashiro, Norichika; Natsubori, Tatsunobu; Aoki, Yuta; Takao, Hidemasa; Sasaki, Hiroki; Gonoi, Wataru; Murakami, Mizuho; Katsura, Masaki; Kunimatsu, Akira; Kawakubo, Yuki; Matsuzaki, Hideo; Tsuchiya, Kenji J.; Kato, Nobumasa; Kano, Yukiko; Miyashita, Yasushi; Kasai, Kiyoto; Yamasue, Hidenori

2012-01-01

Individuals with autism spectrum disorders (ASD) tend to make inadequate social judgments, particularly when the nonverbal and verbal emotional expressions of other people are incongruent. Although previous behavioral studies have suggested that ASD individuals have difficulty in using nonverbal cues when presented with incongruent verbal-nonverbal information, the neural mechanisms underlying this symptom of ASD remain unclear. In the present functional magnetic resonance imaging study, we compared brain activity in 15 non-medicated adult males with high-functioning ASD to that of 17 age-, parental-background-, socioeconomic-, and intelligence-quotient-matched typically-developed (TD) male participants. Brain activity was measured while each participant made friend or foe judgments of realistic movies in which professional actors spoke with conflicting nonverbal facial expressions and voice prosody. We found that the ASD group made significantly less judgments primarily based on the nonverbal information than the TD group, and they exhibited significantly less brain activity in the right inferior frontal gyrus, bilateral anterior insula, anterior cingulate cortex/ventral medial prefrontal cortex (ACC/vmPFC), and dorsal medial prefrontal cortex (dmPFC) than the TD group. Among these five regions, the ACC/vmPFC and dmPFC were most involved in nonverbal-information-biased judgments in the TD group. Furthermore, the degree of decrease of the brain activity in these two brain regions predicted the severity of autistic communication deficits. The findings indicate that diminished activity in the ACC/vmPFC and dmPFC underlies the impaired abilities of individuals with ASD to use nonverbal content when making judgments regarding other people based on incongruent social information. PMID:22745788

Active inference and cognitive-emotional interactions in the brain.

PubMed

Pezzulo, Giovanni; Barca, Laura; Friston, Karl J

2015-01-01

All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the "affective tuning" of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

Inferring multi-scale neural mechanisms with brain network modelling

PubMed Central

Schirner, Michael; McIntosh, Anthony Randal; Jirsa, Viktor; Deco, Gustavo

2018-01-01

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies. PMID:29308767

Dismissing Attachment Characteristics Dynamically Modulate Brain Networks Subserving Social Aversion.

PubMed

Krause, Anna Linda; Borchardt, Viola; Li, Meng; van Tol, Marie-José; Demenescu, Liliana Ramona; Strauss, Bernhard; Kirchmann, Helmut; Buchheim, Anna; Metzger, Coraline D; Nolte, Tobias; Walter, Martin

2016-01-01

Attachment patterns influence actions, thoughts and feeling through a person's "inner working model". Speech charged with attachment-dependent content was proposed to modulate the activation of cognitive-emotional schemata in listeners. We performed a 7 Tesla rest-task-rest functional magnetic resonance imaging (fMRI)-experiment, presenting auditory narratives prototypical of dismissing attachment representations to investigate their effect on 23 healthy males. We then examined effects of participants' attachment style and childhood trauma on brain state changes using seed-based functional connectivity (FC) analyses, and finally tested whether subjective differences in responsivity to narratives could be predicted by baseline network states. In comparison to a baseline state, we observed increased FC in a previously described "social aversion network" including dorsal anterior cingulated cortex (dACC) and left anterior middle temporal gyrus (aMTG) specifically after exposure to insecure-dismissing attachment narratives. Increased dACC-seeded FC within the social aversion network was positively related to the participants' avoidant attachment style and presence of a history of childhood trauma. Anxious attachment style on the other hand was positively correlated with FC between the dACC and a region outside of the "social aversion network", namely the dorsolateral prefrontal cortex, which suggests decreased network segregation as a function of anxious attachment. Finally, the extent of subjective experience of friendliness towards the dismissing narrative was predicted by low baseline FC-values between hippocampus and inferior parietal lobule (IPL). Taken together, our study demonstrates an activation of networks related to social aversion in terms of increased connectivity after listening to insecure-dismissing attachment narratives. A causal interrelation of brain state changes and subsequent changes in social reactivity was further supported by our observation of direct prediction of neuronal responses by individual attachment and trauma characteristics and reversely prediction of subjective experience by intrinsic functional connections. We consider these findings of activation of within-network and between-network connectivity modulated by inter-individual differences as substantial for the understanding of interpersonal processes, particularly in clinical settings.

Prediction of individual brain maturity using fMRI.

PubMed

Dosenbach, Nico U F; Nardos, Binyam; Cohen, Alexander L; Fair, Damien A; Power, Jonathan D; Church, Jessica A; Nelson, Steven M; Wig, Gagan S; Vogel, Alecia C; Lessov-Schlaggar, Christina N; Barnes, Kelly Anne; Dubis, Joseph W; Feczko, Eric; Coalson, Rebecca S; Pruett, John R; Barch, Deanna M; Petersen, Steven E; Schlaggar, Bradley L

2010-09-10

Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.

Resting-state connectivity predicts visuo-motor skill learning.

PubMed

Manuel, Aurélie L; Guggisberg, Adrian G; Thézé, Raphaël; Turri, Francesco; Schnider, Armin

2018-08-01

Spontaneous brain activity at rest is highly organized even when the brain is not explicitly engaged in a task. Functional connectivity (FC) in the alpha frequency band (α, 8-12 Hz) during rest is associated with improved performance on various cognitive and motor tasks. In this study we explored how FC is associated with visuo-motor skill learning and offline consolidation. We tested two hypotheses by which resting-state FC might achieve its impact on behavior: preparing the brain for an upcoming task or consolidating training gains. Twenty-four healthy participants were assigned to one of two groups: The experimental group (n = 12) performed a computerized mirror-drawing task. The control group (n = 12) performed a similar task but with concordant cursor direction. High-density 156-channel resting-state EEG was recorded before and after learning. Subjects were tested for offline consolidation 24h later. The Experimental group improved during training and showed offline consolidation. Increased α-FC between the left superior parietal cortex and the rest of the brain before training and decreased α-FC in the same region after training predicted learning. Resting-state FC following training did not predict offline consolidation and none of these effects were present in controls. These findings indicate that resting-state alpha-band FC is primarily implicated in providing optimal neural resources for upcoming tasks. Copyright © 2018 Elsevier Inc. All rights reserved.

Decoding natural images from evoked brain activities using encoding models with invertible mapping.

PubMed

Li, Chao; Xu, Junhai; Liu, Baolin

2018-05-21

Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be directly decoded. To solve this problem, we designed a sparse framework-based encoding model that predicted brain activities from a complete feature representation. Moreover, according to the distribution and activation rules of neurons in the primary visual cortex (V1), three key transformations were introduced into the basic feature to improve the model performance. In this setting, the mapping was simple enough that it could be inverted using a closed-form formula. Using this mapping, we designed a hybrid identification method based on the support vector machine (SVM), and tested it on a published functional magnetic resonance imaging (fMRI) dataset. The experiments confirmed the rationality of our encoding model, and the identification accuracies for 2 subjects increased from 92% and 72% to 98% and 92% with the chance level only 0.8%. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dynamics of modularity of neural activity in the brain during development

NASA Astrophysics Data System (ADS)

Deem, Michael; Chen, Man

2014-03-01

Theory suggests that more modular systems can have better response functions at short times. This theory suggests that greater cognitive performance may be achieved for more modular neural activity, and that modularity of neural activity may, therefore, likely increase with development in children. We study the relationship between age and modularity of brain neural activity in developing children. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. We interpret these results as evidence of selection for plasticity in the cognitive function of the human brain. We present a model to illustrate how modularity can provide greater cognitive performance at short times and enhance fast, low-level, automatic cognitive processes. Conversely, high-level, effortful, conscious cognitive processes may not benefit from modularity. We use quasispecies theory to predict how the average modularity evolves with age, given a fitness function extracted from the model. We suggest further experiments exploring the effect of modularity on cognitive performance and suggest that modularity may be a potential biomarker for injury, rehabilitation, or disease.

A Polygenic Risk Score of glutamatergic SNPs associated with schizophrenia predicts attentional behavior and related brain activity in healthy humans.

PubMed

Rampino, Antonio; Taurisano, Paolo; Fanelli, Giuseppe; Attrotto, Mariateresa; Torretta, Silvia; Antonucci, Linda Antonella; Miccolis, Grazia; Pergola, Giulio; Ursini, Gianluca; Maddalena, Giancarlo; Romano, Raffaella; Masellis, Rita; Di Carlo, Pasquale; Pignataro, Patrizia; Blasi, Giuseppe; Bertolino, Alessandro

2017-09-01

Multiple genetic variations impact on risk for schizophrenia. Recent analyses by the Psychiatric Genomics Consortium (PGC2) identified 128 SNPs genome-wide associated with the disorder. Furthermore, attention and working memory deficits are core features of schizophrenia, are heritable and have been associated with variation in glutamatergic neurotransmission. Based on this evidence, in a sample of healthy volunteers, we used SNPs associated with schizophrenia in PGC2 to construct a Polygenic-Risk-Score (PRS) reflecting the cumulative risk for schizophrenia, along with a Polygenic-Risk-Score including only SNPs related to genes implicated in glutamatergic signaling (Glu-PRS). We performed Factor Analysis for dimension reduction of indices of cognitive performance. Furthermore, both PRS and Glu-PRS were used as predictors of cognitive functioning in the domains of Attention, Speed of Processing and Working Memory. The association of the Glu-PRS on brain activity during the Variable Attention Control (VAC) task was also explored. Finally, in a second independent sample of healthy volunteers we sought to confirm the association between the Glu-PRS and both performance in the domain of Attention and brain activity during the VAC.We found that performance in Speed of Processing and Working Memory was not associated with any of the Polygenic-Risk-Scores. The Glu-PRS, but not the PRS was associated with Attention and brain activity during the VAC. The specific effects of Glu-PRS on Attention and brain activity during the VAC were also confirmed in the replication sample.Our results suggest a pathway specificity in the relationship between genetic risk for schizophrenia, the associated cognitive dysfunction and related brain processing. Copyright © 2017 Elsevier B.V. and ECNP. All rights reserved.

Brain MRI lesions and atrophy are associated with employment status in patients with multiple sclerosis.

PubMed

Tauhid, Shahamat; Chu, Renxin; Sasane, Rahul; Glanz, Bonnie I; Neema, Mohit; Miller, Jennifer R; Kim, Gloria; Signorovitch, James E; Healy, Brian C; Chitnis, Tanuja; Weiner, Howard L; Bakshi, Rohit

2015-11-01

Multiple sclerosis (MS) commonly affects occupational function. We investigated the link between brain MRI and employment status. Patients with MS (n = 100) completed a Work Productivity and Activity Impairment (WPAI) (general health version) survey measuring employment status, absenteeism, presenteeism, and overall work and daily activity impairment. Patients "working for pay" were considered employed; "temporarily not working but looking for work," "not working or looking for work due to age," and "not working or looking for work due to disability" were considered not employed. Brain MRI T1 hypointense (T1LV) and T2 hyperintense (T2LV) lesion volumes were quantified. To assess lesional destructive capability, we calculated each subject's ratio of T1LV to T2LV (T1/T2). Normalized brain parenchymal volume (BPV) assessed brain atrophy. The mean (SD) age was 45.5 (9.7) years; disease duration was 12.1 (8.1) years; 75 % were women, 76 % were relapsing-remitting, and 76 % were employed. T1LV, T1/T2, Expanded Disability Status Scale (EDSS) scores, and activity impairment were lower and BPV was higher in the employed vs. not employed group (Wilcoxon tests, p < 0.05). Age, disease duration, MS clinical subtype, and T2LV did not differ between groups (p > 0.05). In multivariable logistic regression modeling, adjusting for age, sex, and disease duration, higher T1LV predicted a lower chance of employment (p < 0.05). Pearson correlations showed that EDSS was associated with activity impairment (p < 0.05). Disease duration, age, and MRI measures were not correlated with activity impairment or other WPAI outcomes (p > 0.05). We report a link between brain atrophy and lesions, particularly lesions with destructive potential, to MS employment status.

Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity.

PubMed

Chen, Qiu-Feng; Chen, Hua-Jun; Liu, Jun; Sun, Tao; Shen, Qun-Tai

2016-01-01

Machine learning-based approaches play an important role in examining functional magnetic resonance imaging (fMRI) data in a multivariate manner and extracting features predictive of group membership. This study was performed to assess the potential for measuring brain intrinsic activity to identify minimal hepatic encephalopathy (MHE) in cirrhotic patients, using the support vector machine (SVM) method. Resting-state fMRI data were acquired in 16 cirrhotic patients with MHE and 19 cirrhotic patients without MHE. The regional homogeneity (ReHo) method was used to investigate the local synchrony of intrinsic brain activity. Psychometric Hepatic Encephalopathy Score (PHES) was used to define MHE condition. SVM-classifier was then applied using leave-one-out cross-validation, to determine the discriminative ReHo-map for MHE. The discrimination map highlights a set of regions, including the prefrontal cortex, anterior cingulate cortex, anterior insular cortex, inferior parietal lobule, precentral and postcentral gyri, superior and medial temporal cortices, and middle and inferior occipital gyri. The optimized discriminative model showed total accuracy of 82.9% and sensitivity of 81.3%. Our results suggested that a combination of the SVM approach and brain intrinsic activity measurement could be helpful for detection of MHE in cirrhotic patients.

Amplitude-integrated EEG in newborns with critical congenital heart disease predicts preoperative brain magnetic resonance imaging findings.

PubMed

Mulkey, Sarah B; Yap, Vivien L; Bai, Shasha; Ramakrishnaiah, Raghu H; Glasier, Charles M; Bornemeier, Renee A; Schmitz, Michael L; Bhutta, Adnan T

2015-06-01

The study aims are to evaluate cerebral background patterns using amplitude-integrated electroencephalography in newborns with critical congenital heart disease, determine if amplitude-integrated electroencephalography is predictive of preoperative brain injury, and assess the incidence of preoperative seizures. We hypothesize that amplitude-integrated electroencephalography will show abnormal background patterns in the early preoperative period in infants with congenital heart disease that have preoperative brain injury on magnetic resonance imaging. Twenty-four newborns with congenital heart disease requiring surgery at younger than 30 days of age were prospectively enrolled within the first 3 days of age at a tertiary care pediatric hospital. Infants had amplitude-integrated electroencephalography for 24 hours beginning close to birth and preoperative brain magnetic resonance imaging. The amplitude-integrated electroencephalographies were read to determine if the background pattern was normal, mildly abnormal, or severely abnormal. The presence of seizures and sleep-wake cycling were noted. The preoperative brain magnetic resonance imaging scans were used for brain injury and brain atrophy assessment. Fifteen of 24 infants had abnormal amplitude-integrated electroencephalography at 0.71 (0-2) (mean [range]) days of age. In five infants, the background pattern was severely abnormal. (burst suppression and/or continuous low voltage). Of the 15 infants with abnormal amplitude-integrated electroencephalography, 9 (60%) had brain injury. One infant with brain injury had a seizure on amplitude-integrated electroencephalography. A severely abnormal background pattern on amplitude-integrated electroencephalography was associated with brain atrophy (P = 0.03) and absent sleep-wake cycling (P = 0.022). Background cerebral activity is abnormal on amplitude-integrated electroencephalography following birth in newborns with congenital heart disease who have findings of brain injury and/or brain atrophy on preoperative brain magnetic resonance imaging. Copyright © 2015 Elsevier Inc. All rights reserved.

How much detail is needed in modeling a transcranial magnetic stimulation figure-8 coil: Measurements and brain simulations

PubMed Central

Mandija, Stefano; Sommer, Iris E. C.; van den Berg, Cornelis A. T.; Neggers, Sebastiaan F. W.

2017-01-01

Background Despite TMS wide adoption, its spatial and temporal patterns of neuronal effects are not well understood. Although progress has been made in predicting induced currents in the brain using realistic finite element models (FEM), there is little consensus on how a magnetic field of a typical TMS coil should be modeled. Empirical validation of such models is limited and subject to several limitations. Methods We evaluate and empirically validate models of a figure-of-eight TMS coil that are commonly used in published modeling studies, of increasing complexity: simple circular coil model; coil with in-plane spiral winding turns; and finally one with stacked spiral winding turns. We will assess the electric fields induced by all 3 coil models in the motor cortex using a computer FEM model. Biot-Savart models of discretized wires were used to approximate the 3 coil models of increasing complexity. We use a tailored MR based phase mapping technique to get a full 3D validation of the incident magnetic field induced in a cylindrical phantom by our TMS coil. FEM based simulations on a meshed 3D brain model consisting of five tissues types were performed, using two orthogonal coil orientations. Results Substantial differences in the induced currents are observed, both theoretically and empirically, between highly idealized coils and coils with correctly modeled spiral winding turns. Thickness of the coil winding turns affect minimally the induced electric field, and it does not influence the predicted activation. Conclusion TMS coil models used in FEM simulations should include in-plane coil geometry in order to make reliable predictions of the incident field. Modeling the in-plane coil geometry is important to correctly simulate the induced electric field and to correctly make reliable predictions of neuronal activation PMID:28640923

Contributions of glycogen to astrocytic energetics during brain activation.

PubMed

Dienel, Gerald A; Cruz, Nancy F

2015-02-01

Glycogen is the major store of glucose in brain and is mainly in astrocytes. Brain glycogen levels in unstimulated, carefully-handled rats are 10-12 μmol/g, and assuming that astrocytes account for half the brain mass, astrocytic glycogen content is twice as high. Glycogen turnover is slow under basal conditions, but it is mobilized during activation. There is no net increase in incorporation of label from glucose during activation, whereas label release from pre-labeled glycogen exceeds net glycogen consumption, which increases during stronger stimuli. Because glycogen level is restored by non-oxidative metabolism, astrocytes can influence the global ratio of oxygen to glucose utilization. Compensatory increases in utilization of blood glucose during inhibition of glycogen phosphorylase are large and approximate glycogenolysis rates during sensory stimulation. In contrast, glycogenolysis rates during hypoglycemia are low due to continued glucose delivery and oxidation of endogenous substrates; rates that preserve neuronal function in the absence of glucose are also low, probably due to metabolite oxidation. Modeling studies predict that glycogenolysis maintains a high level of glucose-6-phosphate in astrocytes to maintain feedback inhibition of hexokinase, thereby diverting glucose for use by neurons. The fate of glycogen carbon in vivo is not known, but lactate efflux from brain best accounts for the major metabolic characteristics during activation of living brain. Substantial shuttling coupled with oxidation of glycogen-derived lactate is inconsistent with available evidence. Glycogen has important roles in astrocytic energetics, including glucose sparing, control of extracellular K(+) level, oxidative stress management, and memory consolidation; it is a multi-functional compound.

Contributions of Glycogen to Astrocytic Energetics during Brain Activation

PubMed Central

Dienel, Gerald A.; Cruz, Nancy F.

2014-01-01

Glycogen is the major store of glucose in brain and is mainly in astrocytes. Brain glycogen levels in unstimulated, carefully-handled rats are 10-12 mol/g, and assuming that astrocytes account for half the brain mass, astrocytic glycogen content is twice as high. Glycogen turnover is slow under basal conditions, but it is mobilized during activation. There is no net increase in incorporation of label from glucose during activation, whereas label release from pre-labeled glycogen exceeds net glycogen consumption, which increases during stronger stimuli. Because glycogen level is restored by non-oxidative metabolism, astrocytes can influence the global ratio of oxygen to glucose utilization. Compensatory increases in utilization of blood glucose during inhibition of glycogen phosphorylase are large and approximate glycogenolysis rates during sensory stimulation. In contrast, glycogenolysis rates during hypoglycemia are low due to continued glucose delivery and oxidation of endogenous substrates; rates that preserve neuronal function in the absence of glucose are also low, probably due to metabolite oxidation. Modeling studies predict that glycogenolysis maintains a high level of glucose-6-phosphate in astrocytes to maintain feedback inhibition of hexokinase, thereby diverting glucose for use by neurons. The fate of glycogen carbon in vivo is not known, but lactate efflux from brain best accounts for the major metabolic characteristics during activation of living brain. Substantial shuttling coupled with oxidation of glycogen-derived lactate is inconsistent with available evidence. Glycogen has important roles in astrocytic energetics, including glucose sparing, control of extracellular K+ level, oxidative stress management, and memory consolidation; it is a multi-functional compound. PMID:24515302

Emotions promote social interaction by synchronizing brain activity across individuals

PubMed Central

Nummenmaa, Lauri; Glerean, Enrico; Viinikainen, Mikko; Jääskeläinen, Iiro P.; Hari, Riitta; Sams, Mikko

2012-01-01

Sharing others’ emotional states may facilitate understanding their intentions and actions. Here we show that networks of brain areas “tick together” in participants who are viewing similar emotional events in a movie. Participants’ brain activity was measured with functional MRI while they watched movies depicting unpleasant, neutral, and pleasant emotions. After scanning, participants watched the movies again and continuously rated their experience of pleasantness–unpleasantness (i.e., valence) and of arousal–calmness. Pearson’s correlation coefficient was used to derive multisubject voxelwise similarity measures [intersubject correlations (ISCs)] of functional MRI data. Valence and arousal time series were used to predict the moment-to-moment ISCs computed using a 17-s moving average. During movie viewing, participants' brain activity was synchronized in lower- and higher-order sensory areas and in corticolimbic emotion circuits. Negative valence was associated with increased ISC in the emotion-processing network (thalamus, ventral striatum, insula) and in the default-mode network (precuneus, temporoparietal junction, medial prefrontal cortex, posterior superior temporal sulcus). High arousal was associated with increased ISC in the somatosensory cortices and visual and dorsal attention networks comprising the visual cortex, bilateral intraparietal sulci, and frontal eye fields. Seed-voxel–based correlation analysis confirmed that these sets of regions constitute dissociable, functional networks. We propose that negative valence synchronizes individuals’ brain areas supporting emotional sensations and understanding of another’s actions, whereas high arousal directs individuals’ attention to similar features of the environment. By enhancing the synchrony of brain activity across individuals, emotions may promote social interaction and facilitate interpersonal understanding. PMID:22623534

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

PubMed Central

Just, Marcel Adam; Cherkassky, Vladimir L.; Aryal, Sandesh; Mitchell, Tom M.

2010-01-01

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3–4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind. PMID:20084104

Lasting EEG/MEG Aftereffects of Rhythmic Transcranial Brain Stimulation: Level of Control Over Oscillatory Network Activity

PubMed Central

Veniero, Domenica; Vossen, Alexandra; Gross, Joachim; Thut, Gregor

2015-01-01

A number of rhythmic protocols have emerged for non-invasive brain stimulation (NIBS) in humans, including transcranial alternating current stimulation (tACS), oscillatory transcranial direct current stimulation (otDCS), and repetitive (also called rhythmic) transcranial magnetic stimulation (rTMS). With these techniques, it is possible to match the frequency of the externally applied electromagnetic fields to the intrinsic frequency of oscillatory neural population activity (“frequency-tuning”). Mounting evidence suggests that by this means tACS, otDCS, and rTMS can entrain brain oscillations and promote associated functions in a frequency-specific manner, in particular during (i.e., online to) stimulation. Here, we focus instead on the changes in oscillatory brain activity that persist after the end of stimulation. Understanding such aftereffects in healthy participants is an important step for developing these techniques into potentially useful clinical tools for the treatment of specific patient groups. Reviewing the electrophysiological evidence in healthy participants, we find aftereffects on brain oscillations to be a common outcome following tACS/otDCS and rTMS. However, we did not find a consistent, predictable pattern of aftereffects across studies, which is in contrast to the relative homogeneity of reported online effects. This indicates that aftereffects are partially dissociated from online, frequency-specific (entrainment) effects during tACS/otDCS and rTMS. We outline possible accounts and future directions for a better understanding of the link between online entrainment and offline aftereffects, which will be key for developing more targeted interventions into oscillatory brain activity. PMID:26696834

A neurosemantic theory of concrete noun representation based on the underlying brain codes.

PubMed

Just, Marcel Adam; Cherkassky, Vladimir L; Aryal, Sandesh; Mitchell, Tom M

2010-01-13

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3-4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.

Specificity of regional brain activity in anxiety types during emotion processing.

PubMed

Engels, Anna S; Heller, Wendy; Mohanty, Aprajita; Herrington, John D; Banich, Marie T; Webb, Andrew G; Miller, Gregory A

2007-05-01

The present study tested the hypothesis that anxious apprehension involves more left- than right-hemisphere activity and that anxious arousal is associated with the opposite pattern. Behavioral and fMRI responses to threat stimuli in an emotional Stroop task were examined in nonpatient groups reporting anxious apprehension, anxious arousal, or neither. Reaction times were longer for negative than for neutral words. As predicted, brain activation distinguished anxious groups in a left inferior frontal region associated with speech production and in a right-hemisphere inferior temporal area. Addressing a second hypothesis about left-frontal involvement in emotion, distinct left frontal regions were associated with anxious apprehension versus processing of positive information. Results support the proposed distinction between the two types of anxiety and resolve an inconsistency about the role of left-frontal activation in emotion and psychopathology.

Competing with peers: mentalizing-related brain activity reflects what is at stake.

PubMed

Halko, Marja-Liisa; Hlushchuk, Yevhen; Hari, Riitta; Schürmann, Martin

2009-06-01

Competition imposes constraints for humans who make decisions. Concomitantly, people do not only maximize their personal profit but they also try to punish unfair conspecifics. In bargaining games, subjects typically accept equal-share offers but reject unduly small offers; competition affects this balance. Here we used functional magnetic resonance imaging (fMRI) to study adjustment to competition in a bargaining game where subjects competed against another person for a share of the stake. For medium-sized, but not for minimum offers, competition increased the likelihood of acceptance and thus shifted behavior towards maximizing personal profits, emphasizing the importance of financial incentives. Specifically for medium-sized offers, competition was associated with increased brain activation bilaterally in the temporo-parietal junction, a region associated with mentalizing. In the right inferior frontal region, competition-related brain activation was strongest in subjects whose high acceptance rates in the standard ultimatum game hinted at a profit-oriented approach. The results suggest a network of brain areas supporting decision making under competition, with incentive-dependent mentalizing engaged when the competitor's behavior is difficult to predict and when the stake is attractive enough to justify the effort.

Perplexities and Provocations of Eating Disorders

ERIC Educational Resources Information Center

Halmi, Katherine A.

2009-01-01

Background: Etiological hypotheses of eating disorders, anorexia nervosa and bulimia nervosa have not produced informative research for predictably effective treatment. Methods: The rationale for applying a model of allostasis, a dysregulation of reward circuits with activation of brain and hormonal stress responses to maintain apparent stability,…

A probabilistic, distributed, recursive mechanism for decision-making in the brain

PubMed Central

Gurney, Kevin N.

2018-01-01

Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics. PMID:29614077

What we think before a voluntary movement.

PubMed

Schneider, Logan; Houdayer, Elise; Bai, Ou; Hallett, Mark

2013-06-01

A central feature of voluntary movement is the sense of volition, but when this sense arises in the course of movement formulation and execution is not clear. Many studies have explored how the brain might be actively preparing movement before the sense of volition; however, because the timing of the sense of volition has depended on subjective and retrospective judgments, these findings are still regarded with a degree of scepticism. EEG events such as beta event-related desynchronization and movement-related cortical potentials are associated with the brain's programming of movement. Using an optimized EEG signal derived from multiple variables, we were able to make real-time predictions of movements in advance of their occurrence with a low false-positive rate. We asked participants what they were thinking at the time of prediction: Sometimes they were thinking about movement, and other times they were not. Our results indicate that the brain can be preparing to make voluntary movements while participants are thinking about something else.

fMRI brain response during sentence reading comprehension in children with benign epilepsy with centro-temporal spikes.

PubMed

Malfait, D; Tucholka, A; Mendizabal, S; Tremblay, J; Poulin, C; Oskoui, M; Srour, M; Carmant, L; Major, P; Lippé, S

2015-11-01

Children with benign epilepsy with centro-temporal spikes (BECTS) often have language problems. Abnormal epileptic activity is found in central and temporal brain regions, which are involved in reading and semantic and syntactic comprehension. Using functional magnetic resonance imaging (fMRI), we examined reading networks in BECTS children with a new sentence reading comprehension task involving semantic and syntactic processing. Fifteen children with BECTS (age=11y 1m ± 16 m; 12 boys) and 18 healthy controls (age=11 y 8m ± 20 m; 11 boys) performed an fMRI reading comprehension task in which they read a pair of syntactically complex sentences and decided whether the target sentence (the second sentence in the pair) was true or false with respect to the first sentence. All children also underwent an exhaustive neuropsychological assessment. We demonstrated weaknesses in several cognitive domains in BECTS children. During the sentence reading fMRI task, left inferior frontal regions and bilateral temporal areas were activated in BECTS children and healthy controls. However, additional brain regions such as the left hippocampus and precuneus were activated in BECTS children. Moreover, specific activation was found in the left caudate and putamen in BECTS children but not in healthy controls. Cognitive results and accuracy during the fMRI task were associated with specific brain activation patterns. BECTS children recruited a wider network to perform the fMRI sentence reading comprehension task, with specific activation in the left dorsal striatum. BECTS cognitive performance differently predicted functional activation in frontal and temporal regions compared to controls, suggesting differences in brain network organisation that contribute to reading comprehension. Crown Copyright © 2015. Published by Elsevier B.V. All rights reserved.

Cocaine users with comorbid Cluster B personality disorders show dysfunctional brain activation and connectivity in the emotional regulation networks during negative emotion maintenance and reappraisal.

PubMed

Albein-Urios, Natalia; Verdejo-Román, Juan; Soriano-Mas, Carles; Asensio, Samuel; Martínez-González, José Miguel; Verdejo-García, Antonio

2013-12-01

Cocaine dependence often co-occurs with Cluster B personality disorders. Since both disorders are characterized by emotion regulation deficits, we predicted that cocaine comorbid patients would exhibit dysfunctional patterns of brain activation and connectivity during reappraisal of negative emotions. We recruited 18 cocaine users with comorbid Cluster B personality disorders, 17 cocaine users without comorbidities and 21 controls to be scanned using functional magnetic resonance imaging (fMRI) during performance on a reappraisal task in which they had to maintain or suppress the emotions induced by negative affective stimuli. We followed region of interest (ROI) and whole-brain approaches to investigate brain activations and connectivity associated with negative emotion experience and reappraisal. Results showed that cocaine users with comorbid personality disorders had reduced activation of the subgenual anterior cingulate cortex during negative emotion maintenance and increased activation of the lateral orbitofrontal cortex and the amygdala during reappraisal. Amygdala activation correlated with impulsivity and antisocial beliefs in the comorbid group. Connectivity analyses showed that in the cocaine comorbid group the subgenual cingulate was less efficiently connected with the amygdala and the fusiform gyri and more efficiently connected with the anterior insula during maintenance, whereas during reappraisal the left orbitofrontal cortex was more efficiently connected with the amygdala and the right orbitofrontal cortex was less efficiently connected with the dorsal striatum. We conclude that cocaine users with comorbid Cluster B personality disorders have distinctive patterns of brain activation and connectivity during maintenance and reappraisal of negative emotions, which correlate with impulsivity and dysfunctional beliefs. Copyright © 2013 Elsevier B.V. and ECNP. All rights reserved.

Following the crowd: Brain Substrates of Long-Term Memory Conformity

PubMed Central

Edelson, Micah; Sharot, Tali; Dolan, Raymond J; Dudai, Yadin

2012-01-01

Human memory is strikingly susceptible to social influences, yet we know little about the underlying mechanisms. We examined how socially induced memory errors are generated in the brain by studying the memory of individuals exposed to recollections of others. Participants exhibited a strong tendency to conform to erroneous recollections of the group, producing both long-lasting and temporary errors, even when their initial memory was strong and accurate. Functional brain imaging revealed that social influence modified the neuronal representation of memory. Specifically, a particular brain signature of enhanced amygdala activity and enhanced amygdala-hippocampus connectivity predicted long-lasting, but not temporary memory alterations. Our findings reveal how social manipulation can alter memory and extend the known functions of the amygdala to encompass socially-mediated memory distortions. PMID:21719681

Operator State Estimation for Adaptive Aiding in Uninhabited Combat Air Vehicles

DTIC Science & Technology

2005-09-01

1992). Van Boxtel, A., W. Waterink, and I.J.T. Veldhuizen . “Tonic Facial EMG Activity As An Index of Mental Effort: Effects of Work Rate, Time-On...the ‘normal’ functioning of brain activity (Beaumont, Burov, Carter, Cheuvront, Sawka, Wilson, Van Orden, Hockey, Balkin and Gundel, 2004). For...by the sympathetic nervous system. Electromyographic activity has been shown to predict arousal accurately ( Veldhuizen , Gaillard, and de Vries, 2003

Single-task fMRI overlap predicts concurrent multitasking interference.

PubMed

Nijboer, Menno; Borst, Jelmer; van Rijn, Hedderik; Taatgen, Niels

2014-10-15

There is no consensus regarding the origin of behavioral interference that occurs during concurrent multitasking. Some evidence points toward a multitasking locus in the brain, while other results imply that interference is the consequence of task interactions in several brain regions. To investigate this issue, we conducted a functional MRI (fMRI) study consisting of three component tasks, which were performed both separately and in combination. The results indicated that no specific multitasking area exists. Instead, different patterns of activation across conditions could be explained by assuming that the interference is a result of task interactions. Additionally, similarity in single-task activation patterns correlated with a decrease in accuracy during dual-task conditions. Taken together, these results support the view that multitasking interference is not due to a bottleneck in a single "multitasking" brain region, but is a result of interactions between concurrently running processes. Copyright © 2014 Elsevier Inc. All rights reserved.

Hippocampus activation related to 'real-time' processing of visuospatial change.

PubMed

Beudel, M; Leenders, K L; de Jong, B M

2016-12-01

The delay associated with cerebral processing time implies a lack of real-time representation of changes in the observed environment. To bridge this gap for motor actions in a dynamical environment, the brain uses predictions of the most plausible future reality based on previously provided information. To optimise these predictions, adjustments to actual experiences are necessary. This requires a perceptual memory buffer. In our study we gained more insight how the brain treats (real-time) information by comparing cerebral activations related to judging past-, present- and future locations of a moving ball, respectively. Eighteen healthy subjects made these estimations while fMRI data was obtained. All three conditions evoked bilateral dorsal-parietal and premotor activations, while judgment of the location of the ball at the moment of judgment showed increased bilateral posterior hippocampus activation relative to making both future and past judgments at the one-second time-sale. Since the condition of such 'real-time' judgments implied undistracted observation of the ball's actual movements, the associated hippocampal activation is consistent with the concept that the hippocampus participates in a top-down exerted sensory gating mechanism. In this way, it may play a role in novelty (saliency) detection. Copyright © 2016 Elsevier B.V. All rights reserved.

Anti-Aß immunotherapy in Alzheimer's disease; relevance of transgenic mouse studies to clinical trials

PubMed Central

Wilcock, Donna M.; Colton, Carol A.

2009-01-01

Therapeutic approaches to the treatment of Alzheimer's disease are focused primarily on the Aß peptide which aggregates to form amyloid deposits in the brain. The amyloid hypothesis states that amyloid is the precipitating factor that results in the other pathologies of Alzheimer's, namely neurofibrillary tangles and neurodegeneration, as well as the clinical dementia. One such therapy that has attracted significant attention is anti-Aß immunotherapy. First described in 1999, immunotherapy uses anti-Aß antibodies to lower brain amyloid levels. Active immunization, in which Aß is combined with an adjuvant to stimulate an immune response producing antibodies and passive immunization, in which antibodies are directly injected, were shown to lower brain amyloid levels and improve cognition in multiple transgenic mouse models. Mechanisms of action were studied in these mice and revealed a complex set of mechanisms that depended on the type of antibody used. When active immunization advanced to clinical trials a subset of patients developed meningoencephalitis; an event not predicted in mouse studies. However, it was suspected that a T-cell response due to the type of adjuvant used was the cause of the meningoencephalitis and studies in mice indicated alternative methods of vaccination. Passive immunization has also advanced to phase III clinical trials on the basis of successful transgenic mouse studies. Reports from the active immunization clinical trial indicated that, indeed, amyloid levels in brain were reduced. While APP transgenic mouse models are useful in studying amyloid pathology these mice do not generate significant tau pathology or neuron loss. Continued development of new mouse models that do generate all of these pathologies will be critical in more accurately testing therapeutics and predicting the clinical outcome of such therapeutics. PMID:19096156

Cerebral responses to local and global auditory novelty under general anesthesia

PubMed Central

Uhrig, Lynn; Janssen, David; Dehaene, Stanislas; Jarraya, Béchir

2017-01-01

Primate brains can detect a variety of unexpected deviations in auditory sequences. The local-global paradigm dissociates two hierarchical levels of auditory predictive coding by examining the brain responses to first-order (local) and second-order (global) sequence violations. Using the macaque model, we previously demonstrated that, in the awake state, local violations cause focal auditory responses while global violations activate a brain circuit comprising prefrontal, parietal and cingulate cortices. Here we used the same local-global auditory paradigm to clarify the encoding of the hierarchical auditory regularities in anesthetized monkeys and compared their brain responses to those obtained in the awake state as measured with fMRI. Both, propofol, a GABAA-agonist, and ketamine, an NMDA-antagonist, left intact or even enhanced the cortical response to auditory inputs. The local effect vanished during propofol anesthesia and shifted spatially during ketamine anesthesia compared with wakefulness. Under increasing levels of propofol, we observed a progressive disorganization of the global effect in prefrontal, parietal and cingulate cortices and its complete suppression under ketamine anesthesia. Anesthesia also suppressed thalamic activations to the global effect. These results suggest that anesthesia preserves initial auditory processing, but disturbs both short-term and long-term auditory predictive coding mechanisms. The disorganization of auditory novelty processing under anesthesia relates to a loss of thalamic responses to novelty and to a disruption of higher-order functional cortical networks in parietal, prefrontal and cingular cortices. PMID:27502046

Time of Day Differences in Neural Reward Functioning in Healthy Young Men.

PubMed

Byrne, Jamie E M; Hughes, Matthew E; Rossell, Susan L; Johnson, Sheri L; Murray, Greg

2017-09-13

Reward function appears to be modulated by the circadian system, but little is known about the neural basis of this interaction. Previous research suggests that the neural reward response may be different in the afternoon; however, the direction of this effect is contentious. Reward response may follow the diurnal rhythm in self-reported positive affect, peaking in the early afternoon. An alternative is that daily reward response represents a type of prediction error, with neural reward activation relatively high at times of day when rewards are unexpected (i.e., early and late in the day). The present study measured neural reward activation in the context of a validated reward task at 10.00 h, 14.00 h, and 19.00 h in healthy human males. A region of interest BOLD fMRI protocol was used to investigate the diurnal waveform of activation in reward-related brain regions. Multilevel modeling found, as expected, a highly significant quadratic time-of-day effect focusing on the left putamen ( p < 0.001). Consistent with the "prediction error" hypothesis, activation was significantly higher at 10.00 h and 19.00 h compared with 14.00 h. It is provisionally concluded that the putamen may be particularly important in endogenous priming of reward motivation at different times of day, with the pattern of activation consistent with circadian-modulated reward expectancies in neural pathways (i.e., greater activation to reward stimuli at unexpected times of day). This study encourages further research into circadian modulation of reward and underscores the methodological importance of accounting for time of day in fMRI protocols. SIGNIFICANCE STATEMENT This is one of the first studies to use a repeated-measures imaging procedure to explore the diurnal rhythm of reward activation. Although self-reported reward (most often operationalized as positive affect) peaks in the afternoon, the present findings indicate that neural activation is lowest at this time. We conclude that the diurnal neural activation pattern may reflect a prediction error of the brain, where rewards at unexpected times (10.00 h and 19.00 h) elicit higher activation in reward brain regions than at expected (14.00 h) times. These data also have methodological significance, suggesting that there may be a time of day influence, which should be accounted for in neural reward studies. Copyright © 2017 the authors 0270-6474/17/378895-06$15.00/0.

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

PubMed

De Feo, Vito; Boi, Fabio; Safaai, Houman; Onken, Arno; Panzeri, Stefano; Vato, Alessandro

2017-01-01

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

Brain processing of meter and rhythm in music. Electrophysiological evidence of a common network.

PubMed

Kuck, Heleln; Grossbach, Michael; Bangert, Marc; Altenmüller, Eckart

2003-11-01

To determine cortical structures involved in "global" meter and "local" rhythm processing, slow brain potentials (DC potentials) were recorded from the scalp of 18 musically trained subjects while listening to pairs of monophonic sequences with both metric structure and rhythmic variations. The second sequence could be either identical to or different from the first one. Differences were either of a metric or a rhythmic nature. The subjects' task was to judge whether the sequences were identical or not. During processing of the auditory tasks, brain activation patterns along with the subjects' performance were assessed using 32-channel DC electroencephalography. Data were statistically analyzed using MANOVA. Processing of both meter and rhythm produced sustained cortical activation over bilateral frontal and temporal brain regions. A shift towards right hemispheric activation was pronounced during presentation of the second stimulus. Processing of rhythmic differences yielded a more centroparietal activation compared to metric processing. These results do not support Lerdhal and Jackendoff's two-component model, predicting a dissociation of left hemispheric rhythm and right hemispheric meter processing. We suggest that the uniform right temporofrontal predominance reflects auditory working memory and a pattern recognition module, which participates in both rhythm and meter processing. More pronounced parietal activation during rhythm processing may be related to switching of task-solving strategies towards mental imagination of the score.

Cumulative stress in childhood is associated with blunted reward-related brain activity in adulthood.

PubMed

Hanson, Jamie L; Albert, Dustin; Iselin, Anne-Marie R; Carré, Justin M; Dodge, Kenneth A; Hariri, Ahmad R

2016-03-01

Early life stress (ELS) is strongly associated with negative outcomes in adulthood, including reduced motivation and increased negative mood. The mechanisms mediating these relations, however, are poorly understood. We examined the relation between exposure to ELS and reward-related brain activity, which is known to predict motivation and mood, at age 26, in a sample followed since kindergarten with annual assessments. Using functional neuroimaging, we assayed individual differences in the activity of the ventral striatum (VS) during the processing of monetary rewards associated with a simple card-guessing task, in a sample of 72 male participants. We examined associations between a cumulative measure of ELS exposure and VS activity in adulthood. We found that greater levels of cumulative stress during childhood and adolescence predicted lower reward-related VS activity in adulthood. Extending this general developmental pattern, we found that exposure to stress early in development (between kindergarten and grade 3) was significantly associated with variability in adult VS activity. Our results provide an important demonstration that cumulative life stress, especially during this childhood period, is associated with blunted reward-related VS activity in adulthood. These differences suggest neurobiological pathways through which a history of ELS may contribute to reduced motivation and increased negative mood. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Representation of visual gravitational motion in the human vestibular cortex.

PubMed

Indovina, Iole; Maffei, Vincenzo; Bosco, Gianfranco; Zago, Myrka; Macaluso, Emiliano; Lacquaniti, Francesco

2005-04-15

How do we perceive the visual motion of objects that are accelerated by gravity? We propose that, because vision is poorly sensitive to accelerations, an internal model that calculates the effects of gravity is derived from graviceptive information, is stored in the vestibular cortex, and is activated by visual motion that appears to be coherent with natural gravity. The acceleration of visual targets was manipulated while brain activity was measured using functional magnetic resonance imaging. In agreement with the internal model hypothesis, we found that the vestibular network was selectively engaged when acceleration was consistent with natural gravity. These findings demonstrate that predictive mechanisms of physical laws of motion are represented in the human brain.

Transformation of Cortex-wide Emergent Properties during Motor Learning.

PubMed

Makino, Hiroshi; Ren, Chi; Liu, Haixin; Kim, An Na; Kondapaneni, Neehar; Liu, Xin; Kuzum, Duygu; Komiyama, Takaki

2017-05-17

Learning involves a transformation of brain-wide operation dynamics. However, our understanding of learning-related changes in macroscopic dynamics is limited. Here, we monitored cortex-wide activity of the mouse brain using wide-field calcium imaging while the mouse learned a motor task over weeks. Over learning, the sequential activity across cortical modules became temporally more compressed, and its trial-by-trial variability decreased. Moreover, a new flow of activity emerged during learning, originating from premotor cortex (M2), and M2 became predictive of the activity of many other modules. Inactivation experiments showed that M2 is critical for the post-learning dynamics in the cortex-wide activity. Furthermore, two-photon calcium imaging revealed that M2 ensemble activity also showed earlier activity onset and reduced variability with learning, which was accompanied by changes in the activity-movement relationship. These results reveal newly emergent properties of macroscopic cortical dynamics during motor learning and highlight the importance of M2 in controlling learned movements. Copyright © 2017 Elsevier Inc. All rights reserved.

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

PubMed

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Particle Swarm Optimization for Programming Deep Brain Stimulation Arrays

PubMed Central

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-01-01

Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. Significance The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts. PMID:28068291

Particle swarm optimization for programming deep brain stimulation arrays

NASA Astrophysics Data System (ADS)

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-02-01

Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n  =  3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of  <1% between approaches. Significance. The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

Your Brain on the Movies: A Computational Approach for Predicting Box-office Performance from Viewer's Brain Responses to Movie Trailers.

PubMed

Christoforou, Christoforos; Papadopoulos, Timothy C; Constantinidou, Fofi; Theodorou, Maria

2017-01-01

The ability to anticipate the population-wide response of a target audience to a new movie or TV series, before its release, is critical to the film industry. Equally important is the ability to understand the underlying factors that drive or characterize viewer's decision to watch a movie. Traditional approaches (which involve pilot test-screenings, questionnaires, and focus groups) have reached a plateau in their ability to predict the population-wide responses to new movies. In this study, we develop a novel computational approach for extracting neurophysiological electroencephalography (EEG) and eye-gaze based metrics to predict the population-wide behavior of movie goers. We further, explore the connection of the derived metrics to the underlying cognitive processes that might drive moviegoers' decision to watch a movie. Towards that, we recorded neural activity-through the use of EEG-and eye-gaze activity from a group of naive individuals while watching movie trailers of pre-selected movies for which the population-wide preference is captured by the movie's market performance (i.e., box-office ticket sales in the US). Our findings show that the neural based metrics, derived using the proposed methodology, carry predictive information about the broader audience decisions to watch a movie, above and beyond traditional methods. In particular, neural metrics are shown to predict up to 72% of the variance of the films' performance at their premiere and up to 67% of the variance at following weekends; which corresponds to a 23-fold increase in prediction accuracy compared to current neurophysiological or traditional methods. We discuss our findings in the context of existing literature and hypothesize on the possible connection of the derived neurophysiological metrics to cognitive states of focused attention, the encoding of long-term memory, and the synchronization of different components of the brain's rewards network. Beyond the practical implication in predicting and understanding the behavior of moviegoers, the proposed approach can facilitate the use of video stimuli in neuroscience research; such as the study of individual differences in attention-deficit disorders, and the study of desensitization to media violence.

Attraction Effect in Risky Choice Can Be Explained by Subjective Distance Between Choice Alternatives.

PubMed

Mohr, Peter N C; Heekeren, Hauke R; Rieskamp, Jörg

2017-08-21

Individuals make decisions under risk throughout daily life. Standard models of economic decision making typically assume that people evaluate choice options independently. There is, however, substantial evidence showing that this independence assumption is frequently violated in decision making without risk. The present study extends these findings to the domain of decision making under risk. To explain the independence violations, we adapted a sequential sampling model, namely Multialternative Decision Field Theory (MDFT), to decision making under risk and showed how this model can account for the observed preference shifts. MDFT not only better predicts choices compared with the standard Expected Utility Theory, but it also explains individual differences in the size of the observed context effect. Evidence in favor of the chosen option, as predicted by MDFT, was positively correlated with brain activity in the medial orbitofrontal cortex (mOFC) and negatively correlated with brain activity in the anterior insula (aINS). From a neuroscience perspective, the results of the present study show that specific brain regions, such as the mOFC and aINS, not only code the value or risk of a single choice option but also code the evidence in favor of the best option compared with other available choice options.

Regional amplitude of the low-frequency fluctuations at rest predicts word-reading skill.

PubMed

Xu, M; De Beuckelaer, A; Wang, X; Liu, L; Song, Y; Liu, J

2015-07-09

Individuals' reading skills are critical for their educational development, but variation in reading skills is known to be large. The present study used functional magnetic resonance imaging (fMRI) to examine the role of spontaneous brain activity at rest in individual differences in reading skills in a large sample of participants (N=263). Specifically, we correlated individuals' word-reading skill with their fractional amplitude of low-frequency fluctuation (fALFF) of the whole brain at rest and found that the fALFFs of both the bilateral precentral gyrus (PCG) and superior temporal plane (STP) were positively associated with reading skills. The fALFF-reading association observed in these two regions remained after controlling for general cognitive abilities and in-scanner head motion. A cross-validation confirmed that the individual differences in word-reading skills were reliably correlated with the fALFF values of the bilateral PCG and STP. A follow-up task-based fMRI experiment revealed that the reading-related regions overlapped with regions showing a higher response to sentences than to pseudo-sentences (strings of pseudo-words), suggesting the resting-state brain activity partly captures the characteristics of task-based brain activity. In short, our study provides one of the first pieces of evidence that links spontaneous brain activity to reading behavior and offers an easy-to-access neural marker for evaluating reading skill. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Multivariate representation of food preferences in the human brain.

PubMed

Pogoda, Luca; Holzer, Matthias; Mormann, Florian; Weber, Bernd

2016-12-01

One major goal in decision neuroscience is to investigate the neuronal mechanisms being responsible for the computation of product preferences. The aim of the present fMRI study was to investigate whether similar patterns of brain activity, reflecting category dependent and category independent preference signals, can be observed in case of different food product categories (i.e. chocolate bars and salty snacks). To that end we used a multivariate searchlight approach in which a linear support vector machine (l-SVM) was trained to distinguish preferred from non-preferred chocolate bars and subsequently tested its predictive power in case of chocolate bars (within category prediction) and salty snacks (across category prediction). Preferences were measured by a binary forced choice decision paradigm before the fMRI task. In the scanner, subjects saw only one product per trial which they had to rate after presentation. Consistent with previous multi voxel pattern analysis (MVPA) studies, we found category dependent preference signals in the ventral parts of medial prefrontal cortex (mPFC), but also in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (dlPFC). Category independent preference signals were observed in the dorsal parts of mPFC, dACC, and dlPFC. While the first two results have also been reported in a closely related study, the activation in dlPFC is new in this context. We propose that the dlPFC activity does not reflect the products' value computation per se, but rather a modulatory signal which is computed in anticipation of the forthcoming product rating after stimulus presentation. Furthermore we postulate that this kind of dlPFC activation emerges only if the anticipated choices fall into the domain of primary rewards, such as foods. Thus, in contrast to previous studies which investigated preference decoding for stimuli from utterly different categories, the present study revealed some food domain specific aspects of preference processing in the human brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Activation of Group II Metabotropic Glutamate Receptors Induces Depotentiation in Amygdala Slices and Reduces Fear-Potentiated Startle in Rats

ERIC Educational Resources Information Center

Lin, Chia-Ho; Lee, Chia-Ching; Huang, Ya-Chun; Wang, Su-Jane; Gean, Po-Wu

2005-01-01

There is a close correlation between long-term potentiation (LTP) in the synapses of lateral amygdala (LA) and fear conditioning in animals. We predict that reversal of LTP (depotentiation) in this area of the brain may ameliorate conditioned fear. Activation of group II metabotropic glutamate receptors (mGluR II) with DCG-IV induces…

Temperatures Achieved in Human and Canine Neocortex During Intraoperative Passive or Active Focal Cooling

PubMed Central

Han, Rowland H.; Yarbrough, Chester K.; Patterson, Edward E.; Yang, Xiao-Feng; Miller, John W.; Rothman, Steven M.; D'Ambrosio, Raimondo

2015-01-01

Focal cortical cooling inhibits seizures and prevents acquired epileptogenesis in rodents. To investigate the potential clinical utility of this treatment modality, we examined the thermal characteristics of canine and human brain undergoing active and passive surface cooling in intraoperative settings. Four patients with intractable epilepsy were treated in a standard manner. Before the resection of a neocortical epileptogenic focus, multiple intraoperative studies of active (custom-made cooled irrigation-perfused grid) and passive (stainless steel probe) cooling were performed. We also actively cooled the neocortices of two dogs with perfused grids implanted for 2 hours. Focal surface cooling of the human brain causes predictable depth-dependent cooling of the underlying brain tissue. Cooling of 0.6–2°C was achieved both actively and passively to a depth of 10–15 mm from the cortical surface. The perfused grid permitted comparable and persistent cooling of canine neocortex when the craniotomy was closed. Thus, the human cortex can easily be cooled with the use of simple devices such as a cooling grid or a small passive probe. These techniques provide pilot data for the design of a permanently implantable device to control intractable epilepsy. PMID:25902001

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models.

PubMed

van Luijtelaar, Gilles; Lüttjohann, Annika; Makarov, Vladimir V; Maksimenko, Vladimir A; Koronovskii, Alexei A; Hramov, Alexander E

2016-02-15

Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures. Various methods for automated off and on-line analyses of ECoG in rodent models are reviewed, as well as data on how to interfere with the spike-wave discharges by different types of invasive and non-invasive electrical, magnetic, and optical brain stimulation. Also a new method for seizure prediction is proposed. Many selective and specific methods for off- and on-line spike-wave discharge detection seem excellent, with possibilities to overcome the issue of individual differences. Moreover, electrical deep brain stimulation is rather effective in interrupting ongoing spike-wave discharges with low stimulation intensity. A network based method is proposed for absence seizures prediction with a high sensitivity but a low selectivity. Solutions that prevent false alarms, integrated in a closed loop brain stimulation system open the ways for experimental seizure control. The presence of preictal cursor activity detected with state of the art time frequency and network analyses shows that spike-wave discharges are not caused by sudden and abrupt transitions but that there are detectable dynamic events. Their changes in time-space-frequency characteristics might yield new options for seizure prediction and seizure control. Copyright © 2015 Elsevier B.V. All rights reserved.

Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training

PubMed Central

Curado, Marco Rocha; Cossio, Eliana Garcia; Broetz, Doris; Agostini, Manuel; Cho, Woosang; Brasil, Fabricio Lima; Yilmaz, Oezge; Liberati, Giulia; Lepski, Guilherme

2015-01-01

Background Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies. Methods Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity. Results Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001). Conclusions Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice. PMID:26495971

Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training.

PubMed

Curado, Marco Rocha; Cossio, Eliana Garcia; Broetz, Doris; Agostini, Manuel; Cho, Woosang; Brasil, Fabricio Lima; Yilmaz, Oezge; Liberati, Giulia; Lepski, Guilherme; Birbaumer, Niels; Ramos-Murguialday, Ander

2015-01-01

Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies. Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity. Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001). Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice.

Early functional and morphological brain disturbances in late-onset intrauterine growth restriction.

PubMed

Starčević, Mirta; Predojević, Maja; Butorac, Dražan; Tumbri, Jasna; Konjevoda, Paško; Kadić, Aida Salihagić

2016-02-01

To determine whether the brain disturbances develop in late-onset intrauterine growth restriction (IUGR) before blood flow redistribution towards the fetal brain (detected by Doppler measurements in the middle cerebral artery and umbilical artery). Further, to evaluate predictive values of Doppler arterial indices and umbilical cord blood gases and pH for early functional and/or morphological brain disturbances in late-onset IUGR. This cohort study included 60 singleton term pregnancies with placental insufficiency caused late-onset IUGR (IUGR occurring after 34 gestational weeks). Umbilical artery resistance index (URI), middle cerebral artery resistance index (CRI), and cerebroumbilical (C/U) ratio (CRI/URI) were monitored once weekly. Umbilical blood cord samples (arterial and venous) were collected for the analysis of pO2, pCO2 and pH. Morphological neurological outcome was evaluated by cranial ultrasound (cUS), whereas functional neurological outcome by Amiel-Tison Neurological Assessment at Term (ATNAT). 50 fetuses had C/U ratio>1, and 10 had C/U ratio≤1; among these 10 fetuses, 9 had abnormal neonatal cUS findings and all 10 had non-optimal ATNAT. However, the total number of abnormal neurological findings was much higher. 32 neonates had abnormal cUS (53.37%), and 42 (70.00%) had non-optimal ATNAT. Furthermore, Doppler indices had higher predictive validity for early brain disturbances than umbilical cord blood gases and pH. C/U ratio had the highest predictive validity with threshold for adverse neurological outcome at value 1.13 (ROC analysis), i.e., 1.18 (party machine learning algorithm). Adverse neurological outcome at average values of C/U ratios>1 confirmed that early functional and/or structural brain disturbances in late-onset IUGR develop even before activation of fetal cardiovascular compensatory mechanisms, i.e., before Doppler signs of blood flow redistribution between the fetal brain and the placenta. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

GABA predicts inhibition of frequency-specific oscillations in schizophrenia.

PubMed

Rowland, Laura M; Edden, Richard A E; Kontson, Kimberly; Zhu, He; Barker, Peter B; Hong, L Elliot

2013-01-01

This study is the first to show a relationship between in-vivo brain gamma-amino butyric acid (GABA) levels and auditory inhibitory electrophysiological measures in schizophrenia. Results revealed a strong association between GABA levels and gating of the theta-alpha and beta activities in schizophrenia.

Neural circuits underlying mother’s voice perception predict social communication abilities in children

PubMed Central

Abrams, Daniel A.; Chen, Tianwen; Odriozola, Paola; Cheng, Katherine M.; Baker, Amanda E.; Padmanabhan, Aarthi; Ryali, Srikanth; Kochalka, John; Feinstein, Carl; Menon, Vinod

2016-01-01

The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child’s life is mother's voice: Infants discriminate their mother’s voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother’s voice or whether this brain activity is related to children’s social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (<1 s) nonsense words produced by their biological mother and two female control voices and explored relationships between speech-evoked neural activity and social function. Compared to female control voices, mother’s voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother’s voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired. PMID:27185915

Neural circuits underlying mother's voice perception predict social communication abilities in children.

PubMed

Abrams, Daniel A; Chen, Tianwen; Odriozola, Paola; Cheng, Katherine M; Baker, Amanda E; Padmanabhan, Aarthi; Ryali, Srikanth; Kochalka, John; Feinstein, Carl; Menon, Vinod

2016-05-31

The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child's life is mother's voice: Infants discriminate their mother's voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother's voice or whether this brain activity is related to children's social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (<1 s) nonsense words produced by their biological mother and two female control voices and explored relationships between speech-evoked neural activity and social function. Compared to female control voices, mother's voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother's voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired.

A common neural code for similar conscious experiences in different individuals

PubMed Central

Naci, Lorina; Cusack, Rhodri; Anello, Mimma; Owen, Adrian M.

2014-01-01

The interpretation of human consciousness from brain activity, without recourse to speech or action, is one of the most provoking and challenging frontiers of modern neuroscience. We asked whether there is a common neural code that underpins similar conscious experiences, which could be used to decode these experiences in the absence of behavior. To this end, we used richly evocative stimulation (an engaging movie) portraying real-world events to elicit a similar conscious experience in different people. Common neural correlates of conscious experience were quantified and related to measurable, quantitative and qualitative, executive components of the movie through two additional behavioral investigations. The movie’s executive demands drove synchronized brain activity across healthy participants’ frontal and parietal cortices in regions known to support executive function. Moreover, the timing of activity in these regions was predicted by participants’ highly similar qualitative experience of the movie’s moment-to-moment executive demands, suggesting that synchronization of activity across participants underpinned their similar experience. Thus we demonstrate, for the first time to our knowledge, that a neural index based on executive function reliably predicted every healthy individual’s similar conscious experience in response to real-world events unfolding over time. This approach provided strong evidence for the conscious experience of a brain-injured patient, who had remained entirely behaviorally nonresponsive for 16 y. The patient’s executive engagement and moment-to-moment perception of the movie content were highly similar to that of every healthy participant. These findings shed light on the common basis of human consciousness and enable the interpretation of conscious experience in the absence of behavior. PMID:25225384

Diagnostic, prognostic and predictive relevance of molecular markers in gliomas.

PubMed

Brandner, Sebastian; von Deimling, Andreas

2015-10-01

The advances of genome-wide 'discovery platforms' and the increasing affordability of the analysis of significant sample sizes have led to the identification of novel mutations in brain tumours that became diagnostically and prognostically relevant. The development of mutation-specific antibodies has facilitated the introduction of these convenient biomarkers into most neuropathology laboratories and has changed our approach to brain tumour diagnostics. However, tissue diagnosis will remain an essential first step for the correct stratification for subsequent molecular tests, and the combined interpretation of the molecular and tissue diagnosis ideally remains with the neuropathologist. This overview will help our understanding of the pathobiology of common intrinsic brain tumours in adults and help guiding which molecular tests can supplement and refine the tissue diagnosis of the most common adult intrinsic brain tumours. This article will discuss the relevance of 1p/19q codeletions, IDH1/2 mutations, BRAF V600E and BRAF fusion mutations, more recently discovered mutations in ATRX, H3F3A, TERT, CIC and FUBP1, for diagnosis, prognostication and predictive testing. In a tumour-specific topic, the role of mitogen-activated protein kinase pathway mutations in the pathogenesis of pilocytic astrocytomas will be covered. © 2015 British Neuropathological Society.

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

Automatic processing of political preferences in the human brain.

PubMed

Tusche, Anita; Kahnt, Thorsten; Wisniewski, David; Haynes, John-Dylan

2013-05-15

Individual political preferences as expressed, for instance, in votes or donations are fundamental to democratic societies. However, the relevance of deliberative processing for political preferences has been highly debated, putting automatic processes in the focus of attention. Based on this notion, the present study tested whether brain responses reflect participants' preferences for politicians and their associated political parties in the absence of explicit deliberation and attention. Participants were instructed to perform a demanding visual fixation task while their brain responses were measured using fMRI. Occasionally, task-irrelevant images of German politicians from two major competing parties were presented in the background while the distraction task was continued. Subsequent to scanning, participants' political preferences for these politicians and their affiliated parties were obtained. Brain responses in distinct brain areas predicted automatic political preferences at the different levels of abstraction: activation in the ventral striatum was positively correlated with preference ranks for unattended politicians, whereas participants' preferences for the affiliated political parties were reflected in activity in the insula and the cingulate cortex. Using an additional donation task, we showed that the automatic preference-related processing in the brain extended to real-world behavior that involved actual financial loss to participants. Together, these findings indicate that brain responses triggered by unattended and task-irrelevant political images reflect individual political preferences at different levels of abstraction. Copyright © 2013 Elsevier Inc. All rights reserved.

Global connectivity of prefrontal cortex predicts cognitive control and intelligence

PubMed Central

Cole, Michael W.; Yarkoni, Tal; Repovs, Grega; Anticevic, Alan; Braver, Todd S.

2012-01-01

Control of thought and behavior is fundamental to human intelligence. Evidence suggests a fronto-parietal brain network implements such cognitive control across diverse contexts. We identify a mechanism – global connectivity – by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) region’s activity was found to predict performance in a high control demand working memory task, and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the fronto-parietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brain-wide influence that facilitates the ability to implement control processes central to human intelligence. PMID:22745498

Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance.

PubMed

Keerativittayayut, Ruedeerat; Aoki, Ryuta; Sarabi, Mitra Taghizadeh; Jimura, Koji; Nakahara, Kiyoshi

2018-06-18

Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30-40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding. © 2018, Keerativittayayut et al.

Attentional control activation relates to working memory in attention-deficit/hyperactivity disorder.

PubMed

Burgess, Gregory C; Depue, Brendan E; Ruzic, Luka; Willcutt, Erik G; Du, Yiping P; Banich, Marie T

2010-04-01

Attentional control difficulties in individuals with attention-deficit/hyperactivity disorder (ADHD) might reflect poor working memory (WM) ability, especially because WM ability and attentional control rely on similar brain regions. The current study examined whether WM ability might explain group differences in brain activation between adults with ADHD and normal control subjects during attentional demand. Participants were 20 adults with ADHD combined subtype with no comorbid psychiatric or learning disorders and 23 control subjects similar in age, IQ, and gender. The WM measures were obtained from the Wechsler Adult Intelligence Scale-III and Wechsler Memory Scale-Revised. Brain activation was assessed with functional magnetic resonance imaging (fMRI) while performing a Color-Word Stroop task. Group differences in WM ability explained a portion of the activation in left dorsolateral prefrontal cortex (DLPFC), which has been related to the creation and maintenance of an attentional set for task-relevant information. In addition, greater WM ability predicted increased activation of brain regions related to stimulus-driven attention and response selection processes in the ADHD group but not in the control group. The inability to maintain an appropriate task set in young adults with combined type ADHD, associated with decreased activity in left DLPFC, might in part be due to poor WM ability. Furthermore, in individuals with ADHD, higher WM ability might relate to increased recruitment of stimulus-driven attention and response selection processes, perhaps as a compensatory strategy. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Brain reactivity to alcohol and cannabis marketing during sobriety and intoxication.

PubMed

de Sousa Fernandes Perna, Elizabeth B; Theunissen, Eef L; Kuypers, Kim P C; Evers, Elisabeth A; Stiers, Peter; Toennes, Stefan W; Witteman, Jurriaan; van Dalen, Wim; Ramaekers, Johannes G

2017-05-01

Drugs of abuse stimulate striatal dopamine release and activate reward pathways. This study examined the impact of alcohol and cannabis marketing on the reward circuit in alcohol and cannabis users while sober and intoxicated. It was predicted that alcohol and cannabis marketing would increase striatal activation when sober and that reward sensitivity would be less during alcohol and cannabis intoxication. Heavy alcohol (n = 20) and regular cannabis users (n = 21) participated in a mixed factorial study involving administration of alcohol and placebo in the alcohol group and cannabis and placebo in the cannabis group. Non-drug users (n = 20) served as between group reference. Brain activation after exposure to alcohol and cannabis marketing movies was measured using functional magnetic resonance imaging and compared between groups while sober and compared with placebo while intoxicated. Implicit alcohol and cannabis cognitions were assessed by means of a single-category implicit association test. Alcohol and cannabis marketing significantly increased striatal BOLD activation across all groups while sober. Striatal activation however decreased during intoxication with alcohol and cannabis. Implicit associations with cannabis marketing cues were significantly more positive in alcohol and cannabis users as compared with non-drug using controls. Public advertising of alcohol or cannabis use elicits striatal activation in the brain's reward circuit. Reduction of marketing would reduce brain exposure to reward cues that motivate substance use. Conversely, elevated dopamine levels protect against the reinforcing potential of marketing. © 2016 Society for the Study of Addiction.

Predicting Epileptic Seizures in Advance

PubMed Central

Moghim, Negin; Corne, David W.

2014-01-01

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance. PMID:24911316

The Pattern and Loci of Training-Induced Brain Changes in Healthy Older Adults Are Predicted by the Nature of the Intervention

PubMed Central

Belleville, Sylvie; Mellah, Samira; de Boysson, Chloé; Demonet, Jean-Francois; Bier, Bianca

2014-01-01

There is enormous interest in designing training methods for reducing cognitive decline in healthy older adults. Because it is impaired with aging, multitasking has often been targeted and has been shown to be malleable with appropriate training. Investigating the effects of cognitive training on functional brain activation might provide critical indication regarding the mechanisms that underlie those positive effects, as well as provide models for selecting appropriate training methods. The few studies that have looked at brain correlates of cognitive training indicate a variable pattern and location of brain changes - a result that might relate to differences in training formats. The goal of this study was to measure the neural substrates as a function of whether divided attentional training programs induced the use of alternative processes or whether it relied on repeated practice. Forty-eight older adults were randomly allocated to one of three training programs. In the SINGLE REPEATED training, participants practiced an alphanumeric equation and a visual detection task, each under focused attention. In the DIVIDED FIXED training, participants practiced combining verification and detection by divided attention, with equal attention allocated to both tasks. In the DIVIDED VARIABLE training, participants completed the task by divided attention, but were taught to vary the attentional priority allocated to each task. Brain activation was measured with fMRI pre- and post-training while completing each task individually and the two tasks combined. The three training programs resulted in markedly different brain changes. Practice on individual tasks in the SINGLE REPEATED training resulted in reduced brain activation whereas DIVIDED VARIABLE training resulted in a larger recruitment of the right superior and middle frontal gyrus, a region that has been involved in multitasking. The type of training is a critical factor in determining the pattern of brain activation. PMID:25119464

Neural effects of environmental advertising: An fMRI analysis of voice age and temporal framing.

PubMed

Casado-Aranda, Luis-Alberto; Martínez-Fiestas, Myriam; Sánchez-Fernández, Juan

2018-01-15

Ecological information offered to society through advertising enhances awareness of environmental issues, encourages development of sustainable attitudes and intentions, and can even alter behavior. This paper, by means of functional Magnetic Resonance Imaging (fMRI) and self-reports, explores the underlying mechanisms of processing ecological messages. The study specifically examines brain and behavioral responses to persuasive ecological messages that differ in temporal framing and in the age of the voice pronouncing them. The findings reveal that attitudes are more positive toward future-framed messages presented by young voices. The whole-brain analysis reveals that future-framed (FF) ecological messages trigger activation in brain areas related to imagery, prospective memories and episodic events, thus reflecting the involvement of past behaviors in future ecological actions. Past-framed messages (PF), in turn, elicit brain activations within the episodic system. Young voices (YV), in addition to triggering stronger activation in areas involved with the processing of high-timbre, high-pitched and high-intensity voices, are perceived as more emotional and motivational than old voices (OV) as activations in anterior cingulate cortex and amygdala. Messages expressed by older voices, in turn, exhibit stronger activation in areas formerly linked to low-pitched voices and voice gender perception. Interestingly, a link is identified between neural and self-report responses indicating that certain brain activations in response to future-framed messages and young voices predicted higher attitudes toward future-framed and young voice advertisements, respectively. The results of this study provide invaluable insight into the unconscious origin of attitudes toward environmental messages and indicate which voice and temporal frame of a message generate the greatest subconscious value. Copyright © 2017 Elsevier Ltd. All rights reserved.

Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications

PubMed Central

Papalexakis, Evangelos E.; Faloutsos, Christos; Mitchell, Tom M.; Talukdar, Partha Pratim; Sidiropoulos, Nicholas D.; Murphy, Brian

2016-01-01

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like ’edible’, ’fits in hand’)? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem. Can we enhance any CMTF solver, so that it can operate on potentially very large datasets that may not fit in main memory? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, produces sparse and interpretable solutions, and parallelizes any CMTF algorithm, producing sparse and interpretable solutions (up to 65 fold). Additionally, we improve upon ALS, the work-horse algorithm for CMTF, with respect to efficiency and robustness to missing values. We apply Turbo-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Turbo-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy. Finally, we demonstrate the generality of Turbo-SMT, by applying it on a Facebook dataset (users, ’friends’, wall-postings); there, Turbo-SMT spots spammer-like anomalies. PMID:27672406

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model.

PubMed

Wang, Fang; Han, Yong; Wang, Bingyu; Peng, Qian; Huang, Xiaoqun; Miller, Karol; Wittek, Adam

2018-05-12

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example

PubMed Central

Gunalan, Kabilar; Chaturvedi, Ashutosh; Howell, Bryan; Duchin, Yuval; Lempka, Scott F.; Patriat, Remi; Sapiro, Guillermo; Harel, Noam; McIntyre, Cameron C.

2017-01-01

Background Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Objective Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Methods Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson’s disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Results Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Conclusion Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation. PMID:28441410

Social information changes stress hormone receptor expression in the songbird brain.

PubMed

Cornelius, Jamie M; Perreau, Gillian; Bishop, Valerie R; Krause, Jesse S; Smith, Rachael; Hahn, Thomas P; Meddle, Simone L

2018-01-01

Social information is used by many vertebrate taxa to inform decision-making, including resource-mediated movements, yet the mechanisms whereby social information is integrated physiologically to affect such decisions remain unknown. Social information is known to influence the physiological response to food reduction in captive songbirds. Red crossbills (Loxia curvirostra) that were food reduced for several days showed significant elevations in circulating corticosterone (a "stress" hormone often responsive to food limitation) only if their neighbors were similarly food restricted. Physiological responses to glucocorticoid hormones are enacted through two receptors that may be expressed differentially in target tissues. Therefore, we investigated the influence of social information on the expression of the mineralocorticoid receptor (MR) and glucocorticoid receptor (GR) mRNA in captive red crossbill brains. Although the role of MR and GR in the response to social information may be highly complex, we specifically predicted social information from food-restricted individuals would reduce MR and GR expression in two brain regions known to regulate hypothalamic-pituitary-adrenal (HPA) activity - given that reduced receptor expression may lessen the efficacy of negative feedback and release inhibitory tone on the HPA. Our results support these predictions - offering one potential mechanism whereby social cues could increase or sustain HPA-activity during stress. The data further suggest different mechanisms by which metabolic stress versus social information influence HPA activity and behavioral outcomes. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.