Masked features of task states found in individual brain networks

Completing complex tasks requires flexible integration of functions across brain regions. While studies have shown that functional networks are altered across tasks, recent work highlights that brain networks exhibit substantial individual differences. Here we asked whether individual differences are important for predicting brain network interactions across cognitive states. We trained classifiers to decode state using data from single person "precision" fMRI datasets across 5 diverse cognitive states. Classifiers were then tested on either independent sessions from the same person or new individuals. Classifiers were able to decode task states in both the same and new participants above chance. However, classification performance was significantly higher within a person, a pattern consistent across model types, datasets, tasks, and feature subsets. This suggests that individualized approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individualized approaches have potential to deepen our understanding of brain interactions during complex cognition.

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The diversity and multiplexity of edge communities within and between brain systems

10.1101/2020.05.05.067777 ◽

2020 ◽

Author(s):

Youngheun Jo ◽

Farnaz Zamani Esfahlani ◽

Joshua Faskowitz ◽

Evgeny J. Chumin ◽

Olaf Sporns ◽

...

Keyword(s):

Cerebral Cortex ◽

Individual Differences ◽

Human Brain ◽

Cognitive Processes ◽

Brain Networks ◽

Brain Regions ◽

System Level ◽

Future Research ◽

Behavior Development ◽

Using Data

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions’ “edge community profiles”, we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions’ edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brain’s system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.

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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Rieke Fruengel ◽

Timo Bröhl ◽

Thorsten Rings ◽

Klaus Lehnertz

Keyword(s):

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Concept ◽

Global Network ◽

Time Resolved ◽

Functional Connections ◽

Node Centrality ◽

Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

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Psychological resilience correlates with EEG source-space brain network flexibility

Network Neuroscience ◽

10.1162/netn_a_00079 ◽

2019 ◽

Vol 3 (2) ◽

pp. 539-550 ◽

Cited By ~ 5

Author(s):

Véronique Paban ◽

Julien Modolo ◽

Ahmad Mheich ◽

Mahmoud Hassan

Keyword(s):

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Personality Testing ◽

Functional Networks ◽

Psychological Resilience ◽

Full Spectrum ◽

Sliding Windows ◽

Resilience Scale ◽

Network Flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

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Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/3609821 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 11

Author(s):

Xin Wang ◽

Yanshuang Ren ◽

Wensheng Zhang

Keyword(s):

Pearson Correlation ◽

Brain Regions ◽

Brain Network ◽

Classification Performance ◽

Low Rank ◽

Fmri Data ◽

Healthy Controls ◽

Functional Brain ◽

Functional Brain Network ◽

Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

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Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

International Journal of Image and Graphics ◽

10.1142/s021946782240006x ◽

2021 ◽

Author(s):

A. Thushara ◽

C. Ushadevi Amma ◽

Ansamma John

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Graph Theory ◽

Neurodegenerative Disorder ◽

Brain Networks ◽

Network Connectivity ◽

Brain Regions ◽

Brain Network ◽

Fiber Tracts ◽

The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

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Unifying the Notions of Modularity and Core–Periphery Structure in Functional Brain Networks during Youth

Cerebral Cortex ◽

10.1093/cercor/bhz150 ◽

2019 ◽

Vol 30 (3) ◽

pp. 1087-1102

Author(s):

Shi Gu ◽

Cedric Huchuan Xia ◽

Rastko Ciric ◽

Tyler M Moore ◽

Ruben C Gur ◽

...

Keyword(s):

Individual Differences ◽

Brain Function ◽

Brain Networks ◽

Brain Regions ◽

Imaging Data ◽

Modular Architecture ◽

Functional Brain ◽

Rich Club ◽

Functional Brain Networks ◽

Integrative Processing

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

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Multivariate Granger Causality Analysis of Acupuncture Effects in Mild Cognitive Impairment Patients: An fMRI Study

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2013/127271 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Shangjie Chen ◽

Lijun Bai ◽

Maosheng Xu ◽

Fang Wang ◽

Liang Yin ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Granger Causality ◽

Effective Connectivity ◽

Brain Networks ◽

Brain Regions ◽

Causality Analysis ◽

Granger Causality Analysis ◽

Cognitive States ◽

Dorsolateral Prefrontal

Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments.

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Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLoS ONE ◽

10.1371/journal.pone.0242985 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242985

Author(s):

Howard Muchen Hsu ◽

Zai-Fu Yao ◽

Kai Hwang ◽

Shulan Hsieh

Keyword(s):

Individual Differences ◽

Brain Networks ◽

Network Connectivity ◽

Brain Regions ◽

Motor Inhibition ◽

Attention Network ◽

Functional Brain ◽

Graph Theoretic ◽

Dorsal Attention Network ◽

Functional Brain Networks

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

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Modelling a Multiplex Brain Network by Local Transfer Entropy

10.21203/rs.3.rs-116693/v1 ◽

2020 ◽

Author(s):

Fabrizio Parente ◽

Colosimo Alfredo

Keyword(s):

Information Transfer ◽

Brain Regions ◽

Brain Network ◽

Transfer Entropy ◽

Clustering Methods ◽

Time Step ◽

Cognitive States ◽

Functional Brain Network ◽

Hierarchical Clustering Methods ◽

Transfer Mechanisms

Abstract In this work we report on a systematic study of the causal relations in information transfer mechanisms between brain regions under resting condition. The 1000 Functional Connectomes Beijing Zang dataset was used, which includes brain functional images of 180 healthy individuals. We first characterize the information transfer mechanisms by means of Transfer Entropy concepts and, on this basis, propose a set of indexes concerning the whole functional brain network in the frame of a multilayer description. By exploring the influence of a set of states in two given regions at time t (At; Bt.) over the state of one of them at a following time step (Bt+1), a series of time-dependent events can be observed pointing to four kinds of significant interactions, namely:- (de)activation in the same state (ActS); - (de)activation in the oppostive state (ActO);- turn off in the same state (TfS); - turn off in the opposite state (TfO).This leads to four specific rules and to a directional multilayer network based upon four interaction matrices, one for each rule. By hierarchical clustering methods the four rules can be reduced to two sharing some similarities with positive and negative functional connectivity. The global architecture of the four interactions and the features of single nodes were initially explored under stationary conditions. The information transfer mechanisms on the ensuing functional network were studied by specific indexes describing in a multilayer frame the effects of the network structure in several dynamical processes. The healthy subjects database was used to carefully calibrate and validate the proposed approach, whose final aim remains the detection of clinical differences among individuals, as well as among different cognitive states.

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A novel dynamic brain network in arousal for brain states and emotion analysis

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021368 ◽

2021 ◽

Vol 18 (6) ◽

pp. 7440-7463

Author(s):

Yunyuan Gao ◽

◽

Zhen Cao ◽

Jia Liu ◽

Jianhai Zhang ◽

...

Keyword(s):

Parietal Lobe ◽

Brain Networks ◽

Brain Network ◽

Brain State ◽

High Arousal ◽

Emotion Analysis ◽

Significant Difference ◽

Brain States ◽

The Brain ◽

Channel Norm

<abstract> <sec><title>Background</title>Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals. </sec> <sec><title>New Method</title>Time factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain. </sec> <sec><title>Results</title>The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal. </sec> <sec><title>Comparison with Existing Method(s)</title>Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis. </sec> <sec><title>Conclusions</title>The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis. </sec> </abstract>

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