scholarly journals Multi-Subject Stochastic Blockmodels for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure

2019 ◽  
Author(s):  
Dragana M. Pavlović ◽  
Bryan R. L. Guillaume ◽  
Emma K. Towlson ◽  
Nicole M. Y. Kuek ◽  
Soroosh Afyouni ◽  
...  
Keyword(s):  
Individual Differences ◽  
Human Brain ◽  
Cluster Structure ◽  
Permutation Tests ◽  
Brain Regions ◽  
Brain Network ◽  
Type I ◽  
Network Cluster ◽  
Diagnostic Status ◽  
Stochastic Blockmodel

AbstractThere is great interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of a group average network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two novel extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects on cluster structure of individual differences on subject-level covariates. Multi-subject Stochastic Blockmodels (MS-SBM) can flexibly account for between-subject variability in terms of a homogenous or heterogeneous effect on connectivity of covariates such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on Wald, likelihood ratio and Monte Carlo permutation tests. We show that multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition. Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; N = 268 brain regions), we show that the Heterogeneous Stochastic Blockmodel estimates ‘core-on-modules’ architecture. The intra-block and inter-block connection weights vary between individual participants and can be modelled as a logistic function of subject-level covariates like age or diagnostic status. Multi-subject Stochastic Blockmodels are likely to be useful tools for statistical analysis of individual differences in human brain graphs and other networks whose prior cluster structure needs to be estimated from the data.

scholarly journals Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure

NeuroImage ◽  
2020 ◽  
Vol 220 ◽  
pp. 116611 ◽  
Author(s):  
Dragana M. Pavlović ◽  
Bryan R.L. Guillaume ◽  
Emma K. Towlson ◽  
Nicole M.Y. Kuek ◽  
Soroosh Afyouni ◽  
...  
Keyword(s):  
Individual Differences ◽  
Human Brain ◽  
Cluster Structure ◽  
Brain Network ◽  
Network Cluster ◽  
Adaptive Analysis

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has 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.

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

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.

scholarly journals Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhongliang Yin ◽  
Yue Wang ◽  
Minghao Dong ◽  
Shenghan Ren ◽  
Haihong Hu ◽  
...  
Keyword(s):  
Human Brain ◽  
Face Processing ◽  
Information Transfer ◽  
Minimum Spanning Tree ◽  
Time Window ◽  
Time Windows ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
The Face

Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0–100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100–200 and 200–300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.

scholarly journals A connectome-based neuromarker of the non-verbal number acuity and arithmetic skills

2021 ◽  
Author(s):  
Dai Zhang ◽  
Liqin Zhou ◽  
Anmin Yang ◽  
Shanshan Li ◽  
Chunqi Chang ◽  
...  
Keyword(s):  
Individual Differences ◽  
Number System ◽  
Brain Regions ◽  
Brain Network ◽  
Large Sample Size ◽  
Neural Basis ◽  
Mathematical Abilities ◽  
Human Connectome Project ◽  
Survival And Reproduction ◽  
Numerosity Perception

The approximate number system (ANS) is vital for survival and reproduction in animals and crucial in constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, there lacks a neuromarker to characterize an individual's ANS acuity, especially one based on the whole-brain functional connectivity (FC). Here, we identified a distributed brain network (i.e., numerosity network) using a connectome-based predictive modeling (CPM) analysis on the resting-state functional magnetic resonance imaging (rs-fMRI) data based on a large sample size. The summed strength of all FCs within the numerosity network could reliably predict individual differences of the ANS acuity in behavior. Furthermore, in an independent dataset from the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network could also predict individual differences in arithmetic skills. Our findings illustrate that the numerosity network we identified could be an applicable neuromarker of the non-verbal number acuity and might serve as the neural basis underlying the known link between the non-verbal number acuity and mathematical abilities.

scholarly journals Masked features of task states found in individual brain networks

2021 ◽  
Author(s):  
Alexis Porter ◽  
Ashley M. Nielsen ◽  
Caterina Gratton
Keyword(s):  
Individual Differences ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Cognitive States ◽  
Single Person ◽  
Flexible Integration ◽  
Brain States ◽  
Using Data

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.

scholarly journals Mapping individual differences across brain network structure to function and behavior with connectome embedding

2021 ◽  
Author(s):  
Gidon Levakov ◽  
Joshua Faskowitz ◽  
Galia Avidan ◽  
Olaf Sporns
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Predictive Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Global Network ◽  
Age Related ◽  
And Behavior ◽  
The Brain

AbstractThe connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

scholarly journals Research of Hubs Location Method for Weighted Brain Network Based on NoS-FA

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Zhengkui Weng ◽  
Bin Wang ◽  
Jie Xue ◽  
Baojie Yang ◽  
Hui Liu ◽  
...  
Keyword(s):  
Human Brain ◽  
Information Transfer ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Importance Value ◽  
Global Features ◽  
Single Node ◽  
Location Method ◽  
The Brain

As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study. The number of streamlines (NoS) is fused with normalized fractional anisotropy (FA) for more comprehensive brain bioinformation. The brain region importance contribution matrix and information transfer efficiency value are constructed, respectively, and then by combining these two factors together we can calculate the importance value of each node and locate the hubs. Profiting from both local and global features of the nodes and the multi-information fusion of human brain biosignals, the experiment results show that this method can detect the brain hubs more accurately and reasonably compared with other methods. Furthermore, the proposed location method is used in impaired brain hubs connectivity analysis of schizophrenia patients and the results are in agreement with previous studies.

scholarly journals Mapping epileptic directional brain networks using intracranial EEG data

Biostatistics ◽  
2019 ◽  
Author(s):  
Huazhang Li ◽  
Yaotian Wang ◽  
Seiji Tanabe ◽  
Yinge Sun ◽  
Guofen Yan ◽  
...  
Keyword(s):  
Cluster Structure ◽  
Brain Networks ◽  
Expectation Maximization Algorithm ◽  
Brain Regions ◽  
Brain Network ◽  
Accurate Identification ◽  
Directional Information ◽  
Epileptic Patients ◽  
Directional Connectivity ◽  
Multivariate Autoregression

Summary The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another as directional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. As such, effective epilepsy diagnosis and treatment require accurate identification of directional connections among regions, i.e., mapping of epileptic patients’ brain networks. This article aims to understand the epileptic brain network using intracranial electroencephalographic data—recordings of epileptic patients’ brain activities in many regions. The most popular models for directional connectivity use ordinary differential equations (ODE). However, ODE models are sensitive to data noise and computationally costly. To address these issues, we propose a high-dimensional state-space multivariate autoregression (SSMAR) model for the brain’s directional connectivity. Different from standard multivariate autoregression and SSMAR models, the proposed SSMAR features a cluster structure, where the brain network consists of several clusters of densely connected brain regions. We develop an expectation–maximization algorithm to estimate the proposed model and use it to map the interregional networks of epileptic patients in different seizure stages. Our method reveals the evolution of brain networks during seizure development.

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