scholarly journals A Generative Network Model of the Human Brain Normal Aging Process

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Xiao Liu ◽  
Shuaizong Si ◽  
Bo Hu ◽  
Hai Zhao ◽  
Jian Zhu
Keyword(s):  
Human Brain ◽  
Network Model ◽  
Aging Process ◽  
Brain Aging ◽  
Brain Networks ◽  
Normal Aging ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Generative Mechanisms

The human brain is approximately a symmetric structure, although the functional brain does not exhibit symmetry. Functional brain aging process modelling is essential for the understanding of hypothesized generative mechanisms for human brain networks throughout one’s lifespan. We present a novel generative network model of the human functional brain network, which is the hybrid of the local naïve Bayes model and the anatomical similarity correction (LNBE). We use LNBE, as well as published generative network models to simulate the aging process of the functional brain network, to construct artificial brain networks and to reveal the generative mechanisms and evolutionary patterns of human functional brain across human lifespans. It is suggested that the idea of classifying common neighbours while considering anatomical distances during network formation can provide a much more similar generative mechanism of the human fMRI brain aging process as well as a more practical generative network model of it. We hold that studies on brain normal aging process modelling have the potential to improve the way in which early warnings for latent injury or disease are practised today and advance healthcare.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

2021 ◽  
pp. 1-11
Author(s):  
Yi Liu ◽  
Zhuoyuan Li ◽  
Xueyan Jiang ◽  
Wenying Du ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Decline ◽  
Path Length ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Subjective Cognitive Decline ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p <  0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

2020 ◽  
Vol 30 (10) ◽  
pp. 2050051
Author(s):  
Feng Fang ◽  
Thomas Potter ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Emotion Regulation ◽  
Large Scale ◽  
Brain Networks ◽  
Cognitive Reappraisal ◽  
Brain Network ◽  
Affective Processing ◽  
Functional Brain ◽  
Affective Stimuli ◽  
Functional Brain Network ◽  
Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

Longitudinal study of functional brain network reorganization in clinically isolated syndrome

2018 ◽  
Vol 26 (2) ◽  
pp. 188-200 ◽  
Author(s):  
Ismail Koubiyr ◽  
Mathilde Deloire ◽  
Pierre Besson ◽  
Pierrick Coupé ◽  
Cécile Dulau ◽  
...  
Keyword(s):  
Betweenness Centrality ◽  
Early Stage ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Early Stages ◽  
Global Efficiency ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Brain Network Reorganization

Background: There is a lack of longitudinal studies exploring the topological organization of functional brain networks at the early stages of multiple sclerosis (MS). Objective: This study aims to assess potential brain functional reorganization at rest in patients with CIS (PwCIS) after 1 year of evolution and to characterize the dynamics of functional brain networks at the early stage of the disease. Methods: We prospectively included 41 PwCIS and 19 matched healthy controls (HCs). They were scanned at baseline and after 1 year. Using graph theory, topological metrics were calculated for each region. Hub disruption index was computed for each metric. Results: Hub disruption indexes of degree and betweenness centrality were negative at baseline in patients ( p < 0.05), suggesting brain reorganization. After 1 year, hub disruption indexes for degree and betweenness centrality were still negative ( p < 0.00001), but such reorganization appeared more pronounced than at baseline. Different brain regions were driving these alterations. No global efficiency differences were observed between PwCIS and HCs either at baseline or at 1 year. Conclusion: Dynamic changes in functional brain networks appear at the early stages of MS and are associated with the maintenance of normal global efficiency in the brain, suggesting a compensatory effect.

scholarly journals F37. CACNA1C HYPOFUNCTION IN KETAMINE-ACTIVATED BRAIN NETWORKS IMPAIRS MEMORY CONSOLIDATION AND FUNCTIONAL BRAIN NETWORK CONNECTIVITY

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S233-S233
Author(s):  
Rebecca Hughes ◽  
Cosima Willi ◽  
Jayde Whittingham-Dowd ◽  
Susan Broughton ◽  
Greg Bristow ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Brain Network Connectivity

scholarly journals Functional Hubs in Mild Cognitive Impairment

2015 ◽  
Vol 25 (03) ◽  
pp. 1550034 ◽  
Author(s):  
Adrián Navas ◽  
David Papo ◽  
Stefano Boccaletti ◽  
F. Del-Pozo ◽  
Ricardo Bajo ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Short Term Memory ◽  
Brain Networks ◽  
Memory Task ◽  
Brain Network ◽  
Control Group ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

We investigate how hubs of functional brain networks are modified as a result of mild cognitive impairment (MCI), a condition causing a slight but noticeable decline in cognitive abilities, which sometimes precedes the onset of Alzheimer's disease. We used magnetoencephalography (MEG) to investigate the functional brain networks of a group of patients suffering from MCI and a control group of healthy subjects, during the execution of a short-term memory task. Couplings between brain sites were evaluated using synchronization likelihood, from which a network of functional interdependencies was constructed and the centrality, i.e. importance, of their nodes was quantified. The results showed that, with respect to healthy controls, MCI patients were associated with decreases and increases in hub centrality respectively in occipital and central scalp regions, supporting the hypothesis that MCI modifies functional brain network topology, leading to more random structures.

scholarly journals GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Shoubin Dong ◽  
Ping Li
Keyword(s):  
Brain Networks ◽  
Graph Model ◽  
Brain Network ◽  
Learning Stage ◽  
Attention Network ◽  
Graph Learning ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Feature Importance ◽  
Functional Brain Networks

Abstract Background Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis. Results In this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model’s performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC. Conclusion We propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios.

scholarly journals Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

2021 ◽  
Vol 11 (8) ◽  
pp. 1066
Author(s):  
Han Li ◽  
Qizhong Zhang ◽  
Ziying Lin ◽  
Farong Gao
Keyword(s):  
Graph Theory ◽  
Brain Networks ◽  
Tensor Decomposition ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Dynamic Methods ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Elm Classifier

Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

scholarly journals Gaussian embedding-based functional brain connectomic analysis for amnestic mild cognitive impairment patients with cognitive training

2019 ◽  
Author(s):  
Mengjia Xu ◽  
Zhijiang Wang ◽  
Haifeng Zhang ◽  
Dimitrios Pantazis ◽  
Huali Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Stage ◽  
Brain Networks ◽  
Disease Diagnosis ◽  
Brain Regions ◽  
Brain Network ◽  
Amnestic Mild Cognitive Impairment ◽  
Functional Brain ◽  
Functional Brain Network

AbstractIdentifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer’s disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a new quantitative functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration.

scholarly journals Rich Club Characteristics of Alcohol-Naïve Functional Brain Networks Predict Future Drinking Phenotypes in Rhesus Macaques

2021 ◽  
Vol 15 ◽  
Author(s):  
Jared A. Rowland ◽  
Jennifer R. Stapleton-Kotloski ◽  
Greg E. Alberto ◽  
April T. Davenport ◽  
Phillip M. Epperly ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Brain Networks ◽  
Alcohol Exposure ◽  
Brain Network ◽  
Free Access ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
The Rich

Purpose: A fundamental question for Alcohol use disorder (AUD) is how and when naïve brain networks are reorganized in response to alcohol consumption. The current study aimed to determine the progression of alcohol’s effect on functional brain networks during transition from the naïve state to chronic consumption.Procedures: Resting-state brain networks of six female rhesus macaque (Macaca mulatta) monkeys were acquired using magnetoencephalography (MEG) prior to alcohol exposure and after free-access to alcohol using a well-established model of chronic heavy alcohol consumption. Functional brain network metrics were derived at each time point.Results: The average connection frequency (p &lt; 0.024) and membership of the Rich Club (p &lt; 0.022) changed significantly over time. Metrics describing network topology remained relatively stable from baseline to free-access drinking. The minimum degree of the Rich Club prior to alcohol exposure was significantly predictive of future free-access drinking (r = −0.88, p &lt; 0.001).Conclusions: Results suggest naïve brain network characteristics may be used to predict future alcohol consumption, and that alcohol consumption alters functional brain networks, shifting hubs and Rich Club membership away from previous regions in a non-systematic manner. Further work to refine these relationships may lead to the identification of a high-risk drinking phenotype.

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