scholarly journals Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification

2021 ◽  
Vol 15 ◽  
Author(s):  
Yangyang Zhang ◽  
Xiao Jiang ◽  
Lishan Qiao ◽  
Mingxia Liu
Keyword(s):  
Clustering Algorithm ◽  
Early Stage ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Brain Diseases ◽  
Support Vector ◽  
Functional Brain Network ◽  
Mri Scans ◽  
Lasso Method

Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the topological structure (such as modularity) of FBNs. To address this issue, we propose a modular-LASSO feature selection (MLFS) framework that can explicitly model the modularity information to identify discriminative and interpretable features from FBNs for automated AD/MCI classification. Specifically, the proposed MLFS method first searches the modular structure of FBNs through a signed spectral clustering algorithm, and then selects discriminative features via a modularity-induced group LASSO method, followed by a support vector machine (SVM) for classification. To evaluate the effectiveness of the proposed method, extensive experiments are performed on 563 resting-state functional MRI scans from the public ADNI database to identify subjects with AD/MCI from normal controls and predict the future progress of MCI subjects. Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
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.

scholarly journals Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Satoru Hiwa ◽  
Shogo Obuchi ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Support Vector ◽  
Neural Basis ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

2021 ◽  
pp. 2250029
Author(s):  
Geng Zhang ◽  
Qi Zhu ◽  
Jing Yang ◽  
Ruting Xu ◽  
Zhiqiang Zhang ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Medical Image Analysis ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Structural Brain Network ◽  
The Brain ◽  
Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

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 Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking

2019 ◽  
Author(s):  
Erlend S. Dørum ◽  
Tobias Kaufmann ◽  
Dag Alnæs ◽  
Geneviève Richard ◽  
Knut K. Kolskår ◽  
...  
Keyword(s):  
Brain Function ◽  
Repeated Measures ◽  
High Sensitivity ◽  
Brain Regions ◽  
Brain Network ◽  
System Level ◽  
Cerebral Stroke ◽  
Significant Group ◽  
Functional Brain Network ◽  
Regularized Linear Discriminant Analysis

AbstractA cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes.We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n=44) and matched controls (n=100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tacking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load.MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.

scholarly journals Modelling a Multiplex Brain Network by Local Transfer Entropy

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.

Relapse risk revealed by degree centrality and cluster analysis in heroin addicts undergoing methadone maintenance treatment

2021 ◽  
pp. 1-13
Author(s):  
Lei Wang ◽  
Feng Hu ◽  
Wei Li ◽  
Qiang Li ◽  
Yongbin Li ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Methadone Maintenance Treatment ◽  
Maintenance Treatment ◽  
Clustering Algorithm ◽  
Methadone Maintenance ◽  
Clinical Information ◽  
Brain Regions ◽  
Brain Network ◽  
Degree Centrality ◽  
Network Patterns

Abstract Background Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis. Methods Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse. Results Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse. Conclusion MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.

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 Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

2020 ◽  
Vol 10 (11) ◽  
pp. 777
Author(s):  
Nicholas John Simos ◽  
Stavros I. Dimitriadis ◽  
Eleftherios Kavroulakis ◽  
Georgios C. Manikis ◽  
George Bertsias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Lupus Erythematosus ◽  
Statistical Power ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Brain ◽  
Graph Metrics ◽  
Functional Brain Network

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

scholarly journals Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-27 ◽  
Author(s):  
Jin Liu ◽  
Min Li ◽  
Yi Pan ◽  
Wei Lan ◽  
Ruiqing Zheng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Brain Networks ◽  
Small World ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Brain Disorders ◽  
Comprehensive Overview ◽  
Rich Club ◽  
New Perspective

It is well known that most brain disorders are complex diseases, such as Alzheimer’s disease (AD) and schizophrenia (SCZ). In general, brain regions and their interactions can be modeled as complex brain network, which describe highly efficient information transmission in a brain. Therefore, complex brain network analysis plays an important role in the study of complex brain diseases. With the development of noninvasive neuroimaging and electrophysiological techniques, experimental data can be produced for constructing complex brain networks. In recent years, researchers have found that brain networks constructed by using neuroimaging data and electrophysiological data have many important topological properties, such as small-world property, modularity, and rich club. More importantly, many brain disorders have been found to be associated with the abnormal topological structures of brain networks. These findings provide not only a new perspective to explore the pathological mechanisms of brain disorders, but also guidance for early diagnosis and treatment of brain disorders. The purpose of this survey is to provide a comprehensive overview for complex brain network analysis and its applications to brain disorders.

Sign in / Sign up

Export Citation Format

Share Document