Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Abstract

Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.

ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

“…Therefore, despite the fact that in some studies the reported classification performance is relatively high [15] , [22] , in general the obtained proposed accuracy rates and suggested biomarkers are diverse [19] – [21] , [23] . There are several reasons why this happens.…”

“…Until today, many studies have proposed several classification/diagnostic biomarkers of schizophrenia [15] – [19] . A typical pipeline for the construction of FCN includes a standard preprocessing routine (e.g.…”

“…They reported an accuracy of 79% using a single leave one-out cross-validation (LOOCV) and linear standard SVM (LSVM). Xiang et al [15] also used correlation of time series extracted in accordance with a 246 node anatomical atlas from the COBRE dataset. They computed similar local graph measures to the ones computed here, namely, the degree of node, the participation coefficient, the local clustering coefficient, the betweenness centrality and the local efficiency.…”

“…We constructed FCN by (a) the standard approach, i.e. by-cross-correlating the rsfMRI time series [15] , [18] , [19] and, (b) using ISOMAP [16] , [27] , [33] to produce low dimensional embeddings of the correlation matrices. Based on the constructed FCN, we calculated five local graph theoretic measures, namely the participation coefficient, the strength of node, the betweenness centrality, the nodal efficiency and the local clustering coefficient.…”

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks

“…Since magnetic resonance imaging (MRI) can noninvasively measure brain structural and functional changes related to brain disorder development i n v i v o , in recent years it has been widely used in the study of brain disorders [ 3 ], such as AD/MCI [ 4 , 5 ], schizophrenia [ 6 , 7 ] and autism [ 8 ]. Therefore, MRI can provide phenotypes that can be used to diagnose such disorders.…”