Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study

Abstract

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

“…The rs-fMRI images were preprocessed using SPM8 ( https://www.fil.ion.ucl.ac.uk/spm; Institute of Neurology, University College London) and Data Processing & Analysis for Brain Imaging (DPABI; Yan et al, 2016 ). The preprocessing steps of sMRI images and rs-fMRI images were the same as those in our previous studies ( Wu et al, 2018 ; Kong et al, 2021 ; Huang et al, 2022 ; Wang et al, 2022 ).…”

“…Three sMRI measurements, including GMV, WMV and structural degree centrality (sDC), and three fMRI measurements, including ReHo, ALFF and functional degree centrality (fDC), were calculated in each ROI of the AAL and BNA atlases. The calculation methods of the ROI features used in this study were the same as those in our previous studies ( Wu et al, 2018 ; Zang et al, 2021 ; Huang et al, 2022 ; Wang et al, 2022 ).…”

“…The brain network can be modeled as a graph consisting of brain regions as the nodes and their connectivity as the edges. The construction methods of the brain network based on MRI data were the same as those in our previous studies ( Kong et al, 2021 ; Wang et al, 2022 ). In this section, we constructed brain connectivity matrices as edge features, including structural connectivity matrices, functional connectivity matrices and structural-functional connectivity matrices, based on sMRI and fMRI data.…”

“…ucl.ac.uk/spm; Institute of Neurology, University College London) and Data Processing & Analysis for Brain Imaging (DPABI; Yan et al, 2016). The preprocessing steps of sMRI images and rs-fMRI images were the same as those in our previous studies (Wu et al, 2018;Kong et al, 2021;Huang et al, 2022;Wang et al, 2022). Due to the different brain morphologies of subjects, a brain atlas was proposed to register the brain morphology to the same standard space.…”

Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?

“…Due to its capability to deal with high-dimensional data, it has also been applied to classify between recent-onset depression and recent-onset psychosis using both neuroanatomical information and clinical data ( 51 ), to find neurocognitive subtypes based on cognitive performance and neurocognitive alterations in recent onset psychosis ( 52 , 53 ), to identify schizophrenia patients based on subcortical regions ( 54 ) or functional network connectivity data ( 55 ). A multimodal approach combining structural MRI, diffusion tensor imaging, and resting-state functional MRI data was tested to classify patients with chronic schizophrenia vs. patients with FEP comparing different algorithms such as Random Forest (RF), LR, Linear Discriminant Analysis (LDA), and K-Nearest Neighbor classification (KNN), and SVM, resulting in the latter as the best performing one ( 56 ). Steardo et al in a recent systematic review, analyzed 22 studies using SVM on fMRI as biomarkers to classify between schizophrenia patients and controls, where 19 studies reported a promising >70% accuracy ( 57 ).…”