Abstract
Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.
Highlights
MATERIALS AND METHODSSchizophrenia (SZ) is a chronic psychiatric disorder, characterized by disabling mental symptoms such as auditory delusions, hallucinations and disrupted higher-order cognitive functions (Austin, 2005; Leucht et al, 2007)
It was discovered that: (1) the groupwise whole-brain (GWB) atlas was the optimal atlas for both classifications and the best results by the human brainnetome (HBN) atlas (SZ vs normal controls (NC): recursive feature elimination (RFE)-linear discriminant analysis (LDA)-10-fold cross validation (10FCV); firstepisode schizophrenia (FESZ) vs chronic schizophrenia (CSZ): RFELR-leaveone-out cross validation (LOOCV)) were comparable; (2) logistic regression (LR) and RFE showed a slight advantage over the others, but generally the results with the various combinations of the classifiers and dimensionality reduction algorithms were quite similar; and (3) LOOCV was the best method to identify the best hyperparameter set for both classifications
Dimensionality reduction algorithms were quite similar; (3) the gray matter volume (GMV) and regional homogeneity (ReHo) features in the prefrontal and temporal gyri made the greatest contributions in both classifications; and (4) the ensemble learning method substantially improved classification performance
Summary
MATERIALS AND METHODSSchizophrenia (SZ) is a chronic psychiatric disorder, characterized by disabling mental symptoms such as auditory delusions, hallucinations and disrupted higher-order cognitive functions (Austin, 2005; Leucht et al, 2007). Support vector machine (SVM) is the most widely used method to distinguish SZ patients from normal controls (NCs) (Liu Y. et al, 2017; Chen et al, 2020) or to differentiate illness stages of SZ, such as firstepisode schizophrenia (FESZ) and chronic schizophrenia (CSZ) (Lu et al, 2018; Wu et al, 2018) Other classifiers such as random forest (Deanna et al, 2012) and linear discriminant analysis (LDA) (Kasparek et al, 2011; Ota et al, 2012) have been utilized in discriminative analyses of SZ patients. To achieve an optimal performance of a discriminative analysis, a systematic evaluation with multiple machine learning methods is essential and of great importance
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