Diagnosing schizophrenia with network analysis and a machine learning method

Abstract

Objective: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research.

Methods: We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls.

Results: In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns.

Conclusion: In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.

Keywords: brain imaging; machine learning; network analysis; schizophrenia.

Figure 1

Relative importance of each network property in the machine learning model. Random forest models are denoted in grey and XGBoost models in black. Error bars indicate the standard deviation. Error bars cannot be visualized in the XGBoost models due to small standard deviations

Figure 2

Relative importance of each network property—regions of interest (ROIs) in the machine learning model. The 10 ranking ROIs in each machine learning model are presented. Random forest models are indicated in grey. XGBoost models are denoted in black. The error bars indicate the standard deviation. Error bars could not be visualized for the XGBoost models due to small standard deviations. The ROIs contributing to both RF and XGBoost models for each nodal feature are indicated in red. (a) Local efficiency (RF), (b) local efficiency (XGBoost), (c) degree (RF), (d) degree (XGBoost), (e) betweenness centrality (RF), and (f) betweenness centrality (XGBoost)