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.

Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges

“…Based on the summary table provided in Table 5, there are 6 articles that applied various machine learning approaches to identify and predict the schizophrenia patients with different data sets [20][21][22][23][24][25]. Several research projects have been conducted to analyze and classify depression and anxiety.…”

“…In terms of sample data sets used by the researchers, the data sets used for the classification are mostly small size, which is below 100 subjects. For example, the authors Jo et al [21], Yang et al [22], Rocha-Rego et al [34], Grotegerd et al [35], Mouraō-Miranda et al [37], Akinci et al [39], Wu et al [40], Vergyri et al [46], Salminen et al [47], and Rangaprakash et al [48] have applied small size of sample data for the classifications. Moreover, some studies are conducted by using a partial large size of the data set, which is above 100 subjects.…”

“…e accuracy of 73.7% is obtained after the classification. In one of the research works conducted by Jo et al, they used network analysis and machine learning approaches to identify 48 schizophrenia patients and 24 healthy controls [21]. e network properties were rebuilt using the probabilistic brain tractography.…”

Functional Connectivity Combined With a Machine Learning Algorithm Can Classify High-Risk First-Degree Relatives of Patients With Schizophrenia and Identify Correlates of Cognitive Impairments

“…Combining cortical thickness, gyrification of gray matter, and fractional anisotropy and mean diffusivity of white matter, Liang et al (2019) used a gradient boosting decision tree to identify SCZ patients, reaching an average accuracy of 76.54%. Using global and nodal network properties derived from a graph theory analysis, Jo et al (2020) revealed that functional network properties had a high discriminatory ability for classifying SCZ patients and HCs. Using betweenness centrality from graph theoretical approaches and a SVM algorithm, Cheng et al (2015) found a classification accuracy of around 80% for differentiating SCZ patients from non-psychiatric HCs.…”

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

“…In terms of ML models, Jo et al [117] used network analysis and ML methods to classify SCZ and HC. Bae et al [118] used an SVM model, and nine features were selected as input.…”