Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods

Geetha Soujanya Chilla,Kang Sim,K N Bhanu Prakash,Qian Hui Chew,Ling Yun Yeow

doi:10.1038/s41598-022-06651-4

Abstract

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features, several machine learning based investigations have attempted to classify schizophrenia from healthy controls but the range of neuroanatomical measures employed have been limited in range to date. In this study, we sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance. Additionally, we correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. With Ensemble methods and diverse neuroanatomical measures, we achieved classification accuracies ranging from 83 to 87%, sensitivities and specificities varying between 90–98% and 65–70% respectively. In addition to lower QoL scores within schizophrenia cohort, significant correlations were found between specific neuroanatomical measures and psychological health, social relationship subscale domains of QoL. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia.

Highlights

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers
In a bid to improve on the accuracy and sensitivity of classification between schizophrenia and healthy controls, we employed a wider range of neuroanatomical features with Ensemble methodology to improve overall performance of classification
When independent measure sets like Subcortical volumes (SV), Cortical areas (CA), Cortical volumes (CV) etc. were used, classification accuracy and sensitivity were above 70% and F1 score greater than 0.70 was achieved

Summary

Introduction

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. We sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance We correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia Psychotic spectrum disorders such as schizophrenia affect individuals in multiple domains including cognitive domains, interpersonal relationships and daily psychosocial functioning[1]. In a bid to improve on the accuracy and sensitivity of classification between schizophrenia and healthy controls, we employed a wider range of neuroanatomical features (cortical thickness, surface area, volume, mean curvature, subcortical volumes) with Ensemble methodology to improve overall performance of classification. We further correlated neuroimaging measures with quality of life measures to gain further insights into the relationship between neuroimaging measures and the functional status of our subjects

Methods

Results

Conclusion