Volumetric Integrated Classification Index: An Integrated Voxel-Based Morphometry and Machine Learning Interpretable Biomarker for Post-Traumatic Stress Disorder

TL;DR

This study introduces a new interpretable biomarker for PTSD using brain imaging and machine learning, showing promise for diagnosis and understanding the condition's biology.

Contribution

The study introduces the Volumetric Integrated Classification Index (VICI), an interpretable machine learning-based biomarker for PTSD diagnosis.

Findings

Random Forest achieved high accuracy in classifying PTSD patients using structural brain data.

Prefrontal brain abnormalities were prominent in PTSD patients compared to healthy controls.

VICI showed diagnostic performance comparable to top machine learning models and linked PTSD risk genes to brain structure changes.

Abstract

PTSD is a complex mental health condition triggered by individuals’ traumatic experiences, with long-term and broad impacts on sufferers’ psychological health and quality of life. Despite decades of research providing partial understanding of the pathobiological aspects of PTSD, precise neurobiological markers and imaging indicators remain challenging to pinpoint. This study employed VBM analysis and machine learning algorithms to investigate structural brain changes in PTSD patients. Data were sourced ADNI-DoD database for PTSD cases and from the ADNI database for healthy controls. Various machine learning models, including SVM, RF, and LR, were utilized for classification. Additionally, the VICI was proposed to enhance model interpretability, incorporating SHAP analysis. The association between PTSD risk genes and VICI values was also explored through gene expression data analysis. Among the tested machine learning algorithms, RF emerged as the top performer, achieving high accuracy in classifying PTSD patients. Structural brain abnormalities in PTSD patients were predominantly observed in prefrontal areas compared to healthy controls. The proposed VICI demonstrated classification efficacy comparable to the optimized RF model, indicating its potential as a simplified diagnostic tool. Analysis of gene expression data revealed significant associations between PTSD risk genes and VICI values, implicating synaptic integrity and neural development regulation. This study reveals neuroimaging and genetic characteristics of PTSD, highlighting the potential of VBM analysis and machine learning models in diagnosis and prognosis. The VICI offers a promising approach to enhance model interpretability and guide clinical decision-making. These findings contribute to a better understanding of the pathophysiological mechanisms of PTSD and provide new avenues for future diagnosis and treatment.

Figures