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Machine learning accurately detects Acute Stress Disorder (ASD) using resting-state fMRI data. This novel approach improves early diagnosis, potentially preventing progression to post-traumatic stress disorder (PTSD).

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Area of Science:

Neuroimaging and Machine Learning
Psychiatric Diagnostics

Background:

Early diagnosis of Acute Stress Disorder (ASD) is crucial to prevent its progression to post-traumatic stress disorder (PTSD).
Current diagnostic methods for ASD exhibit subjectivity in assessing trauma responses and stress severity.
Resting-state functional magnetic resonance imaging (rs-fMRI) offers a potential objective biomarker for neurological conditions.

Purpose of the Study:

To develop and validate a machine learning model for the early detection of ASD.
To utilize rs-fMRI data and advanced feature extraction techniques for objective ASD assessment.
To identify specific brain regions and imaging features indicative of ASD.

Main Methods:

rs-fMRI data from 48 subjects and PTSD Check List - Civilian Version (PCL-C) scores were analyzed.
Frequency-domain and graph-based features were extracted from blood-oxygen-level dependent (BOLD) signals across cortical and subcortical regions.
A multi-layer perceptron model was trained and evaluated using a leave-one-subject-out cross-validation scheme.

Main Results:

Eighteen extracted features showed significant differences (p<0.05) between groups.
The machine learning model achieved high diagnostic performance: 91.7% accuracy, 96.8% sensitivity, and 82.4% specificity.
The Right Accumbens and Lingual Gyrus demonstrated significant impact and large effect sizes within the predictive model.

Conclusions:

Machine learning analysis of rs-fMRI data provides a highly accurate and objective method for detecting ASD.
This approach can overcome the limitations of subjective clinical assessments.
The findings highlight the potential of neuroimaging biomarkers for early ASD diagnosis and intervention.