Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic...

Area of Science:

• Neuroimaging
• Psychiatry
• Computational Neuroscience

Background:

• Major depressive disorder (MDD) treatment response varies significantly among patients.
• Thalamic abnormalities are observed in MDD, but their predictive value for treatment outcomes remains unclear.

Purpose of the Study:

• To investigate the predictive value of thalamic neuroimaging features for antidepressant treatment response in MDD patients.
• To assess if gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) can predict treatment outcomes.

Main Methods:

• Multivariate pattern analysis (MVPA) and Gaussian process regression (GPR) were employed.
• Baseline thalamic GMD, GMV, ALFF, and fALFF data from 74 MDD patients were used to predict treatment response.
• Treatment response was defined as the percentage decrease in Hamilton Depression Scale (HAMD) scores after 3-month selective serotonin reuptake inhibitor (SSRI) treatment.

Main Results:

• GPR models trained with baseline thalamic GMD showed significant correlations between predicted and actual HAMD score decreases (p < 0.01, r² = 0.11).
• No significant predictive value was found for GMV, ALFF, or fALFF of the thalamus.
• The findings suggest GMD is a potential biomarker for predicting SSRI treatment response.

Conclusions:

• Thalamic gray matter density shows promise as a predictive biomarker for individual antidepressant treatment response in MDD.
• These findings may contribute to more personalized treatment strategies for major depressive disorder.