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Resting-state fMRI (rs-fMRI) functional connectivity (FC) analysis providesvaluable insights into the relationships between different brain regions andtheir potential implications for neurological or psychiatric disorders.However, specific design efforts to predict treatment response from rs-fMRIremain limited due to difficulties in understanding the current brain state andthe underlying mechanisms driving the observed patterns, which limited theclinical application of rs-fMRI. To overcome that, we propose a graph learningframework that captures comprehensive features by integrating both correlationand distance-based similarity measures under a contrastive loss. This approachresults in a more expressive framework that captures brain dynamic features atdifferent scales and enables more accurate prediction of treatment response.Our experiments on the chronic pain and depersonalization disorder datasetsdemonstrate that our proposed method outperforms current methods in differentscenarios. To the best of our knowledge, we are the first to explore theintegration of distance-based and correlation-based neural similarity intograph learning for treatment response prediction.

Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI