Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI
This work addresses the challenge of limited clinical application of rs-fMRI for treatment response prediction in neurological or psychiatric disorders, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled the problem of predicting treatment response from resting-state fMRI data by proposing a graph learning framework that integrates correlation and distance-based similarity measures, which outperformed current methods on chronic pain and depersonalization disorder datasets.
Resting-state fMRI (rs-fMRI) functional connectivity (FC) analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design efforts to predict treatment response from rs-fMRI remain limited due to difficulties in understanding the current brain state and the underlying mechanisms driving the observed patterns, which limited the clinical application of rs-fMRI. To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss. This approach results in a more expressive framework that captures brain dynamic features at different scales and enables more accurate prediction of treatment response. Our experiments on the chronic pain and depersonalization disorder datasets demonstrate that our proposed method outperforms current methods in different scenarios. To the best of our knowledge, we are the first to explore the integration of distance-based and correlation-based neural similarity into graph learning for treatment response prediction.