Correlation-Distance Graph Learning for Treatment Response Prediction
  from rs-fMRI
Xiatian Zhang Sisi Zheng Hubert P. H. Shum Haozheng Zhang Nan Song Mingkang Song Hongxiao Jia
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.