Consecutive-Contrastive Spherical U-Net: Enhancing Reliability of Individualized Functional Brain Parcellation for...

Individualized brain parcellations derived from functional MRI (fMRI) are essential for discerning unique functional patterns of individuals, facilitating personalized diagnoses and treatments. Unfortunately, as fMRI signals are inherently noisy, establishing reliable individualized parcellations typically necessitates long-duration fMRI scan (> 25 min), posing a major challenge and resulting in the exclusion of numerous short-duration fMRI scans from individualized studies. To address this issue, we develop a novel Consecutive-Contrastive Spherical U-net (CC-SUnet) to enable the prediction of reliable individualized brain parcellation using short-duration fMRI data, greatly expanding its practical applicability. Specifically, 1) the widely used functional diffusion map (DM), obtained from functional connectivity, is carefully selected as the predictive feature, for its advantage in tracing the transitions between regions while reducing noise. To ensure a robust depiction of brain network, we propose a dual-task model to predict DM and cortical parcellation simultaneously, fully utilizing their reciprocal relationship. 2) By constructing a stepwise dataset to capture the gradual changes of DM over increasing scan durations, a consecutive prediction framework is designed to realize the prediction from short-to-long gradually. 3) A stepwise-denoising-prediction module is further proposed. The noise representations are separated and replaced by the latent representations of a group-level diffusion map, realizing informative guidance and denoising concurrently. 4) Additionally, an N-pair contrastive loss is introduced to strengthen the discriminability of the individualized parcellations. Extensive experimental results demonstrated the superiority of our proposed CC-SUnet in enhancing the reliability of the individualized parcellation with short-duration fMRI data, thereby significantly boosting their utility in individualized studies.