A Robust Multi-Subject fMRI Analysis Method Using Dimensional Optimization

In blind source separation (BSS) for multisubject functional magnetic resonance imaging (fMRI) data, dimensionality reduction is generally performed for multiple times. This leads to the challenge of determining the number of the retained dimensionality, i.e. the order of BSS models, which dramatically influences the validity and performance of BSS models. In this study, a multisubject analysis method robust to order selection is developed. This approach remains effective for slight dimensionality reduction and thus utilizes more information from original data. Inspired by the idea of signal-intensity-maximization technology, which can suppress the overfitting that occurs during insufficient dimensionality reduction, we rotate the reduced dimensions to the optimized direction so that rotated components have the most significant intensity and smoothness. Because the optimized dimensions contain more useful information, involving dimensional optimization stage can reduce the negative impact of dimensionality reduction in multisubject data analysis. The experiments on simulated data and real fMRI data showed that involving dimensional optimization improves the validity and performance of the BSS model in analyzing multisubject data. The proposed method works better across a wide range of dimensionality reduction levels, allows inaccurate order selection, maintains more useful information, and is suitable for multisubject fMRI analysis, which requires multiple dimensionality reduction.