Abstract: In recent years, functional connectivity networks (FCN) based on functional magnetic resonance imaging (fMRI) have provided an important tool for the early intervention of brain disorders, such as Alzheimer's disorder (AD) and Autism spectrum disorder (ASD). However, the obtained data are inevitably introduced into structural noises due to participants’ breath, heartbeats and head motions during the scan, which often brings great challenges to the final construction of FCNs. Although conventional data preprocessing methods have been utilized to improve the quality of the data, they still operate in the original data space and separate the data denoising from the FCNs estimation, and thus breaking the internal connection between two steps. Researches shows that data in a certain transform domain may be low-noisy and more informative. Inspired by the transform domain, we propose an adaptive brain network learning model in the light of the transform domain (TD-FCN), which not only improves the quality of the observed data, but also learns the adaptive brain graph in a single framework simultaneously. To verify the effectiveness of the proposed method, we conduct experiments on two public datasets (i.e., ADNI and ABIDE) to identify the patients with mild cognitive impairments (MCIs) and ASDs from health controls (HCs). Experimental results demonstrate that the proposed approach yields statistically significant improvement in multiple performance metrics over traditional methods.

Key words: functional magnetic resonance imaging, functional connectivity network, Pearson's correlation coefficient, sparse representation, Alzheimer's disorder, autism spectrum disorder