A Telescopic Independent Component Analysis on Functional Magnetic Resonance Imaging Data Set

Brain function can be modeled as the dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed "telescopic independent component analysis (TICA)," designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive ICA strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of DMN, VS, and RFPN. In addition, TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources. Author summaryOur study introduces "telescopic independent component analysis (TICA)", a novel approach using a recursive ICA strategy for exploring brain function across multiple spatial scales using fMRI data. TICA constructs spatial hierarchies and identifies functional sources such as default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). By applying TICA, we uncovered hierarchical structures within these networks and revealed differences in the DMN between healthy individuals and those with schizophrenia. This approach offers a promising new tool for studying function dynamics comprehensively.