ArticleNetwork neuroscience (Cambridge, Mass.)2025
A telescopic independent component analysis on functional magnetic resonance imaging dataset.
Article in Network neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Aberrant cortical-subcortical-cerebellar connectivity in resting-state fMRI as an imaging marker of schizophrenia and psychosis: a systematic review of data-driven whole-brain functional connectivity analyses.Frontiers in neuroimaging · 2025Pooled it
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- Toward Granular Brain Intrinsic Connectivity Networks and Insights into Schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning.bioRxiv : the preprint server for biology · 2025Article
- Ultrafine brain intrinsic connectivity networks template via very-high-order independent component analysis of large-scale resting-state functional magnetic resonance imaging data.Frontiers in neuroscience · 2025Article
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Abstract
Brain function can be modeled as 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 independent component analysis (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 the DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of the DMN, VN, and RFPN. In addition, the 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.
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