Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders
arXiv:2603.24603v2 Announce Type: replace-cross Abstract: Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging (fMRI) has been extensively utilized in brain science research. The sliding window correlation (SWC) method is a widely used approach for constructing dFC by computing correlation coefficients between amplitude time series of signals from pairs of brain regions. In this study, we propose an integrated approach that incorporates both amplitude and phase information of fMRI signals to improve the detection of brain disorders. Specificall
Advances in computational neuroscience and AI are enabling more nuanced analysis of complex biological signals like fMRI, pushing the boundaries of diagnostic capabilities.
This research suggests a more robust method for early and accurate identification of brain disorders, potentially revolutionizing clinical diagnostics and personalized medicine in neurology.
Traditional fMRI analysis, which primarily uses amplitude, now has a proposed enhancement by integrating phase information, leading to more comprehensive and potentially more accurate diagnostic models.
- · Neurology research
- · Medical AI development
- · Patients with brain disorders
- · Diagnostic imaging companies
- · Traditional fMRI diagnostic methods
Improved early detection and differential diagnosis of complex neurological and psychiatric conditions.
Accelerated development of targeted therapies and interventions for various brain disorders based on more precise diagnostic profiles.
Potential for population-scale screening or preventative measures for neurological diseases, driven by cost-effective and accurate fMRI analysis.
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Read at arXiv cs.AI