This study introduces instantaneous frequency (IF) analysis as a novel method for characterizing dynamic brain causal networks from functional magnetic resonance imaging blood-oxygen-level-dependent signals.
Methods:
Effective connectivity, estimated using dynamic causal modeling, is analyzed to derive IF sequences, with the average IF across brain regions serving as a potential biomarker for global network oscillatory behavior.
Results:
Analysis of data from the Alzheimer’s Disease (AD) Neuroimaging Initiative, Open Access Series of Imaging Studies, and Human Connectome Project demonstrates the method’s efficacy in distinguishing between clinical and demographic groups, such as cognitive decline stages (e.g., normal control, early mild cognitive impairment [MCI], late MCI, and AD), sex differences, and sleep quality levels.
Conclusion:
Statistical analyses reveal significant group differences in IF metrics, highlighting its potential as a sensitive indicator for early diagnosis and monitoring of neurodegenerative and cognitive conditions.
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