Keywords: fMRI Analysis, Multimodal, Brain; Data Analysis; fMRI; Neuroscience; PET/MR
Motivation: Latest advances in functional PET (fPET)-FDG and PET-MRI have enabled us to map stimulus-driven metabolic and hemodynamic changes simultaneously within a single scan. However, existing PET-MRI studies have focused on the static, time-averaged (de)couplings of fPET-fMRI signals, neglecting the rich information embedded in their temporal dependence.
Goal(s): The goal of this study is to propose and validate an analytical framework linking dynamic, concurrent variations in fPET and fMRI signals.
Approach: The efficacy of the framework was tested on visual task and naturalistic arousal fPET-fMRI datasets.
Results: Our results demonstrated that metabolic changes modeled by concurrent fMRI signals could successfully predict instantaneous fPET-FDG dynamics.
Impact: The statistical framework proposed by our study will enable broad functional PET-MRI studies to elucidate the dynamic interplays amongst metabolic and hemodynamic processes that are otherwise obscured in the conventional, time-averaged analysis.
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