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Global brain-wide signals in functional magnetic resonance imaging (fMRI) are influenced by temporal variations in vigilance, peripheral physiological processes, head motion, and other potential neuronal and non-neuronal sources. These effects are challenging to disentangle as fluctuations in vigilance and peripheral physiology are difficult to detect with fMRI alone. In this study, we leveraged multimodal neuroimaging data (simultaneous fMRI, EEG, respiratory, and cardiac recordings) to investigate the ability of dimensionality reduction techniques to separate influences of vigilance, physiology, and other global effects in fMRI. Our study included resting-state fMRI from 30 subjects, parcellated into 317 brain regions. Two different methods, temporal independent component analysis (tICA) and a fully connected autoencoder, were used to project the atlas-based data into a lower dimensional latent space. The correlation of each latent component with the EEG alpha/theta power ratio (a marker of vigilance), physiological signals (respiratory volume and heart rate), and the global fMRI signal was computed. LASSO regression was additionally employed to reconstruct the alpha/theta ratio from the latent components. Our results showed that tICA, but not the autoencoder, was able to disentangle a vigilance-related component from other global effects. Both the vigilance and global components exhibited a moderate relationship with physiological activity. Therefore, tICA is useful for isolating vigilance-related influences in fMRI, which may aid in discovering novel clinical biomarkers linked to vigilance dysregulation as well as assist in explaining intersubject variability due to in-scanner state.
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