Key result
Head motion and physiological fluctuations induce artifactual functional connectivity, and removing these effects at the preprocessing stage improves subject identifiability to 52-99%.
Why the study?
It was unclear whether fMRI-based connectivity fingerprinting patterns mainly reflect neural activity or the effects of physiological and head motion processes.
Observational
Physiological and motion artifacts significantly confound fMRI functional connectivity, but aggressive preprocessing strategies can mitigate these effects and improve subject identifiability.
Physiological confounds warrant stricter preprocessing in fMRI studies; leaves open the neural specificity of connectivity fingerprints for clinical translation.
Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.
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Xifra‐Porxas et al. (2020) reported an observational. Physiological and head motion artifacts vs. Preprocessed fMRI data (neural datasets) was evaluated on Subject identification accuracy. Head motion and physiological fluctuations induce artifactual functional connectivity, and removing these effects at the preprocessing stage improves subject identifiability to 52-99%.
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