Resting-state “physiological networks”

• Published in: NeuroImage (Orlando, Fla.) Vol. 213; p. 116707
• Main Authors: Chen, Jingyuan E., Lewis, Laura D., Chang, Catie, Tian, Qiyuan, Fultz, Nina E., Ohringer, Ned A., Rosen, Bruce R., Polimeni, Jonathan R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-06-2020; Elsevier Limited; Elsevier
• Subjects: Adult; Brain; Brain - physiology; Brain mapping; Connectome; Cortex (temporal); Female; fMRI; Functional magnetic resonance imaging; Global signal regression; Heart rate; Heart Rate - physiology; Hemodynamics; Humans; Magnetic Resonance Imaging; Male; Nerve Net - physiology; Neural networks; Physiology; Respiration; Respiratory variation; Rest - physiology; Resting state functional connectivity; Heart rate; fMRI; Resting state functional connectivity; Respiratory variation; Global signal regression
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116707

Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which manifest as structured spatial patterns of temporal correlations between distant brain regions. Here, we investigated whether such “physiological networks”—sets of segregated brain regions that exhibit similar responses following slow changes in systemic physiology—resemble patterns associated with large-scale networks typically attributed to remotely synchronized neuronal activity. By analyzing a large group of subjects from the 3T Human Connectome Project (HCP) database, we demonstrate brain-wide and noticeably heterogenous dynamics tightly coupled to either respiratory variation or heart rate changes. We show, using synthesized data generated from physiological recordings across subjects, that these physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the “physiological networks” seem to mimic the neuronal networks. Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent “physiological connectivity” cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses. Our results challenge previous notions that physiological confounds are either localized to large veins or globally coherent across the cortex, therefore emphasizing the necessity to consider potential physiological contributions in fMRI-based functional connectivity studies. The rich spatiotemporal patterns carried by such “physiological” dynamics also suggest great potential for clinical biomarkers that are complementary to large-scale neuronal networks.