Resting-state \"Physiological Networks\"
Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which may manifest as structured spatial patterns of temporal correlations between distant brain regions. These correlations can appear similar to large-scale networks typically attributed to coupled neuronal activity. However, little effort has been devoted to a systematic investigation of such \"physiological networks\"--sets of segregated brain regions that exhibit similar physiological responses--and their potential influence on estimates of resting-state brain networks. Here, by analyzing a large group of subjects from the 3T Human Connectome Project database, we demonstrate brain-wide and noticeably heterogenous dynamics attributable to either respiratory variation or heart rate changes. We show that these physiologic dynamics can give rise to apparent \"connectivity\" patterns that resemble previously reported resting-state networks derived from fMRI data. Further, we show 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 physiological responses. Possible mechanisms causing these apparent \"physiological networks\", and their broad implications for interpreting functional connectivity studies are discussed.