However, BOLD signals rely on changes in local cerebral blood flow (CBF) to infer underlying changes in neuronal activity, and according to a recent study, at least 50% of the spontaneous hemodynamic signal is unrelated to ongoing neural activity ( Winder et al., 2017). Therefore, it is widely assumed that resting-state FC measured using BOLD fMRI reflects spontaneous co-fluctuations of the underlying neuronal networks. In human studies, direct measurements of macroscale neural activity have revealed a spatial correlation structure similar to that of spontaneous BOLD fluctuations ( Brookes et al., 2011 Hacker et al., 2017 He et al., 2008 Hipp et al., 2012 Kucyi et al., 2018), even during transient (50–200 ms) events ( Baker et al., 2014a Hunyadi et al., 2019 Vidaurre et al., 2018). Furthermore, a recent study suggested a close correspondence between windowed FC calculated from simultaneously recorded hemodynamic signals and calcium transients ( Matsui et al., 2019). For instance, in animal models, a strong association between spontaneous BOLD fluctuations and neural activity, in particular band-limited local field potentials and firing rates, has been reported ( Logothetis et al., 2001 Schölvinck et al., 2010 Shmuel and Leopold, 2008 Thompson et al., 2013b). However, several researchers challenged this assumption ( Chang and Glover, 2010 Sakoğlu et al., 2010), and recent studies have been focusing on FC dynamics, quantified over shorter time scales than the scan duration (time-varying FC) ( Hutchison et al., 2013 Lurie et al., 2020).Īlthough the neurophysiological basis of resting-state FC measured with fMRI is not yet fully understood, many studies have provided evidence to support its neuronal origin. Initially, FC was viewed as a stationary phenomenon (static FC) and was commonly measured as the correlation between brain regions over an entire scan. FC has been observed even in the absence of any explicit stimulus or task, giving rise to the so-called resting-state networks (RSNs) ( Biswal et al., 1995 Fox and Raichle, 2007 Smith et al., 2009). The BOLD signal exhibits low frequency (~0.01–0.15 Hz) fluctuations that are synchronized across different regions of the brain, a phenomenon known as functional connectivity (FC). Introductionįunctional magnetic resonance imaging (fMRI) is based on the blood-oxygenation-level-dependent (BOLD) contrast mechanism ( Ogawa et al., 1990), and is widely viewed as the gold standard for studying brain function because of its high spatial resolution and non-invasive nature. 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. 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. 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. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. Human brain connectivity yields significant potential as a noninvasive biomarker.
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