Abstract
Definitions of what constitutes the ‘signal of interest’ in neuroscience can be controversial, due in part to continuously evolving notions regarding the significance of spontaneous neural activity. This review highlights how the challenge of separating brain signal from noise has led to new conceptualizations of brain functional organization at both the micro- and macroscopic level. Recent debates in the functional neuroimaging community surrounding artifact removal processes have revived earlier discussions surrounding how to appropriately isolate and measure neuronal signals against a background of noise from various sources. Insights from electrophysiological studies and computational modeling can inform current theory and data analytic practices in human functional neuroimaging, given that signal and noise may be inextricably linked in the brain.
Separating neural signal from noise is methodologically and theoretically challenging.Spontaneous activity can be observed at the level of individual neurons and at the level of large-scale brain networks.Spontaneous activity and signal variability used to be considered noise, but both are now known to critically contribute to brain function.Theoretical frameworks and data analytic approaches that focus on trajectories of whole-brain activity and do not rely on evoked/intrinsic activity dichotomies are promising.
