Global network influences on local functional connectivity

Abstract

A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales – from cortical columns to whole-brain networks – research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp EEG in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a surprising non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely-used network model we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we report that spike count correlation explains nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

Monkey EEG links neuronal color and motion information across species and scales

“…High electrode density and methods identical to those used in human M/EEG enabled us to perform source reconstruction and directly relate measures across species. The few available previous studies measuring EEG in nonhuman primates were typically restricted to only a few electrodes (Bimbi et al, 2017;Snyder et al, 2015Snyder et al, , 2018 and used diverging methods such as skull-screw electrodes, or both (Godlove et al, 2011;Musall et al, 2014;Reinhart et al, 2012;Whittingstall and Logothetis, 2009;Woodman et al, 2007). We show how monkey EEG can serve as a missing link to enable the disentangling of species differences from differences in measurement modality.…”

“…Second, invasive and non-invasive electrophysiology are largely separate research fields. Comparable experiments performed on both levels and in the same species are rare, with few recent exceptions (Bimbi et al, 2017;Godlove et al, 2011;Reinhart et al, 2012;Shin et al, 2017;Snyder et al, 2015Snyder et al, , 2018. Third, studies employing invasive and non-invasive methods in parallel suffer from sparse sampling of recording sites.…”

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

“…Our study could be extended to scaling trends in evoked activity, in which visual stimuli are presented during the V1 recordings and non-zero inputs are used in the spiking network models. Previous studies have found that shared variance tends to decrease after stimulus presentation [20, 35, 38–40] and that the scaling properties of PCA dimensionality change after stimulus presentation [19]. However, under certain conditions, the population activity patterns expressed in spontaneous activity can resemble those expressed in evoked activity [9, 41].…”

Differential modulation of global and local neural oscillations in REM sleep by homeostatic sleep regulation

“…Slow oscillations (<15 Hz) repeated the behavioral pattern of REM sleep alterations (i.e., a persistent increase throughout SR1 to SR5, whereas high-frequency oscillations showed progressive increases over the course of CSR). CFC of slow and fast oscillations during REM sleep were preserved or even enhanced in CSR with a matching topography [i.e., a prominent (8-10 Hz) theta2-gamma2 (70-90 Hz)] nesting was detected in the areas overlaying the hippocampus, narrow-band delta range oscillations (2 and 4 Hz) were modulating gamma1 (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45) in the PFC and a weaker slow (∼1 Hz) modulation of beta2 (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) was observed in the sensory-motor area. Collectively, these findings suggest that in REM sleep, slow oscillations closely following the behavioral pattern imposed by REM sleep regulation may serve to translate this top-down homeostatic control to a wide range of cortical networks whereas fast oscillations escaping this control might serve as an instrument granting relative independence for local ensembles to synchronize on shorter spatiotemporal scales.…”

“…Simultaneous EEG and multiunit recordings demonstrated that correlated firing within small cortical ensembles has the strongest relationship with beta2 and gamma amplitude at the nearest EEG electrode (35,36). The frequency is determined by the architecture of the local network.…”