Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration

Abstract

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate sourceseparation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, interregional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration. Significance statementNeural activity is synchronized over space, time, and frequency. To characterize the dynamics of large-scale networks spanning multiple brain regions, we recorded data from the prefrontal cortex, parietal cortex, and hippocampus in awake behaving mice, and pooled data from spiking activity and local field potentials into one data matrix. Frequency-specific multivariate decomposition methods revealed a cornucopia of neural networks defined by coherent spatiotemporal patterns over time.These findings reveal a rich, dynamic, and multivariate landscape of large-scale neural activity patterns during foraging behavior.

A Fully Adapted Headstage With Custom Electrode Arrays Designed for Electrophysiological Experiments

“…According to the records, each brain region showed a distinct oscillatory pattern, without visible crosstalk contamination between channels ( Figures 6A–C ). While the largest amplitude theta oscillations were observed at the dHPC ( Buzsáki, 2002 ), theta and other rhythms can also be observed at both substrates with different features ( Soltani Zangbar et al, 2020 ; Cohen et al, 2021 ).…”

Transient cortical Beta-frequency oscillations associated with contextual novelty in high density mouse EEG

“…Hence, it is presently unclear the degree to which the localization of novelty-associated connectivity may be associated with localization of beta bursting and should be the target of future work. The exploration of a novel environment is likely to elicit the activity of numerous, multidimensional networks across a range of oscillatory frequencies 5 and the complexity of such a system is difficult to quantify with individual metrics.…”

“…More recently, several other cortical regions have been shown to participate in these bursts. Beta bursts have been detected in parietal and mid-frontal cortices and are highly coherent with those in the hippocampus 2,5 . In addition, beta bursting has been detected in the retrosplenial cortex, where they increase in prevalence during contextual novelty and are associated with transient increases in neuronal spiking 6 , further highlighting this region as an important area for context discrimination.…”

A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology

“…Multimodal datasets GED does not "know" or "care" about the origin or type of data. It is possible to include multiple sources of data into the same data matrix, for example, EEG+MEG (Zuure et al, 2020), LFP+single units (Cohen et al, 2021), EEG+FMRI, or any other combinations of brain, behavior, and body readouts. Any continuous signals that are relevant for an hypothesis can be included in the covariance matrices.…”