Variability and stability of large-scale cortical oscillation patterns.

Anna C Schapiro,Robert Stickgold,Roy Cox

doi:10.1162/netn_a_00046

Anna C Schapiro, Robert Stickgold + Show 1 more

Open Access

https://doi.org/10.1162/netn_a_00046

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Abstract

Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior.

Highlights

Human brains are very similar, every brain is distinct
We replicated this pattern of results for SB and second visit (SC) (Supporting Information Table 4B and C, Cox et al, 2018) with 92.5 ± 8.2% and 91.7 ± 8.0% of subjects, respectively, showing clustering across frequency bands and metrics. (We could not assess the existence of metric-specific networks within individuals for task networks because of the low number of possible permutations; see Methods.) these findings demonstrate that oscillatory profiles based on different oscillatory features are reliably distinct, even when derived from the same frequency band, for almost all individuals
Employing a data-driven approach with internal replications, we have demonstrated that oscillatory network patterns differ across frequency bands and oscillation metrics, suggesting that distinct network types, defined by these parameters, capture separate processing streams operating in parallel

Summary

Introduction

Magnetic resonance imaging (MRI) techniques indicate both individual variability in anatomical white matter connectivity (Bürgel et al, 2006) and marked differences in interregional functional connectivity (Gordon, Laumann, Adeyemo, & Petersen, 2015) that relate to cognitive functioning (Finn et al, 2015; Mueller et al, 2013; Schultz & Cole, 2016). MRI network approaches have yielded powerful insights into the brain’s macroscopic connectivity pattern, or connectome, and its relation to behavior Functional connectivity: Statistical association between time-varying signals measured at distinct sites (e.g., electrodes or voxels), suggestive of direct or indirect communication between the pertaining neural regions. Connectome: Comprehensive map of connections among brain regions, based on anatomical or functional connectivity. Cross-frequency coupling: Statistical association between time-varying activity in different frequency bands, suggestive of information transfer between neural processes operating at different timescales. Relatively little is known about the detailed properties of such oscillatory networks, their variability from person to person, or their long-term stability

Methods

Results

Discussion

Conclusion