Biology subjects

Zanesco, A. P.

Publications and source records attributed to Zanesco, A. P..

3 recordsLinked to original sources

bioRxiv2026

Macroscale dynamics of EEG microstates determine the periodic and aperiodic features of the neural power spectrum

The global signal characteristics of scalp-recorded electroencephalography (EEG) are composed of periodic oscillatory rhythms and aperiodic broadband fluctuations that together constitute the neural power spectrum. Spectral decomposition of these features has long served as the primary window into the macroscale characteristics of human brain activity. However, prevailing interpretations of spectral features lack a unifying mechanistic framework and often conflate activity resulting from distinct neural sources. Here, we propose that the primary periodic rhythms and majority share of broadband spectral power within the brains dominant frequencies originate from the brain network architecture responsible for generating EEG microstates. These microstates consist of a small repertoire of quasi-stable topographic voltage configurations that each reflect the momentary functional state of the cortex, and it is their dynamics that generate periodic and aperiodic spectral features. To computationally test this generating mechanism, we isolated and removed the spatial projections of microstates from high-density EEG using orthogonal subspace projection applied to both the surface scalp recordings and their modeled cortical generators. Spectral parameterization of the residual power spectral density revealed that removing seven distinct microstates strongly attenuated alpha and theta rhythms and features of the aperiodic 1/f background. Selectively removing specific topographic configurations also demonstrated that each microstate possesses independent oscillatory generators and unique 1/f aperiodic structures. Together, our findings suggest that dominant periodic and aperiodic spectral features are more accurately understood as the frequency-domain expressions of the distributed brain networks generating EEG microstates.

neuroscience↗

bioRxiv2026

The Maintenance of Attention Over Time Influences the Dynamics of EEG Microstates

Human attention is inherently transient and limited in span to only a few moments without lapsing. The intrinsic dynamics of large-scale neurocognitive networks are thought to contribute to these lapses and result in the unavoidable fluctuations in attention that constrain its span. However, it remains unclear how the millisecond temporal dynamics of specific electrophysiological brain states contribute to the endogenous maintenance of attention or the onset of attentional lapses. In the present study, we investigated whether the strength and millisecond dynamics of brain electric microstates differentiate states of focus from inattention and contribute to the endogenous maintenance of attention over short and long timescales. We recorded 128-channel EEG while participants maintained their attention during the wait time delay of trials in the Sustained Attention to Cue Task (SACT) and segmented the EEG into a categorized time series of microstates based on data-driven clustering of topographic voltage patterns. The findings revealed that the prevalence and rate of occurrence of microstates C and E in the wait time delay of trials differentiated trials in which the target stimulus was correctly detected from incorrectly detected. These same microstates were also implicated in the maintenance of attention over short and long timescales, with their time-varying dynamics changing systematically during the wait time delay of trials and over the course of the task session. Together, these findings demonstrate the sensitivity of microstates to variation in attentional states and suggest that the millisecond dynamics of these brain states contribute to the maintenance of attention over time.

neuroscience↗

bioRxiv2019

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

Microstates reflect transient brain states resulting from the activity of synchronously active brain networks that predominate in the broadband EEG time series. Despite increasing interest in understanding how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent, comparatively little research has examined within and between-person correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyes-closed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five data-driven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with within-person factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of whole-brain neuronal networks vary considerably within-individuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on between-person differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to within-person variation.

neuroscience↗