The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.
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http://dx.doi.org/10.1007/s10548-023-01004-4 | DOI Listing |
Appl Environ Microbiol
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Department of Forest Mycology and Plant Pathology, Uppsala BioCenter, Swedish University of Agricultural Sciences, Uppsala, Sweden.
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View Article and Find Full Text PDFNano Lett
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University Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay 91405, France.
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Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Mathematical models of viral dynamics are crucial in understanding infection trajectories. However, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load data often includes limited sparse observations with significant heterogeneity. This study aims to: (1) understand the impact of patient characteristics in shaping the temporal viral load trajectory and (2) establish a data collection protocol (DCP) to reliably reconstruct individual viral load trajectories.
View Article and Find Full Text PDFACS ES T Water
January 2025
Department of Statistics & Data Science, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Since the start of the coronavirus-19 pandemic, the use of wastewater-based epidemiology (WBE) for disease surveillance has increased throughout the world. Because wastewater measurements are affected by external factors, processing WBE data typically includes a normalization step in order to adjust wastewater measurements (e.g.
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Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon 97331, United States.
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