Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture
Abstract
Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex, and are relevant for cognition in both short- and long-terms, bridging microcircuit physiology with macroscale dynamics and behavior.
Data availability
All raw data are previously published and taken from publicly available repositories (see Table 1), all intermediate data produced from this manuscript are available on Github, with the associated analysis and visualization code
-
MNI Open iEEGMNI, https://mni-open-ieegatlas.research.mcgill.ca/.
-
Human Connectome Project S1200 ReleaseHCP, https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release.
-
Neurotycho Anesthesia and Sleep TaskNeurotycho, Anesthesia and Sleep.
-
Whole brain gene expressionhttp://www.meduniwien.ac.at/neuroimaging/mRNA.html.
Article and author information
Author details
Funding
Natural Sciences and Engineering Research Council of Canada (CGSD3-488052-2016)
- Richard Gao
Katzin Prize
- Richard Gao
Alexander von Humboldt-Stiftung
- Ruud L van den Brink
Alfred P. Sloan Foundation (FG-2015-66057)
- Bradley Voytek
Whitehall Foundation (2017-12-73)
- Bradley Voytek
National Science Foundation (BCS-1736028)
- Bradley Voytek
National Institutes of Health (R01GM134363-01)
- Bradley Voytek
Shiley-Marcos Alzheimer's Disease Research Center
- Bradley Voytek
Halicioglu Data Science Institute Fellowship
- Bradley Voytek
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Gao et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 10,735
- views
-
- 1,174
- downloads
-
- 170
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Immunology and Inflammation
Mass cytometry is a cutting-edge high-dimensional technology for profiling marker expression at the single-cell level, advancing clinical research in immune monitoring. Nevertheless, the vast data generated by cytometry by time-of-flight (CyTOF) poses a significant analytical challenge. To address this, we describe ImmCellTyper (https://github.com/JingAnyaSun/ImmCellTyper), a novel toolkit for CyTOF data analysis. This framework incorporates BinaryClust, an in-house developed semi-supervised clustering tool that automatically identifies main cell types. BinaryClust outperforms existing clustering tools in accuracy and speed, as shown in benchmarks with two datasets of approximately 4 million cells, matching the precision of manual gating by human experts. Furthermore, ImmCellTyper offers various visualisation and analytical tools, spanning from quality control to differential analysis, tailored to users’ specific needs for a comprehensive CyTOF data analysis solution. The workflow includes five key steps: (1) batch effect evaluation and correction, (2) data quality control and pre-processing, (3) main cell lineage characterisation and quantification, (4) in-depth investigation of specific cell types; and (5) differential analysis of cell abundance and functional marker expression across study groups. Overall, ImmCellTyper combines expert biological knowledge in a semi-supervised approach to accurately deconvolute well-defined main cell lineages, while maintaining the potential of unsupervised methods to discover novel cell subsets, thus facilitating high-dimensional immune profiling.
-
- Computational and Systems Biology
- Ecology
Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern next-generation sequencing data. We present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.