Topographic gradients of intrinsic dynamics across neocortex
Abstract
The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6,000 statistical properties of individual brain regions' time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.
Data availability
All data used in this study is publicly available. Detailed information about the datasets is available in the manuscript.
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Human Connectome Project (HCP)ConnectomeDB, https://db.humanconnectome.org/app/template/Login.vm;jsessionid=5925BF444CE79AFD10B0D723CEBBD1CB.
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Midnight Scan Club (MSC)OpenfMRI database, accession number: ds000224.
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Allen Institute Human Brain Atlas (AHBA)https://human.brain-map.org/static/download.
Article and author information
Author details
Funding
Natural Sciences and Engineering Research Council of Canada
- Golia Shafiei
Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN #017-04265)
- Bratislav Misic
Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative
- Bratislav Misic
Canada Research Chairs Program
- Bratislav Misic
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Lucina Q Uddin, University of Miami, United States
Ethics
Human subjects: Informed consent and consent to publish were obtained during data acquisition process (all data used in this study were obtained from publicly available datasets).
Version history
- Received: August 14, 2020
- Accepted: December 16, 2020
- Accepted Manuscript published: December 17, 2020 (version 1)
- Version of Record published: December 29, 2020 (version 2)
Copyright
© 2020, Shafiei 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.
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Further reading
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- Neuroscience
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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- Neuroscience
Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.