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.
All data used in this study is publicly available. Detailed information about the datasets is available in the manuscript.
Human Connectome Project (HCP)ConnectomeDB, https://db.humanconnectome.org/app/template/Login.vm;jsessionid=5925BF444CE79AFD10B0D723CEBBD1CB.
Midnight Scan Club (MSC)OpenfMRI database, accession number: ds000224.
Allen Institute Human Brain Atlas (AHBA)https://human.brain-map.org/static/download.
- Golia Shafiei
- Bratislav Misic
- Bratislav Misic
- Bratislav Misic
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
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).
- Lucina Q Uddin, University of Miami, United States
© 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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