Topographic gradients of intrinsic dynamics across neocortex.

Golia Shafiei,Reinder Vos De Wael,Bratislav Misic,Ben D Fulcher,Boris C Bernhardt,Ross D Markello

doi:10.7554/elife.62116

Golia Shafiei, Reinder Vos De Wael + Show 4 more

Open Access

https://doi.org/10.7554/elife.62116

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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 6000 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.

Highlights

The brain is a complex network of anatomically connected and perpetually interacting neuronal populations (Sporns et al, 2005)
All analyses were performed on four resting state fMRI runs from the Human Connectome Project (Van Essen et al, 2013)
We first investigate whether regions that are structurally and functionally connected display similar intrinsic dynamics

Summary

Introduction

The brain is a complex network of anatomically connected and perpetually interacting neuronal populations (Sporns et al, 2005). Neuronal populations are organized into a hierarchy of increasingly polyfunctional neural circuits (Jones and Powell, 1970; Mesulam, 1998; Hilgetag and Goulas, 2020; Bazinet et al, 2020), manifesting as topographic gradients of molecular and cellular properties that smoothly vary between unimodal and transmodal cortices (Huntenburg et al, 2018). Cell type composition, their morphology and their configuration in local circuits determine how signals are generated, transmitted and integrated (Payeur et al, 2019). These micro-architectural properties – increasingly measured directly from histology or inferred from other measurements, such as microarray gene expression – provide a unique opportunity to relate circuit architecture to temporal dynamics and computation. Multiple studies have focused on how intrinsic timescales vary in relation to microscale and macroscale attributes (Murray et al, 2014; Mahjoory et al, 2019; Shine et al, 2019; Gao et al, 2020; Ito et al, 2020; Raut et al, 2020)

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