Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain.

John Fallon,Alex Fornito,Phillip G D Ward,Ben D Fulcher,Stuart Oldham,Aurina Arnatkevičiūtė,Linden Parkes

doi:10.1162/netn_a_00151

John Fallon, Alex Fornito + Show 5 more

Open Access

https://doi.org/10.1162/netn_a_00151

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Abstract

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

Highlights

Structural connectivity: The set of physical connections between all pairs of neural elements.The brain’s complex spatiotemporal dynamics unfold on an intricate web of axonal connections: the connectome (Fornito, Zalesky, & Bullmore, 2016; Sporns, Tononi, & Kötter, 2005)
While much is known about the structure–function relationship at the level of pairs of brain regions, and how structural and functional connectivity architecture shape cognitive function and are affected in disease (Fornito, Zalesky, & Breakspear, 2015; Li et al, 2009; Penke et al, 2012; van den Heuvel & Sporns, 2019), relatively little is known about how structural connectivity affects the information-processing dynamics of individual brain areas
Structural connectivity was estimated from the diffusion data using MRtrix3 (Tournier, Calamante, & Connelly, 2012) and the FMRIB Software Library (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012), performing tractography with 10 million streamlines using Fibre Assignment by Continuous Tracking (FACT), Anatomically Constrained Tractography (ACT), and SIFT-2, yielding a 34 × 34 left-hemisphere connectome

Summary

Introduction

The brain’s complex spatiotemporal dynamics unfold on an intricate web of axonal connections: the connectome (Fornito, Zalesky, & Bullmore, 2016; Sporns, Tononi, & Kötter, 2005) These pathways facilitate information transfer between brain regions, manifesting in a complex relationship between connectome structure and neural dynamics. While much is known about the structure–function relationship at the level of pairs of brain regions, and how structural and functional connectivity architecture shape cognitive function and are affected in disease (Fornito, Zalesky, & Breakspear, 2015; Li et al, 2009; Penke et al, 2012; van den Heuvel & Sporns, 2019), relatively little is known about how structural connectivity affects the information-processing dynamics of individual brain areas. We do not yet understand the role structural connections play in the organization of timescales

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