Abstract
Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.
Highlights
IntroductionThe representation of the human brain as a network, in which cortical and subcortical areas (nodes) communicate via white matter tracts that carry neuronal signals (connections or edges), has been used extensively to study the brains of healthy people and of patients that suffer from neurological and neuropsychiatric conditions (e.g., Aerts, Fias, Caeyenberghs, & Marinazzo, 2016; Baker et al, 2015; Caeyenberghs & Leemans, 2014; Collin et al, 2016; Drakesmith, Caeyenberghs, Dutt, Zammit, et al, 2015; Fischer, Wolf, Scheurich, & Fellgiebel, 2014; Griffa, Baumann, Thiran, & Hagmann, 2013; Hagmann et al, 2008; Imms et al, 2019; Nelson, Bassett, Camchong, Bullmore, & Lim, 2017; van den Heuvel & Fornito, 2014; Vidaurre et al, 2018; Yuan, Wade, & Babcock, 2014)
Observed Functional Connectivity The strength of the connections of FCo depended on the Euclidean distance between brain areas (Figure 2), a result that is in agreement with what has been observed for functional MRI (fMRI) functional connectivity (e.g., Gõni et al, 2014; Alexander-Bloch et al, 2012; note a discussion of the relationship between MEG-derived FC and Euclidean distance in Tewarie et al, 2019)
We used magnetic resonance imaging (MRI) and MEG data to investigate the relationship between brain structure and resting-state functional connectivity in the human brain
Summary
The representation of the human brain as a network, in which cortical and subcortical areas (nodes) communicate via white matter tracts that carry neuronal signals (connections or edges), has been used extensively to study the brains of healthy people and of patients that suffer from neurological and neuropsychiatric conditions (e.g., Aerts, Fias, Caeyenberghs, & Marinazzo, 2016; Baker et al, 2015; Caeyenberghs & Leemans, 2014; Collin et al, 2016; Drakesmith, Caeyenberghs, Dutt, Zammit, et al, 2015; Fischer, Wolf, Scheurich, & Fellgiebel, 2014; Griffa, Baumann, Thiran, & Hagmann, 2013; Hagmann et al, 2008; Imms et al, 2019; Nelson, Bassett, Camchong, Bullmore, & Lim, 2017; van den Heuvel & Fornito, 2014; Vidaurre et al, 2018; Yuan, Wade, & Babcock, 2014). Comparing structural and functional networks can lead to an understanding of the role of the structural connectome on the evocation of functional connectivity, both in healthy and in diseased brains It can shed light into whether, in diseased brains, the local synaptic disruptions and the excitation-inhibition imbalance, and the resulting disrupted functional connectome, lead to structural impairments, or whether it is the structural impairments that lead to functional deficiencies. Such knowledge can inform possible interventions that target structural or functional deficiencies in patients (Friston, Brown, Siemerkus, & Stephan, 2016)
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