Relationships between cortical myeloarchitecture and electrophysiological networks

Abstract

The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology.

Keywords: MRI; functional connectivity; magnetoencephalography; myelination; network.

Fig. S1.

Predicting myelination based on cross-subject covariance. (A) (Left) A schematic representation of a connectivity strength calculation. Connectivity strength is derived as the linear sum of a MEG adjacency matrix in one direction. (Right) Mean (across subjects) connectivity strength, plotted as a function of brain region for all five frequency bands. (B) The matrices show cross-subject correlation of connectivity strength (i.e., high values between two regions, A and B, indicate that subjects with high connectivity strength at region A also indicate high connectivity strength at region B). These matrices thus form a measure of cross-subject covariance in connectivity strength, which we term strength covariance. Note that a dominating feature, in particular in the beta band, is interhemispheric coupling. This is also shown by the inset images in the lower panel. These depict seed-based strength-covariance profiles (single columns in the matrices in the upper panel). Arrows mark the seed AAL region. (C) The link between strength covariance and structural covariance. The bar chart shows correlation between the structural network (Fig. 1D) and the strength-covariance matrices in B. Note correlation is measured over the whole matrix (i.e., for all possible seed regions) and is shown for all frequency bands. The inset images show which seed regions drive the relationship in the bar chart [i.e., red indicates a region whose structural connectivity (MT) profile and strength-covariance profile is highly correlated].

Fig. 1.

In vivo myelination measures and the structural network. (A) Mean MT contrast percentage for all AAL regions. High MT is reflective of high myelination. Note high levels of myelination in primary cortical regions. (B) Correlation between MT and handedness. Positive correlations show regions where myelin is higher in right handers. Negative correlations show regions where myelin is higher in left handers. (C) Example plot showing correlation over subjects between MT measured in the AAL regions capturing Broca’s and Wernicke’s areas. These correlations are the basis of the structural network. (D) The myelin structural network, represented as a matrix. Each element denotes cross-subject correlation in MT between two brain regions.

Fig. 2.

MEG functional connectivity matrices. Matrices represent AEC in the (A) theta (4–8 Hz), (B) alpha (8–13 Hz), (C) beta (13–30 Hz), (D) low–gamma (30–70 Hz), and (E) high-gamma (70–120 Hz) bands. All matrices show Pearson correlation between AAL region pairs. The 3D plots shown depict all connections within 5% of the maximum value in each band.

Fig. 3.

The relationship between MEG networks and myelination. (A) Structural covariance between a seed region in right inferior parietal cortex and all other brain regions. (B) Seed-based functional connectivity, calculated using MEG in the beta band between the same seed region in inferior parietal cortex and all other regions. Note the similarity between A and B. (C) The bar chart shows correlation between the structural network (Fig. 1D) and the functional networks (Fig. 2). Correlation is measured over the whole matrix (i.e., for all possible seed regions) and is shown for all frequency bands. ** indicates a significant relationship; * indicates a trend. The inset images show which seed regions drive the relationship in the bar chart [i.e., red indicates a region whose structural connectivity (MT) profile and functional connectivity profile are highly correlated].

Fig. 4.

Predicting myelination based on integrated MEG networks. (A–C) Matrices representing (A) the MT network, (B) the best linear combination of MEG frequency bands to estimate MT, and (C) the best nonlinear prediction of MT. (D and E) Seed-based visualizations of structural and functional networks with seed regions in right lateral visual cortex (D) and left superior frontal cortex (E). The lower three rows show beta band and the linear and nonlinear predictions of the MT network (Top). (F) r² values describing goodness-of-fit between structure and function for the best-fitting single frequency band (beta) and predictions based upon linear and nonlinear combinations of MEG frequency bands. The inset images show the seed regions driving these correlations. (G–I) The r² values (circles) plotted against the null distributions for the beta band (G) linear (H) and nonlinear (I) combinations.

Fig. S2.

Using fMRI-derived RSNs to predict structural covariance and MEG. (A) Vector (Upper) and adjacency matrix (Lower) representations of 10 commonly observed RSNs. (B) (Left) The structural covariance (MT) matrix and a fit to that matrix based on the 10 RSNs in A. (Center) The measured correlation between the MT matrix and the fit (yellow) alongside an empirical null distribution. (Right) The fitted parameters, $\mathit{\beta }$ (bar chart) and the networks with the highest contribution. (C) (Center) Correlation between the five MEG-derived adjacency matrices (Fig. 2) and their best fit based on the 10 fMRI-derived RSNs in A. The inset images show which RSNs contribute maximally to each band.

Fig. S3.

Uncorrected MT maps. (A) Correlation between MT and handedness. Positive correlations show regions where myelin is higher in right handers. Negative correlations show regions where myelin is higher in left handers. (B–D) Structural covariance between a seed region and all other brain regions. (B) Parietal seed. (C) Occipital seed. (D) Frontal seed.

Fig. S4.

A real MEG matrix (Left) and a single example of a pseudomatrix (Right).

Fig. S5.

Linear and nonlinear fits of MEG to MT data. (A) The five MEG-derived adjacency matrices (Left) and their squares (Right). (B) Results of our rate-of-improvement analysis. (Right) The yellow dashed line represents the improvement in fit with model complexity in real data. The blue lines represent the equivalent analysis applied to pseudomatrices. (Left) The gradient of the dashed line (in yellow) and the equivalent gradients of all of the blue lines in the null distribution (blue). Note that the improvement afforded by increasing model complexity in real data is greater than what would be expected by chance. (C) Results of our cross-validation procedure. (Upper) The case for the linear fit. (Lower) The case for the nonlinear fit. The blue curve shows the estimation of MT data from group A, using linear and nonlinear combinations of MEG matrices, with parameters derived from group A. The red curve shows the estimation of MT data from group B, using linear and nonlinear combinations of MEG matrices, but with parameters derived independently from group A. Yellow curves show the null distributions. (D) The contributions of the original and the squared terms to the nonlinear fit.