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.network | functional connectivity | myelination | magnetoencephalography | MRI T he way in which integration of functionally specific brain regions supports ongoing cognition is one of the most important questions in neuroscience, and noninvasive in vivo imaging provides a tool to investigate this interregional connectivity in terms of both brain function and structure. Functional connectivity refers to statistical interdependencies between patterns of brain "activity" measured at separate cortical locations (1) and, even in the "resting state," measured spontaneous brain activity defines nonrandom networks that are related to cognitive processes (2). The way in which these functional networks are supported by structural white-matter pathways is reasonably well understood (3). However, it is likely that the structure-function association extends to graymatter morphology, for which fundamental understanding is lacking. Structural morphology of the cortex is known to vary significantly between individuals, and does so in an organized fashion. For example, individuals with a high cortical volume in Broca's area typically exhibit high cortical volume in Wernicke's area, reflecting a language network (4). Similar observations can be made between other associated cortical regions (5, 6). This is known as structural covariance (7-9) and it allows the formation of matrices showing how structural properties of individual brain regions covary over subjects. In this paper, we assess structural covariance based upon cortical myeloarchitecture and probe its relationship to functional networks that are assessed based upon measured spontaneous brain activity.Noninvasive map...

Cross-population myelination covariance of human cerebral cortex

“…Interestingly, stronger myelination covariance–RSFC correlation was observed in sensory and motor networks than in cognitive and polymodal association networks. This result is consistent with a recent study comparing structural covariance of myelination measured by magnetization transfer with RSNs measured by magnetoencephalography, and showed stronger structure‐function relationship in the occipital and parietal lobes but weaker relationship in the frontal areas [Hunt et al, ]. Our result is also consistent with another report comparing gray matter density covariance and RSFC at the network level, which demonstrated high spatial overlaps between structure covariance and RSFC in the medial and lateral visual cortices and the supplementary motor area of the human brain [Segall et al, ].…”

“…Previous structural covariance analysis predominantly used bulky morphological measures without differentiating separate compartments, whereas our knowledge of the structural covariance of a specific cortical component such as myelin content remains rather limited [Accolla et al, ; Carmeli et al, ; Hunt et al, ]. Bridging this knowledge gap is of great interest, as characterizing myelination covariance will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions.…”

Optimising experimental design for MEG resting state functional connectivity measurement

“…In addition, the comparatively large effect of recording duration can be seen clearly. It is noteworthy that the highest between session reliability is observed in the parietal and occipital lobes and this is consistent with functional connectivity in beta band being dominated by these brain areas, as shown in Figure 5 (see also Hunt et al, 2016). However it is important to point out that this is likely because results are derived from resting state data.…”

“…All results presented in this paper are based upon this band. The reason for this choice was a weight of literature that points towards connections in many long range networks being mediated by neural oscillations in this range Hipp et al, 2012;Hunt et al, 2016).…”

High‐Frequency Oscillations in the Normal Human Brain

“…32,33 There have also been reports suggesting the existence of resting levels of HFOs/HFA that also vanish or decrease during involvement in a task. 38 Physiological ripples are ubiquitous in normal regions, with particularly high rates in eloquent cortex. Interestingly, the inhibitory system is largely γ-aminobutyric acidergic (GABAergic), so the concentration or receptor density of GABA, which differs across brain areas, might be related to the spatial pattern of spontaneously occurring HFA.…”