Microstructural and functional gradients are increasingly dissociated in transmodal cortices

Abstract

While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While nonhuman primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses, which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive magnetic resonance imaging (MRI) data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta-analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition, such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.

Fig 1. Histology-based MPC_HIST analysis.

(A) Pial (purple) and WM (yellow) surfaces displayed against a sagittal slice of the BigBrain (left) and with the midsurface (blue) in magnified view (right). (B) Mean and SD in residual intensity at each node are displayed on the cortex (left). Cortex-wide intensity profiles were calculated by systematic intensity sampling across intracortical surfaces (rows) and nodes (columns). (C) The MPC_HIST matrix depicts node-wise partial correlations in intensity profiles, controlling for the average intensity profile. Exemplary patterns of microstructural similarity from S1, ACC, V1, and the temporal pole. Seed nodes are shown in white. Histological data is openly available as part of the BigBrain repository (https://bigbrain.loris.ca/main.php). ACC, anterior cingulate cortex; HIST, histology-based; MPC, microstructure profile covariance; S1, primary somatosensory; V1, primary visual; WM, white matter.

Fig 2. The G1_HIST of the histology-based MPC_HIST.

(A) Identification: the MPC_HIST matrix was transformed into an affinity matrix, which captures similarities in MPC_HIST between nodes; this affinity matrix was subjected to diffusion map embedding, a nonlinear compression algorithm that sorts nodes based on MPC_HIST similarity. (B) Variance explained by embedding components (left). The first component, G1_HIST, describes a gradual transition from primary sensory and motor (blue) to transmodal and limbic areas (red), corresponding to changes in intensity profiles, illustrated with the mean residual intensity profile across 10 discrete bins of the gradient (right). (C) Spatial associations between G1_HIST and levels of laminar differentiation (left; [40]) and cytoarchitectural taxonomy (right; [13,41]), ordered by median. Histological data is openly available as part of the BigBrain repository (https://bigbrain.loris.ca/main.php). G1, first principal gradient; HIST, histology-based; MPC, microstructure profile covariance.

Fig 3. In vivo MPC_MRI.

(A) Left hemisphere pial, mid, and WM surfaces superimposed on a T1w/T2w image (left); whole-cortex intensity profiles were calculated by systematic sampling across surfaces (rows) and vertices and then averaged with each node (columns). Mean at each node (centre right); MPC_MRI matrix depicts node-wise partial correlations in intensity profiles, covaried for mean whole-cortex intensity profile (right). (B) Normalised angle matrix sorted by the principal gradient (left); variance explained by the diffusion-embedding components (left centre) and the principal gradient (right centre); mean residual intensity profiles within 10 discrete bins of the gradient (right). (C) Similarity of histological and in vivo gradients (G1_HIST, G1_MRI) shown in a density plot (left) and node-wise rank differences shown on the cortical surfaces (right). (D) Associations of G1_MRI to levels of laminar differentiation [40] and cytoarchitectural class [13,41] ordered by median. In vivo imaging data is openly available as part of the HCP S900 release (https://www.humanconnectome.org/study/hcp-young-adult/document/900-subjects-data-release). GI, first principal gradient; HCP, Human Connectome Project; HIST, histology-based; MPC, microstructure profile covariance; MRI, magnetic resonance imaging; WM, white matter.

Fig 4. Cross-modal correspondence of the MPC_MRI and intrinsic functional gradients.

(A) Transformation from individual functional connectomes (left) to a group average normalised angle matrix (centre) to diffusion-embedding components (right). (B) The group-level G1_MRI (left), group-level G1_FUNC (centre), and density plots depicting the correlation between the gradients (right). (C) Consistency across three example subjects and (D) interindividual variability of the gradients and cross-modal correspondence in the Replication data set. In vivo imaging data is openly available as part of the HCP S900 release (https://www.humanconnectome.org/study/hcp-young-adult/document/900-subjects-data-release). FUNC, functional; HCP, Human Connectome Project; MPC, microstructure profile covariance; MRI, magnetic resonance imaging.

Fig 5. Divergent representations of the cortical hierarchy derived from microstructure and function.

(A) Differences in nodal ranks between G1_MRI (blue) and G1_FUNC (red). (B) Radar plot depicting the difference in mean node ranks of functional communities [26] between G1_MRI (blue) and G1_FUNC (red), with 95% confidence intervals calculated across individuals presented with dotted lines; stacked bar plots depicting the proportion of each bin accounted for by intrinsic functional communities. (C) Meta-analysis maps for diverse cognitive terms were obtained from Neurosynth [28]. We calculated node-wise z-statistics, capturing node-term associations, and calculated the centre of gravity of each term along G1_FUNC and G1_MRI. The density plots depict the mean difference in the centre of gravity of meta-analysis maps in G1_FUNC and G1_MRI space across subjects. In vivo imaging data is openly available as part of the HCP S900 release (https://www.humanconnectome.org/study/hcp-young-adult/document/900-subjects-data-release). FUNC, functional; GI, first principal gradient; HCP, Human Connectome Project; MPC, microstructure profile covariance; MRI, magnetic resonance imaging.