A cortical wiring space links cellular architecture, functional dynamics and hierarchies in humans

Abstract

The vast net of fibres within and underneath the cortical mantle is optimised to support the convergence of different levels of brain organisation, specifically through linking cellular and architectural features of different cortical areas to give rise to more integrated neural function. Leveraging multimodal neuroimaging and machine learning techniques, we identified a novel manifold space that is governed by geometric, microstructural, and connectional aspects of cortico-cortical wiring. Principal dimensions of this manifold recapitulated established sensory-limbic and anterior-posterior gradients of brain organisation, and were found to reflect neural and glial gradients of cytoarchitectural organisation and gene expression. Manifold features were accurate in predicting cortico-cortical functional interactions derived from resting-state functional neuroimaging. Furthermore, application of this approach to a group of neurological patients, who underwent intracerebral encephalographic recordings, established that manifold features also explained the direction of information flow between different cortical areas. These results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. RESULTSA multi-scale model of cortical wiring The wiring model was first derived from a Discovery subset of the Human Connectome Project dataset (n=100 unrelated adults) that offers high resolution structural magnetic resonance imaging (MRI), diffusion MRI, and microstructurally sensitive T1w/T2w maps 38 (Figure 1A, see Methods for details). and diffusion-based tractography strength (TS) were estimated between all pairs of nodes. (B) Normalised matrices were concatenated and transformed into an affinity matrix. Manifold learning identified a lower dimensional representation of cortical wiring. (C) Left. Node positions in this newly-discovered structural manifold, coloured according to proximity to axis limits. Closeness to the maximum of the second eigenvector is redness, towards the minimum of the first eigenvector is greenness and towards the maximum of the first eigenvector is blueness. The first two eigenvectors are shown on the respective axes. Right. Equivalent cortical surface representation. (D) Calculation of inter-regional distances in the structural manifold from specific seeds to other regions of cortex (left). Overall distance to all other nodes can also be quantified to index centrality of different regions, with more integrative areas having shorter distances to nodes (right). Figure 4: Hierarchical information processing is organised by the structural manifold. (A)Intracerebral implantations of ten epileptic patients were mapped to cortical surface. Intracortical EEG recordings were selected across five minutes of rest. (B) Boxplots show the variance explained in coherence by the structural manifold using adaboost machine learning across all nodes. (C) The phase slope ...

Topographic gradients of intrinsic dynamics across neocortex

“…Our results build on this literature, demonstrating that microscale and connectional hierarchies leave an indelible mark on intrinsic dynamics [59], perhaps through variation in local excitability [102]. How these patterns are related to underlying cell types and subcortical afferent input -in particular, thalamocortical feedback -is an important ongoing question [1,34,70,73,85,101].…”

Topographic gradients of intrinsic dynamics across neocortex

“…Our results build on this literature, demonstrating that microscale and connectional hierarchies leave an indelible mark on intrinsic dynamics [70], perhaps through variation in local excitability [30,31,118,119]. How these patterns are related to underlying cell types and subcortical afferent input -in particular, thalamocortical feedback -is an important ongoing question [1,43,83,86,102,118].…”

“…Our results build on this literature, demonstrating that microscale and connectional hierarchies leave an indelible mark on intrinsic dynamics ( Lurie and D’Esposito, 2020 ), perhaps through variation in local excitability ( Demirtaş et al, 2019 ; Wang et al, 2019 ; Wang, 2020 ; Deco et al, 2020 ). How these patterns are related to underlying cell types and subcortical afferent input – in particular, thalamocortical feedback – is an important ongoing question ( Abeysuriya et al, 2015 ; Garrett et al, 2018 ; Shine et al, 2019 ; Wang et al, 2019 ; Muller et al, 2020 ; Paquola et al, 2020 ).…”

A tutorial and tool for exploring feature similarity gradients with MRI data

“…These techniques are appealing to the neuroimaging community since they have the potential to provide a flexible, unified framework for understanding similarities of neural structure or function across the brain (c.f. Paquola et al, 2020Paquola et al, , 2019Vázquez-Rodríguez et al, 2019 for examples of how flexible these concepts can be used across multiple modalities). In this article we further extend this framework by introducing a way to measure how sharply defined each area is, showing the full spectrum of possibilities between the ideas of the Vogts, and those of Bailey and von Bonin; The Vogt-Bailey index.…”