Decoupling of brain function from structure reveals regional behavioral specialization in humans

Preti, Maria Giulia; Ville, Dimitri Van De

doi:10.1038/s41467-019-12765-7

Cited by 244 publications

(341 citation statements)

References 46 publications

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“…Predictive performance decreased towards transmodal networks, a finding indicating that more higher-level systems may escape (currently measurable) structural constraints. Such conclusions are in line with recent work showing that transmodal areas exhibit lower microstructure-function correspondence 7 and reduced correlations between diffusion tractography and resting state connectivity 85,86 , potentially contributing to greater behavioural flexibility 7,87 . The hierarchical nature of the structural manifold was further supported by analysing its correspondence to direct measurements of neural dynamics and information flow via intracerebral stereo-electroencephalography.…”

Section: Discussionsupporting

confidence: 90%

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

Paquola

Seidlitz

Benkarim

et al. 2020

Preprint

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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 ...

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Section: Discussionsupporting

confidence: 90%

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

Paquola

Seidlitz

Benkarim

et al. 2020

Preprint

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“…These observations are in line with recent findings that show that the alignment between SC and FC, i.e. the degree to which FC is shaped by SC, differs across regions (Preti & Van De Ville, 2019) , with sensory regions -i.e. the visual and somatosensory/motor cortices -are more strongly aligned with the SC than higher cognitive areas.…”

Section: Euclidean Distance and Fiber Count Differentially Affect Fc supporting

confidence: 93%

Using structural connectivity to augment community structure in EEG functional connectivity

Glomb

Mullier

Carboni

et al. 2019

Preprint

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Recently, EEG recording techniques (high-density EEG) as well as source analysis have improved markedly. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG signals suffer from volume conduction, introducing spurious functional connections (statistical dependencies) between nearby sources. Genuine functional connectivity (FC) reflecting functional relationships is impossible to disentangle from spurious FC, impairing the applicability of network neuroscience to EEG. Here, we use information from white matter structural connectivity (SC) to attenuate the impact of volume conduction on EEG-FC. We confirm that FC (power envelope correlations) is predicted by the SC beyond the impact of Euclidean distance. We then smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions is increased while FC between non-connected regions remains unchanged. We hypothesize that this results in an increase in genuine relative to spurious FC. We analyze changes in FC due to smoothing by assessing the resemblance between EEG-and fMRI-FC, and find that smoothing increases resemblance, both in terms of overall correlation and community structure. This suggests that information from the SC can indeed be used to attenuate the effect of volume conduction on EEG source signals. Author summaryIn this study, we combine high-density EEG recorded during resting state with white matter connectivity obtained from diffusion MRI and fiber tracking. We leverage the additional information contained in the structural connectome towards augmenting the source level EEG functional connectivity. In particular, it is known -and confirmed in this study -that the activity of brain regions that possess a direct anatomical connection is, on average, more strongly correlated than that of regions that have no such direct link. We use the structural connectome to define a graph and smooth the source reconstructed EEG signal in the space spanned by this graph. We compare the resulting "filtered" signal correlation matrices to those obtained from fMRI and find that such "graph filtering" improves the agreement between EEG and fMRI functional connectivity structure. Thus suggests that structural connectivity can be used to attenuate some of the limitations imposed by volume conduction.

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“…The original HCP-MMP atlas consists of 360 cortical ROIs delineated by their distinct structural, functional and connectivity profiles (Glasser et al, 2016), which offers good neuroanatomical precision essential for understanding macroscopic brain network dynamics. As such, the HCP-MMP atlas has been widely used for parcellation of resting-state fMRI data (Ito et al, 2017;Dubois et al, 2018;Dermitaş et al, 2019;Preti and Van De Ville, 2019;Watanabe et al, 2019). Nevertheless, the fine spatial resolution of this atlas becomes a key limiting factor for apply it to MEG, because MEG data acquired in a typical scanner with 200-300 sensors would lead to rank deficiency if parcellated into 360 regions.…”

Section: The Reduced Hcp-mmp Atlas Optimized For Megmentioning

confidence: 99%

“…One commonly used template for cortical parcellation is the AAL atlas (Tzourio-Mazoyer et al, 2002), which is based on anatomical landmarks. Recently, there is a surge of interest in applying the Human Connectome Project Multimodal Parcelation (HCP-MMP) atlas (Glasser et al, 2016) for parcellation of cortical dynamics (Ito et al, 2017;Dubois et al, 2018;Dermitaş et al, 2019;Preti and Van De Ville, 2019;Watanabe et al, 2019). The ROI boundaries in the HCP-MMP atlas are defined by combined characteristics in cortical architecture, function, connectivity, and topography, offering better neuroanatomical precision and functional segregation.…”

Section: Introductionmentioning

confidence: 99%

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

Tait

Özkan

Szul

et al. 2020

Preprint

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Non-invasive functional neuroimaging of the human brain at rest can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with very high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms for task-based MEG data are available. However, no consensus yet exists on the optimum algorithm for resting-state data.Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. In the context of human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project's multimodal parcellation of the human cortex. This procedure produced a reduced cortical atlas with 250 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners.For both voxel-wise and parcellated source reconstructions, we showed that the eLORETA inverse algorithm had zero localization error, high spatial resolution, and superior performance in predicting sensor-level activity. Our comprehensive comparisons and recommandations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.

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Decoupling of brain function from structure reveals regional behavioral specialization in humans

Cited by 244 publications

References 46 publications

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

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

Using structural connectivity to augment community structure in EEG functional connectivity

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

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