Shaping brain structure: Genetic and phylogenetic axes of macroscale organization of cortical thickness

Valk, Sofie L.; Xu, Ting; Margulies, Daniel S.; Masouleh, Shahrzad Kharabian; Paquola, Casey; Goulas, Alexandros; Kochunov, Peter; Smallwood, Jonathan; Yeo, B.T. Thomas; Bernhardt, Boris C.; Eickhoff, Simon B.

doi:10.1126/sciadv.abb3417

Cited by 115 publications

(132 citation statements)

References 88 publications

(176 reference statements)

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“…They both exhibit a general sensory-fugal organization (Mesulam, 1998) placing sensory/motor areas at one end and transmodal and paralimbic systems at the other. However, the microstructural gradient describes decreasing laminar differentiation, more in line with Mesulam’s notion of synaptic distance from external input, whereas the functional gradient depicts a transition from locally-connected sensory regions towards a default mode core with longer-rage connectivity (Valk et al, 2020).…”

Section: Introductionmentioning

confidence: 80%

Heritability of cortical morphology reflects a sensory-fugal plasticity gradient

Vainik

Paquola

Wang

et al. 2020

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Human brain plastically adapts to environmental demands. Here, we propose that naturally occuring plasticity in certain brain areas should be reflected by higher environmental influence and therefore lower heritability of the structure of those brain areas. Mesulam's (1998) seminal overview proposed a hierarchy of plasticity, where higher-order multimodal areas should be more plastic than lower-order sensory areas. Using microstructural and functional gradients as proxies for Mesulam's hierarchy, we seek to test whether these gradients predict heritability of brain structure. We test this model simultaneously across multiple measures of cortical structure and microstructure derived from structural magnet resonance imaging. We also account for multiple other explanations of heritability differences, such as signal-to-noise ratio and spatial autocorrelation. We estimated heritability of brain areas using 984 participants from the Human Connectome Project. Multi-level modelling of heritability differences demonstrated that heritability is explained by both signal quality, as well as by the primary microstructural gradient. Namely, sensory areas had higher heritability and limbic/heteromodal areas had lower heritability. Given the increasing availability of genetically informed imaging data, heritability could be a quick method assess brain plasticity.

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Section: Introductionmentioning

confidence: 80%

Heritability of cortical morphology reflects a sensory-fugal plasticity gradient

Vainik

Paquola

Wang

et al. 2020

Preprint

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“…Therefore, various groups (including ours) have most frequently utilized the 400-region Schaefer group-level parcellation (Varikuti et al, 2018; Franzmeier et al, 2019; Kebets et al, 2019; Murphy et al, 2020; Orban et al, 2020). Other studies have opted to utilize different resolutions of the Schaefer group-level parcellation, e.g., 100 regions (Chin Fatt et al, 2019), 200 regions (Anderson et al, 2020; Faskowitz et al, 2020) and 800 regions (Valk et al, 2020). Despite our focus on the 400-region areal-level parcellations in the current study, we do not believe that there is an optimal number of cortical parcels because of the multi-resolution organization of the cerebral cortex (Churchland and Sejnowski, 1988; van den Heuvel and Yeo, 2017).…”

Section: Discussionmentioning

confidence: 99%

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Kong

Yang

Gordon

et al. 2021

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Resting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (GITHUB_LINK).

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“…These co-relationships between brain regions can also be non-linear, and manifold learning techniques have been applied to elucidate them (see e.g. Valk et al., 2020 ). The independent variables

and

may be more advantageous in terms of its reliability and comparability ( Carmon et al., 2019 ) for structural covariance analysis.…”

Section: Discussionmentioning

confidence: 99%

Independent components of human brain morphology

et al. 2021

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Quantification of brain morphology has become an important cornerstone in understanding brain structure. Measures of cortical morphology such as thickness and surface area are frequently used to compare groups of subjects or characterise longitudinal changes. However, such measures are often treated as independent from each other. A recently described scaling law, derived from a statistical physics model of cortical folding, demonstrates that there is a tight covariance between three commonly used cortical morphology measures: cortical thickness, total surface area, and exposed surface area. We show that assuming the independence of cortical morphology measures can hide features and potentially lead to misinterpretations. Using the scaling law, we account for the covariance between cortical morphology measures and derive novel independent measures of cortical morphology. By applying these new measures, we show that new information can be gained; in our example we show that distinct morphological alterations underlie healthy ageing compared to temporal lobe epilepsy, even on the coarse level of a whole hemisphere. We thus provide a conceptual framework for characterising cortical morphology in a statistically valid and interpretable manner, based on theoretical reasoning about the shape of the cortex.

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Shaping brain structure: Genetic and phylogenetic axes of macroscale organization of cortical thickness

Cited by 115 publications

References 88 publications

Heritability of cortical morphology reflects a sensory-fugal plasticity gradient

Heritability of cortical morphology reflects a sensory-fugal plasticity gradient

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Independent components of human brain morphology

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