Biology subjects

Vos de Wael, R.

Publications and source records attributed to Vos de Wael, R..

At least 19 recordsLinked to original sources

bioRxiv2024

Brain Charts for the Rhesus Macaque Lifespan

Recent efforts to chart human brain growth across the lifespan using large-scale MRI data have provided reference standards for human brain development. However, similar models for nonhuman primate (NHP) growth are lacking. The rhesus macaque, a widely used NHP in translational neuroscience due to its similarities in brain anatomy, phylogenetics, cognitive, and social behaviors to humans, serves as an ideal NHP model. This study aimed to create normative growth charts for brain structure across the macaque lifespan, enhancing our understanding of neurodevelopment and aging, and facilitating cross-species translational research. Leveraging data from the PRIMatE Data Exchange (PRIME-DE) and other sources, we aggregated 1,522 MRI scans from 1,024 rhesus macaques. We mapped non-linear developmental trajectories for global and regional brain structural changes in volume, cortical thickness, and surface area over the lifespan. Our findings provided normative charts with centile scores for macaque brain structures and revealed key developmental milestones from prenatal stages to aging, highlighting both species-specific and comparable brain maturation patterns between macaques and humans. The charts offer a valuable resource for future NHP studies, particularly those with small sample sizes. Furthermore, the interactive open resource (https://interspeciesmap.childmind.org) supports cross-species comparisons to advance translational neuroscience research.

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bioRxiv2023

The Human Brain Connectome Weighted by the Myelin Content and Total Intra-Axonal Cross-Sectional Area of White Matter Tracts

A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features. Computational models support in vivo investigation of the mechanisms mediating this relationship but currently lack the requisite biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features to assess their potential joint utilization in computational models. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, tractometry-derived networks weighted by either tensor-based metrics or neurite density show no relationship with whole-brain functional connectivity. We conclude that structure-function brain models are likely to be improved by the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content. We anticipate that the proposed microstructure-weighted computational modeling approach will support mechanistic understanding of the structure-function relationship of the human brain. AUTHOR SUMMARYFor computational network models to provide mechanistic links between brain structure and function, they must be informed by networks in which edge weights quantify structural features relevant to brain function. Here, we characterized several weighted structural networks capturing multiscale features of white matter connectivity. We describe these networks in terms of edge weight distribution, variance and network topology, as well as their relationships with each other, edge length and function. Overall, these findings support the joint use of structural networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts in structure-function models. This thorough characterization serves as a benchmark for future investigations of weighted structural brain networks.

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bioRxiv2023

Variation in spatial dependencies across the cortical mantle discriminates the functional behaviour of primary and association cortex

Recent theories of cortical organisation maintain that important features of brain function emerge through the spatial arrangement of regions of cortex. For example, areas of association cortex are located in regions of cortex furthest from sensory and motor cortex. Association cortex is also interdigitated since adjacent regions can have relatively different patterns of functional connectivity. It is assumed that topographic properties such as distance between cortical regions constrain their functions. For example, large distances between association and sensory and motor systems may enable these areas of cortex to maintain differentiable neural patterns, while an interdigitated organisation may enable association cortex to contain many functional systems in a relatively compact space. We currently lack a formal understanding of how spatial organisation impacts brain function, limiting the ability to leverage cortical topography to facilitate better interpretations of a regions function. Here we use variograms, a quantification of spatial autocorrelation, to develop a cortex-wide profile of how functional similarity changes as a function of the distance between regions. We establish that function changes gradually within sensory and motor cortex as the distance between regions increases, while in association cortex function changes rapidly over shorter distances. Subsequent analysis suggests these differential classes of spatial dependency are related to variation in intracortical myelin between sensory motor and association cortex. Our study suggests primary and association cortex are differentiated by the degree to which function varies over space, emphasising the need to formally account for spatial properties when estimating a systems contribution to cognition and behaviour. Significance statementThe spatial arrangements of regions in the human brain are hypothesised to underpin important features of a brain regions function. Currently, however, we lack a formal understanding of how topography shapes brain function, limiting our ability to leverage topographical perspectives to inform better theories of brain function. Here we use a formal mathematical approach to establish that in regions of association cortex function varies across the cortex more rapidly than in sensory and motor cortex, a phenomenon linked to levels of intracortical myelin. This result highlights how topographical features distinguish between cortical regions with different functional profiles and provides a formal account of how spatial differences support different features of brain function.

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bioRxiv2022

Macro-scale patterns in functional connectivity associated with ongoing thought patterns and dispositional traits

Complex macro-scale patterns of brain activity that emerge during periods of wakeful rest provide insight into the organisation of neural function, how these differentiate individuals based on their traits, and the neural basis of different types of self-generated thoughts. Although brain activity during wakeful rest is valuable for understanding important features of human cognition, its unconstrained nature makes it difficult to disentangle neural features related to personality traits from those related to the thoughts occurring at rest. Our study builds on recent perspectives from work on ongoing conscious thought that highlight the interactions between three brain networks - ventral and dorsal attention networks, as well as the default mode network. We combined measures of personality with state-of-the-art indices of ongoing thoughts at rest and brain imaging analysis and explored whether this tri-partite view can provide a framework within which to understand the contribution of states and traits to observed patterns of neural activity at rest. To capture macro-scale relationships between different brain systems, we calculated cortical gradients to describe brain organisation in a low dimensional space. Our analysis established that for more introverted individuals, regions of the ventral attention network were functionally more aligned to regions of the somatomotor system and the default mode network. At the same time, a pattern of detailed self-generated thought was associated with a decoupling of regions of dorsal attention from regions in the default mode network. Our study, therefore, establishes that interactions between attention systems and the default mode network are important influences on ongoing thought at rest and highlights the value of integrating contemporary perspectives on conscious experience when understanding patterns of brain activity at rest.

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bioRxiv2022

Atypical intrinsic neural timescales in temporal lobe epilepsy

ObjectiveTemporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Here, we aimed to profile local neural function in TLE in vivo, building on prior evidence that has identified widespread structural alterations. Using multimodal MRI, we mapped intrinsic neural timescales (INT) at rest, examined associations to TLE-related structural compromise, and evaluated the clinical utility of INT. MethodsWe studied 46 TLE patients and 44 healthy controls from two independent sites, and mapped INT changes in patients relative to controls across hippocampal, subcortical, and neocortical regions. We examined region-specific associations to structural alterations and explored effects of age and epilepsy duration. A supervised machine learning paradigm assessed utility of INT for classifying patients-vs-controls and seizure focus lateralization. ResultsRelative to controls, TLE showed marked INT reductions across multiple regions bilaterally, indexing faster changing resting activity, with strongest effects in ipsilateral medial and lateral temporal regions, and sensorimotor cortices. Findings were consistent in each site and robust, albeit with reduced effect sizes, when correcting for structural alterations. TLE-related INT reductions increased with advancing disease duration, yet findings differed from aging effects seen in controls. Classifiers based on INT distinguished patients-vs-controls (balanced accuracy, 5-fold: 76{+/-}2.65%; cross-site, 72-83%) and lateralized the focus in TLE (balanced accuracy, 5-fold: 96{+/-}2.10%; cross-site, 95-97%) with high accuracies and generalization. ConclusionsOur findings robustly demonstrate atypical macroscale function in TLE in a topography that extends beyond mesiotemporal epicenters. INT measurements can assist in TLE diagnosis, seizure focus lateralization, and monitoring of disease progression, which suggests clinical utility.

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bioRxiv2022

Assortative mixing in micro-architecturally annotated brain connectomes

The wiring of the brain connects micro-architecturally diverse neuronal populations. The conventional graph model encodes macroscale brain connectivity as a network of nodes and edges, but abstracts away the rich biological detail of each regional node. Regions are different in terms of their microscale attributes, many of which are readily available through modern technological advances and data-sharing initiatives. How is macroscale connectivity related to nodal attributes? Here we investigate the systematic arrangement of white-matter connectivity with respect to multiple biological annotations. Namely, we formally study assortative mixing in annotated connectomes by quantifying the tendency for regions to be connected with each other based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species (human, macaque and mouse), and consider a range of molecular, cellular and laminar annotations, including gene expression, neurotransmitter receptors, neuron density, laminar thickness and intracortical myelin. Importantly, we disentangle the relationship between neural wiring, regional heterogeneity and spatial embedding using spatial autocorrelation-preserving null models. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections. Using meta-analytic decoding, we find that the arrangement of connectivity patterns with respect to biological annotations shape patterns of regional functional specialization. Specifically, regions that connect to biologically similar regions are associated with executive function; conversely, regions that connect with biologically dissimilar regions are associated with memory function. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.

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bioRxiv2022

Micapipe: a pipeline for multimodal neuroimaging and connectome analysis

AO_SCPLOWBSTRACTC_SCPLOWMultimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain structure, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and different spatial scales. Here, we present micapipe, an open processing pipeline for BIDS-conform multimodal MRI datasets. micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. These matrices are routinely generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, and connectivity.

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bioRxiv2022

BrainStat: A toolbox for brain-wide statistics and neuroscientific contextualization

AO_SCPLOWBSTRACTC_SCPLOWAnalysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

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bioRxiv2021

Heritability of hippocampal functional and microstructural organisation

The hippocampus is a uniquely infolded allocortical structure in the medial temporal lobe that consists of the microstructurally and functionally distinct subregions: subiculum, cornu ammonis, and dentate gyrus. The hippocampus is a remarkably plastic region that is implicated in learning and memory. At the same time it has been shown that hippocampal subregion volumes are heritable, and that genetic expression varies along a posterior to anterior axis. Here, we studied how a heritable, stable, hippocampal organisation may support its flexible function in healthy adults. Leveraging the twin set-up of the Human Connectome Project with multimodal neuroimaging, we observed that the functional connectivity between hippocampus and cortex was heritable and that microstructure of the hippocampus genetically correlated with cortical microstructure. Moreover, both functional and microstructural organisation could be consistently captured by anterior-to-posterior and medial-to-lateral axes across individuals. However, heritability of functional, relative to microstructural, organisation was found reduced, suggesting individual variation in functional organisation of subfields is under low genetic control. Last, we demonstrate that structure and function couple along its genetic axes, suggesting an interplay of stability and plasticity within the hippocampus. Our study provides new insights on the heritability of the hippocampal formation and illustrates how genetic axes may scaffold hippocampal structure and function.

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bioRxiv2021

A Riemannian approach to predicting brain function from the structural connectome

AO_SCPLOWBSTRACTC_SCPLOWOngoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono-as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.

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bioRxiv2021

Long-range connections mirror and link microarchitectural and cognitive hierarchies in the human brain

AO_SCPLOWBSTRACTC_SCPLOWCore features of higher-order cognition are hypothesized to be implemented via distributed cortical networks that are linked via long-range connections. However, these connections are biologically expensive, and it is unknown how the computational advantages long-range connections provide overcome the associated wiring costs. Our study investigated this question by exploring the relationship between long-range functional connections and local cortical microarchitecture. Specifically, our work (i) profiled distant cortical connectivity using resting-state fMRI and cortico-cortical geodesic distance mapping, (ii) assessed how long-range connections reflect local brain microarchitecture, and (iii) studied the microarchitectural similarity of regions connected through long-range connections. Analysis of two independent datasets indicated that sensory and motor areas had more clustered short-range connectivity patterns, while transmodal association cortices, including regions of the default mode network, were characterized by distributed, long-range connections. Confirmatory meta-analysis suggested that this topographical difference mirrored a shift in cognitive function, from perception/action towards emotional and social cognitive processing. Analysis of myelin-sensitive in vivo MRI in the same participants as well as post mortem histology and gene expression established that gradients in functional connectivity distance are paralleled by those present in cortical microarchitecture. Moreover, long-range connections were found to link together spatially remote regions of association cortex with an unexpectedly similar microarchitecture. These findings provide novel insights into how the organization of distributed functional networks in transmodal association cortex contribute to cognition, because they suggest that long-range connections link together distant islands of association cortex with similar microstructural features.

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bioRxiv2021

An open MRI dataset for multiscale neuroscience

AO_SCPLOWBSTRACTC_SCPLOWMultimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54{+/-}5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from i) resting-state functional imaging, ii) diffusion tractography, iii) microstructure covariance analysis, and iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and macroscale function. MICA-MICs is available on the Canadian Open Neuroscience Platforms data portal (https://portal.conp.ca).

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bioRxiv2021

The Cost of Untracked Diversity in Brain-Imaging Prediction

Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

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bioRxiv2021

Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex

Brain structure scaffolds intrinsic function, supporting cognition and ultimately behavioral flexibility. However, it remains unclear how a static, genetically controlled architecture supports flexible cognition and behavior. Here, we synthesize genetic, phylogenetic and cognitive analyses to understand how the macroscale organization of structure-function coupling across the cortex can inform its role in cognition. In humans, structure-function coupling was highest in regions of unimodal cortex and lowest in transmodal cortex, a pattern that was mirrored by a reduced alignment with heritable connectivity profiles. Structure-function uncoupling in non-human primates had a similar spatial distribution, but we observed an increased coupling between structure and function in association regions in macaques relative to humans. Meta-analysis suggested regions with the least genetic control (low heritable correspondence and different across primates) are linked to social cognition and autobiographical memory. Our findings establish the genetic and evolutionary uncoupling of structure and function in different transmodal systems may support the emergence of complex, culturally embedded forms of cognition.

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bioRxiv2021

A tale of two gradients: Differences between the left and right hemispheres predict semantic cognition and visual reasoning

Decomposition of whole-brain functional connectivity patterns reveals a principal gradient that captures the separation of sensorimotor cortex from heteromodal regions in the default mode network (DMN); this gradient captures the systematic order of networks on the cortical surface. Functional homotopy is strongest in sensorimotor areas, and weakest in heteromodal cortices, suggesting there may be differences between the left and right hemispheres (LH/RH) in the principal gradient, especially towards its apex. This study characterised hemispheric differences in the position of large-scale cortical networks along the principal gradient, and their functional significance. We collected resting-state fMRI and semantic and non-verbal reasoning task performance in 175+ healthy volunteers. We then extracted the principal gradient of connectivity for each participant and tested which networks showed significant hemispheric differences in gradient value. We investigated the functional associations of these differences by regressing participants behavioural efficiency in tasks outside the scanner against their interhemispheric gradient difference for each network. LH showed a higher overall principal gradient value, consistent with its role in heteromodal semantic cognition. One frontotemporal control subnetwork was linked to individual differences in semantic cognition: when it was nearer heteromodal DMN on the principal gradient in LH, participants showed more efficient semantic retrieval. In contrast, when a dorsal attention subnetwork was closer to the heteromodal end of the principal gradient in RH, participants showed better visual reasoning. Lateralization of function may reflect differences in connectivity between control and heteromodal regions in LH, and attention and visual regions in RH.

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bioRxiv2021

A convergent structure-function substrate of cognitive imbalances in autism

Autism is a common neurodevelopmental condition characterized by substantial phenotypic heterogeneity, which hinders diagnosis, research, and intervention. A leading example can be found in marked imbalances in language and perceptual skills, where deficits in one domain often co-exist with normal or even superior performance in the other domain. The current work capitalized on multiple data analytics including data-driven subtyping and dimensional approaches to quantify cognitive imbalances in multi-site datasets of individuals diagnosed with autism spectrum disorder (ASD) and neurotypical controls, and assessed structural and functional brain network substrates. Studying cognitive dimensions as well as multimodal neuroimaging signatures in 155 ASD and 151 neurotypical individuals, we observed robust evidence for a structure-function substrate of cognitive imbalances in ASD. Specifically, ASD presented with marked imbalances in cognitive profiles relative to neurotypical controls, characterized by verbal to non-verbal intelligence discrepancy. Different analytical approaches including subtyping and dimensional regression methods converged in showing that these imbalances were reflected in atypical cortical thickening and functional integration of language networks, alongside with sensory and higher cognitive networks. Phenotypic findings could be replicated in an independent sample of 325 ASD and 569 neurotypical controls. Although verbal and non-verbal intelligence are currently considered as specifiers unrelated to the categorical diagnosis of autism, our results show that intelligence disparities are accentuated in ASD and relate to a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity of autism and potentially inform intervention-oriented subtyping.

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bioRxiv2020

The ENIGMA Toolbox: Cross-disorder integration and multiscale neural contextualization of multisite neuroimaging datasets

Among big data initiatives, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium--a worldwide alliance of over 2,000 scientists diversified into over 50 Working Groups--has yielded some of the largest studies of the healthy and diseased brain. Integration of multisite datasets to assess transdiagnostic similarities and differences and to contextualize findings with respect to neural organization, however, have been limited. Here, we introduce the ENIGMA Toolbox, a Python/Matlab ecosystem for (i) accessing 100+ ENIGMA datasets, facilitating cross-disorder analysis, (ii) visualizing data on brain surfaces, and (iii) contextualizing findings at the microscale (postmortem cytoarchitecture and gene expression) and macroscale (structural and functional connectomes). Our Toolbox equips scientists with tutorials to explore molecular, histological, and network correlates of noninvasive neuroimaging markers of brain disorders. Moreover, our Toolbox bridges the gap between standardized data processing protocols and analytic workflows and facilitates cross-consortia initiatives. The Toolbox is documented and openly available at http://enigma-toolbox.readthedocs.io. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/423838v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@183a3f3org.highwire.dtl.DTLVardef@180d368org.highwire.dtl.DTLVardef@b752a3org.highwire.dtl.DTLVardef@1928c53_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2020

Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autism

Autism spectrum disorder (ASD) is commonly understood as a network disorder, yet case-control analyses against typically-developing controls (TD) have yielded somewhat inconsistent patterns of results. The current work was centered on a novel approach to profile functional network idiosyncrasy, the inter-individual variability in the association between functional network organization and brain anatomy, and we tested the hypothesis that idiosyncrasy contributes to connectivity alterations in ASD. Studying functional network idiosyncrasy in a multi-centric dataset with 157 ASD and 172 TD, our approach revealed higher idiosyncrasy in ASD in the default mode, somatomotor and attention networks together with reduced idiosyncrasy in the lateral temporal lobe. Idiosyncrasy was found to increase with age in both ASD and TD, and was significantly correlated with symptom severity in the former group. Association analysis with structural and molecular brain features indicated that patterns of functional network idiosyncrasy were not correlated with ASD-related cortical thickness alterations, but closely with the spatial expression patterns of intracortical ASD risk genes. In line with our main hypothesis, we could demonstrate that idiosyncrasy indeed plays a strong role in the manifestation of connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between the heterogeneity of individuals with an autism diagnosis and group-level functional signatures, and help to consolidate prior research findings on the highly variable nature of the functional connectome in ASD. Our study promotes idiosyncrasy as a potential individualized diagnostic marker of atypical brain network development.

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