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Royer, J.

Publications and source records attributed to Royer, J..

58 records · Page 4Linked to original sources

Myeloarchitecture gradients in the human insula serve as blueprints for its diverse connectivity and function

AO_SCPLOWBSTRACTC_SCPLOWInsular cortex is a core hub involved in multiple cognitive and socio-affective processes. Yet, the anatomical mechanisms that explain how it is involved in such a diverse array of functions remain incompletely understood. Here, we define a novel framework to test the hypothesis that changes in myeloarchitecture across the insular cortex explain how it can be involved in many different facets of cognitive function. Detailed intracortical profiling, performed across hundreds of insular locations on the basis of myelin-sensitive magnetic resonance imaging (MRI), was compressed into a lower-dimensional space uncovering principal axes of myeloarchitectonic variation. Leveraging two datasets with different high-resolution MRI contrasts, we obtained robust support for two principal dimensions of insular myeloarchitectonic differentiation in vivo, one running from ventral anterior to posterior banks and one radiating from dorsal anterior towards both ventral anterior and posterior subregions. Analyses of post mortem 3D histological data showed that the antero-posterior axis was mirrored in cytoarchitectural markers, even when controlling for anatomical landmarks and sulco-gyral folding. Resting-state functional connectomics in the same individuals and ad hoc meta-analyses showed that myelin gradients in the insula constrained affiliation to macroscale intrinsic functional systems, showing differential shifts in functional network embedding across each myelin-derived gradient. Collectively, our findings offer a novel approach to capture structure-function interactions of a key node of the limbic system, and suggest a multidimensional structural basis underlying the diverse functional roles of the insula.

neuroscience↗

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact manner through the use of multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. More generally, its macroscale perspective on brain organization offers novel possibilities to investigate the complex relationships between brain structure, function, and cognition in a quantified manner. Here, we present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Our toolbox also allows for controlled association studies between gradients with other brain-level features, adjusted with respect to several null models that account for spatial autocorrelation. The toolbox is implemented in both Python and Matlab, programming languages widely used by the neuroimaging and network neuroscience communities. Several use-case examples and validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.

neuroscience↗

Targeting age-related differences in brain and cognition with multimodal imaging and connectome topography profiling

I.AO_SCPLOWBSTRACTC_SCPLOWAging is characterised by accumulation of structural and metabolic changes in the brain. Recent studies suggest transmodal brain networks are especially sensitive to aging, which, we hypothesise, may be due to their apical position in the cortical hierarchy. Studying an open-access healthy cohort (n=102, age range = 30-89 years) with MRI and A{beta} PET data, we estimated age-related cortical thinning, hippocampal atrophy and A{beta} deposition. In addition to carrying out surface-based morphological and metabolic mapping, we stratified effects along neocortical and hippocampal resting-state functional connectome gradients derived from independent datasets. The cortical gradient depicts an axis of functional differentiation from sensory-motor regions to transmodal regions, whereas the hippocampal gradient recapitulates its long-axis. While age-related thinning and increased A{beta} deposition occurred across the entire cortical topography, increased A{beta} deposition was especially pronounced towards higher-order transmodal regions. Age-related atrophy was greater towards the posterior end of the hippocampal long-axis. No significant effect of age on A{beta} deposition in the hippocampus was observed. Imaging markers correlated with behavioural measures of fluid intelligence and episodic memory in a topography-specific manner. Our results strengthen existing evidence of structural and metabolic change in the aging brain and support the use of connectivity gradients as a compact framework to analyse and conceptualize brain-based biomarkers of aging.

neuroscience↗

Deep Learning Predicts Tuberculosis Drug Resistance Status from Whole-Genome Sequencing Data

BackgroundThe diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data.\n\nMethods and FindingsUsing targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3,601 Mycobacterium tuberculosis strains, 1,228 of which were multidrug resistant, we investigated the use of machine learning to predict phenotypic drug resistance to 10 anti-tuberculosis drugs. The final model, a multitask wide and deep neural network (MD-WDNN), achieved improved high predictive performance: the average AUCs were 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the MD-WDNN showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. In addition to being able to learn from samples that have only been partially phenotyped, our proposed multidrug architecture shares information across different anti-tuberculosis drugs and genes to provide a more accurate phenotypic prediction. We use t-distributed Stochastic Neighbor Embedding (t-SNE) visualization and feature importance analyses to examine inter-drug similarities.\n\nConclusionsMachine learning is capable of accurately predicting resistant status using genomic information and holds promise in bringing sequencing technologies closer to the bedside.

bioinformatics↗