An Open MRI Dataset for Multiscale Neuroscience

Abstract

Multimodal 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 Platform data portal (https://portal.conp.ca).

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

“…Our main analyses were based on a sample of 50 healthy young adults (29.82 ± 5.73 years, 21 females) evaluated in our institute who had high definition functional and microstructural in vivo MRI data available 39 . The replication analysis was based on an independent dataset of 200 unrelated healthy young adults from the Human Connectome Project (HCP) dataset 40 (28.68 ± 3.69 years, 119 females).…”

“…A detailed description of the Subjects inclusions, image processing, and analysis methodology can be found in the SI Appendix, in the Supplemental Materials and Methods. In brief, we studied 50 unrelated healthy adults (age: 29.82±5.73 years, 21 females) 39 . All participants had quality-controlled multimodal MRI data available (see below).…”

A Riemannian approach to predicting brain function from the structural connectome

“…We evaluated our proposed approach on diffusion magnetic resonance imaging (dMRI) and resting-state functional MRI (rs-fMRI) data provided by the Human Connectome Project (HCP) repository (Van Essen et al, 2013), and on the Microstructure-Informed Connectomics (MICs) dataset (Royer et al, 2021).…”

“…Participants were instructed to keep their eyes open, look at a fixation cross, and not fall asleep. A complete list of acquisition parameters can be found in the detailed imaging protocol provided by (Royer et al, 2021).…”

“…The workflow was evaluated on the prediction of FC from SC at the individual level rather than group level. Results are reported for two different datasets, namely: 326 unrelated subjects from the Human Connectome project (Van Essen et al, 2013) and 50 unrelated subjects from the Microstructure-Informed Connectomics dataset (Royer et al, 2021). We used the proposed approach to: i) study the relationship between the length of the random walks (i.e., length of indirect paths between brain regions) and prediction accuracy (quantified via Pearson's correlation coefficient), ii) test the contribution and identify the weighting schemes to define the SC that best explain the observed brain function, iii) perform region-and network-specific analyses of FC prediction as a function of path length, and iv) compare the prediction performance of the proposed approach with several state-of-the-art methods.…”