The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students

Tsuchida, Ami; Laurent, Alexandre; Crivello, Fabrice; Petit, Laurent; Joliot, Marc; Pepe, Antonietta; Beguedou, Naka; Gueye, Marie-Fateye; Verrecchia, Violaine; Nozais, Victor; Mellet, Emmanuel; Debette, Stéphanie; Tzourio, Christophe; Mazoyer, Bernard

doi:10.1007/s00429-021-02334-4

Cited by 18 publications

(42 citation statements)

References 118 publications

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“…The MRi-Share database is a subcomponent of a larger, prospective cohort study on French university students' health, called iShare (internet-based Student Health Research enterprise, www.i-share.fr). The detailed study and MRI acquisition protocol have been described in Tsuchida et al, (2021). For the training and evaluation of the SHIVA-WMH detector, we used the MRI data and manually-traced WMH from the same sub-sample of 50 subjects described in Boutinaud et al, (2021), drawn from the total sample of 1,867 MRi-Share subjects (mean age 22.1 years, range 18 to 35, 72% female).…”

Section: Mri-sharementioning

confidence: 99%

“…Because our MWC test dataset is part of the training dataset in the challenge (and therefore in the PGS model), evaluation of this tool in the MWC test dataset was not performed. Since this tool had been trained on the bias-field corrected data prepared by the challenge organizers (using SPM12), we used the bias-field corrected T1w and FLAIR images of the MRi-Share (based on SPM12, as described in Tsuchida et al, (2021)) and UKB datasets (based on FSL FAST, as described in Alfaro-Almagro et al, (2018)) as inputs for this tool.…”

Section: Pgs (Park Et Al 2021)mentioning

confidence: 99%

“…In the present study, we took advantage of the unique, large neuroimaging database of French university students called MRi-Share (Tsuchida et al, 2021). We first characterize small WMH visible on the 1mm isotropic 3D FLAIR scans in a subsample of 50 MRi-Share participants.…”

Section: Introductionmentioning

confidence: 99%

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Early detection of white matter hyperintensities using SHIVA-WMH detector

Tsuchida

Boutinaud

Verrecchia

et al. 2023

Preprint

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White matter hyperintensities (WMH) are well-established markers of cerebral small vessel disease (cSVD), and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for the detection of small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e. with confluency) in evaluation dataset from three distinct databases: MRi-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with the wide-rangind WMH load, our tool achieved the voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LST-LPA: Schmidt, 2017), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al, 2021), and HyperMapper tool (HPM: Mojiri Forooshani et al., 2022), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.

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Section: Mri-sharementioning

confidence: 99%

Section: Pgs (Park Et Al 2021)mentioning

confidence: 99%

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Early detection of white matter hyperintensities using SHIVA-WMH detector

Tsuchida

Boutinaud

Verrecchia

et al. 2023

Preprint

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“…To examine the impact of PVS risk variants across the lifecourse, we explored the association of the aforementioned PVS risk loci identi ed in older adults (mean age 66.3±8.6 years) with PVS burden in 1,748 young healthy adults participating in the i-Share cohort (mean age 22.1±2.3 years). 10,37 A WM PVS weighted genetic risk score (wGRS) derived from the PVS GWAS was signi cantly associated with WM PVS in young adults (OR=1.11…”

Section: Implication Across the Lifespan And Clinical Correlates Of I...mentioning

confidence: 99%

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Duperron

Knol

Grand

et al. 2022

Alzheimer's & Dementia

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Perivascular space burden (PVS) is an emerging and possibly the earliest magnetic resonance imaging (MRI)marker of cerebral small vessel disease (cSVD), a leading cause of stroke and dementia. Its molecular underpinnings are unknown. Genome-wide and whole-exome association studies in 40,095 participants (21 population-based cohorts, 66.3±8.6 years) revealed 24 genome-wide signi cant PVS risk loci. These showed association with white matter PVS already at age 20, suggesting an important role of early-life factors. PVS loci were enriched in genes causing early-onset leukodystrophies and genes expressed in fetal brain endothelial cells.Mendelian randomization analyses supported causal associations of high blood pressure with basal ganglia (BG) and hippocampal PVS, and of BG PVS with stroke. Transcriptome-wide association studies suggest causal implication of 11 genes, to prioritize for experimental follow-up as putative biotargets for cSVD. Two-thirds of PVS loci point to novel pathways, involving extracellular matrix, membrane transport, and developmental processes, with enrichment in targets of existing drugs for vascular/cognitive disorders.

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“…Following initial participation, the i-Share participants were invited to complete a follow-up questionnaire every year. Those recruited at the Bordeaux site were also given the information regarding MRi-Share, the i-Share neuroanatomical substudy held between November 2015 and November 2017 and approved by the Bordeaux CPP SOMIII Ethics Committee (Tsuchida et al, 2021). Students interested in contributing to MRi-Share who received all the pertinent information were checked for the absence of any cause for exclusion (i.e., (1) age over 35 years; (2) pregnancy or nursing; (3) claustrophobia; and (4) contraindications for head MRI), gave their informed written consent, and received compensation for their participation.…”

Section: Design Study Population and Data Collectionmentioning

confidence: 99%

Cluster-based characterization of consistencies in individuals' thought profiles at rest in a cohort of 1779 French university students

2022

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Is ongoing conscious thought spontaneous and situation-related, or is it recurrent and dependent on psychological dispositions? The answer is critical for resting-state functional connectivity (RSFC) paradigms that seek to correlate neuroanatomical states with conscious mental states. The goal of the present study was to characterize individual resting state thought profiles (RSTPs) and identify the recurrent ones, i.e., that could both be predicted by personality traits and predict subsequent negative affective states. The 1779 participants had a mean age of 22.1 years, 71.8% were females, and 71.8% were undergraduates. We collected the form and content of their thoughts during a 15-min RSFC session with a computerized retrospective self-questionnaire (ReSQ 2.0). Subsamples of participants also completed online autoquestionnaires assessing their psychological maturity and trait negative affectivity (with a four-day gap on average, N = 1270) and subsequent depressive and anxious states (1.4 years later on average, N = 922). Based on the multiple correspondence and clustering analyses of the ReSQ 2.0 responses, we identified six RSTPs distinctive by their content scope, temporal orientation, empathetic concern, and emotional valence. Multivariate analyses revealed that the probability of experiencing five of the six RSTPs was predicted by trait negative affectivity interacting with psychological maturity. Among them, a negatively valenced RSTP also increased the likelihood of subsequent negative affective states, suggesting its stable and recurrent nature. Identifying recurrent RSTPs is helpful for the future understanding of RSTPs’ contribution to RSFC. Additionally, it will be relevant to test whether acting on psychological maturity can alter the relationship between ongoing conscious thought and negative affectivity.

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The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students

Cited by 18 publications

References 118 publications

Early detection of white matter hyperintensities using SHIVA-WMH detector

Early detection of white matter hyperintensities using SHIVA-WMH detector

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Cluster-based characterization of consistencies in individuals' thought profiles at rest in a cohort of 1779 French university students

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