TOWARDS A DATA MODEL FOR PLM APPLICATION IN BIO-MEDICAL IMAGING

International audienceBio-Medical Imaging (BMI) is currently confronted to data issues similar to those of the manufacturing industry twenty years ago. In particular, the need for data sharing and reuse has never been so strong to foster major discoveries in neuroimaging. Some data management systems have been developed to meet the requirements of BMI large-scale research studies. However, many efforts to integrate the data provenance along a research study, from the specifications to the published results, are to be done. Product Lifecycle Management (PLM) systems are designed to comply with manufacturing industry expectations of providing the right information at the right time and in the right context. Consequently PLM systems are proposed to be relevant for the management of BMI data. From a need analysis led with the GIN research group, the BMI-LM data model is designed: it is PLM-oriented, generic (enabling the management of many types of data such as imaging, clinical, psychology or genetics), flexible (enabling users’ customisation) and it covers the whole stages of a BMI study from specifications to publication. The test implementation of the BMI-LM model into a PLM system is detailed. The preliminary feed-back of the GIN researchers is discussed in this paper: the BMI-LM data model and the PLM concepts are relevant to manage BMI data, but PLM systems interfaces are unsuitable for BMI researchers

Neural correlates of perceiving and interpreting engraved prehistoric patterns as human production: Effect of archaeological expertise

It has been suggested that engraved abstract patterns dating from the Middle and Lower Palaeolithic served as means of representation and communication. Identifying the brain regions involved in visual processing of these engravings can provide insights into their function. In this study, brain activity was measured during perception of the earliest known Palaeolithic engraved patterns and compared to natural patterns mimicking human-made engravings. Participants were asked to categorise marks as being intentionally made by humans or due to natural processes (e.g. erosion, root etching). To simulate the putative familiarity of our ancestors with the marks, the responses of expert archaeologists and control participants were compared, allowing characterisation of the effect of previous knowledge on both behaviour and brain activity in perception of the marks. Besides a set of regions common to both groups and involved in visual analysis and decision-making, the experts exhibited greater activity in the inferior part of the lateral occipital cortex, ventral occipitotemporal cortex, and medial thalamic regions. These results are consistent with those reported in visual expertise studies, and confirm the importance of the integrative visual areas in the perception of the earliest abstract engravings. The attribution of a natural rather than human origin to the marks elicited greater activity in the salience network in both groups, reflecting the uncertainty and ambiguity in the perception of, and decision-making for, natural patterns. The activation of the salience network might also be related to the process at work in the attribution of an intention to the marks. The primary visual area was not specifically involved in the visual processing of engravings, which argued against its central role in the emergence of engraving production.publishedVersio

Hum Brain Mapp

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, 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 detecting 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 datasets from three distinct databases: magnetic resonance imaging-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 a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), 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.Etude de cohorte sur la santé des étudiantsStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseLaboratoire pour les applications en imagerie biomédicaleTranslational Research and Advanced Imaging LaboratoryInitiative d'excellence de l'Université de Bordeau

Cortical Terminations of the Inferior Fronto-Occipital and Uncinate Fasciculi: Anatomical Stem-Based Virtual Dissection

International audienceWe combined the neuroanatomists' approach of defining a fascicle as all fibers passing through its compact stem with diffusion-weighted tractography to investigate the cortical terminations of two association tracts, the inferior fronto-occipital fasciculus (IFOF) and the uncinate fasciculus (UF), which have recently been implicated in the ventral language circuitry. The aim was to provide a detailed and quantitative description of their terminations in 60 healthy subjects and to do so to apply an anatomical stem-based virtual dissection, mimicking classical post-mortem dissection, to extract with minimal a priori the IFOF and UF from tractography datasets. In both tracts, we consistently observed more extensive termination territories than their conventional definitions, within the middle and superior frontal, superior parietal and angular gyri for the IFOF and the middle frontal gyrus and superior, middle and inferior temporal gyri beyond the temporal pole for the UF. We revealed new insights regarding the internal organization of these tracts by investigating for the first time the frequency, distribution and hemispheric asymmetry of their terminations. Interestingly, we observed a dissociation between the lateral right-lateralized and medial left-lateralized fronto-occipital branches of the IFOF. In the UF, we observed a rightward lateralization of the orbito-frontal and temporal branches. We revealed a more detailed map of the terminations of these fiber pathways that will enable greater specificity for correlating with diseased populations and other behavioral measures. The limitations of the diffusion tensor model in this study are also discussed. We conclude that anatomical stem-based virtual dissection with diffusion tractography is a fruitful method for studying the structural anatomy of the human white matter pathways

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm(3) and 0.95 for PVSs larger than 15 mm(3). We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.Stopping cognitive decline and dementia by fighting covert cerebral small vessel diseas

Imaging genetics of language network functional connectivity reveals links with language-related abilities, dyslexia and handedness

Published on 28 september 2024Language is supported by a distributed network of brain regions with a particular contribution from the left hemisphere. A multi-level understanding of this network requires studying its genetic architecture. We used resting-state imaging data from 29,681 participants (UK Biobank) to measure connectivity between 18 left-hemisphere regions involved in multimodal sentence-level processing, as well as their right-hemisphere homotopes, and interhemispheric connections. Multivariate genome-wide association analysis of this total network, based on genetic variants with population frequencies >1%, identified 14 genomic loci, of which three were also associated with asymmetry of intrahemispheric connectivity. Polygenic dispositions to lower language-related abilities, dyslexia and left-handedness were associated with generally reduced leftward asymmetry of functional connectivity. Exome-wide association analysis based on rare, protein-altering variants (frequencies <1%) suggested 7 additional genes. These findings shed new light on genetic contributions to language network organization and related behavioural traits.This research was funded by the Max Planck Society (Germany), together with grants from the Netherlands Organisation for Scientific Research (NWO) (grant number 054-15-101) and French National Research Agency (ANR, grant No. 15-HBPR-0001-03) as part of the FLAG-ERA consortium project “MULTI-LATERAL”, a Partner Project to the European Union’s Flagship Human Brain Project, and the Language in Interaction consortium (NWO Gravitation grant number 024-001-006). The study was conducted using the UK Biobank resource under application no. 16066 with C.F. as the principal applicant. Our study made use of quality-controlled brain images generated by an image-processing pipeline developed and run on behalf of the UK Biobank. The funders had no role in study design, data collection and analysis, and the decision to publish or preparation of the manuscript. The authors thank Else Eising, Giacomo Bignardi and Tristan Looden for their thoughts on the methodology. The authors thank Fabrice Crivello and Antonietta Pepe for their involvement in the inception of this project. The authors would like to thank the research participants and employees of 23andMe, Inc. for making this work possible

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

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.Etude de cohorte sur la santé des étudiantsStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseStudy on Environmental and GenomeWide predictors of early structural brain Alterations in Young student