Biology subjects

Tzourio, C.

Publications and source records attributed to Tzourio, C..

10 recordsLinked to original sources

bioRxiv2025

Resting State connectivity patterns associated with trait anxiety in adolescence

Anxiety symptoms can vary across different life stages, with a higher frequency during adolescence and early adulthood, increasing the risk of developing future anxiety disorders. To date, neuroscientific research on anxiety has primarily focused on adulthood, thus limiting our understanding of how anxiety may characterize earlier stages of life, and employing mostly univariate approaches, thus discounting large-scale alterations of the brain. One intriguing hypothesis is that adolescents with trait anxiety may display similar abnormalities shown by adults in brain regions ascribed to the Default Mode Network (DMN) associated with self-referential thinking, awareness, and rumination-related processes. The present study aims to expand our previous knowledge on this topic using a large sample of young individuals to uncover the resting-state connectivity patterns associated with trait anxiety in a network approach. To test our hypotheses, we analyzed the rs-fMRI images of 1263 adolescents (mean age 20.55 years) as well as their scores on anxiety trait. A significant association between trait anxiety and resting-state functional connectivity in two networks was found, with some regions overlapping with the Default Mode Network. The first network included regions such as the cingulate gyrus and the middle temporal gyri known to be involved in self-referential processing and emotional perception and control, both altered in anxiety disorders. The second network included the precuneus, possibly related to rumination that characterizes anxiety. Of note, the higher the trait anxiety, the lower the connectivity within both networks, suggesting abnormal self-referential processing, awareness, and emotion regulation abilities in adolescents with high anxiety trait. These findings provided a better understanding of the association between trait anxiety and brain rs-functional connectivity, and may pave the way for the development of potential biomarkers in adolescents with anxiety.

neuroscience↗

bioRxiv2023

Drowsiness during resting-state fMRI: a major confounder of functional connectivity

This research explores the effects of drowsiness on variability in functional connectivity (FC) during resting-state functional magnetic resonance imaging. The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy (N=68), alert (N=96), and mixed/undetermined states based on observed respiratory oscillations. Five different processing methods were employed, the reference method, two correction methods based on physiological and global regression approaches, and two based on Gaussian standardizations. According to the reference methodology, the results indicate that drowsy individuals exhibit higher cortico-cortical FC than alert individuals. However, the differences between drowsy and alert states were reduced when applying correction methods based on physiological and global regression approaches. The global regression-based strategy was the most effective among these correction methods, minimizing significant FC differences to only 3.3% of the total FCs. Utilizing the Gaussian-based methods, both cortico-subcortical and intra-default mode network regions demonstrated significantly greater FCs in awake than drowsy subjects. These findings align with previous studies suggesting that, in the descent to sleep, the cortex isolates itself to facilitate the transition into deeper sleep stages while also disconnecting the default mode network. The Gaussian standardization methods and the global regression-based correction approach efficiently address the hemodynamic variations caused by the rapid alternation between the N1 stage and wakefulness. These variations contribute to the measurement of cortico-cortical pseudo connectivity observed in the reference methodology. In summary, these findings underscore the importance of considering drowsiness in rs-fMRI studies and demonstrate that there is no single optimal correction methodology for processing fMRI data

neuroscience↗

bioRxiv2023

Early detection of white matter hyperintensities using SHIVA-WMH detector

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 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: 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 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 (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. HighlightsO_LIWe propose a novel 3D Unet-based model, SHIVA-WMH detector, with much improved detection of small WMH across subjects with a wide range of WMH burden compared to existing methods C_LIO_LIWe characterize microstructural properties of small white matter hyperintensities in young adults from MRi-Share study C_LI

neuroscience↗

bioRxiv2021

Changes in regional white matter volumetry and microstructure during the post-adolescence period: a cross-sectional study of a cohort of 1,713 university students

Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilised to study white matter changes during development and ageing. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18 to 26. We assessed the age and sex dependence, as well as hemispheric asymmetry of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterised by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. We also report a robust hemispheric asymmetry in both the volume and microstructural properties in many regions. These findings emphasise the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.

neuroscience↗

bioRxiv2020

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 mm3 and 0.95 for PVSs larger than 15 mm3. 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.

neuroscience↗

bioRxiv2020

Deep learning-based classification of resting-state fMRI independent-component analysis

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92%. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96% of the gray matter. Second, we proposed an individualbased analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

neuroscience↗

bioRxiv2020

The MRi-Share database: brain imaging in a cross-sectional cohort of 1,870 university students

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.

neuroscience↗

bioRxiv2020

Age-related changes of Peak width Skeletonized Mean Diffusivity (PSMD) across the adult life span: a multi-cohort study

Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and meta-analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing DTI metrics. For each individual MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40 and 50, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.

neuroscience↗

bioRxiv2019

The Genetic History of France

The study of the genetic structure of different countries within Europe has provided significant insights into their demographic history and their actual stratification. Although France occupies a particular location at the end of the European peninsula and at the crossroads of migration routes, few population genetic studies have been conducted so far with genome-wide data. In this study, we analyzed SNP-chip genetic data from 2 184 individuals born in France who were enrolled in two independent population cohorts. Using FineStructure, six different genetic clusters of individuals were found that were very consistent between the two cohorts. These clusters match extremely well the geography and overlap with historical and linguistic divisions of France. By modeling the relationship between genetics and geography using EEMS software, we were able to detect gene flow barriers that are similar in the two cohorts and corresponds to major French rivers or mountains. Estimations of effective population sizes using IBDNe program also revealed very similar patterns in both cohorts with a rapid increase of effective population sizes over the last 150 generations similar to what was observed in other European countries. A marked bottleneck is also consistently seen in the two datasets starting in the fourteenth century when the Black Death raged in Europe. In conclusion, by performing the first exhaustive study of the genetic structure of France, we fill a gap in the genetic studies in Europe that would be useful to medical geneticists but also historians and archeologists.

genetics↗

bioRxiv2017

Genome-wide Association Study Links APOEϵ4 and BACE1 Variants with Plasma Amyloid β Levels

INTRODUCTIONThere is increasing interest in plasma A{beta} as an endophenotype and biomarker of Alzheimers disease (AD). Identifying the genetic determinants of plasma A{beta} levels may elucidate important processes that determine plasma A{beta} measures. METHODSWe included 12,369 non-demented participants derived from eight population-based studies. Imputed genetic data and plasma A{beta}1-40, A{beta}1-42 levels and A{beta}1-42/A{beta}1-40 ratio were used to perform genome-wide association studies, gene-based and pathway analyses. Significant variants and genes were followed-up for the association with PET A{beta} deposition and AD risk. RESULTSSingle-variant analysis identified associations across APOE for A{beta}1-42 and A{beta}1-42/A{beta}1-40 ratio, and BACE1 for A{beta}1-40. Gene-based analysis of A{beta}1-40 additionally identified associations for APP, PSEN2, CCK and ZNF397. There was suggestive interaction between a BACE1 variant and APOE{varepsilon}4 on brain A{beta} deposition. DISCUSSIONIdentification of variants near/in known major A{beta}-processing genes strengthens the relevance of plasma-A{beta} levels both as an endophenotype and a biomarker of AD.

genetics↗