Biology subjects

Coupe, P.

Publications and source records attributed to Coupe, P..

6 recordsLinked to original sources

bioRxiv2025

The link between gender inequality and the distribution of brain regions' relative sizes across the lifespan and the world

Evidence is emerging that the socioeconomic environment in general, and gender inequality in particular, can be a shaping force on brain structure. However, our understanding of the nature of this influence throughout the lifespan is often limited because most current data sets are geographically and demographically narrow, making it unclear whether results hold across distinct world populations. Here we analyse, for the first time, data from an online MRI analysis platform comprising 13277 subjects from 52 countries and the five continents, across ages that range from childhood to late life. We examined how gender inequality, jointly examined with economic inequality, relates to differences in brain grey matter between males and females. We found that the association between female-male brain differences and gender inequality increases with age, suggesting a cumulative effect of gender inequality throughout life. Further, by considering additional variables that are specifically related to the economy, we found that this effect was, as per current data, dominated by the economic aspects of inequality.

neuroscience↗

bioRxiv2023

Dynamic evolution of infarct volumes at MRI in ischemic stroke due to large vessel occlusion

Background and ObjectivesThe typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes. MethodsWe conducted a secondary analysis from prospectively collected databases (FRAME, 2017- 2019; ETIS, 2015-2022). We selected acute MRI data from anterior LVO stroke patients with witnessed onset which were divided into training- and independent validation-datasets. In the training-dataset, using Gaussian mixture analysis, we classified the patients into three growth groups based on their rate of infarct growth (diffusion volume / time-to-imaging). Subsequently, we extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth. We used these charts to attribute a growth group to the independent patients from the validation-dataset. We compared their 3-month modified Rankin scale (mRS) with the predicted values based on a multivariable regression model from the training-dataset that used growth group as independent variable. ResultsWe included 804 patients (median age, 73.0 years [IQR, 61.2-82.0 years]; 409 men). The training-dataset revealed non-supervised clustering into 11% (74/703) slow, 62% (437/703) intermediate, and 27% (192/703) fast progressors. Infarct volume evolutions were best fitted with a linear (r=0.809; P<.001), cubic (r=0.471; P<.001), and power (r=0.63; P<.001) functions for the slow, intermediate and fast progressors, respectively. Notably, the deep nuclei and insular cortex were rapidly affected in the intermediate and fast groups with further cortical involvement in the fast group. The variable "growth group" significantly predicted 3-month mRS (multivariate OR, 0.51; 95% CI: 0.37-0.72, P<.0001) in the training-dataset, yielding a mean AUC of 0.78 (95% CI: 0.66-0.88) in the independent validation-dataset. ConclusionsWe revealed spatiotemporal archetype dynamic evolutions following large vessel occlusion stroke according to three growth phenotypes called slow, intermediate and fast progressors, providing insight into anticipating clinical outcome. We expect this could help in designing neuroprotective trials aiming at modulating infarct growth prior EVT.

pathology↗

bioRxiv2023

Deep Learning disconnectomes to accelerate and improve long-term predictions for post-stroke symptoms

Deep learning as a truly transformative force is revolutionizing a wide range of fields, making a significant difference in medical imaging, where recent advancements have yielded some truly remarkable outcomes. In a connected brain, maps of white matter damage -- otherwise known as disconnectomes -- are essential for capturing the effects of focal lesions. However, the current tools for obtaining such information are prohibitively slow and not admitted for clinical usage. Here, we have explored the potential of deep-learning models to accurately generate disconnectomes in a population of stroke survivors. We trained a 3D U-Net algorithm to produce deep-disconnectomes from binary lesion masks. This artificial neural network was able to capture most information obtained in conventional disconnectomes, i.e., statistical maps filtering normative white-matter networks, but output a deep-disconnectome 170 times faster - compared to disconnectome computation with the state-of-the-art BCBToolkit software. Moreover, the deep-disconnectomes were challenged to predict cognitive and behavioral outcomes one-year post-stroke. In an additional cohort of N=139 stroke survivors, N=86 neuropsychological scores were predicted from deep-disconnectomes achieving, on average, 85.2% of accuracy and R2= 0.208. The deep-disconnectomes predictivity power outperformed the conventional disconnectome predictions for clinical scores. In summary, we have achieved a significant milestone for clinical neuroimaging by accelerating and ameliorating the creation of disconnectome maps using deep learning. By integrating deep learning into the management of stroke, one of the most prevailing catalysts for acquired disabilities, we deepen our understanding of its impact on the brain. This novel approach may offer potential avenues for acute intervention, ultimately enhancing patients overall quality of life.

neuroscience↗

bioRxiv2023

Lifespan Neurodegeneration Of The Human Brain In Multiple Sclerosis

BackgroundAtrophy related to Multiple Sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown. MethodsWe modeled the volumetric trajectories of brain structures across the entire lifespan using 40944 subjects (38295 healthy controls and 2649 MS patients). Then, we estimated the chronological progression of MS by assessing the divergence of lifespan trajectories between normal brain charts and MS brain charts. ResultsChronologically, the first affected structure was the thalamus, then the putamen and the pallidum (3 years later), followed by the ventral diencephalon (7 years after thalamus) and finally the brainstem (9 years after thalamus). To a lesser extent, the anterior cingulate gyrus, insular cortex, occipital pole, caudate and hippocampus were impacted. Finally, the precuneus and accumbens nuclei exhibited a limited atrophy pattern. ConclusionSubcortical atrophy was more pronounced than cortical atrophy. The thalamus was the most impacted structure with a very early divergence in life. It paves the way toward utilization of these lifespan models for future preclinical/prodromal prognosis and monitoring of MS.

neuroscience↗

bioRxiv2018

Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimers disease (AD). While anatomical MRI captures structural alterations, studies demonstrated the ability of diffusion MRI to capture microstructural modifications at an earlier stage. Several methods have focused on hippocampus structure to detect AD. To date, the patch-based grading framework provides the best biomarker based on the hippocampus. However, this structure is complex since the hippocampus is divided into several heterogeneous subfields not equally impacted by AD. Former in-vivo imaging studies only investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to study the efficiency of hippocampal subfields compared to the whole hippocampus structure with a multimodal patch-based framework that enables to capture subtler structural and microstructural alterations. To this end, we analyze the significance of the different hippocampal subfields for AD diagnosis and prognosis with volumetric, diffusivity measurements and a novel multimodal patch-based grading framework that combines structural and diffusion MRI. The experiments conducted in this work showed that the whole hippocampus provides the most discriminant biomarkers for advanced AD detection while biomarkers applied into subiculum obtain the best results for AD prediction, improving by 2% the accuracy compared to the whole hippocampus.

neuroscience↗

bioRxiv2017

TIMELINE OF BRAIN ALTERATIONS IN ALZHEIMER’S DISEASE ACROSS THE ENTIRE LIFESPAN

Brain imaging studies have shown that progressive cerebral atrophy characterized the development of Alzheimers Disease (AD). The key question is how long before the diagnosis of AD the neurodegenerative process started leading to these structural alterations. To answer this question, we proposed an innovative way by inferring brain structure volume trajectories across the entire lifespan using massive number of MRI (N=4714). Our study provides evidences of early divergence of the AD model from the control model for the hippocampus before 40 years, followed by the lateral ventricles and the amygdala around 40 years for the AD model. Moreover, our lifespan investigation reveals the dynamic of the evolution of these biomarkers and suggest close abnormality trajectories for the hippocampus and the amygdala. Finally, our results highlight that the temporal lobe atrophy, a key biomarker in AD, is a very early pathophysiological event potentially associated to early life exposures to risk factors.

neuroscience↗