Showing papers by "Pierrick Coupé published in 2022"
TL;DR: It is concluded that this MRI scheme of atrophy progression in Alzheimer’s disease was close but did not entirely overlap with Braak staging of tauopathy, with a ‘reverse chronology’ between limbic and entorhinal stages.
Abstract: Abstract The chronological progression of brain atrophy over decades, from pre-symptomatic to dementia stages, has never been formally depicted in Alzheimer’s disease. This is mainly due to the lack of cohorts with long enough MRI follow-ups in cognitively unimpaired young participants at baseline. To describe a spatiotemporal atrophy staging of Alzheimer’s disease at the whole-brain level, we built extrapolated lifetime volumetric models of healthy and Alzheimer’s disease brain structures by combining multiple large-scale databases (n = 3512 quality controlled MRI from 9 cohorts of subjects covering the entire lifespan, including 415 MRI from ADNI1, ADNI2 and AIBL for Alzheimer’s disease patients). Then, we validated dynamic models based on cross-sectional data using external longitudinal data. Finally, we assessed the sequential divergence between normal aging and Alzheimer’s disease volumetric trajectories and described the following staging of brain atrophy progression in Alzheimer’s disease: (i) hippocampus and amygdala; (ii) middle temporal gyrus; (iii) entorhinal cortex, parahippocampal cortex and other temporal areas; (iv) striatum and thalamus and (v) middle frontal, cingular, parietal, insular cortices and pallidum. We concluded that this MRI scheme of atrophy progression in Alzheimer’s disease was close but did not entirely overlap with Braak staging of tauopathy, with a ‘reverse chronology’ between limbic and entorhinal stages. Alzheimer’s disease structural progression may be associated with local tau accumulation but may also be related to axonal degeneration in remote sites and other limbic-predominant associated proteinopathies.
12 citations
TL;DR: In this article , the authors performed a dimensional analysis of cerebellar anatomy in an independent cohort of 352 individuals with autism-related symptoms and found no significant difference in the cerebellum when comparing individuals with ASD and control subjects using linear models.
10 citations
TL;DR: An innovative MRI‐based method for Alzheimer disease detection and mild cognitive impairment (MCI) prognostic, using lifespan trajectories of brain structures, which demonstrated better classification performance and HAVAs simplicity makes it fully understandable and thus well‐suited for clinical practice or future pharmaceutical trials.
Abstract: In this article, we present an innovative MRI‐based method for Alzheimer disease (AD) detection and mild cognitive impairment (MCI) prognostic, using lifespan trajectories of brain structures. After a full screening of the most discriminant structures between AD and normal aging based on MRI volumetric analysis of 3,032 subjects, we propose a novel Hippocampal‐Amygdalo‐Ventricular Atrophy score (HAVAs) based on normative lifespan models and AD lifespan models. During a validation on three external datasets on 1,039 subjects, our approach showed very accurate detection (AUC ≥ 94%) of patients with AD compared to control subjects and accurate discrimination (AUC = 78%) between progressive MCI and stable MCI (during a 3‐year follow‐up). Compared to normative modeling, classical machine learning methods and recent state‐of‐the‐art deep learning methods, our method demonstrated better classification performance. Moreover, HAVAs simplicity makes it fully understandable and thus well‐suited for clinical practice or future pharmaceutical trials.
6 citations
TL;DR: In this article , the authors identify magnetic resonance (MR) metrics that are most sensitive to early changes in the brain in spinocerebellar ataxia type 1 and type 3 using an advanced multimodal MR imaging (MRI) protocol in the multisite trial setting.
Abstract: This study was undertaken to identify magnetic resonance (MR) metrics that are most sensitive to early changes in the brain in spinocerebellar ataxia type 1 (SCA1) and type 3 (SCA3) using an advanced multimodal MR imaging (MRI) protocol in the multisite trial setting.SCA1 or SCA3 mutation carriers and controls (n = 107) underwent MR scanning in the US-European READISCA study to obtain structural, diffusion MRI, and MR spectroscopy data using an advanced protocol at 3T. Morphometric, microstructural, and neurochemical metrics were analyzed blinded to diagnosis and compared between preataxic SCA (n = 11 SCA1, n = 28 SCA3), ataxic SCA (n = 14 SCA1, n = 37 SCA3), and control (n = 17) groups using nonparametric testing accounting for multiple comparisons. MR metrics that were most sensitive to preataxic abnormalities were identified using receiver operating characteristic (ROC) analyses.Atrophy and microstructural damage in the brainstem and cerebellar peduncles and neurochemical abnormalities in the pons were prominent in both preataxic groups, when patients did not differ from controls clinically. MR metrics were strongly associated with ataxia symptoms, activities of daily living, and estimated ataxia duration. A neurochemical measure was the most sensitive metric to preataxic changes in SCA1 (ROC area under the curve [AUC] = 0.95), and a microstructural metric was the most sensitive metric to preataxic changes in SCA3 (AUC = 0.92).Changes in cerebellar afferent and efferent pathways underlie the earliest symptoms of both SCAs. MR metrics collected with a harmonized advanced protocol in the multisite trial setting allow detection of disease effects in individuals before ataxia onset with potential clinical trial utility for subject stratification. ANN NEUROL 2022.
6 citations
TL;DR: A fully automatic pipeline for whole brain segmentation and analysis, which densely labels the brain while being robust to the presence of WML, based on a fast and multiscale multi-atlas label fusion technology with systematic error correction.
Abstract: Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels (N > 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide.
5 citations
TL;DR: In this article , the authors evaluated the relationship between normal-appearing white matter integrity and cognitive outcome after post-ischemic stroke (IS) and found that patients who decreased their NAWM FA more over the year had a slower cognitive improvement on MoCA (β = - 0.11, p = 0.05).
Abstract: Normal-appearing white matter (NAWM) is a hub of plasticity, but data relating to its influence on post-ischemic stroke (IS) outcome remain scarce. The aim of this study was to evaluate the relationship between NAWM integrity and cognitive outcome after an IS. A longitudinal study was conducted including supra-tentorial IS patients. A 3-Tesla brain MRI was performed at baseline and 1 year, allowing the analyses of mean fractional anisotropy (FA) and mean diffusivity (MD) in NAWM masks, along with the volume of white matter hyperintensities (WMH) and IS. A Montreal Cognitive Assessment (MoCA), an Isaacs set test, and a Zazzo's cancellation task were performed at baseline, 3 months and 1 year. Mixed models were built, followed by Tract-based Spatial Statistics (TBSS) analyses. Ninety-five patients were included in the analyses (38% women, median age 69 ± 20). FA significantly decreased, and MD significantly increased between baseline and 1 year, while cognitive scores improved. Patients who decreased their NAWM FA more over the year had a slower cognitive improvement on MoCA (β = - 0.11, p = 0.05). The TBSS analyses showed that patients who presented the highest decrease of FA in various tracts of white matter less improved their MoCA performances, regardless of WMH and IS volumes, demographic confounders, and clinical severity. NAWM integrity deteriorates over the year after an IS, and is associated with a cognitive recovery slowdown. The diffusion changes recorded here in patients starting with an early preserved white matter structure could have long term impact on cognition.
1 citations
TL;DR: Denoising using deep learning–based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.
Abstract: BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning–based reconstruction could provide a relevant strategy while shortening the imaging examination. MATERIALS AND METHODS: Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning–based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. RESULTS: Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning–based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning–based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning–based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. CONCLUSIONS: Denoising using deep learning–based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.
1 citations
TL;DR: Thalamic nuclei closest to the third ventricle are more affected, with cognitive consequences, in multiple sclerosis, and medial and posterior thalamic groups were significantly more affected than anterior and lateral groups.
Abstract: Objectives: Investigating differential vulnerability of thalamic nuclei in multiple sclerosis (MS). Methods: In a secondary analysis of prospectively collected datasets, we pooled 136 patients with MS or clinically isolated syndrome and 71 healthy controls all scanned with conventional 3D-T1 and white-matter-nulled magnetization-prepared rapid gradient echo (WMn-MPRAGE) and tested for cognitive performance. T1-based thalamic segmentation was compared with the reference WMn-MPRAGE method. Volumes of thalamic nuclei were compared according to clinical phenotypes and cognitive profile. Results: T1- and WMn-MPRAGE provided comparable segmentations (0.84 ± 0.13 < volume-similarity-index < 0.95 ± 0.03). Medial and posterior thalamic groups were significantly more affected than anterior and lateral groups. Cognitive impairment related to volume loss of the anterior group. Conclusion: Thalamic nuclei closest to the third ventricle are more affected, with cognitive consequences.
1 citations
TL;DR: In this paper , the authors report volumetric and functional connectivity alterations of the midbrain in patients with IGE, which is consistent with previous studies demonstrating pathophysiological abnormalities of the lower basal ganglia in animal models of generalised epilepsy.
TL;DR: In this article , the authors evaluated the integrity of gray matter (GM) on longitudinal data using mean diffusivity (MD) and its influence on post-ischemic stroke cognitive performances and found that GM integrity deterioration was associated with processing speed slowdown, and appears to be a biomarker of cognitive frailty.
Abstract: Microstructural changes after an ischemic stroke (IS) have mainly been described in white matter. Data evaluating microstructural changes in gray matter (GM) remain scarce. The aim of the present study was to evaluate the integrity of GM on longitudinal data using mean diffusivity (MD), and its influence on post-IS cognitive performances. A prospective study was conducted, including supra-tentorial IS patients without pre-stroke disability. A cognitive assessment was performed at baseline and 1 year, including a Montreal Cognitive Assessment, an Isaacs set test, and a Zazzo cancelation task (ZCT): completion time and number of errors. A 3-T brain MRI was performed at the same two time-points, including diffusion tensor imaging for the assessment of GM MD. GM volume was also computed, and changes in GM volume and GM MD were evaluated, followed by the assessment of the relationship between these structural changes and changes in cognitive performances. One hundred and four patients were included (age 68.5 ± 21.5, 38.5% female). While no GM volume loss was observed, GM MD increased between baseline and 1 year. The increase of GM MD in left fronto-temporal regions (dorsolateral prefrontal cortex, superior and medial temporal gyrus, p < 0.05, Threshold-Free Cluster Enhancement, 5000 permutations) was associated with an increase time to complete ZCT, regardless of demographic confounders, IS volume and location, GM, and white matter hyperintensity volume. GM integrity deterioration was thus associated with processing speed slowdown, and appears to be a biomarker of cognitive frailty. This broadens the knowledge of post-IS cognitive impairment mechanisms.
TL;DR: In this paper , the authors introduced a new methodology that holds a promise to be used in hippocampus-aging studies using sub-millimeter super-resolution hybrid diffusion imaging (HYDI) MRI, which was acquired in two groups of older and younger healthy participants recruited from the Indiana Alzheimer's Disease Research Center and community.
Abstract: The goal of the current study was to introduce a new methodology that holds a promise to be used in hippocampus-aging studies using sub-millimeter super-resolution hybrid diffusion imaging (HYDI) MRI.HYDI diffusion data were acquired in two groups of older and younger healthy participants recruited from the Indiana Alzheimer's Disease Research Center and community. These data were then transformed into super-resolution diffusion images before the hippocampal subfield analyses. We studied the correlation between the subjects' age and the structural connectivity involving the hippocampal subfields and the connectivity between the whole hippocampus and the cerebral cortex.Structural integrity derived from the tractography streamlines between the hippocampal subfields was reduced in older than younger adults.The findings offered a new promising framework, and they opened avenues for future studies to explore the relationship between the structural connectivity in the hippocampal area and different types of dementia.
TL;DR: In this paper , the authors investigated the navigation behavior of a population of ASD and typically developing (TD) adults related to their brain anatomy as assessed by structural and functional MRI at rest.
Abstract: Cerebellar abnormalities have been reported in autism spectrum disorder (ASD). Beyond its role in hallmark features of ASD, the cerebellum and its connectivity with forebrain structures also play a role in navigation. However, the current understanding of navigation abilities in ASD is equivocal, as is the impact of the disorder on the functional anatomy of the cerebellum. In the present study, we investigated the navigation behavior of a population of ASD and typically developing (TD) adults related to their brain anatomy as assessed by structural and functional MRI at rest. We used the Starmaze task, which permits assessing and distinguishing two complex navigation behaviors, one based on allocentric learning and the other on egocentric learning of a route with multiple decision points. Compared to TD controls, individuals with ASD showed similar exploration, learning, and strategy performance and preference. In addition, there was no difference in the structural or functional anatomy of the cerebellar circuits involved in navigation between the two groups. The findings of our work suggest that navigation abilities, spatio‐temporal memory, and their underlying circuits are preserved in individuals with ASD.
TL;DR: In this article , an atlas morphométriques des IRM pondérées T1 have been proposed for the diagnosis of the maladie de Parkinson. But, le diagnosis of Parkinson is a difficult task and the problem of finding the right set of moteurs débutants is intérêt.