Severely brain-injured patients may enter a spectrum of conditions collectively known as disorders of consciousness. This spectrum includes clinical conditions such as unresponsive wakefulness syndrome or minimally conscious state, where the behavioural assessment of consciousness can often be deceptive. To bridge this dissociation, neuroimaging techniques are employed to identify the residual brain functions. Each neuroimaging modality imperfectly captures distinct aspects of brain preservation—functional, anatomical, or both. In this study, we adopt a comprehensive approach by integrating the neurophysiology and neuroimaging modalities available from the standard and advanced clinical assessments through interpretable machine learning. The electrophysiological modalities included high-density EEG (resting state and task), whereas neuroimaging modalities included anatomical and resting-state functional MRI, diffusion MRI and 18F-fluorodeoxyglucose PET. Our investigation reveals that specific modalities, such as functional assessments, provide comprehensive insights into the currently evaluated state of consciousness, the diagnosis of the patients. Conversely, structural modalities offer valuable information about the patient's evolution within the consciousness spectrum. We validate the proposed analysis with data coming from other centres with different acquisition parameters. Importantly, we demonstrate that model performance improves with an increase in the number of modalities. We observe a higher inter-modality disagreement for minimally conscious state patients and those patients who improve. Lastly, we observe a difference in feature importances between diagnosis and prognosis, with an interaction between modality and anatomical structures: some subcortical markers tend to contribute more to prognosis, while other cortical markers are more informative for diagnosis. This integrative multimodal and machine learning methodology presents a promising avenue for a more nuanced understanding of disorders of consciousness, contributing to enhanced diagnostic precision, prognostic capabilities and the personalization of rehabilitative strategies in clinical practice.

OBJECTIVE:Impairment of consciousness is frequent in patients with supratentorial brain injury, but underlying mechanisms are poorly understood. Large-scale network dysfunction has been suggested as a unifying concept for the diverse focal infra- and supratentorial brain lesions associated with unconsciousness. Functional imaging studies have revealed that lesions in different locations that cause the same symptom can be linked to common networks using lesion network mapping (LNM). METHODS:To identify a commonly injured network for impaired consciousness, LNM was used in a prospective, bi-center observational cohort of 115 humans suffering from focal brain injury due to supratentorial intracerebral hemorrhage (ICH). RESULTS:Contrasting patients with preserved (n = 45) and impaired consciousness (n = 70), we identified a common network of brain regions more likely to be connected to focal brain lesions associated with impaired consciousness. Lesions in unconscious patients were more likely connected to a network including fronto-insulo-limbic association cortex, subcortical arousal and neuromodulatory nodes, and the ventral striato-pallidal circuitry. Data suggested preferential connectivity to a combination of arousal circuitry and association cortices. INTERPRETATION:Patients with impaired consciousness shared a common pattern of distributed network involvement that we infer to be mechanistically relevant to the regulation of consciousness that may serve as promising targets of neuromodulatory therapies. ANN NEUROL 2026;100:206-221.

A reliable outcome prognostication tool for patients in coma of various etiologies would facilitate ICU treatment by providing objective information to caregivers and patients' relatives. This study aimed to predict outcome based on supervised machine learning and magnetic resonance diffusion tensor imaging (DTI) metrics. In this multicenter international study, a training set of 531 patients not responding to simple orders at day 5 after coma onset underwent diffusion-weighted MRI between day 5 and 45. A classifier was developed using DTI metrics, patient age, and delay between admission and MRI as features. Unfavorable outcome (UFO) was defined as GOSE 1–4 at one year. Three prognosis areas were defined: a “red” zone (specificity for UFO above 95

In the growing field of neuroprognosis in intensive care patients, the use of diffusion tensor imaging (DTI) MRI to quantify white matter integrity is particularly promising, with solid demonstrations for predicting outcomes in cardiac arrest and severe traumatic brain injury. However, as with any new technique available to practitioners in routine practice, it is essential to provide clinicians and future users with the validity criteria they need to master for an appropriate interpretation of their results. This pragmatic review is aimed at providing clinicians and future users with a comprehensive understanding of the medical and technical conditions necessary for the accurate interpretation of DTI-derived metrics. Although DTI offers significant potential, its reliability can be influenced by various factors, including previous neurological disorders, acquisition artifacts, and data preprocessing failures. This review highlights the importance of considering classical MRI contraindications and the impact of physiological conditions such as age and the timing of MRI scans. Additionally, it discusses the influence of preexisting neurological disorders and associated lesions on DTI metrics, which can lead to classification biases and prognostication errors. Technical considerations, including the necessity for rigorous quality control and preprocessing steps, are also emphasized to ensure the precision and accuracy of DTI metrics. By addressing these factors, this review is aimed at enhancing the interpretability and clinical utility of DTI in neuroprognostication, ultimately facilitating more informed decision-making for intensive care unit patients. Furthermore, ongoing research and data sharing are needed to refine DTI techniques and improve their predictive accuracy in various clinical contexts.

In the growing field of neuroprognosis in intensive care patients, the use of diffusion tensor imaging (DTI) MRI to quantify white matter integrity is particularly promising, with solid demonstrations for predicting outcomes in cardiac arrest and severe traumatic brain injury. However, as with any new technique available to practitioners in routine practice, it is essential to provide clinicians and future users with the validity criteria they need to master for an appropriate interpretation of their results. This pragmatic review is aimed at providing clinicians and future users with a comprehensive understanding of the medical and technical conditions necessary for the accurate interpretation of DTI-derived metrics. Although DTI offers significant potential, its reliability can be influenced by various factors, including previous neurological disorders, acquisition artifacts, and data preprocessing failures. This review highlights the importance of considering classical MRI contraindications and the impact of physiological conditions such as age and the timing of MRI scans. Additionally, it discusses the influence of preexisting neurological disorders and associated lesions on DTI metrics, which can lead to classification biases and prognostication errors. Technical considerations, including the necessity for rigorous quality control and preprocessing steps, are also emphasized to ensure the precision and accuracy of DTI metrics. By addressing these factors, this review is aimed at enhancing the interpretability and clinical utility of DTI in neuroprognostication, ultimately facilitating more informed decision-making for intensive care unit patients. Furthermore, ongoing research and data sharing are needed to refine DTI techniques and improve their predictive accuracy in various clinical contexts.

BACKGROUND AND OBJECTIVES:Post-traumatic brain injury (TBI) lesions, which combine brain atrophy and white matter injuries, can lead to progressive post-traumatic encephalopathy. However, the specific involvement of the cerebellum, which participates in cognitive, executive, and sensory functions, has been little studied. The aim of this work was to explore the long-term cerebellar consequences of severe TBI. METHODS:In this retrospective study, patients included were hospitalized for a severe TBI and reassessed after discharge with a clinical examination and a MRI with diffusion tensor imaging. Patients were compared with a population of healthy volunteers. For a subgroup of this cohort, we analyzed the evolution of late post-TBI lesions on MRI up to 10 years after TBI. RESULTS:Ninety-seven patients reassessed 5 [3; 6] years after the TBI were included. Volume loss was revealed in the whole cerebellum ( P = .01) and especially in the anterior lobe ( P < .005) with a decrease in grey matter volume ( P = 6.10 -3 ). The mean diffusivity was increased in 4 cerebellar areas which are the right lower, right upper, left lower, and left upper cerebellar peduncles while the fractional anisotropy was decreased in all studied areas ( P < 10 -3 ). The longitudinal analysis (n = 17 patients) showed no progression of MRI lesions beyond the acute phase. CONCLUSION:This work shows that even if direct cerebellar damage is rare, long-term post-TBI cerebellar lesions can be observed. Therefore, clinical correlates of cerebellar lesions should be considered more systematically.

X-linked adrenoleukodystrophy (X-ALD) is caused by ABCD1 pathogenic variants, leading to accumulation of very long-chain fatty acids (VLCFAs). Phenotypes include cerebral ALD (CALD) and adrenomyeloneuropathy (AMN). We assessed if quantitative MRI (qMRI) parameters from an automated tool (BrainQuant) could differentiate CALD from non-CALD and reflects myelopathy severity. Adult males from a prospective study (2015-2024) underwent annual neurological exams and brain MRI. Exclusion criteria were: fewer than two MRIs, non-ALD lesions, or prior transplantation. BrainQuant processed diffusion tensor imaging (DTI) to yield global metrics: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Patients were stratified by Loes score (0 vs. > 0) and expanded disability status scale (EDSS ≤ 2 vs. > 2). Longitudinal changes were modeled. Correlations with EDSS, severity score for progressive myelopathy (SSPROM), and 6-min walk test (6-MWT) were analyzed. The cohort included 62 patients (median age 36.5); 15 had CALD. Global DTI metrics did not differ significantly between CALD and non-CALD. A trend for higher radial diffusivity (RD) in the splenium (p = 0.037) was seen, but results were not significant after Bonferroni correction for multiple comparisons (p = 0.748). Patients with EDSS > 2 showed significantly worse global DTI values (p < 0.05), correlating with clinical scores (r = 0.40-0.69). Longitudinally, RD-global increased significantly (p < 0.001) at similar rates across EDSS groups. BrainQuant qMRI did not distinguish CALD and non-CALD but effectively tracked myelopathy. Radial diffusivity (RD)-global is a promising biomarker for monitoring X-ALD progression.

Brain connectivity, allowing information to be shared between distinct cortical areas and thus to be processed in an integrated way, has long been considered critical for consciousness. However, the relationship between functional intercortical interactions and the structural connections thought to underlie them is poorly understood. In the present work, we explore both functional (with an EEG-based metric: the median weighted symbolic mutual information in the theta band) and structural (with a brain MRI-based metric: fractional anisotropy) connectivities in a cohort of 78 patients with disorders of consciousness. Both metrics could distinguish patients in a vegetative state from patients in minimally conscious state. Crucially, we discovered a significant positive correlation between functional and structural connectivities. Furthermore, we showed that this structure-function relationship is more specifically observed when considering structural connectivity within the intra- and inter-hemispheric long-distance cortico-cortical bundles involved in the Global Neuronal Workspace (GNW) theory of consciousness, thus supporting predictions of this model. Altogether, these results support the interest of multimodal assessments of brain connectivity in refining the diagnostic evaluation of patients with disorders of consciousness.

Severely brain-injured patients may enter a spectrum of conditions collectively known as disorders of consciousness (DoC). This spectrum includes clinical categories such as unresponsive wakefulness syndrome or minimally conscious state, where the behavioral assessment of consciousness can often be deceptive. To bridge this dissociation, neuroimaging techniques are employed to look for the residual brain functions. Each neuroimaging modality imperfectly captures distinct aspects of brain preservation - functional, anatomical, or both. In this study, we adopt a comprehensive approach by integrating the neurophysiology and neuroimaging modalities available from the standard and advanced clinical assessment through interpretable machine learning (ML). The electrophysiological modalities included high-density electroencephalography (EEG) (resting state and task), whereas neuroimaging modalities included anatomical and resting-state functional magnetic resonance imaging (MRI), diffusion MRI, and 18F-fluoro-deoxy-glucose positron emission tomography (FDG PET). Our investigation reveals that specific modalities, such as functional assessments provide comprehensive insights into the currently evaluated state of consciousness - the diagnosis of the patients. Conversely, structural modalities offer valuable information about the patient's evolution within the consciousness spectrum. We validate the proposed analysis with data coming from other centers with different acquisition parameters. Importantly, we show that there is an improved model performance with the increase in the number of modalities. We observe a higher inter-modality disagreement for MCS patients and those patients who improve. Lastly, we observe a difference in feature importances in diagnosis and prognosis. This integrative multimodal and ML methodology presents a promising avenue for a more nuanced understanding of DoC, contributing to enhanced diagnostic precision and prognostic capabilities in clinical practice. ### Competing Interest Statement Jacobo D. Sitt and Lionel Naccache are scientific co-founders of NeuroMeters (have scientific advisory activity but no executive or management activity). ### Funding Statement This work was supported by the Ecole Doctorale Frontieres de l'Innovation en Recherche et Education-Programme Bettencourt (to D.M.). This project is part of the multicentric application for the EU ERAPerMed Joint Translational Call for Proposals for "Personalised Medicine: Multidisciplinary research towards implementation" (ERA PerMed JTC2019). It is funded by local funding agencies of the participating countries (for France it is the Agence Nationale de Recherche ANR, funding code: ANR-19-PERM-0002, for Germany the Federal Ministry of Education and Research BMBF, funding code: 01KU2003). This project is supported by the Human Brain Project (HBP) MODELDxConsciousness Consortium (Agence Nationale de Recherche ANR, funding code: S.1600.ANR.HBPR). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Pitie-Salpetriere Hospital (Comite de Protection des Personnes, Ile de France 1, Paris, France) under the French label of routine care research (protocol number 2013-A01385-40, code Recherche en soins courants, protocol number M-Neuro-DOC, CE SRLF 20-2). Medical Faculty of Ludwig-Maximilians-Universitat Munchen (Germany) (protocol numbers 20-634 and 20-635). IRCCS Fondazione Don Carlo Gnocchi (IRCCS Regione Lombardia, Italy) (protocol number 32/2021/CE_FdG/FC/SA). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data is not publicly available.

Cerebral adrenoleukodystrophy (CALD) is an X-linked rapidly progressive demyelinating disease leading to death usually within a few years. The standard of care is haematopoietic stem cell transplantation (HSCT), but many men are not eligible due to age, absence of a matched donor or lesions of the corticospinal tracts (CST). Based on the ADVANCE study showing that leriglitazone decreases the occurrence of CALD, we treated 13 adult CALD patients (19-67 years of age) either not eligible for HSCT (n = 8) or awaiting HSCT (n = 5).Patients were monitored every 3 months with standardized neurological scores, plasma biomarkers and brain MRI comprising lesion volumetrics and diffusion tensor imaging.The disease stabilized clinically and radiologically in 10 patients with up to 2 years of follow-up. Five patients presented with gadolinium enhancing CST lesions that all turned gadolinium negative and, remarkably, regressed in four patients. Plasma neurofilament light chain levels stabilized in all 10 patients and correlated with lesion load. The two patients who continued to deteriorate were over 60 years of age with prominent cognitive impairment. One patient died rapidly from coronavirus disease 2019.These results suggest that leriglitazone can arrest disease progression in adults with early-stage CALD and may be an alternative treatment to HSCT. Graphical Abstract Cerebral adrenoleukodystrophy (CALD) is an X-linked, rapidly progressive, fatal white matter disease. Bone marrow transplants can halt neuroinflammation in CALD, but are risky and many men are not eligible. Golse et al. show that leriglitazone can arrest disease progression in CALD and may be an alternative to a bone marrow transplant.

Dataset and R codes necessary to reproduce the Tables and Figures of the publication entitled "Multimodal MRI characterizes clinical outcome in chronic traumatic brain injury". Please consult the text file README_first.

Background: Traumatic Brain Injury (TBI) is a major cause of acquired disability and can cause devastating and progressive post-traumatic encephalopathy. TBI is a dynamic condition that continues to evolve over time. A better understanding of the pathophysiology of these late lesions is important for the development of new therapeutic strategies.Objectives: The primary objective was to compare the ability of fluid-attenuated reversion recovery (FLAIR) and diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) markers to identify participants with a Glasgow outcome scale extended (GOS-E) score of 7-8, up to 10 years after their original TBI. The secondary objective was to study the brain regionalization of DTI markers. Finally, we analyzed the evolution of latedeveloping brain lesions using repeated MRI images, also taken up to 10 years after the TBI. Methods: In this retrospective study, participants were included from a cohort of people hospitalized following a severe TBI. Following their discharge, they were followed-up and clinically assessed, including a DTIMRI scan, between 2012 and 2016. We performed a cross-sectional analysis on 97 participants at a median (IQR) of 5 years (3-6) post-TBI, and a further post-TBI longitudinal analysis over 10 years on a subpopulation (n = 17) of the cohort.Results: Although the area under the curve (AUC) of FLAIR, fractional anisotropy (FA), and mean diffusivity (MD) were not significantly different, only the AUC of FA was statistically greater than 0.5. In addition, only the FA was correlated with clinical outcomes as assessed by GOS-E score (P<10-4). On the cross-sectional analysis, DTI markers allowed study post-TBI white matter lesions by region. In the longitudinal subpopulation analysis, the observed number of brain lesions increased for the first 5 years post-TBI, before stabilizing over the next 5 years.Conclusions: This study has shown for the first time that post-TBI lesions can present in a two-phase evolution. These results must be confirmed in larger studies. French Data Protection Agency (Commission nationale de l'informatique et des libert = es; CNIL) study registration no: 1934708v0. (c) 2023 Elsevier Masson SAS. All rights reserved.

Cerebellum is a key structure for functional motor recovery after stroke. Enhancing the cerebello-motor pathway by paired associative stimulation (PAS) might improve upper limb function. Here, we conducted a randomized, double-blind, sham-controlled pilot trial investigating the efficacy of a 5-day treatment of cerebello-motor PAS coupled with physiotherapy for promoting upper limb motor function compared to sham stimulation. The secondary objectives were to determine in the active treated group (i) whether improvement of upper limb motor function was associated with changes in corticospinal excitability or changes in functional activity in the primary motor cortex and (ii) whether improvements were correlated to the structural integrity of the input and output pathways. To that purpose, hand dexterity and maximal grip strength were assessed along with TMS recordings and multimodal magnetic resonance imaging, before the first treatment, immediately after the last one and a month later. Twenty-seven patients were analyzed. Cerebello-motor PAS was effective compared to sham in improving hand dexterity (p: 0.04) but not grip strength. This improvement was associated with increased activation in the ipsilesional primary motor cortex (p: 0.04). Moreover, the inter-individual variability in clinical improvement was partly explained by the structural integrity of the afferent (p: 0.06) and efferent pathways (p: 0.02) engaged in this paired associative stimulation (i.e., cortico-spinal and dentato-thalamo-cortical tracts). In conclusion, cerebello-motor-paired associative stimulation combined with physiotherapy might be a promising approach to enhance upper limb motor function after stroke. Clinical Trial Registration URL: http://www.clinicaltrials.gov . Unique identifier: NCT 02284087.

Background: Traumatic brain injury (TBI) lesions are known to evolve over time, but the duration and consequences of cerebral remodelling are unclear. Degenerative mechanisms occurring in the chronic phase after TBI could constitute "tertiary" lesions related to the neurological outcome. Objective: The objective of this prospective study of severe TBI was to longitudinally evaluate the volume of white and grey matter structures and white matter integrity with 2 time-point multimodal MRI. Methods: Longitudinal MRI follow-up was obtained for 11 healthy controls (HCs) and 22 individuals with TBI (mean [SD] 60 [15] months after injury) along with neuropsychological assessments. TBI individuals were classified in the "favourable" recovery group (Glasgow Outcome Scale Extended [GOSE] 6-8) and "unfavourable" recovery group (GOSE 3-5) at 5 years. Variation in brain volumes (3D T1-weighted image) and white matter integrity (diffusion tensor imaging [DTI]) were quantitatively assessed over time and used to predict neurological outcome. Results: TBI individuals showed a marked decrease in volumes of whole white matter (median -11.4% [interquartile range -5.8; -14.6]; p < 0.001) and deep grey nuclear structures (-17.1% [-10.6; -20.5]; p < 0.001). HCs did not show any significant change over the same time period. Median volumetric loss in several brain regions was higher with GOSE 3-5 than 6-8. These lesions were associated with lower fractional anisotropy and higher mean diffusivity at baseline. Volumetric variations were positively correlated with normalized fractional anisotropy and negatively with normalized mean diffusivity at baseline and follow-up. A computed predictive model with baseline DTI showed good accuracy to predict neurological outcome (area under the receiver operating characteristic curve 0.82 [95% confidence interval 0.81-0.83]) Conclusions: We characterised the striking atrophy of deep brain structures after severe TBI. DTI imaging in the subacute phase can predict the occurrence and localization of these tertiary lesions as well as long-term neurological outcome. Trial registration: ClinicalTrials.gov: NCT00577954. Registered on October 2006. (c) 2021 Elsevier Masson SAS. All rights reserved.

Background Disorders of consciousness due to severe hypoglycemia are rare but challenging to treat. The aim of this retrospective cohort study was to describe our multimodal neurological assessment of patients with hypoglycemic encephalopathy hospitalized in the intensive care unit and their neurological outcomes. Methods Consecutive patients with disorders of consciousness related to hypoglycemia admitted for neuroprognostication from 2010 to 2020 were included. Multimodal neurological assessment included electroencephalography, somatosensory and cognitive event-related potentials, and morphological and quantitative magnetic resonance imaging (MRI) with quantification of fractional anisotropy. Neurological outcomes at 28 days, 3 months, 6 months, 1 year, and 2 years after hypoglycemia were retrieved. Results Twenty patients were included. After 2 years, 75% of patients had died, 5% remained in a permanent vegetative state, 10% were in a minimally conscious state, and 10% were conscious but with severe disabilities (Glasgow Outcome Scale-Extended scores 3 and 4). All patients showed pathologic electroencephalography findings with heterogenous patterns. Morphological brain MRI revealed abnormalities in 95% of patients, with various localizations including cortical atrophy in 65% of patients. When performed, quantitative MRI showed decreased fractional anisotropy affecting widespread white matter tracts in all patients. Conclusions The overall prognosis of patients with severe hypoglycemic encephalopathy was poor, with only a small fraction of patients who slowly improved after intensive care unit discharge. Of note, patients who did not improve during the first 6 months did not recover consciousness. This study suggests that a multimodal approach capitalizing on advanced brain imaging and bedside electrophysiology techniques could improve diagnostic and prognostic performance in severe hypoglycemic encephalopathy.

Abstract IMPORTANCE: Papez circuit is composed of deep structures of the limbic system which supports episodic memory. Biomechanical modelling suggests that this circuit is particularly exposed to shear forces during traumatic brain injury (TBI). Recent studies showed the relevance of MRIderived measures for improving diagnosis and adapt care of TBI patients. However, the relationship between MRI measures in this specific circuit and memory disorders resulting from TBI remains poorly documented.OBJECTIVE: To relate MRI measurements of the Papez circuit to episodic memory impairment (EMI) in TBI patients, and to assess the relevance of MRI to diagnose EMI consecutive to TBI. DESIGN, SETTING AND PARTICIPANTS: This is a prospective observational study with severe TBI patients enrolled (2006-2012) who did receive neuropsychological assessment and multiparametric brain MRI at distance from the trauma. Patients were classified by neuropsychologist into two groups: those showing an episodic memory impairment (EMI+) and those without impairment (EMI-). We defined an anatomical delineation of the Papez circuit and its sub regions. We extracted MRI measurements in each of these regions and compared statistically between EMI+ and EMI- patients. The same methodology was applied to a control group of 50 healthy controls (HC) to compare with normative values.MAIN OUTCOMES AND MEASURES: Normalized fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) were derived from diffusion tensor imaging (DTI) data. The volume of the Papez circuit was extracted from anatomical MRI.RESULTS: Over the study period (2009-2016), 100 patients received an MRI and a neuropsychological assessment 63±22 months after the initial TBI. Patients EMI+ had significantly lower FA and higher MD, RD and AD values than the EMI- and HC in the Papez circuit. Volume measurements showed no difference between EMI+ and EMI-. We also reported abnormalities pattern across sub regions of this circuit between EMI+ and EMI-. The potential of MRI measures in Papez circuit to help diagnosis of EMI in severe TBI patients was confirmed by a multivariate model combining clinical data at baseline and MRI features.CONCLUSIONS AND RELEVANCE: Our study shows a high incidence of damage to the Papez circuit following severe TBI associated with episodic memory impairments. MRI measures of the Papez circuit constitute relevant information for the diagnosis of EMI in severe TBI patients.

L'identification d'un biomarqueur de l'atteinte centrale dans la sclérose latérale amyotrophique (SLA) est un challenge majeur. L'atteinte du faisceau cortico-spinal peut être objectivée quantitativement par les paramètres IRM de diffusion. Déterminer si les paramètres IRM de diffusion permettent de discriminer les patients SLA et les témoins à travers une plateforme web cliniquement validée. Des acquisitions IRM ont été réalisées sur 24 patients SLA et 22 témoins appareillés en sexe et âge. Les images ont été transférées à une plateforme web (Brainquant) pour obtenir les mesures DTI de deux régions du faisceau cortico-spinal (Bras postérieur de la capsule interne (BPCI) et Pédoncule cérébral (PC). L'objectif primaire était d'objectiver une différence des mesures DTI entre patients et témoins. L'analyse statistique a été réalisée via JMP Pro16. La diffusivité moyenne (MD) était augmentée chez les patients SLA en comparaison avec les témoins sur les régions BPCI et PC. La diminution de Fraction d'Anisotropie (FA) n'était pas significative. En objectifs secondaires, nous avons mis en évidence que La MD du BPIC était corrélée avec la sévérité fonctionnelle (échelle ALSFRS-R) et que La MD du CP était augmentée dans le sous-groupe de patients sans atteinte clinique centrale par rapport aux témoins. Nous avons trouvé des différences significatives dans les paramètres de diffusion de régions du faisceau cortico-spinal (Bras postérieurs de la capsule interne et Pédoncules cérébraux) ainsi qu'une corrélation entre l'atteinte de la substance blanche et le degré de sévérité de la maladie. Une plateforme-web d'analyse IRM peut apporter des indicateurs d'atteinte centrale chez des patients avec ou sans signes cliniques. L'outil proposé pourrait être transposé en clinique pour le diagnostic, pour des prises de décision et pour le recrutement en essais thérapeutiques, et doit être confirmée par l'évaluation d'une large cohorte multicentrique de patients SLA (NCT02360891).