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Di Folco C, Couronné R, Arnulf I, Mangone G, Leu-Semenescu S, Dodet P, Vidailhet M, Corvol JC, Lehéricy S, Durrleman S. Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease. Mov Disord 2024; 39:64-75. [PMID: 38006282 DOI: 10.1002/mds.29662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/03/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers. OBJECTIVE To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD). METHODS Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow-up duration = 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers (dopaminergic denervation). RESULTS PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep-all before diagnosis-and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor symptoms. CONCLUSIONS Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Cécile Di Folco
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Raphaël Couronné
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Isabelle Arnulf
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Graziella Mangone
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Smaranda Leu-Semenescu
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Pauline Dodet
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Marie Vidailhet
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Jean-Christophe Corvol
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stanley Durrleman
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
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Guinebretiere O, Nedelec T, Gantzer L, Lekens B, Durrleman S, Louapre C. Association Between Diseases and Symptoms Diagnosed in Primary Care and the Subsequent Specific Risk of Multiple Sclerosis. Neurology 2023; 101:e2497-e2508. [PMID: 38052493 PMCID: PMC10791050 DOI: 10.1212/wnl.0000000000207981] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/20/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Previous studies have reported a possible prodrome in multiple sclerosis (MS) defined by nonspecific symptoms including mood disorder or genitourinary symptoms and increased health care use detected several years before diagnosis. This study aimed to evaluate agnostically the associations between diseases and symptoms diagnosed in primary care and the risk of MS relative to controls and 2 other autoimmune inflammatory diseases with similar population characteristics, namely lupus and Crohn disease (CD). METHODS A case-control study was conducted using electronic health records from the Health Improvement Network database in the United Kingdom and France. We agnostically assessed the associations between 113 diseases and symptoms in the 5 years before and after diagnosis in patients with subsequent diagnosis of MS. Individuals with a diagnosis of MS were compared with individuals without MS and individuals with 2 other autoimmune diseases, CD and lupus. RESULTS The study population consisted of patients with MS (n = 20,174), patients without MS (n = 54,790), patients with CD (n = 30,477), and patients with lupus (n = 7,337). Twelve ICD-10 codes were significantly positively associated with the risk of MS compared with controls without MS. After considering ICD-10 codes suggestive of neurologic symptoms as the first diagnosis of MS, 5 ICD-10 codes remained significantly associated with MS: depression (UK: odds ratio 1.22, 95% CI 1.11-1.34), sexual dysfunction (1.47, 1.11-1.95), constipation (1.5, 1.27-1.78), cystitis (1.21, 1.05-1.39), and urinary tract infections of unspecified site (1.38, 1.18-1.61). However, none of these conditions was selectively associated with MS in comparisons with both lupus and CD. All 5 ICD-10 codes identified were still associated with MS during the 5 years after diagnosis. DISCUSSION We identified 5 health conditions associated with subsequent MS diagnosis, which may be considered not only prodromal but also early-stage symptoms. However, these health conditions overlap with prodrome of 2 other autoimmune diseases; hence, they lack specificity to MS.
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Affiliation(s)
- Octave Guinebretiere
- From the Sorbonne Université (O.G., T.N., S.D., C.L.), Paris Brain Institute-ICM, Inserm, CNRS, Inria; Cegedim R&D (L.G., B.B.L.), Boulogne-Billancourt; and Department of Neurology (C.L.), CIC Neurosciences, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, France
| | - Thomas Nedelec
- From the Sorbonne Université (O.G., T.N., S.D., C.L.), Paris Brain Institute-ICM, Inserm, CNRS, Inria; Cegedim R&D (L.G., B.B.L.), Boulogne-Billancourt; and Department of Neurology (C.L.), CIC Neurosciences, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, France
| | - Laurene Gantzer
- From the Sorbonne Université (O.G., T.N., S.D., C.L.), Paris Brain Institute-ICM, Inserm, CNRS, Inria; Cegedim R&D (L.G., B.B.L.), Boulogne-Billancourt; and Department of Neurology (C.L.), CIC Neurosciences, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, France
| | - Beranger Lekens
- From the Sorbonne Université (O.G., T.N., S.D., C.L.), Paris Brain Institute-ICM, Inserm, CNRS, Inria; Cegedim R&D (L.G., B.B.L.), Boulogne-Billancourt; and Department of Neurology (C.L.), CIC Neurosciences, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, France
| | - Stanley Durrleman
- From the Sorbonne Université (O.G., T.N., S.D., C.L.), Paris Brain Institute-ICM, Inserm, CNRS, Inria; Cegedim R&D (L.G., B.B.L.), Boulogne-Billancourt; and Department of Neurology (C.L.), CIC Neurosciences, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, France
| | - Celine Louapre
- From the Sorbonne Université (O.G., T.N., S.D., C.L.), Paris Brain Institute-ICM, Inserm, CNRS, Inria; Cegedim R&D (L.G., B.B.L.), Boulogne-Billancourt; and Department of Neurology (C.L.), CIC Neurosciences, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, France
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Ortholand J, Pradat PF, Tezenas du Montcel S, Durrleman S. Interaction of sex and onset site on the disease trajectory of amyotrophic lateral sclerosis. J Neurol 2023; 270:5903-5912. [PMID: 37615751 DOI: 10.1007/s00415-023-11932-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND Studies showed the impact of sex and onset site (spinal or bulbar) on disease onset and survival in ALS. However, they mainly result from cross-sectional or survival analysis, and the interaction of sex and onset site on the different proxies of disease trajectory has not been fully investigated. METHODS We selected all patients with repeated observations in the PRO-ACT database. We divided them into four groups depending on their sex and onset site. We estimated a multivariate disease progression model, named ALS Course Map, to investigate the combined temporal changes of the four sub-scores of the revised ALS functional rating scale (ALSFRSr), the forced vital capacity (FVC), and the body mass index (BMI). We then compared the progression rate, the estimated age at onset, and the relative progression of the outcomes across each group. RESULTS We included 1438 patients from the PRO-ACT database. They were 51% men with spinal onset, 12% men with bulbar onset, 26% women with spinal onset, and 11% women with bulbar onset. We showed a significant influence of both sex and onset site on the ALSFRSr progression. The BMI decreased 8.9 months earlier (95% CI [3.9, 13.8]) in women than men, after correction for the onset site. Among patients with bulbar onset, FVC was impaired 2.6 months earlier (95% CI [0.6, 4.6]) in women. CONCLUSION Using a multivariable disease modelling approach, we showed that sex and onset site are important drivers of the progression of motor function, BMI, and FVC decline.
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Affiliation(s)
- Juliette Ortholand
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, CNRS, InriaInserm, AP-HP, Hôpital de La Pitié Salpêtrière, 75013, Paris, France.
| | - Pierre-François Pradat
- Laboratoire d'Imagerie Biomédicale, Sorbonne Université, CNRS, INSERM, Paris, France
- APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute Ulster University, C-TRIC, Altnagelvin Hospital, Derry, Londonderry, UK
| | - Sophie Tezenas du Montcel
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, CNRS, InriaInserm, AP-HP, Hôpital de La Pitié Salpêtrière, 75013, Paris, France
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, CNRS, InriaInserm, AP-HP, Hôpital de La Pitié Salpêtrière, 75013, Paris, France
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Sauty B, Durrleman S. Impact of sex and APOE- ε4 genotype on patterns of regional brain atrophy in Alzheimer's disease and healthy aging. Front Neurol 2023; 14:1161527. [PMID: 37333001 PMCID: PMC10272760 DOI: 10.3389/fneur.2023.1161527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/19/2023] [Indexed: 06/20/2023] Open
Abstract
Alzheimer's Disease (AD) is a heterogeneous disease that disproportionately affects women and people with the APOE-ε4 susceptibility gene. We aim to describe the not-well-understood influence of both risk factors on the dynamics of brain atrophy in AD and healthy aging. Regional cortical thinning and brain atrophy were modeled over time using non-linear mixed-effect models and the FreeSurfer software with t1-MRI scans from the Alzheimer's Disease Neuroimaging Initiative (N = 1,502 subjects, 6,728 images in total). Covariance analysis was used to disentangle the effect of sex and APOE genotype on the regional onset age and pace of atrophy, while correcting for educational level. A map of the regions mostly affected by neurodegeneration is provided. Results were confirmed on gray matter density data from the SPM software. Women experience faster atrophic rates in the temporal, frontal, parietal lobes and limbic system and earlier onset in the amygdalas, but slightly later onset in the postcentral and cingulate gyri as well as all regions of the basal ganglia and thalamus. APOE-ε4 genotypes leads to earlier and faster atrophy in the temporal, frontal, parietal lobes, and limbic system in AD patients, but not in healthy patients. Higher education was found to slightly delay atrophy in healthy patients, but not for AD patients. A cohort of amyloid positive patients with MCI showed a similar impact of sex as in the healthy cohort, while APOE-ε4 showed similar associations as in the AD cohort. Female sex is as strong a risk factor for AD as APOE-ε4 genotype regarding neurodegeneration. Women experience a sharper atrophy in the later stages of the disease, although not a significantly earlier onset. These findings may have important implications for the development of targeted intervention.
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Poulet PE, Durrleman S. Multivariate disease progression modeling with longitudinal ordinal data. Stat Med 2023. [PMID: 37231622 DOI: 10.1002/sim.9770] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/23/2023] [Accepted: 05/03/2023] [Indexed: 05/27/2023]
Abstract
Disease modeling is an essential tool to describe disease progression and its heterogeneity across patients. Usual approaches use continuous data such as biomarkers to assess progression. Nevertheless, categorical or ordinal data such as item responses in questionnaires also provide insightful information about disease progression. In this work, we propose a disease progression model for ordinal and categorical data. We built it on the principles of disease course mapping, a technique that uniquely describes the variability in both the dynamics of progression and disease heterogeneity from multivariate longitudinal data. This extension can also be seen as an attempt to bridge the gap between longitudinal multivariate models and the field of item response theory. Application to the Parkinson's progression markers initiative cohort illustrates the benefits of our approach: a fine-grained description of disease progression at the item level, as compared to the aggregated total score, together with improved predictions of the patient's future visits. The analysis of the heterogeneity across individual trajectories highlights known disease trends such as tremor dominant or postural instability and gait difficulties subtypes of Parkinson's disease.
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Affiliation(s)
- Pierre-Emmanuel Poulet
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
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Nedelec T, Couvy-Duchesne B, Darves-Bornoz A, Couronne R, Monnet F, Gantzer L, Lekens B, Wu Y, Villain N, Schrag A, Durrleman S, Corvol JC. A comparison between early presentation of dementia with Lewy Bodies, Alzheimer's disease and Parkinson's disease: evidence from routine primary care and UK Biobank data. Ann Neurol 2023. [PMID: 37098633 DOI: 10.1002/ana.26670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 03/31/2023] [Accepted: 04/17/2023] [Indexed: 04/27/2023]
Abstract
OBJECTIVE To simultaneously contrast prediagnostic clinical characteristics of individuals with a final diagnosis of dementia with Lewy Bodies, Parkinson's disease, Alzheimer's disease compared to controls without neurodegenerative disorders. METHODS Using the longitudinal THIN database in the UK, we tested the association of each neurodegenerative disorder with a selected list of symptoms and broad families of treatments, and compared the associations between disorders to detect disease-specific effects. We replicated the main findings in the UK Biobank. RESULTS We used data of 28,222 patients with PD, 20,214 with AD, 4,682 with DLB and 20,214 controls. All neurodegenerative disorders were significantly associated with the presence of multiple clinical characteristics before their diagnosis including sleep disorders, falls, psychiatric symptoms and autonomic dysfunctions. When comparing DLB patients with patients with PD and AD patients, falls, psychiatric symptoms and autonomic dysfunction were all more strongly associated with DLB in the five years preceding the first neurodegenerative diagnosis. The use of statins was lower in patients who developed PD and higher in patients who developed DLB compared to AD. In PD patients, the use of statins was associated with the development of dementia in the five years following PD diagnosis. INTERPRETATION Prediagnostic presentations of falls, psychiatric symptoms and autonomic dysfunctions were more strongly associated with DLB than PD and AD. This study also suggests that whilst several associations with medications are similar in neurodegenerative disorders, statin usage is negatively associated with Parkinson's Disease but positively with DLB and AD as well as development of dementia in PD. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Thomas Nedelec
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Inria, Paris, France
| | - Baptiste Couvy-Duchesne
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Inria, Paris, France
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia
| | - Aube Darves-Bornoz
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Inria, Paris, France
| | - Raphaël Couronne
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Inria, Paris, France
| | | | | | | | - Yeda Wu
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia
| | - Nicolas Villain
- Department of Neurology, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Anette Schrag
- Department of Clinical Neurosciences, UCL Queen Square Institute of Neurology, University College London, UK
| | - Stanley Durrleman
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Inria, Paris, France
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Maheux E, Koval I, Ortholand J, Birkenbihl C, Archetti D, Bouteloup V, Epelbaum S, Dufouil C, Hofmann-Apitius M, Durrleman S. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun 2023; 14:761. [PMID: 36765056 PMCID: PMC9918533 DOI: 10.1038/s41467-022-35712-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.
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Affiliation(s)
- Etienne Maheux
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Igor Koval
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Juliette Ortholand
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Colin Birkenbihl
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Damiano Archetti
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Vincent Bouteloup
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Stéphane Epelbaum
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences, Paris, France
| | - Carole Dufouil
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Martin Hofmann-Apitius
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
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Koval I, Dighiero-Brecht T, Tobin AJ, Tabrizi SJ, Scahill RI, Tezenas du Montcel S, Durrleman S, Durr A. Forecasting individual progression trajectories in Huntington disease enables more powered clinical trials. Sci Rep 2022; 12:18928. [PMID: 36344508 PMCID: PMC9640581 DOI: 10.1038/s41598-022-18848-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/22/2022] [Indexed: 11/09/2022] Open
Abstract
Variability in neurodegenerative disease progression poses great challenges for the evaluation of potential treatments. Identifying the persons who will experience significant progression in the short term is key for the implementation of trials with smaller sample sizes. We apply here disease course mapping to forecast biomarker progression for individual carriers of the pathological CAG repeat expansions responsible for Huntington disease. We used data from two longitudinal studies (TRACK-HD and TRACK-ON) to synchronize temporal progression of 15 clinical and imaging biomarkers from 290 participants with Huntington disease. We used then the resulting HD COURSE MAP to forecast clinical endpoints from the baseline data of 11,510 participants from ENROLL-HD, an external validation cohort. We used such forecasts to select participants at risk for progression and compute the power of trials for such an enriched population. HD COURSE MAP forecasts biomarkers 5 years after the baseline measures with a maximum mean absolute error of 10 points for the total motor score and 2.15 for the total functional capacity. This allowed reducing sample sizes in trial up to 50% including participants with a higher risk for progression ensuring a more homogeneous group of participants.
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Affiliation(s)
- Igor Koval
- Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, 75013, Paris, France
| | - Thomas Dighiero-Brecht
- Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, 75013, Paris, France
| | - Allan J Tobin
- Biological Adaptation and Ageing, Sorbonne Université, Paris, France
- Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarah J Tabrizi
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK
| | - Rachael I Scahill
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK
| | - Sophie Tezenas du Montcel
- Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, 75013, Paris, France
| | - Stanley Durrleman
- Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, 75013, Paris, France.
| | - Alexandra Durr
- Department of Neurology, DMU Neurosciences, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France.
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Sakr FA, Grothe MJ, Cavedo E, Jelistratova I, Habert MO, Dyrba M, Gonzalez-Escamilla G, Bertin H, Locatelli M, Lehericy S, Teipel S, Dubois B, Hampel H, Bakardjian H, Benali H, Bertin H, Bonheur J, Boukadida L, Boukerrou N, Cavedo E, Chiesa P, Colliot O, Dubois B, Dubois M, Epelbaum S, Gagliardi G, Genthon R, Habert MO, Hampel H, Houot M, Kas A, Lamari F, Levy M, Lista S, Metzinger C, Mochel F, Nyasse F, Poisson C, Potier MC, Revillon M, Santos A, Andrade KS, Sole M, Surtee M, de Schotten MT, Vergallo A, Younsi N, Aguilar LF, Babiloni C, Baldacci F, Benda N, Black KL, Bokde ALW, Bonuccelli U, Broich K, Bun RS, Cacciola F, Castrillo J, Cavedo E, Ceravolo R, Chiesa PA, Colliot O, Coman CM, Corvol JC, Cuello AC, Cummings JL, Depypere H, Dubois B, Duggento A, Durrleman S, Escott-Price V, Federoff H, Ferretti MT, Fiandaca M, Frank RA, Garaci F, Genthon R, George N, Giorgi FS, Graziani M, Haberkamp M, Habert MO, Hampel H, Herholz K, Karran E, Kim SH, Koronyo Y, Koronyo-Hamaoui M, Lamari F, Langevin T, Lehéricy S, Lista S, Lorenceau J, Mapstone M, Neri C, Nisticò R, Nyasse-Messene F, O’bryant SE, Perry G, Ritchie C, Rojkova K, Rossi S, Saidi A, Santarnecchi E, Schneider LS, Sporns O, Toschi N, Verdooner SR, Vergallo A, Villain N, Welikovitch LA, Woodcock J, Younesi E. Correction: Applicability of in vivo staging of regional amyloid burden in a cognitively normal cohort with subjective memory complaints: the INSIGHT-preAD study. Alzheimers Res Ther 2022; 14:131. [PMID: 36104713 PMCID: PMC9472399 DOI: 10.1186/s13195-022-01025-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Mantoux C, Durrleman S, Allassonnière S. Asymptotic Analysis of a Matrix Latent Decomposition Model. ESAIM-PROBAB STAT 2022. [DOI: 10.1051/ps/2022004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Matrix data sets arise in network analysis for medical applications, where each network belongs to a subject and represents a measurable phenotype. These large dimensional data are often modeled using lower-dimensional latent variables, which explain most of the observed variability and can be used for predictive purposes. In this paper, we provide asymptotic convergence guarantees for the estimation of a hierarchical statistical model for matrix data sets. It captures the variability of matrices by modeling a truncation of their eigendecomposition. We show that this model is identifiable, and that consistent Maximum A Posteriori (MAP) estimation can be performed to estimate the distribution of eigenvalues and eigenvectors. The MAP estimator is shown to be asymptotically normal for a restricted version of the model.
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11
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Nedelec T, Couvy-Duchesne B, Monnet F, Daly T, Ansart M, Gantzer L, Lekens B, Epelbaum S, Dufouil C, Durrleman S. Identifying health conditions associated with Alzheimer's disease up to 15 years before diagnosis: an agnostic study of French and British health records. The Lancet Digital Health 2022; 4:e169-e178. [DOI: 10.1016/s2589-7500(21)00275-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/06/2021] [Accepted: 11/25/2021] [Indexed: 12/30/2022]
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12
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Goparaju A, Iyer K, Bône A, Hu N, Henninger HB, Anderson AE, Durrleman S, Jacxsens M, Morris A, Csecs I, Marrouche N, Elhabian SY. Benchmarking off-the-shelf statistical shape modeling tools in clinical applications. Med Image Anal 2022; 76:102271. [PMID: 34974213 PMCID: PMC8792348 DOI: 10.1016/j.media.2021.102271] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 09/30/2021] [Accepted: 10/15/2021] [Indexed: 02/06/2023]
Abstract
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.
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Affiliation(s)
- Anupama Goparaju
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA
| | - Krithika Iyer
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA
| | - Alexandre Bône
- ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris, France
| | - Nan Hu
- Robert Stempel School of Public Health and Social Work, Florida International University, Miami, FL, USA
| | - Heath B Henninger
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Andrew E Anderson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Stanley Durrleman
- ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris, France
| | - Matthijs Jacxsens
- Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Alan Morris
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Ibolya Csecs
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Nassir Marrouche
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Shireen Y Elhabian
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA.
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13
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Cacciamani F, Valladier A, Maheux E, Koval I, Durrleman S, Epelbaum S. Changes in the awareness of cognitive decline across the course of Alzheimer’s disease: Comparison of two assessment methods. Alzheimers Dement 2021. [DOI: 10.1002/alz.053074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Federica Cacciamani
- ARAMIS Lab, Brain & Spine Institute (ICM), Pitié‐Salpêtrière Hospital Paris France
| | - Arnaud Valladier
- ARAMIS Lab, Brain & Spine Institute (ICM), Pitié‐Salpêtrière Hospital Paris France
| | - Etienne Maheux
- Inria, Aramis project‐team, Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université Paris France
| | - Igor Koval
- Inria, Aramis‐project team, Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital de la Pitié‐Salpêtrière Paris France
| | - Stanley Durrleman
- Sorbonne Universités, Inserm, CNRS, Institut du cerveau et la moelle (ICM), Aramis‐project team, AP‐HP ‐ Hôpital Pitié‐Salpêtrière Paris France
| | - Stéphane Epelbaum
- APHP, Sorbonne Universités, Inserm, CNRS, Institut du cerveau et de la Moelle Epinière (ICM), Aramis project‐team, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié‐Salpêtrière Paris France
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14
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Ansart M, Epelbaum S, Houot M, Nedelec T, Lekens B, Gantzer L, Dormont D, Durrleman S. Changes in the use of psychotropic drugs during the course of Alzheimer's disease: A large-scale longitudinal study of French medical records. Alzheimers Dement (N Y) 2021; 7:e12210. [PMID: 34541292 PMCID: PMC8439142 DOI: 10.1002/trc2.12210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 04/30/2021] [Accepted: 07/28/2021] [Indexed: 11/23/2022]
Abstract
INTRODUCTION We aim to understand how patients with Alzheimer's disease (AD) are treated by identifying in a longitudinal fashion the late-life changes in patients' medical history that precede and follow AD diagnosis. METHODS We use prescription history of 34,782 patients followed between 1996 and 2019 by French general practitioners. We compare patients with an AD diagnosis, patients with mild cognitive impairment (MCI), and patients free of mental disorders. We use a generalized mixed-effects model to study the longitudinal changes in the prescription of eight drug types for a period 15 years before diagnosis and 10 years after. RESULTS In the decades preceding diagnosis, we find that future AD patients are treated significantly more than MCI patients with most psychotropic drugs and that most studied drugs are increasingly prescribed with age. At the time of diagnosis, all psychotropic drugs except benzodiazepines show a significant increase in prescription, while other drugs are significantly less prescribed. In the 10 years after diagnosis, nearly all categories of drugs are less and less prescribed including antidementia drugs. DISCUSSION Pre-diagnosis differences between future AD patients and MCI patients may indicate that subtle cognitive changes are recognized and treated as psychiatric symptoms. The disclosure of AD diagnosis drastically changes patients' care, priority being given to the management of psychiatric symptoms. The decrease of all prescriptions in the late stages may reflect treatment discontinuation and simplification of therapeutic procedures. This study therefore provides new insights into the medical practices for management of AD.
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Affiliation(s)
- Manon Ansart
- Sorbonne UniversitésUPMC Univ Paris 06InsermCNRSInstitut du cerveau et la moelle épinière (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria ParisAramis project‐teamParisFrance
| | - Stéphane Epelbaum
- Sorbonne UniversitésUPMC Univ Paris 06InsermCNRSInstitut du cerveau et la moelle épinière (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria ParisAramis project‐teamParisFrance
- Department of NeurologyAP‐HPHôpital de la Pitié‐SalpêtrièreInstitut de la Mémoire et de la Maladie d'Alzheimer (IM2A)Reference Center for Rare or Early Dementias and Center of Excellence of Neurodegenerative Disease (CoEN)ParisFrance
| | - Marion Houot
- Sorbonne UniversitésUPMC Univ Paris 06InsermCNRSInstitut du cerveau et la moelle épinière (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Sorbonne UniversityAlzheimer Precision Medicine (APM)AP‐HPHôpital de la Pitié‐SalpêtrièreParisFrance
| | - Thomas Nedelec
- Sorbonne UniversitésUPMC Univ Paris 06InsermCNRSInstitut du cerveau et la moelle épinière (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria ParisAramis project‐teamParisFrance
| | | | | | - Didier Dormont
- Sorbonne UniversitésUPMC Univ Paris 06InsermCNRSInstitut du cerveau et la moelle épinière (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria ParisAramis project‐teamParisFrance
- Department of NeuroradiologyAP‐HPHôpital de la Pitié‐SalpêtrièreParisFrance
| | - Stanley Durrleman
- Sorbonne UniversitésUPMC Univ Paris 06InsermCNRSInstitut du cerveau et la moelle épinière (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria ParisAramis project‐teamParisFrance
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15
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Lartigue T, Bottani S, Baron S, Colliot O, Durrleman S, Allassonniere S. Gaussian Graphical Model Exploration and Selection in High Dimension Low Sample Size Setting. IEEE Trans Pattern Anal Mach Intell 2021; 43:3196-3213. [PMID: 32175856 DOI: 10.1109/tpami.2020.2980542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Gaussian graphical models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: the nodewise approach and the penalised likelihood maximisation. We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one. As a result, we propose a composite procedure that explores a family of graphs with a nodewise numerical scheme and selects a candidate among them with an overall likelihood criterion. We demonstrate that, when the number of observations is small, this selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence with regards to the real distribution than the other two. Finally, we show the interest of our algorithm on two concrete cases: first on brain imaging data, then on biological nephrology data. In both cases our results are more in line with current knowledge in each field.
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16
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Routier A, Burgos N, Díaz M, Bacci M, Bottani S, El-Rifai O, Fontanella S, Gori P, Guillon J, Guyot A, Hassanaly R, Jacquemont T, Lu P, Marcoux A, Moreau T, Samper-González J, Teichmann M, Thibeau-Sutre E, Vaillant G, Wen J, Wild A, Habert MO, Durrleman S, Colliot O. Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies. Front Neuroinform 2021; 15:689675. [PMID: 34483871 PMCID: PMC8415107 DOI: 10.3389/fninf.2021.689675] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/19/2021] [Indexed: 12/03/2022] Open
Abstract
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.
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Affiliation(s)
- Alexandre Routier
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Ninon Burgos
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Mauricio Díaz
- Inria, Service d'Expérimentation et de Développement, Paris, France
| | - Michael Bacci
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Simona Bottani
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Omar El-Rifai
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Sabrina Fontanella
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Pietro Gori
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Jérémy Guillon
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Alexis Guyot
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Ravi Hassanaly
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Thomas Jacquemont
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Pascal Lu
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Arnaud Marcoux
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Tristan Moreau
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Jorge Samper-González
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Marc Teichmann
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
- Department of Neurology, Institute for Memory and Alzheimer's Disease, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Elina Thibeau-Sutre
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Ghislain Vaillant
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Junhao Wen
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Adam Wild
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Marie-Odile Habert
- Sorbonne Université, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
- Centre d'Acquisition et Traitement des Images, Paris, France
| | - Stanley Durrleman
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Olivier Colliot
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
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17
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Mantoux C, Couvy-Duchesne B, Cacciamani F, Epelbaum S, Durrleman S, Allassonnière S. Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold. Entropy (Basel) 2021; 23:e23040490. [PMID: 33924060 PMCID: PMC8074266 DOI: 10.3390/e23040490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/08/2021] [Accepted: 04/14/2021] [Indexed: 11/22/2022]
Abstract
Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.e., the structure of connection similarities and differences across a set of networks. We propose a statistical framework to model these variations based on manifold-valued latent factors. Each network adjacency matrix is decomposed as a weighted sum of matrix patterns with rank one. Each pattern is described as a random perturbation of a dictionary element. As a hierarchical statistical model, it enables the analysis of heterogeneous populations of adjacency matrices using mixtures. Our framework can also be used to infer the weight of missing edges. We estimate the parameters of the model using an Expectation-Maximization-based algorithm. Experimenting on synthetic data, we show that the algorithm is able to accurately estimate the latent structure in both low and high dimensions. We apply our model on a large data set of functional brain connectivity matrices from the UK Biobank. Our results suggest that the proposed model accurately describes the complex variability in the data set with a small number of degrees of freedom.
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Affiliation(s)
- Clément Mantoux
- ARAMIS Project Team, Inria, 75013 Paris, France; (B.-C.D.); (F.C.); (S.E.); (S.D.)
- ARAMIS Lab, Brain and Spine Institute, ICM, INSERM UMR 1127, CNRS UMR 7225, Sorbonne University, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
- CMAP, École Polytechnique, 91120 Palaiseau, France
- Correspondence:
| | - Baptiste Couvy-Duchesne
- ARAMIS Project Team, Inria, 75013 Paris, France; (B.-C.D.); (F.C.); (S.E.); (S.D.)
- ARAMIS Lab, Brain and Spine Institute, ICM, INSERM UMR 1127, CNRS UMR 7225, Sorbonne University, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Federica Cacciamani
- ARAMIS Project Team, Inria, 75013 Paris, France; (B.-C.D.); (F.C.); (S.E.); (S.D.)
- ARAMIS Lab, Brain and Spine Institute, ICM, INSERM UMR 1127, CNRS UMR 7225, Sorbonne University, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Stéphane Epelbaum
- ARAMIS Project Team, Inria, 75013 Paris, France; (B.-C.D.); (F.C.); (S.E.); (S.D.)
- ARAMIS Lab, Brain and Spine Institute, ICM, INSERM UMR 1127, CNRS UMR 7225, Sorbonne University, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
- Institute of Memory and Alzheimer’s Disease (IM2A), Centre of Excellence of Neurodegenerative Disease (CoEN), CIC Neurosciences, AP-HP, Department of Neurology, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Stanley Durrleman
- ARAMIS Project Team, Inria, 75013 Paris, France; (B.-C.D.); (F.C.); (S.E.); (S.D.)
- ARAMIS Lab, Brain and Spine Institute, ICM, INSERM UMR 1127, CNRS UMR 7225, Sorbonne University, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Stéphanie Allassonnière
- Centre de Recherche des Cordeliers, Université de Paris, INSERM UMR 1138, Sorbonne Université, 75006 Paris, France;
- HEKA Project Team, Inria, 75006 Paris, France
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18
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Koval I, Bône A, Louis M, Lartigue T, Bottani S, Marcoux A, Samper-González J, Burgos N, Charlier B, Bertrand A, Epelbaum S, Colliot O, Allassonnière S, Durrleman S. AD Course Map charts Alzheimer's disease progression. Sci Rep 2021; 11:8020. [PMID: 33850174 PMCID: PMC8044144 DOI: 10.1038/s41598-021-87434-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/22/2021] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer's disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient's cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer's disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.
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Affiliation(s)
- Igor Koval
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Alexandre Bône
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Maxime Louis
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Thomas Lartigue
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Simona Bottani
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Arnaud Marcoux
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Jorge Samper-González
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Benjamin Charlier
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- Laboratoire Alexandre Grotendieck, Université de Montpellier, Montpellier, France
| | - Anne Bertrand
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphanie Allassonnière
- Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Stanley Durrleman
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France.
- Inria, Aramis project-team, Paris, France.
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19
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Burgos N, Cardoso MJ, Samper-González J, Habert MO, Durrleman S, Ourselin S, Colliot O. Anomaly detection for the individual analysis of brain PET images. J Med Imaging (Bellingham) 2021; 8:024003. [PMID: 33842668 PMCID: PMC8021015 DOI: 10.1117/1.jmi.8.2.024003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/12/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: In clinical practice, positron emission tomography (PET) images are mostly analyzed visually, but the sensitivity and specificity of this approach greatly depend on the observer's experience. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings. We present an anomaly detection framework for the individual analysis of PET images. Approach: We created subject-specific abnormality maps that summarize the pathology's topographical distribution in the brain by comparing the subject's PET image to a model of healthy PET appearance that is specific to the subject under investigation. This model was generated from demographically and morphologically matched PET scans from a control dataset. Results: We generated abnormality maps for healthy controls, patients at different stages of Alzheimer's disease and with different frontotemporal dementia syndromes. We showed that no anomalies were detected for the healthy controls and that the anomalies detected from the patients with dementia coincided with the regions where abnormal uptake was expected. We also validated the proposed framework using the abnormality maps as inputs of a classifier and obtained higher classification accuracies than when using the PET images themselves as inputs. Conclusions: The proposed method was able to automatically locate and characterize the areas characteristic of dementia from PET images. The abnormality maps are expected to (i) help clinicians in their diagnosis by highlighting, in a data-driven fashion, the pathological areas, and (ii) improve the interpretability of subsequent analyses, such as computer-aided diagnosis or spatiotemporal modeling.
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Affiliation(s)
- Ninon Burgos
- Paris Brain Institute, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM, U 1127, Hôpital Pitié-Salpêtrière, Paris, France
- CNRS, UMR 7225, Hôpital Pitié-Salpêtrière, Paris, France
- Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- Inria, Aramis Project-Team, Hôpital Pitié-Salpêtrière, Paris, France
| | - M. Jorge Cardoso
- King’s College London, Department of Imaging and Biomedical Engineering, London, United Kingdom
| | - Jorge Samper-González
- Paris Brain Institute, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM, U 1127, Hôpital Pitié-Salpêtrière, Paris, France
- CNRS, UMR 7225, Hôpital Pitié-Salpêtrière, Paris, France
- Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- Inria, Aramis Project-Team, Hôpital Pitié-Salpêtrière, Paris, France
| | - Marie-Odile Habert
- AP-HP, Hôpital Pitié-Salpêtrière, Department of Nuclear Medicine, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Université, Inserm U 1146, CNRS UMR 7371, Hôpital Pitié-Salpêtrière, Paris, France
- Centre Acquisition et Traitement des Images, Hôpital Pitié-Salpêtrière, Paris, France
| | - Stanley Durrleman
- Paris Brain Institute, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM, U 1127, Hôpital Pitié-Salpêtrière, Paris, France
- CNRS, UMR 7225, Hôpital Pitié-Salpêtrière, Paris, France
- Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- Inria, Aramis Project-Team, Hôpital Pitié-Salpêtrière, Paris, France
| | - Sébastien Ourselin
- King’s College London, Department of Imaging and Biomedical Engineering, London, United Kingdom
| | - Olivier Colliot
- Paris Brain Institute, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM, U 1127, Hôpital Pitié-Salpêtrière, Paris, France
- CNRS, UMR 7225, Hôpital Pitié-Salpêtrière, Paris, France
- Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- Inria, Aramis Project-Team, Hôpital Pitié-Salpêtrière, Paris, France
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20
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Dumoncel J, Subsol G, Durrleman S, Bertrand A, de Jager E, Oettlé AC, Lockhat Z, Suleman FE, Beaudet A. Are endocasts reliable proxies for brains? A 3D quantitative comparison of the extant human brain and endocast. J Anat 2021; 238:480-488. [PMID: 32996582 PMCID: PMC7812123 DOI: 10.1111/joa.13318] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 09/01/2020] [Accepted: 09/04/2020] [Indexed: 12/24/2022] Open
Abstract
Endocasts (i.e., replicas of the inner surface of the bony braincase) constitute a critical proxy for qualifying and quantifying variations in brain shape and organization in extinct taxa. In the absence of brain tissues preserved in the fossil record, endocasts provide the only direct evidence of brain evolution. However, debates on whether or not information inferred from the study of endocasts reflects brain shape and organization have polarized discussions in paleoneurology since the earliest descriptions of cerebral imprints in fossil hominin crania. By means of imaging techniques (i.e., MRIs and CT scans) and 3D modelling methods (i.e., surface-based comparisons), we collected consistent morphological (i.e., shape) and structural (i.e., sulci) information on the variation patterns between the brain and the endocast based on a sample of extant human individuals (N = 5) from the 3D clinical image database of the Steve Biko Academic Hospital in Pretoria (South Africa) and the Hôpitaux Universitaires Pitié Salpêtrière in Paris (France). Surfaces of the brain and endocast of the same individual were segmented from the 3D MRIs and CT images, respectively. Sulcal imprints were automatically detected. We performed a deformation-based shape analysis to compare both the shape and the sulcal pattern of the brain and the endocast. We demonstrated that there is close correspondence in terms of morphology and organization between the brain and the corresponding endocast with the exception of the superior region. By comparatively quantifying the shape and organization of the brain and endocast, this work represents an important reference for paleoneurological studies.
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Affiliation(s)
- Jean Dumoncel
- Laboratoire d’Anthropobiologie Moléculaire et Imagerie de SynthèseUMR 5288 CNRSUniversité Toulouse 3 Paul SabatierToulouseFrance
| | - Gérard Subsol
- Research‐Team ICARLaboratoire d’Informatiquede Robotique et de Microélectronique de MontpellierCNRSUniversité de MontpellierMontpellierFrance
| | - Stanley Durrleman
- Aramis teamINRIA ParisSorbonne UniversitésUPMC Université Paris 06 UMR S 1127Inserm U 1127CNRS UMR 7225Institut du Cerveau et de la Moelle épinièreParisFrance
| | - Anne Bertrand
- Aramis teamINRIA ParisSorbonne UniversitésUPMC Université Paris 06 UMR S 1127Inserm U 1127CNRS UMR 7225Institut du Cerveau et de la Moelle épinièreParisFrance
- Department of NeuroradiologyHôpital Pitié‐SalpêtrièreAssistance Publique–Hôpitaux de ParisParisFrance
| | - Edwin de Jager
- Department of AnatomyFaculty of Health SciencesUniversity of PretoriaPretoriaSouth Africa
| | - Anna C. Oettlé
- Department of AnatomyFaculty of Health SciencesUniversity of PretoriaPretoriaSouth Africa
- Department of Anatomy and HistologySchool of MedicineSefako Makgatho Health Sciences UniversityGa‐RankuwaSouth Africa
| | - Zarina Lockhat
- Department of RadiologyFaculty of Health SciencesUniversity of PretoriaPretoriaSouth Africa
| | - Farhana E. Suleman
- Department of RadiologyFaculty of Health SciencesUniversity of PretoriaPretoriaSouth Africa
| | - Amélie Beaudet
- Department of AnatomyFaculty of Health SciencesUniversity of PretoriaPretoriaSouth Africa
- Department of ArchaeologyUniversity of CambridgeCambridgeUnited Kingdom
- School of Geography, Archaeology and Environmental StudiesUniversity of the WitwatersrandJohannesburgSouth Africa
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21
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Guyot A, Fouquier ABG, Gerardin E, Chupin M, Glaunes JA, Marrakchi-Kacem L, Germain J, Boutet C, Cury C, Hertz-Pannier L, Vignaud A, Durrleman S, Henry TR, van de Moortele PF, Trouve A, Colliot O. A Diffeomorphic Vector Field Approach to Analyze the Thickness of the Hippocampus From 7 T MRI. IEEE Trans Biomed Eng 2021; 68:393-403. [PMID: 32746019 DOI: 10.1109/tbme.2020.2999941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE 7-Tesla MRI of the hippocampus enhances the visualization of its internal substructures. Among these substructures, the cornu Ammonis and subiculum form a contiguous folded ribbon of gray matter. Here, we propose a method to analyze local thickness measurements of this ribbon. METHODS We introduce an original approach based upon the estimation of a diffeomorphic vector field that traverses the ribbon. The method is designed to handle specificities of the hippocampus and corresponding 7-Tesla acquisitions: highly convoluted surface, non-closed ribbon, incompletely defined inner/outer boundaries, anisotropic acquisitions. We furthermore propose to conduct group comparisons using a population template built from the central surfaces of individual subjects. RESULTS We first assessed the robustness of our approach to anisotropy, as well as to inter-rater variability, on a post-mortem scan and on in vivo acquisitions respectively. We then conducted a group study on a dataset of in vivo MRI from temporal lobe epilepsy (TLE) patients and healthy controls. The method detected local thinning patterns in patients, predominantly ipsilaterally to the seizure focus, which is consistent with medical knowledge. CONCLUSION This new technique allows measuring the thickness of the hippocampus from 7-Tesla MRI. It shows good robustness with respect to anisotropy and inter-rater variability and has the potential to detect local atrophy in patients. SIGNIFICANCE As 7-Tesla MRI is increasingly available, this new method may become a useful tool to study local alterations of the hippocampus in brain disorders. It is made freely available to the community (code: https://github.com/aramis-lab/hiplay7-thickness, postmortem segmentation: https://doi.org/10.5281/zenodo.3533264).
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22
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Debavelaere V, Durrleman S, Allassonnière S. On the convergence of stochastic approximations under a subgeometric ergodic Markov dynamic. Electron J Stat 2021. [DOI: 10.1214/21-ejs1827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | - Stanley Durrleman
- Inria Center of Paris, Sorbonne Université, CNRS UMR 7225, Inserm U 1127, Institut du Cerveau (ICM), Paris, France
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23
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Cacciamani F, Valladier A, Maheux E, Koval I, Durrleman S, Epelbaum S. Awareness of cognitive decline through the continuum of Alzheimer’s disease and its association to APOE‐ε4 and amyloid load. Alzheimers Dement 2020. [DOI: 10.1002/alz.042730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Federica Cacciamani
- ARAMIS Lab, Brain & Spine Institute (ICM) Pitié‐Salpêtrière Hospital Paris France
| | - Arnaud Valladier
- ARAMIS Lab, Brain & Spine Institute (ICM) Pitié‐Salpêtrière Hospital Paris France
| | - Etienne Maheux
- Inria, Aramis Project‐Team, Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225 Sorbonne Université F‐75013 Paris France
| | - Igor Koval
- Inria, Aramis‐Project Team Sorbonne Universités UPMC Univ Paris 06, INSERM, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital de la Pitié‐Salpêtrière Paris France
| | - Stanley Durrleman
- Sorbonne Universités Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), Aramis‐Project Team, AP‐HP ‐ Hôpital Pitié‐Salpêtrière Paris France
| | - Stéphane Epelbaum
- APHP, Sorbonne Universités INSERM, CNRS, Institut du Cerveau et de la Moelle Epinière (ICM), Aramis Project‐Team, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié‐Salpêtrière Paris France
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24
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Maheux E, Koval I, Archetti D, Redolfi A, Durrleman S. Towards cross‐cohort estimation of cognitive decline in neurodegenerative diseases. Alzheimers Dement 2020. [DOI: 10.1002/alz.041498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Etienne Maheux
- Inria, Aramis Project Team, Institut du Cerveau et de la Moelle Épinière, ICM, Inserm U 1127, CNRS UMR 7225 Sorbonne Université F‐75013 Paris France
| | - Igor Koval
- Inria, Aramis‐project team, Sorbonne Universités, UPMC University Paris 06, Inserm, CNRS Institut du Cerveau et la Moelle (ICM) ‐ Hôpital de la Pitié‐Salpêtrière Paris France
| | | | - Alberto Redolfi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology ‐ LANE IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy
| | - Stanley Durrleman
- Sorbonne Universités, Inserm, CNRS Institut du Cerveau et la Moelle (ICM), Aramis‐project team AP‐HP ‐ Hôpital Pitié‐Salpêtrière Paris France
- Inria, Aramis Project Team Centre de Recherche Paris‐Rocquencourt Paris France
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25
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Vernhet P, Bilgel M, Durrleman S, Resnick SM, Johnson SC, Jedynak BM. Modeling the early accumulation of amyloid using differential equations in WRAP and BLSA. Alzheimers Dement 2020. [DOI: 10.1002/alz.039536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
| | - Murat Bilgel
- National Institute on Aging NIH Baltimore MD USA
| | - Stanley Durrleman
- Sorbonne Universités, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), Aramis‐Project Team AP‐HP ‐ Hôpital Pitié‐Salpêtrière Paris France
| | | | - Sterling C. Johnson
- Division of Geriatrics and Gerontology University of Wisconsin‐Madison School of Medicine and Public Health Madison WI USA
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Maheux E, Koval I, Archetti D, Redolfi A, Durrleman S. Prediction of the MMSE up to 6 years ahead with cross‐cohort replications. Alzheimers Dement 2020. [DOI: 10.1002/alz.043541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Etienne Maheux
- Inria, Aramis Project Team, Institut du Cerveau et de la Moelle Épinière, ICM, Inserm U 1127, CNRS UMR 7225 Sorbonne Université F‐75013 Paris France
| | - Igor Koval
- Inria, Aramis‐Project Team Sorbonne Universités UPMC University Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital de la Pitié‐Salpêtrière Paris France
| | | | - Alberto Redolfi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology ‐ LANE IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy
| | - Stanley Durrleman
- Sorbonne Universités Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), Aramis‐Project Team, AP‐HP ‐ Hôpital Pitié‐Salpêtrière Paris France
- Inria Paris, Aramis Project Team Paris France
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27
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Ansart M, Epelbaum S, Bassignana G, Bône A, Bottani S, Cattai T, Couronné R, Faouzi J, Koval I, Louis M, Thibeau-Sutre E, Wen J, Wild A, Burgos N, Dormont D, Colliot O, Durrleman S. Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Med Image Anal 2020; 67:101848. [PMID: 33091740 DOI: 10.1016/j.media.2020.101848] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 08/17/2020] [Accepted: 08/31/2020] [Indexed: 11/25/2022]
Abstract
We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice.
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Affiliation(s)
- Manon Ansart
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France.
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, F-75013, France
| | - Giulia Bassignana
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Alexandre Bône
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Simona Bottani
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Tiziana Cattai
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Dept. of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Italy
| | - Raphaël Couronné
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Johann Faouzi
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Igor Koval
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Maxime Louis
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Elina Thibeau-Sutre
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Junhao Wen
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Adam Wild
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, F-75013, France; AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Stanley Durrleman
- Inria, Aramis project-team, Paris, F-75013, France; Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France
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Wei W, Poirion E, Bodini B, Tonietto M, Durrleman S, Colliot O, Stankoff B, Ayache N. Predicting PET-derived myelin content from multisequence MRI for individual longitudinal analysis in multiple sclerosis. Neuroimage 2020; 223:117308. [PMID: 32889117 DOI: 10.1016/j.neuroimage.2020.117308] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/20/2020] [Accepted: 08/21/2020] [Indexed: 12/31/2022] Open
Abstract
Multiple sclerosis (MS) is a demyelinating and inflammatory disease of the central nervous system (CNS). The demyelination process can be repaired by the generation of a new sheath of myelin around the axon, a process termed remyelination. In MS patients, the demyelination-remyelination cycles are highly dynamic. Over the years, magnetic resonance imaging (MRI) has been increasingly used in the diagnosis of MS and it is currently the most useful paraclinical tool to assess this diagnosis. However, conventional MRI pulse sequences are not specific for pathological mechanisms such as demyelination and remyelination. Recently, positron emission tomography (PET) with radiotracer [11C]PIB has become a promising tool to measure in-vivo myelin content changes which is essential to push forward our understanding of mechanisms involved in the pathology of MS, and to monitor individual patients in the context of clinical trials focused on repair therapies. However, PET imaging is invasive due to the injection of a radioactive tracer. Moreover, it is an expensive imaging test and not offered in the majority of medical centers in the world. In this work, by using multisequence MRI, we thus propose a method to predict the parametric map of [11C]PIB PET, from which we derived the myelin content changes in a longitudinal analysis of patients with MS. The method is based on the proposed conditional flexible self-attention GAN (CF-SAGAN) which is specifically adjusted for high-dimensional medical images and able to capture the relationships between the spatially separated lesional regions during the image synthesis process. Jointly applying the sketch-refinement process and the proposed attention regularization that focuses on the MS lesions, our approach is shown to outperform the state-of-the-art methods qualitatively and quantitatively. Specifically, our method demonstrated a superior performance for the prediction of myelin content at voxel-wise level. More important, our method for the prediction of myelin content changes in patients with MS shows similar clinical correlations to the PET-derived gold standard indicating the potential for clinical management of patients with MS.
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Affiliation(s)
- Wen Wei
- Université Côte d'Azur, Inria, Epione Project-Team, Sophia Antipolis, France; Inria, Aramis Project-Team, Paris, France; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France.
| | - Emilie Poirion
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France
| | - Benedetta Bodini
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France; APHP, Hôpital Saint Antoine, Neurology Department, Paris, France
| | - Matteo Tonietto
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France
| | - Stanley Durrleman
- Inria, Aramis Project-Team, Paris, France; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France
| | - Olivier Colliot
- Inria, Aramis Project-Team, Paris, France; Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France
| | - Bruno Stankoff
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France; APHP, Hôpital Saint Antoine, Neurology Department, Paris, France
| | - Nicholas Ayache
- Université Côte d'Azur, Inria, Epione Project-Team, Sophia Antipolis, France
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Debavelaere V, Durrleman S, Allassonnière S. Learning the Clustering of Longitudinal Shape Data Sets into a Mixture of Independent or Branching Trajectories. Int J Comput Vis 2020. [DOI: 10.1007/s11263-020-01337-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Med Image Anal 2020; 63:101694. [PMID: 32417716 DOI: 10.1016/j.media.2020.101694] [Citation(s) in RCA: 178] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 03/23/2020] [Accepted: 03/27/2020] [Indexed: 10/24/2022]
Abstract
Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL.
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Affiliation(s)
- Junhao Wen
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Elina Thibeau-Sutre
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Mauricio Diaz-Melo
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Jorge Samper-González
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Alexandre Routier
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Simona Bottani
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Didier Dormont
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France
| | - Stanley Durrleman
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France; Department of Neurology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France.
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Couvy-Duchesne B, Faouzi J, Martin B, Thibeau-Sutre E, Wild A, Ansart M, Durrleman S, Dormont D, Burgos N, Colliot O. Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge. Front Psychiatry 2020; 11:593336. [PMID: 33384629 PMCID: PMC7770104 DOI: 10.3389/fpsyt.2020.593336] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/20/2020] [Indexed: 12/14/2022] Open
Abstract
We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.
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Affiliation(s)
- Baptiste Couvy-Duchesne
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France.,Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Johann Faouzi
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Benoît Martin
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Elina Thibeau-Sutre
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Adam Wild
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Manon Ansart
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Stanley Durrleman
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Didier Dormont
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Department of Neuroradiology, Paris, France
| | - Ninon Burgos
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Olivier Colliot
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
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Wei W, Poirion E, Bodini B, Durrleman S, Ayache N, Stankoff B, Colliot O. Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis. Med Image Anal 2019; 58:101546. [DOI: 10.1016/j.media.2019.101546] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 07/04/2019] [Accepted: 08/20/2019] [Indexed: 11/24/2022]
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Bertrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O, Le Ber I. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol 2019; 75:236-245. [PMID: 29197216 DOI: 10.1001/jamaneurol.2017.4266] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Importance Presymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72) mutation, the most frequent genetic cause of frontotemporal lobar degeneration and amyotrophic lateral sclerosis, represent the optimal target population for the development of disease-modifying drugs. Preclinical biomarkers are needed to monitor the effect of therapeutic interventions in this population. Objectives To assess the occurrence of cognitive, structural, and microstructural changes in presymptomatic C9orf72 carriers. Design, Setting, and Participants The PREV-DEMALS study is a prospective, multicenter, observational study of first-degree relatives of individuals carrying the C9orf72 mutation. Eighty-four participants entered the study between October 2015 and April 2017; 80 (95%) were included in cross-sectional analyses of baseline data. All participants underwent neuropsychological testing and magnetic resonance imaging; 63 (79%) underwent diffusion tensor magnetic resonance imaging. Gray matter volumes and diffusion tensor imaging metrics were calculated within regions of interest. Anatomical and microstructural differences between individuals who carried the C9orf72 mutation (C9+) and those who did not carry the C9orf72 mutation (C9-) were assessed using linear mixed-effects models. Data were analyzed from October 2015 to April 2017. Main Outcomes and Measures Differences in neuropsychological scores, gray matter volume, and white matter integrity between C9+ and C9- individuals. Results Of the 80 included participants, there were 41 C9+ individuals (24 [59%] female; mean [SD] age, 39.8 [11.1] years) and 39 C9- individuals (24 [62%] female; mean [SD] age, 45.2 [13.9] years). Compared with C9- individuals, C9+ individuals had lower mean (SD) praxis scores (163.4 [6.1] vs 165.3 [5.9]; P = .01) and intransitive gesture scores (34.9 [1.6] vs 35.7 [1.5]; P = .004), atrophy in 8 cortical regions of interest and in the right thalamus, and white matter alterations in 8 tracts. When restricting the analyses to participants younger than 40 years, compared with C9- individuals, C9+ individuals had lower praxis scores and intransitive gesture scores, atrophy in 4 cortical regions of interest and in the right thalamus, and white matter alterations in 2 tracts. Conclusions and Relevance Cognitive, structural, and microstructural alterations are detectable in young C9+ individuals. Early and subtle praxis alterations, underpinned by focal atrophy of the left supramarginal gyrus, may represent an early and nonevolving phenotype related to neurodevelopmental effects of C9orf72 mutation. White matter alterations reflect the future phenotype of frontotemporal lobar degeneration/amyotrophic lateral sclerosis, while atrophy appears more diffuse. Our results contribute to a better understanding of the preclinical phase of C9orf72 disease and of the respective contribution of magnetic resonance biomarkers. Trial Registration clinicaltrials.gov Identifier: NCT02590276.
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Affiliation(s)
- Anne Bertrand
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Aramis Project Team, Inria Research Center of Paris, Paris, France.,Department of Neuroradiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Department of Radiology, Hôpital Saint Antoine, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Junhao Wen
- Aramis Project Team, Inria Research Center of Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France
| | - Daisy Rinaldi
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Centre de Référence des Démences Rares ou Précoces, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Marion Houot
- Institute of Memory and Alzheimer's Disease, Centre of Excellence of Neurodegenerative Disease, Department of Neurology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Sabrina Sayah
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France
| | - Agnès Camuzat
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France
| | - Clémence Fournier
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France
| | - Sabrina Fontanella
- Aramis Project Team, Inria Research Center of Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France
| | - Alexandre Routier
- Aramis Project Team, Inria Research Center of Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France
| | - Philippe Couratier
- Department of Neurology, Amyotrophic Lateral Sclerosis Center, Centre Hospitalier Universitaire de Limoges, Limoges, France.,Limoges University, Institut d'Epidémiologie Neurologique et Neurologie Tropicale, Centre National de la Recherche Scientifique, Fédération de Recherche 3503, Institut Génomique, Environnement, Immunité, Santé et Thérapeutiques, Limoges, France
| | - Florence Pasquier
- Neurology Department, National Reference Center for Young Onset Dementia, Centre Hospitalier Régional Universitaire de Lille, INSERM U1171, Lille, France.,Equipe d'accueil 1046, Maladie d'Alzheimer et Pathologies Vasculaires, Lille University, Lille, France
| | - Marie-Odile Habert
- Department of Nuclear Medicine, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, Université Pierre et Marie Curie Paris 06, INSERM U1146, Centre National de la Recherche Scientifique, UMR 7371, Paris, France
| | - Didier Hannequin
- Centre National de Référence pour les Malades Alzheimer Jeunes, Centre Hospitalier Universitaire de Rouen, INSERM 1245, Rouen, France.,Department of Neurology, Centre Hospitalier Universitaire de Rouen, Rouen, France
| | - Olivier Martinaud
- Centre National de Référence pour les Malades Alzheimer Jeunes, Centre Hospitalier Universitaire de Rouen, INSERM 1245, Rouen, France.,Department of Neurology, Centre Hospitalier Universitaire de Rouen, Rouen, France
| | - Paola Caroppo
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France.,Division of Neurology V and Neuropathology, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
| | - Richard Levy
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Centre de Référence des Démences Rares ou Précoces, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Institute of Memory and Alzheimer's Disease, Centre of Excellence of Neurodegenerative Disease, Department of Neurology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Bruno Dubois
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Centre de Référence des Démences Rares ou Précoces, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Institute of Memory and Alzheimer's Disease, Centre of Excellence of Neurodegenerative Disease, Department of Neurology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Alexis Brice
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Stanley Durrleman
- Aramis Project Team, Inria Research Center of Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Paris, France
| | - Olivier Colliot
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Aramis Project Team, Inria Research Center of Paris, Paris, France.,Centre pour l'Acquisition et le Traitement des Images, Institut du Cerveau et la Moelle, Paris, France
| | - Isabelle Le Ber
- Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Centre de Référence des Démences Rares ou Précoces, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France.,Institute of Memory and Alzheimer's Disease, Centre of Excellence of Neurodegenerative Disease, Department of Neurology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
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Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Houot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. J Neurol Neurosurg Psychiatry 2019; 90:387-394. [PMID: 30355607 DOI: 10.1136/jnnp-2018-318994] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/12/2018] [Accepted: 09/19/2018] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To assess the added value of neurite orientation dispersion and density imaging (NODDI) compared with conventional diffusion tensor imaging (DTI) and anatomical MRI to detect changes in presymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72) mutation. METHODS The PREV-DEMALS (Predict to Prevent Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis) study is a prospective, multicentre, observational study of first-degree relatives of individuals carrying the C9orf72 mutation. Sixty-seven participants (38 presymptomatic C9orf72 mutation carriers (C9+) and 29 non-carriers (C9-)) were included in the present cross-sectional study. Each participant underwent one single-shell, multishell diffusion MRI and three-dimensional T1-weighted MRI. Volumetric measures, DTI and NODDI metrics were calculated within regions of interest. Differences in white matter integrity, grey matter volume and free water fraction between C9+ and C9- individuals were assessed using linear mixed-effects models. RESULTS Compared with C9-, C9+ demonstrated white matter abnormalities in 10 tracts with neurite density index and only 5 tracts with DTI metrics. Effect size was significantly higher for the neurite density index than for DTI metrics in two tracts. No tract had a significantly higher effect size for DTI than for NODDI. For grey matter cortical analysis, free water fraction was increased in 13 regions in C9+, whereas 11 regions displayed volumetric atrophy. CONCLUSIONS NODDI provides higher sensitivity and greater tissue specificity compared with conventional DTI for identifying white matter abnormalities in the presymptomatic C9orf72 carriers. Our results encourage the use of neurite density as a biomarker of the preclinical phase. TRIAL REGISTRATION NUMBER NCT02590276.
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Affiliation(s)
- Junhao Wen
- Inria Paris, Aramis Project-Team, Paris, France
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), Paris, France
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - Daniel C Alexander
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - Stanley Durrleman
- Inria Paris, Aramis Project-Team, Paris, France
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), Paris, France
| | - Alexandre Routier
- Inria Paris, Aramis Project-Team, Paris, France
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), FrontLab, Paris, France
| | - Daisy Rinaldi
- AP-HP, Hôpital Pitié-Salpêtrière, Centre de Référence des Démences Rares ou Précoces, Paris, France
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), AP-HP, Paris, France
| | - Marion Houot
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of Excellence of Neurodegenerative Disease (CoEN), Department of Neurology, ICM, CIC Neurosciences, Paris, France
| | - Philippe Couratier
- Department of Neurology, Centre de Compétences Démences Rares, Centre Hospitalier Universitaire de Limoges, Limoges, France
- Limoges University, UMR1094, Limoges, France
| | - Didier Hannequin
- Centre National de Référence pour les Malades Alzheimer Jeunes, Centre Hospitalier Universitaire de Rouen, INSERM 1245, Rouen, France
- Department of Neurology, Centre Hospitalier Universitaire de Rouen, Rouen, France
| | - Florence Pasquier
- Centre National de Référence pour les Malades Alzheimer Jeunes, Centre Hospitalier Universitaire de Lille, Paris, France
- Université de Lille, INSERM U1171, Labex DistALZ, CoEN LiCEND, Lille, France
| | - Jiaying Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - Olivier Colliot
- Inria Paris, Aramis Project-Team, Paris, France
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), AP-HP, Paris, France
- AP-HP, Departments of Neuroradiology and Neurology, Pitié-Salpêtrière Hospital, Paris, France
| | - Isabelle Le Ber
- AP-HP, Hôpital Pitié-Salpêtrière, Centre de Référence des Démences Rares ou Précoces, Paris, France
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), AP-HP, Paris, France
- AP-HP, Department of Neurology, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), Paris, France
| | - Anne Bertrand
- Inria Paris, Aramis Project-Team, Paris, France
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), AP-HP, Paris, France
- AP-HP,Department of Radiology, Saint-Antoine Hospital, Paris, France
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Cury C, Durrleman S, Cash DM, Lorenzi M, Nicholas JM, Bocchetta M, van Swieten JC, Borroni B, Galimberti D, Masellis M, Tartaglia MC, Rowe JB, Graff C, Tagliavini F, Frisoni GB, Laforce R, Finger E, de Mendonça A, Sorbi S, Ourselin S, Rohrer JD, Modat M. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort. Neuroimage 2019; 188:282-290. [PMID: 30529631 PMCID: PMC6414401 DOI: 10.1016/j.neuroimage.2018.11.063] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 11/15/2018] [Accepted: 11/30/2018] [Indexed: 12/18/2022] Open
Abstract
Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.
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Affiliation(s)
- Claire Cury
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom.
| | - Stanley Durrleman
- Inria Aramis Project-team Centre Paris-Rocquencourt, Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
| | - David M Cash
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom
| | - Marco Lorenzi
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Epione Team, Inria Sophia Antipolis, Sophia Antipolis, France
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom; Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Martina Bocchetta
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom
| | | | | | - Daniela Galimberti
- Dept. of Pathophysiology and Transplantation, "Dino Ferrari" Center, University of Milan, Fondazione C Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Mario Masellis
- Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Department of Medicine, University of Toronto, Canada
| | | | | | - Caroline Graff
- Karolinska Institutet, Stockholm, Sweden; Karolinska Institutet, Department NVS, Center for Alzheimer Research, Division of Neurogeriatrics, Sweden; Department of Geriatric Medicine, Karolinska University Hospital, Stockholm, Sweden
| | | | | | | | | | | | - Sandro Sorbi
- Department of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy; IRCCS Don Gnocchi, Firenze, Italy
| | - Sebastien Ourselin
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom
| | - Jonathan D Rohrer
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom
| | - Marc Modat
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College of London, WC1N 3BG, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom
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Wei W, Poirion E, Bodini B, Durrleman S, Colliot O, Stankoff B, Ayache N. Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis. J Med Imaging (Bellingham) 2019; 6:014005. [PMID: 30820439 DOI: 10.1117/1.jmi.6.1.014005] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 01/29/2019] [Indexed: 11/14/2022] Open
Abstract
Multiple sclerosis (MS) is a white matter (WM) disease characterized by the formation of WM lesions, which can be visualized by magnetic resonance imaging (MRI). The fluid-attenuated inversion recovery (FLAIR) MRI pulse sequence is used clinically and in research for the detection of WM lesions. However, in clinical settings, some MRI pulse sequences could be missed because of various constraints. The use of the three-dimensional fully convolutional neural networks is proposed to predict FLAIR pulse sequences from other MRI pulse sequences. In addition, the contribution of each input pulse sequence is evaluated with a pulse sequence-specific saliency map. This approach is tested on a real MS image dataset and evaluated by comparing this approach with other methods and by assessing the lesion contrast in the synthetic FLAIR pulse sequence. Both the qualitative and quantitative results show that this method is competitive for FLAIR synthesis.
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Affiliation(s)
- Wen Wei
- Université Côte d'Azur, Inria, Epione Project Team, Sophia Antipolis, France.,Sorbonne Université, Inserm, CNRS, Institut du cerveau et la moelle (ICM), AP-HP-Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France.,Inria, Aramis Project Team, Paris, France
| | - Emilie Poirion
- Sorbonne Université, Inserm, CNRS, Institut du cerveau et la moelle (ICM), AP-HP-Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - Benedetta Bodini
- Sorbonne Université, Inserm, CNRS, Institut du cerveau et la moelle (ICM), AP-HP-Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - Stanley Durrleman
- Sorbonne Université, Inserm, CNRS, Institut du cerveau et la moelle (ICM), AP-HP-Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France.,Inria, Aramis Project Team, Paris, France
| | - Olivier Colliot
- Sorbonne Université, Inserm, CNRS, Institut du cerveau et la moelle (ICM), AP-HP-Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France.,Inria, Aramis Project Team, Paris, France
| | - Bruno Stankoff
- Sorbonne Université, Inserm, CNRS, Institut du cerveau et la moelle (ICM), AP-HP-Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - Nicholas Ayache
- Université Côte d'Azur, Inria, Epione Project Team, Sophia Antipolis, France
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Ansart M, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis. Stat Methods Med Res 2019; 29:151-164. [PMID: 30698081 DOI: 10.1177/0962280218823036] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials.
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Affiliation(s)
- Manon Ansart
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Geoffroy Gagliardi
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
- AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Harald Hampel
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
- AXA Research Fund & Sorbonne University Chair, Paris, France
- Sorbonne University, GRC no 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France
| | - Stanley Durrleman
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
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Marcoux A, Burgos N, Bertrand A, Teichmann M, Routier A, Wen J, Samper-González J, Bottani S, Durrleman S, Habert MO, Colliot O. An Automated Pipeline for the Analysis of PET Data on the Cortical Surface. Front Neuroinform 2018; 12:94. [PMID: 30618699 PMCID: PMC6296445 DOI: 10.3389/fninf.2018.00094] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 11/23/2018] [Indexed: 12/14/2022] Open
Abstract
We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run.
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Affiliation(s)
- Arnaud Marcoux
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France
| | - Anne Bertrand
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France.,AP-HP, Departments of Neuroradiology and Neurology, Pitié-Salpétriére Hospital, Paris, France
| | - Marc Teichmann
- Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Institut du Cerveau et de la Moelle épinière, ICM, FrontLab, Paris, France.,Department of Neurology, National Reference Center for "PPA and rare dementias", Institute for Memory and Alzheimer's Disease, Pitié Salpêtrière Hospital, AP-HP, Paris, France
| | - Alexandre Routier
- Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France.,Institut du Cerveau et de la Moelle épinière, ICM, FrontLab, Paris, France
| | - Junhao Wen
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France
| | - Jorge Samper-González
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France
| | - Simona Bottani
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France
| | - Stanley Durrleman
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France
| | - Marie-Odile Habert
- AP-HP, Hôpital Pitié-Salpêtrière, Department of Nuclear Medicine, Paris, France.,Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France.,Centre Acquisition et Traitement des Images, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria, Aramis Project-Team, Paris, France.,AP-HP, Departments of Neuroradiology and Neurology, Pitié-Salpétriére Hospital, Paris, France
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Gori P, Colliot O, Kacem LM, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis. IEEE Trans Med Imaging 2018; 37:2033-2043. [PMID: 29993599 DOI: 10.1109/tmi.2018.2813062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modeled as 3-D surfaces and curves, respectively. Neurodevelopmental disorders involve morphological and organizational alterations which cannot be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3-D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify, and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.
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Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data. Neuroimage 2018; 183:504-521. [PMID: 30130647 DOI: 10.1016/j.neuroimage.2018.08.042] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 07/12/2018] [Accepted: 08/17/2018] [Indexed: 11/29/2022] Open
Abstract
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML.
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Affiliation(s)
- Jorge Samper-González
- Inria, ARAMIS Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France.
| | - Ninon Burgos
- Inria, ARAMIS Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France
| | - Simona Bottani
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France
| | - Sabrina Fontanella
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France
| | - Pascal Lu
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France
| | - Arnaud Marcoux
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France
| | - Alexandre Routier
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France
| | - Jérémy Guillon
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France
| | - Michael Bacci
- Inria, ARAMIS Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France
| | - Junhao Wen
- Inria, ARAMIS Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France
| | - Anne Bertrand
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France; AP-HP, Department of Neuroradiology, Pitié-Salpêtrière Hospital, Paris, France
| | - Hugo Bertin
- Laboratoire d'Imagerie Biomédicale, Inserm, U 1146, CNRS, UMR 7371, Sorbonne Université, F-75013, Paris, France
| | - Marie-Odile Habert
- Laboratoire d'Imagerie Biomédicale, Inserm, U 1146, CNRS, UMR 7371, Sorbonne Université, F-75013, Paris, France; AP-HP, Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, Paris, France
| | - Stanley Durrleman
- Inria, ARAMIS Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France
| | | | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France; Inria, ARAMIS Project-team, F-75013, Paris, France; AP-HP, Department of Neuroradiology, Pitié-Salpêtrière Hospital, Paris, France; AP-HP, Department of Neurology, Pitié-Salpêtrière Hospital, Paris, France.
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Wen J, Samper-González J, Bottani S, Routier A, Burgos N, Jacquemont T, Fontanella S, Durrleman S, Bertrand A, Colliot O. P2‐451: USING DIFFUSION MRI FOR CLASSIFICATION AND PREDICTION OF ALZHEIMER'S DISEASE: A REPRODUCIBLE STUDY. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.1144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Junhao Wen
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la MoëlleParisFrance
| | - Jorge Samper-González
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la MoëlleParisFrance
| | - Simona Bottani
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la MoëlleParisFrance
| | - Alexandre Routier
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la Moëlle, FrontLabParisFrance
| | - Ninon Burgos
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la MoëlleParisFrance
| | - Thomas Jacquemont
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la MoëlleParisFrance
| | - Sabrina Fontanella
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la MoëlleParisFrance
| | - Stanley Durrleman
- Inria Paris, Aramis Project TeamParisFrance
- Sorbonne Universités, University Pierre and Marie Curie InsermCentre National de la Recherche Scientifique, Institut du Cerveau et la Moelle ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
| | - Anne Bertrand
- Inria Paris, Aramis Project TeamParisFrance
- Assistance Publique – Hôpitaux de Paris, Saint‐Antoine HospitalDepartment of RadiologyParisFrance
- Sorbonne Universités, University Pierre and Marie Curie InsermCentre National de la Recherche Scientifique, Institut du Cerveau et la Moelle, Assistance Publique – Hôpitaux de Paris‐Hôpital Pitié‐SalpêtrièreParisFrance
| | - Olivier Colliot
- Inria Paris, Aramis Project TeamParisFrance
- Assistance Publique – Hôpitaux de Paris, Departments of Neuroradiology and NeurologyPitié‐Salpeêtrière HospitalParisFrance
- Sorbonne Université, INSERMCentre National de la Recherche Scientifique, Institut du Cerveau et la Moëlle, Assistance Publique – Hôpitaux de ParisParisFrance
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Ansart M, Koval I, Bertrand A, Dormont D, Durrleman S. P1‐363: DESIGN OF A DECISION SUPPORT SYSTEM FOR PREDICTING THE PROGRESSION OF ALZHEIMER'S DISEASE. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Manon Ansart
- Sorbonne UniversitésUPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria Paris, Aramis Project-teamParisFrance
| | - Igor Koval
- Sorbonne UniversitésUPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria Paris, Aramis Project-teamParisFrance
| | - Anne Bertrand
- Inria Paris, Aramis Project-teamParisFrance
- Sorbonne UniversitésUPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), AP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Didier Dormont
- Inria Paris, Aramis Project-teamParisFrance
- Sorbonne UniversitésUPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM), AP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Stanley Durrleman
- Sorbonne UniversitésUPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital de la Pitié‐SalpêtrièreParisFrance
- Inria Paris, Aramis Project-teamParisFrance
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Koval I, Schiratti JB, Routier A, Bacci M, Colliot O, Allassonnière S, Durrleman S. Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns. Front Neurol 2018; 9:235. [PMID: 29780348 PMCID: PMC5945895 DOI: 10.3389/fneur.2018.00235] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 03/26/2018] [Indexed: 01/19/2023] Open
Abstract
Repeated failures in clinical trials for Alzheimer’s disease (AD) have raised a strong interest for the prodromal phase of the disease. A better understanding of the brain alterations during this early phase is crucial to diagnose patients sooner, to estimate an accurate disease stage, and to give a reliable prognosis. According to recent evidence, structural alterations in the brain are likely to be sensitive markers of the disease progression. Neuronal loss translates in specific spatiotemporal patterns of cortical atrophy, starting in the enthorinal cortex and spreading over other cortical regions according to specific propagation pathways. We developed a digital model of the cortical atrophy in the left hemisphere from prodromal to diseased phases, which is built on the temporal alignment and combination of several short-term observation data to reconstruct the long-term history of the disease. The model not only provides a description of the spatiotemporal patterns of cortical atrophy at the group level but also shows the variability of these patterns at the individual level in terms of difference in propagation pathways, speed of propagation, and age at propagation onset. Longitudinal MRI datasets of patients with mild cognitive impairments who converted to AD are used to reconstruct the cortical atrophy propagation across all disease stages. Each observation is considered as a signal spatially distributed on a network, such as the cortical mesh, each cortex location being associated to a node. We consider how the temporal profile of the signal varies across the network nodes. We introduce a statistical mixed-effect model to describe the evolution of the cortex alterations. To ensure a spatiotemporal smooth propagation of the alterations, we introduce a constrain on the propagation signal in the model such that neighboring nodes have similar profiles of the signal changes. Our generative model enables the reconstruction of personalized patterns of the neurodegenerative spread, providing a way to estimate disease progression stages and predict the age at which the disease will be diagnosed. The model shows that, for instance, APOE carriers have a significantly higher pace of cortical atrophy but not earlier atrophy onset.
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Affiliation(s)
- Igor Koval
- Inria Paris-Rocquencourt, INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMRS 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,INSERM UMRS 1138, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
| | - Jean-Baptiste Schiratti
- Inria Paris-Rocquencourt, INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMRS 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,INSERM UMRS 1138, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
| | - Alexandre Routier
- Inria Paris-Rocquencourt, INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMRS 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Michael Bacci
- Inria Paris-Rocquencourt, INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMRS 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Olivier Colliot
- Inria Paris-Rocquencourt, INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMRS 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,AP-HP, Pitié-Salpétriere Hospital, Department of Neurology, Paris, France.,AP-HP, Pitié-Salpétriere Hospital, Department of Neuroradiology, Paris, France
| | - Stéphanie Allassonnière
- INSERM UMRS 1138, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
| | - Stanley Durrleman
- Inria Paris-Rocquencourt, INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMRS 1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
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Bône A, Louis M, Martin B, Durrleman S. Deformetrica 4: An Open-Source Software for Statistical Shape Analysis. Shape in Medical Imaging 2018. [DOI: 10.1007/978-3-030-04747-4_1] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Hampel H, Toschi N, Babiloni C, Baldacci F, Black KL, Bokde AL, Bun RS, Cacciola F, Cavedo E, Chiesa PA, Colliot O, Coman CM, Dubois B, Duggento A, Durrleman S, Ferretti MT, George N, Genthon R, Habert MO, Herholz K, Koronyo Y, Koronyo-Hamaoui M, Lamari F, Langevin T, Lehéricy S, Lorenceau J, Neri C, Nisticò R, Nyasse-Messene F, Ritchie C, Rossi S, Santarnecchi E, Sporns O, Verdooner SR, Vergallo A, Villain N, Younesi E, Garaci F, Lista S. Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. J Alzheimers Dis 2018; 64:S47-S105. [PMID: 29562524 PMCID: PMC6008221 DOI: 10.3233/jad-179932] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.
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Affiliation(s)
- Harald Hampel
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Department of Radiology, “Athinoula A. Martinos” Center for Biomedical Imaging, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome “La Sapienza”, Rome, Italy
- Institute for Research and Medical Care, IRCCS “San Raffaele Pisana”, Rome, Italy
| | - Filippo Baldacci
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Keith L. Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland
| | - René S. Bun
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Francesco Cacciola
- Unit of Neurosurgery, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Enrica Cavedo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- IRCCS “San Giovanni di Dio-Fatebenefratelli”, Brescia, Italy
| | - Patrizia A. Chiesa
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Olivier Colliot
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Paris, France
| | - Cristina-Maria Coman
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Bruno Dubois
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Stanley Durrleman
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France
| | - Maria-Teresa Ferretti
- IREM, Institute for Regenerative Medicine, University of Zurich, Zürich, Switzerland
- ZNZ Neuroscience Center Zurich, Zürich, Switzerland
| | - Nathalie George
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle Épinière, ICM, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
| | - Remy Genthon
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Marie-Odile Habert
- Département de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Karl Herholz
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Foudil Lamari
- AP-HP, UF Biochimie des Maladies Neuro-métaboliques, Service de Biochimie Métabolique, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | | | - Stéphane Lehéricy
- Centre de NeuroImagerie de Recherche - CENIR, Institut du Cerveau et de la Moelle Épinière - ICM, F-75013, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Paris, France
| | - Jean Lorenceau
- Institut de la Vision, INSERM, Sorbonne Universités, UPMC Univ Paris 06, UMR_S968, CNRS UMR7210, Paris, France
| | - Christian Neri
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, CNRS UMR 8256, Institut de Biologie Paris-Seine (IBPS), Place Jussieu, F-75005, Paris, France
| | - Robert Nisticò
- Department of Biology, University of Rome “Tor Vergata” & Pharmacology of Synaptic Disease Lab, European Brain Research Institute (E.B.R.I.), Rome, Italy
| | - Francis Nyasse-Messene
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Simone Rossi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Department of Medicine, Surgery and Neurosciences, Section of Human Physiology University of Siena, Siena, Italy
| | - Emiliano Santarnecchi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- IU Network Science Institute, Indiana University, Bloomington, IN, USA
| | | | - Andrea Vergallo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicolas Villain
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | | | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Casa di Cura “San Raffaele Cassino”, Cassino, Italy
| | - Simone Lista
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
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Beaudet A, Dumoncel J, de Beer F, Durrleman S, Gilissen E, Oettlé A, Subsol G, Thackeray JF, Braga J. The endocranial shape of Australopithecus africanus: surface analysis of the endocasts of Sts 5 and Sts 60. J Anat 2017; 232:296-303. [PMID: 29148040 DOI: 10.1111/joa.12745] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2017] [Indexed: 11/30/2022] Open
Abstract
Assessment of global endocranial morphology and regional neuroanatomical changes in early hominins is critical for the reconstruction of evolutionary trajectories of cerebral regions in the human lineage. Early evidence of cortical reorganization in specific local areas (e.g. visual cortex, inferior frontal gyrus) is perceptible in the non-human South African hominin fossil record. However, to date, little information is available regarding potential global changes in the early hominin brain. The introduction of non-invasive imaging techniques opens up new perspectives for the study of hominin brain evolution. In this context, our primary aim in this study is to explore the organization of the Australopithecus africanus endocasts, and highlight the nature and extent of the differences distinguishing A. africanus from the extant hominids at both local and global scales. By means of X-ray-based imaging techniques, we investigate two A. africanus specimens from Sterkfontein Member 4, catalogued as Sts 5 and Sts 60, respectively a complete cranium and a partial cranial endocast. Endocrania were virtually reconstructed and compared by using a landmark-free registration method based on smooth and invertible surface deformation. Both local and global information provided by our deformation-based approach are used to perform statistical analyses and topological mapping of inter-specific variation. Statistical analyses indicate that the endocranial shape of Sts 5 and Sts 60 approximates the Pan condition. Furthermore, our study reveals substantial differences with respect to the extant human condition, particularly in the parietal regions. Compared with Pan, the endocranial shape of the fossil specimens differs in the anterior part of the frontal gyri.
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Affiliation(s)
- Amélie Beaudet
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa.,Department of Anatomy, University of Pretoria, Pretoria, South Africa
| | - Jean Dumoncel
- Laboratoire d'Anthropologie Moléculaire et Imagerie de Synthèse, UMR 5288 CNRS-Université de Toulouse (Paul Sabatier), Toulouse Cedex, France.,Institut de Recherche en Informatique de Toulouse, UMR 5505 CNRS-Université de Toulouse (Paul Sabatier), Toulouse Cedex, France
| | - Frikkie de Beer
- Radiation Science Department, South African Nuclear Energy Corporation (Necsa), Pelindaba, South Africa
| | - Stanley Durrleman
- Institut du Cerveau et de la Moelle épinière, Aramis Team, INRIA Paris, Sorbonne Universités, UPMC Université Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Emmanuel Gilissen
- Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium.,Laboratory of Histology and Neuropathology, Université Libre de Bruxelles, Brussels, Belgium
| | - Anna Oettlé
- Department of Anatomy, University of Pretoria, Pretoria, South Africa.,Department of Anatomy and Histology, Sefako Makgatho Health Sciences University, Pretoria, South Africa
| | - Gérard Subsol
- Montpellier Laboratory of Informatics, Robotics and Microelectronics, UMR 5506 CNRS, Université de Montpellier, Montpellier, France
| | - John Francis Thackeray
- Evolutionary Studies Institute and School of Geosciences, University of the Witwatersrand, Johannesburg, South Africa
| | - José Braga
- Laboratoire d'Anthropologie Moléculaire et Imagerie de Synthèse, UMR 5288 CNRS-Université de Toulouse (Paul Sabatier), Toulouse Cedex, France.,Evolutionary Studies Institute and School of Geosciences, University of the Witwatersrand, Johannesburg, South Africa
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48
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Jacquemont T, De Vico Fallani F, Bertrand A, Epelbaum S, Routier A, Dubois B, Hampel H, Durrleman S, Colliot O. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiol Aging 2017; 55:177-189. [DOI: 10.1016/j.neurobiolaging.2017.03.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 03/17/2017] [Accepted: 03/19/2017] [Indexed: 01/01/2023]
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Bertrand A, Wen J, Rinaldi D, Camuzat A, Fontanella S, Routier A, Couratier P, Pasquier F, Martinaud O, Durrleman S, Brice A, Colliot O, Le Ber I. [IC‐P‐126]: ACCELERATED SUBCORTICAL ATROPHY DURING AGING IN PRESYMPTOMATIC CARRIERS OF C9ORF72 MUTATION. Alzheimers Dement 2017. [DOI: 10.1016/j.jalz.2017.06.2400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Anne Bertrand
- Inria Paris, Aramis Project‐TeamParisFrance
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM)AP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Junhao Wen
- Inria Paris, Aramis Project‐TeamParisFrance
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Daisy Rinaldi
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM)AP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | | | - Sabrina Fontanella
- Inria Paris, Aramis Project‐TeamParisFrance
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Alexandre Routier
- Inria Paris, Aramis Project‐TeamParisFrance
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | | | - Florence Pasquier
- INSERM U1171, National Reference Centre for Young Onset Dementia, Neurology DepartmentUniversity HospitalLilleFrance
- Lille UniversityLilleFrance
| | | | - Stanley Durrleman
- Inria Paris, Aramis Project‐TeamParisFrance
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Alexis Brice
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut du Cerveau et la Moelle (ICM) ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Olivier Colliot
- Inria Paris, Aramis Project‐TeamParisFrance
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM)AP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Isabelle Le Ber
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM)AP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
- APHP‐ Groupe Hospitalier Pitie SalpetriereParisFrance
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50
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Jacquemont T, De Vico Fallani F, Epelbaum S, Bertrand A, Routier A, Dubois B, Hampel H, Durrleman S, Colliot O. [P1–388]: DIFFERENT STRUCTURAL CONNECTIVITY PATTERNS IN MILD COGNITIVE IMPAIRMENT STRATIFIED BY AMYLOID AND NEURODEGENERATION BIOMARKERS. Alzheimers Dement 2017. [DOI: 10.1016/j.jalz.2017.06.404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Thomas Jacquemont
- Université Pierre et Marie CurieParisFrance
- Inserm U1127, CNRS UMR 7225Sorbonne Universites, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle epiniere, ICM, Inria Paris‐RocquencourtF‐75013 ParisFrance
| | | | - Stéphane Epelbaum
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle (ICM), Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A)ParisFrance
- Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), ICM, Salpetriere Hospital, AP‐HPUniversity Paris 6ParisFrance
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle (ICM)Département de Neurologie, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Hôpital Pitié‐SalpêtrièreParisFrance
| | - Anne Bertrand
- AP‐HPPitie‐Salpetriere Hospital Service de Neuroradiologie Diagnostique et FonctionnelleF‐75013 ParisFrance
- ARAMIS lab, ICMPitié‐Salpêtrière HospitalParisFrance
| | | | - Bruno Dubois
- APHP‐ Groupe Hospitalier Pitie SalpetriereParisFrance
- Hôpital La SalpêtrièreParisFrance
- Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Institute of Memory and Alzheimer's Disease (IM2A) & Brain and Spine Institute (ICM) UMR S 1127Department of Neurology, Hopital Pitié‐SalpêtrièreParisFrance
- INSERM‐ Universite Pierre et Marie CurieParis 6, IHU‐ICMParisFrance
- Sorbonne Universites, Universite Pierre et Marie Curie‐Paris 6ParisFrance
| | - Harald Hampel
- Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), ICM, Salpetriere Hospital, AP‐HPUniversity Paris 6ParisFrance
- AXA Research Fund & UPMC ChairSorbonne Universities, Pierre et Marie Curie University, Paris 06, Institute of Memory and Alzheimer's Disease (IM2A) & Brain and Spine Institute (ICM) UMR S 1127, Hopital Pitié‐SalpêtrièreParisFrance
- AXA Research Fund & UPMC ChairSorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle (ICM), Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A)ParisFrance
- Sorbonne Universities, Pierre et Marie Curie UniversityParisFrance
- Hopital de la Pitie‐SalpetriereParisFrance
| | - Stanley Durrleman
- Université Pierre et Marie CurieParisFrance
- Institut du Cerveau et de la MoelleParisFrance
- Inria, Aramis Project‐TeamCentre de Recherche Paris‐RocquencourtParisFrance
| | - Olivier Colliot
- CNRS, UMR 7225 ICMParisFrance
- Inserm, U1127F‐75013 ParisFrance
- Institut du Cerveau et de la Moelle EpinièreICMF‐75013 ParisFrance
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du Cerveau et la Moelle (ICM)AP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
- Inria Paris, Aramis Project‐TeamParisFrance
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