Lifespan Tree of Brain Anatomy: Diagnostic Values for Motor and Cognitive Neurodegenerative Diseases

The differential diagnosis of neurodegenerative diseases, characterized by overlapping symptoms, may be challenging. Brain imaging coupled with artificial intelligence has been previously proposed for diagnostic support, but most of these methods have been trained to discriminate only isolated diseases from controls. Here, we develop a novel machine learning framework, named lifespan tree of brain anatomy, dedicated to the differential diagnosis between multiple diseases simultaneously. It integrates the modeling of volume changes for 124 brain structures during the lifespan with nonlinear dimensionality reduction and synthetic sampling techniques to create easily interpretable representations of brain anatomy over the course of disease progression. As clinically relevant proof-of-concept applications, we constructed a cognitive lifespan tree of brain anatomy for the differential diagnosis of six causes of neurodegenerative dementia and a motor lifespan tree of brain anatomy for the differential diagnosis of four causes of parkinsonism using 37,594 MRIs as a training dataset. This original approach significantly enhanced the efficiency of differential diagnosis in the external validation cohort of 1754 cases, outperforming existing state-of-the-art machine learning techniques. Lifespan tree holds promise as a valuable tool for differential diagnosis in relevant clinical conditions, especially for diseases still lacking effective biological markers.

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Publication Details

Journal
Human Brain Mapping
Published
2025-09-01
DOI
https://doi.org/10.1002/hbm.70336
Citations
1
Primary Topic
Mitochondrial Function and Pathology
Type
article
Field-Weighted Citation Impact
0.81

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article

Lifespan Tree of Brain Anatomy: Diagnostic Values for Motor and Cognitive Neurodegenerative Diseases

José V. Manjón, Pierrick Coupé, Boris Mansencal, Patrice Péran et al.
1 citations
Human Brain Mapping
Mitochondrial Function and Pathology
0.81
article

Lifespan Tree of Brain Anatomy: Diagnostic Values for Motor and Cognitive Neurodegenerative Diseases

José V. Manjón, Pierrick Coupé, Boris Mansencal, Patrice Péran, Vincent Planche, Thomas Tourdias, Wassilios G. Meissner
article en
1 citations

Abstract

The differential diagnosis of neurodegenerative diseases, characterized by overlapping symptoms, may be challenging. Brain imaging coupled with artificial intelligence has been previously proposed for diagnostic support, but most of these methods have been trained to discriminate only isolated diseases from controls. Here, we develop a novel machine learning framework, named lifespan tree of brain anatomy, dedicated to the differential diagnosis between multiple diseases simultaneously. It integrates the modeling of volume changes for 124 brain structures during the lifespan with nonlinear dimensionality reduction and synthetic sampling techniques to create easily interpretable representations of brain anatomy over the course of disease progression. As clinically relevant proof-of-concept applications, we constructed a cognitive lifespan tree of brain anatomy for the differential diagnosis of six causes of neurodegenerative dementia and a motor lifespan tree of brain anatomy for the differential diagnosis of four causes of parkinsonism using 37,594 MRIs as a training dataset. This original approach significantly enhanced the efficiency of differential diagnosis in the external validation cohort of 1754 cases, outperforming existing state-of-the-art machine learning techniques. Lifespan tree holds promise as a valuable tool for differential diagnosis in relevant clinical conditions, especially for diseases still lacking effective biological markers.

Human Brain MappingVol. 46(13)
Centre National de la Recherche Scientifique (FR), Université Toulouse III - Paul Sabatier (FR), Université de Bordeaux (FR), Inserm (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Centre Hospitalier Universitaire de Bordeaux (FR), Institut des Maladies Neurodégénératives (FR), New Zealand Brain Research Institute (NZ), Neurocentre Magendie (FR), Laboratoire Bordelais de Recherche en Informatique (FR), Institut Polytechnique de Bordeaux (FR), Universitat Politècnica de València (ES), University of Otago (NZ)
Michael J. Fox Foundation for Parkinson's Research, Alzheimer's Association, Dana Foundation, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, AstraZeneca, GlaxoSmithKline, Novartis, University of Southern California, F. Hoffmann-La Roche, Leon Levy Foundation, Medpace, Synarc, Cincinnati Children's Hospital Medical Center, Alzheimer's Disease Neuroimaging Initiative, University of Calgary, Northern California Institute for Research and Education, Ministerio de Ciencia, Innovación y Universidades, Alzheimer's Society, Child Mind Institute, University of Cambridge, Commonwealth Scientific and Industrial Research Organisation, Agence Nationale de la Recherche, Elan, Eisai, Stavros Niarchos Foundation, Université de Bordeaux, Grand Équipement National De Calcul Intensif, National Institutes of Health, U.S. Food and Drug Administration, Directorate for Biological Sciences, Genentech, University of California, Los Angeles, University of California, San Diego, University of California, San Francisco, Canadian Institutes of Health Research, Medical Research Council, Engineering and Physical Sciences Research Council, National Health and Medical Research Council, National Institute on Aging, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Child Health and Human Development, National Center for Advancing Translational Sciences
Openalex Percentile: Top 24%
Mitochondrial Function and Pathology
0.81
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