Hippocampal‐amygdalo‐ventricular atrophy score: Alzheimer disease detection using normative and pathological lifespan models

• Published in: Human brain mapping Vol. 43; no. 10; pp. 3270 - 3282
• Main Authors: Coupé, Pierrick, Manjón, José V., Mansencal, Boris, Tourdias, Thomas, Catheline, Gwenaëlle, Planche, Vincent
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2022
• Subjects: Age; Aging; Alzheimer Disease - pathology; Alzheimer's disease; Artificial intelligence; Atrophy; Atrophy - diagnostic imaging; Atrophy - pathology; Biomarkers; Clinical trials; Cognitive ability; Cognitive Dysfunction - pathology; Datasets; Decision making; Deep learning; Disease detection; Disease Progression; Gender; Hippocampus; Hippocampus - pathology; Humans; Life span; Longevity; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neurodegeneration; Neurodegenerative diseases; Open access; Pathology; Quality control; Ventricle; Volumetric analysis
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25850

In this article, we present an innovative MRI‐based method for Alzheimer disease (AD) detection and mild cognitive impairment (MCI) prognostic, using lifespan trajectories of brain structures. After a full screening of the most discriminant structures between AD and normal aging based on MRI volumetric analysis of 3,032 subjects, we propose a novel Hippocampal‐Amygdalo‐Ventricular Atrophy score (HAVAs) based on normative lifespan models and AD lifespan models. During a validation on three external datasets on 1,039 subjects, our approach showed very accurate detection (AUC ≥ 94%) of patients with AD compared to control subjects and accurate discrimination (AUC = 78%) between progressive MCI and stable MCI (during a 3‐year follow‐up). Compared to normative modeling, classical machine learning methods and recent state‐of‐the‐art deep learning methods, our method demonstrated better classification performance. Moreover, HAVAs simplicity makes it fully understandable and thus well‐suited for clinical practice or future pharmaceutical trials. In this article, we present an innovative MRI‐based method for Alzheimer disease (AD) detection and mild cognitive impairment (MCI) prognostic, using lifespan trajectories of brain structures. Compared to normative modeling and recent state‐of‐the‐art deep learning methods, our method demonstrated better classification performance. Moreover, it simplicity makes it fully understandable and thus well‐suited for clinical practice or future pharmaceutical trials.