@article{Manouvriez2025,
abstract = {INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically,  genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving therapeutic strategies. This study aims to differentiate EOAD patients with varying rates of progression using Brain Age Gap Estimation (BrainAGE)-based clustering algorithm applied to structural magnetic resonance images (MRI). METHODS: A retrospective analysis of a longitudinal cohort consisting of 142 participants who met the criteria for early-onset probable Alzheimer's disease was conducted. Participants were assessed clinically, neuropsychologically and with structural MRI at baseline and annually for 6 years. A Brain Age Gap Estimation (BrainAGE) deep learning model pre-trained on 3,227 3D T1-weighted MRI of healthy subjects was used to extract encoded MRI representations at baseline. Then, k-means clustering was performed on these encoded representations to stratify the population. The resulting clusters were then analyzed for disease severity, cognitive phenotype and brain volumes at baseline and longitudinally. RESULTS: The optimal number of clusters was determined to be 2. Clusters differed significantly in BrainAGE scores (5.44 [± 8] years vs 15.25 [± 5 years], p < 0.001). The high BrainAGE cluster was associated with older age (p = 0.001) and higher proportion of female patients (p = 0.005), as well as greater disease severity based on Mini Mental State Examination (MMSE) scores (19.32 [±4.62] vs 14.14 [±6.93], p < 0.001) and gray matter volume (0.35 [±0.03] vs 0.32 [±0.02], p < 0.001). Longitudinal analyses revealed significant differences in disease progression (MMSE decline of -2.35 [±0.15] pts/year vs -3.02 [±0.25] pts/year, p = 0.02; CDR 1.58 [±0.10] pts/year vs 1.99 [±0.16] pts/year, p = 0.03). CONCLUSION: K-means clustering of BrainAGE encoded representations stratified EOAD patients based on varying rates of disease progression. These findings underscore the potential of using BrainAGE as a biomarker for better understanding and managing EOAD.},
author = {Manouvriez, Dorian and Kuchcinski, Gr{\'{e}}gory and Roca, Vincent and Sillaire, Adeline Rollin and Bertoux, Maxime and Delbeuck, Xavier and Pruvo, Jean-Pierre and Lecerf, Simon and Pasquier, Florence and Lebouvier, Thibaud and Lopes, Renaud},
doi = {10.1016/j.neurad.2025.101365},
issn = {0150-9861 (Print)},
journal = {Journal of neuroradiology = Journal de neuroradiologie},
keywords = {Age of Onset,Aged,Alzheimer Disease,Brain,Cluster Analysis,Deep Learning,Disease Progression,Female,Humans,Longitudinal Studies,Magnetic Resonance Imaging,Male,Middle Aged,Retrospective Studies,diagnostic imaging,methods,pathology},
language = {eng},
month = {sep},
number = {5},
pages = {101365},
pmid = {40614437},
title = {{BrainAGE latent representation clustering is associated with longitudinal disease  progression in early-onset Alzheimer's disease.}},
volume = {52},
year = {2025}
}

{"_id":"mHfQN4DTeks6KTD2q","bibbaseid":"manouvriez-kuchcinski-roca-sillaire-bertoux-delbeuck-pruvo-lecerf-etal-brainagelatentrepresentationclusteringisassociatedwithlongitudinaldiseaseprogressioninearlyonsetalzheimersdisease-2025","author_short":["Manouvriez, D.","Kuchcinski, G.","Roca, V.","Sillaire, A. R.","Bertoux, M.","Delbeuck, X.","Pruvo, J.","Lecerf, S.","Pasquier, F.","Lebouvier, T.","Lopes, R."],"bibdata":{"bibtype":"article","type":"article","abstract":"INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving therapeutic strategies. This study aims to differentiate EOAD patients with varying rates of progression using Brain Age Gap Estimation (BrainAGE)-based clustering algorithm applied to structural magnetic resonance images (MRI). METHODS: A retrospective analysis of a longitudinal cohort consisting of 142 participants who met the criteria for early-onset probable Alzheimer's disease was conducted. Participants were assessed clinically, neuropsychologically and with structural MRI at baseline and annually for 6 years. A Brain Age Gap Estimation (BrainAGE) deep learning model pre-trained on 3,227 3D T1-weighted MRI of healthy subjects was used to extract encoded MRI representations at baseline. Then, k-means clustering was performed on these encoded representations to stratify the population. The resulting clusters were then analyzed for disease severity, cognitive phenotype and brain volumes at baseline and longitudinally. RESULTS: The optimal number of clusters was determined to be 2. Clusters differed significantly in BrainAGE scores (5.44 [± 8] years vs 15.25 [± 5 years], p < 0.001). The high BrainAGE cluster was associated with older age (p = 0.001) and higher proportion of female patients (p = 0.005), as well as greater disease severity based on Mini Mental State Examination (MMSE) scores (19.32 [±4.62] vs 14.14 [±6.93], p < 0.001) and gray matter volume (0.35 [±0.03] vs 0.32 [±0.02], p < 0.001). Longitudinal analyses revealed significant differences in disease progression (MMSE decline of -2.35 [±0.15] pts/year vs -3.02 [±0.25] pts/year, p = 0.02; CDR 1.58 [±0.10] pts/year vs 1.99 [±0.16] pts/year, p = 0.03). CONCLUSION: K-means clustering of BrainAGE encoded representations stratified EOAD patients based on varying rates of disease progression. These findings underscore the potential of using BrainAGE as a biomarker for better understanding and managing EOAD.","author":[{"propositions":[],"lastnames":["Manouvriez"],"firstnames":["Dorian"],"suffixes":[]},{"propositions":[],"lastnames":["Kuchcinski"],"firstnames":["Grégory"],"suffixes":[]},{"propositions":[],"lastnames":["Roca"],"firstnames":["Vincent"],"suffixes":[]},{"propositions":[],"lastnames":["Sillaire"],"firstnames":["Adeline","Rollin"],"suffixes":[]},{"propositions":[],"lastnames":["Bertoux"],"firstnames":["Maxime"],"suffixes":[]},{"propositions":[],"lastnames":["Delbeuck"],"firstnames":["Xavier"],"suffixes":[]},{"propositions":[],"lastnames":["Pruvo"],"firstnames":["Jean-Pierre"],"suffixes":[]},{"propositions":[],"lastnames":["Lecerf"],"firstnames":["Simon"],"suffixes":[]},{"propositions":[],"lastnames":["Pasquier"],"firstnames":["Florence"],"suffixes":[]},{"propositions":[],"lastnames":["Lebouvier"],"firstnames":["Thibaud"],"suffixes":[]},{"propositions":[],"lastnames":["Lopes"],"firstnames":["Renaud"],"suffixes":[]}],"doi":"10.1016/j.neurad.2025.101365","issn":"0150-9861 (Print)","journal":"Journal of neuroradiology = Journal de neuroradiologie","keywords":"Age of Onset,Aged,Alzheimer Disease,Brain,Cluster Analysis,Deep Learning,Disease Progression,Female,Humans,Longitudinal Studies,Magnetic Resonance Imaging,Male,Middle Aged,Retrospective Studies,diagnostic imaging,methods,pathology","language":"eng","month":"sep","number":"5","pages":"101365","pmid":"40614437","title":"BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.","volume":"52","year":"2025","bibtex":"@article{Manouvriez2025,\nabstract = {INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving therapeutic strategies. This study aims to differentiate EOAD patients with varying rates of progression using Brain Age Gap Estimation (BrainAGE)-based clustering algorithm applied to structural magnetic resonance images (MRI). METHODS: A retrospective analysis of a longitudinal cohort consisting of 142 participants who met the criteria for early-onset probable Alzheimer's disease was conducted. Participants were assessed clinically, neuropsychologically and with structural MRI at baseline and annually for 6 years. A Brain Age Gap Estimation (BrainAGE) deep learning model pre-trained on 3,227 3D T1-weighted MRI of healthy subjects was used to extract encoded MRI representations at baseline. Then, k-means clustering was performed on these encoded representations to stratify the population. The resulting clusters were then analyzed for disease severity, cognitive phenotype and brain volumes at baseline and longitudinally. RESULTS: The optimal number of clusters was determined to be 2. Clusters differed significantly in BrainAGE scores (5.44 [± 8] years vs 15.25 [± 5 years], p < 0.001). The high BrainAGE cluster was associated with older age (p = 0.001) and higher proportion of female patients (p = 0.005), as well as greater disease severity based on Mini Mental State Examination (MMSE) scores (19.32 [±4.62] vs 14.14 [±6.93], p < 0.001) and gray matter volume (0.35 [±0.03] vs 0.32 [±0.02], p < 0.001). Longitudinal analyses revealed significant differences in disease progression (MMSE decline of -2.35 [±0.15] pts/year vs -3.02 [±0.25] pts/year, p = 0.02; CDR 1.58 [±0.10] pts/year vs 1.99 [±0.16] pts/year, p = 0.03). CONCLUSION: K-means clustering of BrainAGE encoded representations stratified EOAD patients based on varying rates of disease progression. These findings underscore the potential of using BrainAGE as a biomarker for better understanding and managing EOAD.},\nauthor = {Manouvriez, Dorian and Kuchcinski, Gr{\\'{e}}gory and Roca, Vincent and Sillaire, Adeline Rollin and Bertoux, Maxime and Delbeuck, Xavier and Pruvo, Jean-Pierre and Lecerf, Simon and Pasquier, Florence and Lebouvier, Thibaud and Lopes, Renaud},\ndoi = {10.1016/j.neurad.2025.101365},\nissn = {0150-9861 (Print)},\njournal = {Journal of neuroradiology = Journal de neuroradiologie},\nkeywords = {Age of Onset,Aged,Alzheimer Disease,Brain,Cluster Analysis,Deep Learning,Disease Progression,Female,Humans,Longitudinal Studies,Magnetic Resonance Imaging,Male,Middle Aged,Retrospective Studies,diagnostic imaging,methods,pathology},\nlanguage = {eng},\nmonth = {sep},\nnumber = {5},\npages = {101365},\npmid = {40614437},\ntitle = {{BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.}},\nvolume = {52},\nyear = {2025}\n}\n","author_short":["Manouvriez, D.","Kuchcinski, G.","Roca, V.","Sillaire, A. R.","Bertoux, M.","Delbeuck, X.","Pruvo, J.","Lecerf, S.","Pasquier, F.","Lebouvier, T.","Lopes, R."],"key":"Manouvriez2025","id":"Manouvriez2025","bibbaseid":"manouvriez-kuchcinski-roca-sillaire-bertoux-delbeuck-pruvo-lecerf-etal-brainagelatentrepresentationclusteringisassociatedwithlongitudinaldiseaseprogressioninearlyonsetalzheimersdisease-2025","role":"author","urls":{},"keyword":["Age of Onset","Aged","Alzheimer Disease","Brain","Cluster Analysis","Deep Learning","Disease Progression","Female","Humans","Longitudinal Studies","Magnetic Resonance Imaging","Male","Middle Aged","Retrospective Studies","diagnostic imaging","methods","pathology"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://nextcloud.univ-lille.fr/index.php/s/ZN4QZ7dH9iNaNWE/download/Interactions.bib","dataSources":["ECsXQHgbAjNJDKiJ7"],"keywords":["age of onset","aged","alzheimer disease","brain","cluster analysis","deep learning","disease progression","female","humans","longitudinal studies","magnetic resonance imaging","male","middle aged","retrospective studies","diagnostic imaging","methods","pathology"],"search_terms":["brainage","latent","representation","clustering","associated","longitudinal","disease","progression","early","onset","alzheimer","disease","manouvriez","kuchcinski","roca","sillaire","bertoux","delbeuck","pruvo","lecerf","pasquier","lebouvier","lopes"],"title":"BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.","year":2025}