Brain Age as a New Measure of Disease Stratification in Huntington's Disease

被引:2
作者
Abeyasinghe, Pubu M. [1 ,2 ]
Cole, James H. [3 ,4 ]
Razi, Adeel [1 ,2 ,5 ,6 ]
Poudel, Govinda R. [7 ]
Paulsen, Jane S. [8 ]
Tabrizi, Sarah J. [9 ]
Long, Jeffrey D. [10 ,11 ]
Georgiou-Karistianis, Nellie [1 ,2 ]
机构
[1] Monash Univ, Sch Psychol Sci, Clayton Campus, Melbourne, Vic 3800, Australia
[2] Monash Univ, Turner Inst Brain & Mental Hlth, Clayton Campus, Melbourne, Vic 3800, Australia
[3] UCL, Ctr Med Image Comp, Dept Comp Sci, London, England
[4] Natl Hosp Neurol & Neurosurg, Dementia Res Ctr, Univ Coll London, London WC1N 3BG, England
[5] Monash Univ, Monash Biomed Imaging, Clayton, Vic, Australia
[6] UCL, Welcome Ctr Human Neuroimaging, London, England
[7] Australian Catholic Univ, Mary MacKillop Inst Hlth Res, Melbourne, Vic, Australia
[8] Univ Wisconsin, Dept Neurol, Madison, WI USA
[9] UCL, UCL Queen Sq Inst Neurol, London, England
[10] Univ Iowa, Carver Coll Med, Dept Psychiat, Iowa City, IA USA
[11] Univ Iowa, Coll Publ Hlth, Dept Biostat, Iowa City, IA USA
基金
英国医学研究理事会; 澳大利亚研究理事会;
关键词
Huntington's disease; brain age; states of progression; COGNITIVE DECLINE; TRACK-HD; PREMANIFEST;
D O I
10.1002/mds.30109
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background: Despite advancements in understanding Huntington's disease (HD) over the past two decades, absence of disease-modifying treatments remains a challenge. Accurately characterizing progression states is crucial for developing effective therapeutic interventions. Various factors contribute to this challenge, including the need for precise methods that can account for the complex nature of HD progression. Objective: This study aims to address this gap by leveraging the concept of the brain's biological age as a foundation for a data-driven clustering method to delineate various states of progression. Brain-predicted age, influenced by somatic expansion and its impact on brain volumes, offers a promising avenue for stratification by stratifying subgroups and determining the optimal timing for interventions. Methods: To achieve this, data from 953 participants across diverse cohorts, including PREDICT-HD, TRACK-HD, and IMAGE-HD, were meticulously analyzed. Brain-predicted age was computed using sophisticated algorithms, and participants were categorized into four groups based on CAG and age product score. Unsupervised k-means clustering with brain-predicted age difference (brain-PAD) was then employed to identify distinct progression states. Results: The analysis revealed significant disparities in brain-predicted age between HD participants and controls, with these differences becoming more pronounced as the disease progressed. Brain-PAD demonstrated a correlation with disease severity, effectively identifying five distinct progression states characterized by significant longitudinal disparities. Conclusions: These findings highlight the potential of brain-PAD in capturing HD progression states, thereby enhancing prognostic methodologies and providing valuable insights for future clinical trial designs and interventions. (c) 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
引用
收藏
页码:627 / 641
页数:15
相关论文
共 32 条
[1]  
Abeyasinghe PM., 2021, TRACKING HUNTINGTONS
[2]  
Ashburner J., 2014, SPM12 MANUAL, DOI [DOI 10.1111/J.1365-294X.2006.02813.X, 10.1111/j.1365-294X.2006.02813.x]
[3]   Refining the diagnosis of Huntington disease: the PREDICT-HD study [J].
Biglan, Kevin M. ;
Zhang, Ying ;
Long, Jeffrey D. ;
Geschwind, Michael ;
Kang, Gail A. ;
Killoran, Annie ;
Lu, Wenjing ;
McCusker, Elizabeth ;
Mills, James A. ;
Raymond, Lynn A. ;
Testa, Claudia ;
Wojcieszek, Joanne ;
Paulsen, Jane S. .
FRONTIERS IN AGING NEUROSCIENCE, 2013, 5
[4]   Motor Abnormalities in Premanifest Persons with Huntington's Disease: The PREDICT-HD Study [J].
Biglan, Kevin M. ;
Ross, Christopher A. ;
Langbehn, Douglas R. ;
Aylward, Elizabeth H. ;
Stout, Julie C. ;
Queller, Sarah ;
Carlozzi, Noelle E. ;
Duff, Kevin ;
Beglinger, Leigh J. ;
Paulsen, Jane S. .
MOVEMENT DISORDERS, 2009, 24 (12) :1763-1772
[5]   Brain-age is associated with progression to dementia in memory clinic patients [J].
Biondo, Francesca ;
Jewell, Amelia ;
Pritchard, Megan ;
Aarsland, Dag ;
Steves, Claire J. ;
Mueller, Christoph ;
Cole, James H. .
NEUROIMAGE-CLINICAL, 2022, 36
[6]   Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups [J].
Clausen, Ashley N. ;
Fercho, Kelene A. ;
Monsour, Molly ;
Disner, Seth ;
Salminen, Lauren ;
Haswell, Courtney C. ;
Rubright, Emily Clarke ;
Watts, Amanda A. ;
Buckley, M. Nicole ;
Maron-Katz, Adi ;
Sierk, Anika ;
Manthey, Antje ;
Suarez-Jimenez, Benjamin ;
Olatunji, Bunmi O. ;
Averill, Christopher L. ;
Hofmann, David ;
Veltman, Dick J. ;
Olson, Elizabeth A. ;
Li, Gen ;
Forster, Gina L. ;
Walter, Henrik ;
Fitzgerald, Jacklynn ;
Theberge, Jean ;
Simons, Jeffrey S. ;
Bomyea, Jessica A. ;
Frijling, Jessie L. ;
Krystal, John H. ;
Baker, Justin T. ;
Phan, K. Luan ;
Ressler, Kerry ;
Han, Laura K. M. ;
Nawijn, Laura ;
Lebois, Lauren A. M. ;
Schmaall, Lianne ;
Densmore, Maria ;
Shenton, Martha E. ;
van Zuiden, Mirjam ;
Stein, Murray ;
Fani, Negar ;
Simons, Raluca M. ;
Neufeld, Richard W. J. ;
Lanius, Ruth ;
van Rooij, Sanne ;
Koch, Saskia B. J. ;
Bonomo, Serena ;
Jovanovic, Tanja ;
DeRoon-Cassini, Terri ;
Ely, Timothy D. ;
Magnotta, Vincent A. ;
He, Xiaofu .
BRAIN AND BEHAVIOR, 2022, 12 (01)
[7]  
Cole JH, 2020, NEUROBIOL AGING, V92, P34, DOI [10.1016/j.neurobiolaging.2020.03.014, 10.1016/j.neurobiolaging.2020.03.014]
[8]   Longitudinal Assessment of Multiple Sclerosis with the Brain-Age Paradigm [J].
Cole, James H. ;
Raffel, Joel ;
Friede, Tim ;
Eshaghi, Arman ;
Brownlee, Wallace J. ;
Chard, Declan ;
De Stefano, Nicola ;
Enzinger, Christian ;
Pirpamer, Lukas ;
Filippi, Massimo ;
Gasperini, Claudio ;
Rocca, Maria Assunta ;
Rovira, Alex ;
Ruggieri, Serena ;
Sastre-Garriga, Jaume ;
Stromillo, Maria Laura ;
Uitdehaag, Bernard M. J. ;
Vrenken, Hugo ;
Barkhof, Frederik ;
Nicholas, Richard ;
Ciccarelli, Olga .
ANNALS OF NEUROLOGY, 2020, 88 (01) :93-105
[9]   Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker [J].
Cole, James H. ;
Poudel, Rudra P. K. ;
Tsagkrasoulis, Dimosthenis ;
Caan, Matthan W. A. ;
Steves, Claire ;
Spector, Tim D. ;
Montana, Giovanni .
NEUROIMAGE, 2017, 163 :115-124
[10]   Prediction of Brain Age Suggests Accelerated Atrophy after Traumatic Brain Injury [J].
Cole, James H. ;
Leech, Robert ;
Sharp, David J. .
ANNALS OF NEUROLOGY, 2015, 77 (04) :571-581