Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns

被引:1
作者
Roh, Hyun Woong [1 ]
Chauhan, Nishant [2 ]
Seo, Sang Won [3 ]
Choi, Seong Hye [4 ]
Kim, Eun-Joo [5 ]
Cho, Soo Hyun [6 ]
Kim, Byeong C. [6 ]
Choi, Jin Wook [7 ]
An, Young-Sil [8 ]
Park, Bumhee [9 ,10 ]
Lee, Sun Min [11 ]
Nam, You Jin [1 ]
Moon, So Young [11 ]
Hong, Sunhwa [1 ]
Son, Sang Joon [1 ]
Hong, Chang Hyung [1 ]
Lee, Dongha [2 ]
机构
[1] Ajou Univ, Sch Med, Dept Psychiat, 164 Worldcup ro, Suwon 441749, South Korea
[2] Korea Brain Res Inst, Cognit Sci Res Grp, 61 Cheomdan ro, Daegu 41062, South Korea
[3] Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Dept Neurol, Seoul, South Korea
[4] Inha Univ, Sch Med, Dept Neurol, Incheon, South Korea
[5] Pusan Natl Univ, Pusan Natl Univ Hosp, Sch Med & Med Res Inst, Dept Neurol, Busan, South Korea
[6] Chonnam Natl Univ, Chonnam Natl Univ Hosp, Dept Neurol, Med Sch, Gwangju, South Korea
[7] Ajou Univ, Sch Med, Dept Radiol, Suwon, South Korea
[8] Ajou Univ, Dept Nucl Med & Mol Imaging, Sch Med, Suwon, South Korea
[9] Ajou Univ, Sch Med, Dept Biomed Informat, Suwon, South Korea
[10] Ajou Univ, Ajou Res Inst Innovat Med, Off Biostat, Med Ctr, Suwon, South Korea
[11] Ajou Univ, Sch Med, Dept Neurol, Suwon, South Korea
基金
新加坡国家研究基金会;
关键词
cognitive impairment; functional disability; MRI and PET imaging; neurodegeneration; white matter pattern; ALZHEIMERS-DISEASE; INSTRUMENTAL ACTIVITIES; DEMENTIA; BIOMARKERS;
D O I
10.1002/alz.14094
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
INTRODUCTIONThis study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults.METHODSWe conducted an in-depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter-subject variability (WM-ISV).RESULTSThe white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer's disease (AD) exhibited higher WM-ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM-ISV correlated significantly with blood-based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]), and cognitive function and disability scores.DISCUSSIONOur results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making and determining cognitive impairment and disability.Highlights The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer's disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter-subject variability (WM-ISV) was significantly correlated with blood-based biomarkers (glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making processes and determining cognitive impairment and disability.
引用
收藏
页码:6032 / 6044
页数:13
相关论文
共 45 条
[1]   Seoul Neuropsychological Screening Battery-Dementia Version (SNSB-D): A Useful Tool for Assessing and Monitoring Cognitive Impairments in Dementia Patients [J].
Ahn, Hyun-Jung ;
Chin, Juhee ;
Park, Aram ;
Lee, Byung Hwa ;
Suh, Mee Kyung ;
Seo, Sang Won ;
Na, Duk L. .
JOURNAL OF KOREAN MEDICAL SCIENCE, 2010, 25 (07) :1071-1076
[2]   Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI) [J].
Arevalo-Rodriguez, Ingrid ;
Smailagic, Nadja ;
Roque-Figuls, Marta ;
Ciapponi, Agustin ;
Sanchez-Perez, Erick ;
Giannakou, Antri ;
Pedraza, Olga L. ;
Cosp, Xavier Bonfill ;
Cullum, Sarah .
COCHRANE DATABASE OF SYSTEMATIC REVIEWS, 2021, (07)
[3]   A fast diffeomorphic image registration algorithm [J].
Ashburner, John .
NEUROIMAGE, 2007, 38 (01) :95-113
[4]   Neuropsychological Measures that Predict Progression from Mild Cognitive Impairment to Alzheimer's type dementia in Older Adults: a Systematic Review and Meta-Analysis [J].
Belleville, Sylvie ;
Fouquet, Celine ;
Hudon, Carol ;
Zomahoun, Herve Tchala Vignon ;
Croteau, Jordie .
NEUROPSYCHOLOGY REVIEW, 2017, 27 (04) :328-353
[5]   Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI [J].
Challis, Edward ;
Hurley, Peter ;
Serra, Laura ;
Bozzali, Marco ;
Oliver, Seb ;
Cercignani, Mara .
NEUROIMAGE, 2015, 112 :232-243
[6]   Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study [J].
Chen, Haifeng ;
Li, Weikai ;
Sheng, Xiaoning ;
Ye, Qing ;
Zhao, Hui ;
Xu, Yun ;
Bai, Feng .
EUROPEAN RADIOLOGY, 2022, 32 (01) :448-459
[7]   The use of neuroimaging techniques in the early and differential diagnosis of dementia [J].
Chouliaras, Leonidas ;
O'Brien, John T. .
MOLECULAR PSYCHIATRY, 2023, 28 (10) :4084-4097
[8]   Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations [J].
Creavin, Sam T. ;
Wisniewski, Susanna ;
Noel-Storr, Anna H. ;
Trevelyan, Clare M. ;
Hampton, Thomas ;
Rayment, Dane ;
Thom, Victoria M. ;
Nash, Kirsty J. E. ;
Elhamoui, Hosam ;
Milligan, Rowena ;
Patel, Anish S. ;
Tsivos, Demitra V. ;
Wing, Tracey ;
Phillips, Emma ;
Kellman, Sophie M. ;
Shackleton, Hannah L. ;
Singleton, Georgina F. ;
Neale, Bethany E. ;
Watton, Martha E. ;
Cullum, Sarah .
COCHRANE DATABASE OF SYSTEMATIC REVIEWS, 2016, (01)
[9]   Vascular Cognitive Impairment [J].
Dichgans, Martin ;
Leys, Didier .
CIRCULATION RESEARCH, 2017, 120 (03) :573-591
[10]   Latent feature representation learning for Alzheimer's disease classification [J].
Dong, Aimei ;
Zhang, Guodong ;
Liu, Jian ;
Wei, Zhonghe .
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 150