Poster No:
120
Submission Type:
Abstract Submission
Authors:
Yuqi GONG1, Jing Li2, Hanna LU2,3, Yuqi GONG1
Institutions:
1Department of Neuroscience, School of Translational Medicine, Monash University, Victoria, Australia, 2Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China, 3The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
First Author:
Yuqi GONG
Department of Neuroscience, School of Translational Medicine, Monash University
Victoria, Australia
Co-Author(s):
Jing Li
Department of Psychiatry, The Chinese University of Hong Kong
Hong Kong SAR, China
Hanna LU
Department of Psychiatry, The Chinese University of Hong Kong|The Affiliated Brain Hospital of Guangzhou Medical University
Hong Kong SAR, China|Guangzhou, China
Introduction:
Early-onset (EOAD) and Late-onset Alzheimer's Disease (LOAD) may represent distinct disease entities beyond the age of onset. By integrating brain age matrices with structural and cognitive analyses, we aimed to characterize potential subtype-specific patterns that may enhance our understanding of the heterogeneity in AD sybtypes.
Methods:
Structural MRI data of EOAD (n=23) and LOAD (n=192) participants were recruited from the OASIS-4 database, we employed brain age matrices (pretrained model calculated from the Cam-CAN dataset, N=609) and regional analysis of gray matter volume (GMV) and cortical thickness (CT) across 66 bilateral regions. Assessment included six standardized cognitive tests spanning multiple domains, including global cognition, executive functions and Logical Memory, behavioral evaluation using GDS, and CSF analysis incorporating 10 biomarkers including standard AD indices (t-tau, p-tau, Aβ42) and biochemical measures (e.g., glucose, protein, cell counts). Morphometry-cognition relationships were examined using partial correlations, adjusting for age and sex.
Results:
EOAD participants showed significant impairments in executive function as measured by TMT-B. Compared to individuals with LOAD, brain-PAD scores were higher in EOAD patients. At the lobe level, there were no significant differences in GMV or CT between the two groups. Further regional CT analysis revealed EOAD-specific patterns in the left cingulate gyrus and right parahippocampal gyrus. Morphometry-function relationships differed between groups: In EOAD, completion time for TMT-A showed negative correlations with temporal and occipital GMV, while GDS weight loss was associated with parietal GMV. In LOAD, BNT performance correlated with frontal, occipital, and parietal GMV, while limbic GMV was associated with verbal fluency. CSF biomarker analysis revealed no significant differences between the two groups.
Conclusions:
This study provides evidence for distinct brain aging patterns and morphometry-cognition relationships between EOAD and LOAD, suggesting different underlying mechanisms that may inform patient stratification and interventions.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Lifespan Development:
Aging
Modeling and Analysis Methods:
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Aging
Cerebro Spinal Fluid (CSF)
Cognition
Cortical Layers
Data analysis
Degenerative Disease
Emotions
Language
MRI
Open Data
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Not applicable
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
Neuropsychological testing
For human MRI, what field strength scanner do you use?
1.5T
3.0T
Which processing packages did you use for your study?
Free Surfer
Provide references using APA citation style.
1. Cole, J. H. (2017). Predicting age using neuroimaging: Innovative brain ageing biomarkers. Trends in Neurosciences, 40(10), 681-690.
2. Koenig, L. N. (2020). Select Atrophied Regions in Alzheimer disease (SARA): An improved volumetric model for identifying Alzheimer disease dementia. NeuroImage: Clinical, 26, 102248.
3. Lu, H. (2024). MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters. Journal of Central Nervous System Disease, 16, 11795735241266556.
4. Seath, P. (2023). Clinical characteristics of early-onset versus late-onset Alzheimer’s disease: A systematic review and meta-analysis. International Psychogeriatrics, 1-17.
5. Sirkis, D. W. (2022). Dissecting the clinical heterogeneity of early-onset Alzheimer’s disease. Molecular Psychiatry, 27(6), 2674-2688.
6. Vieira, R. T. (2013). Epidemiology of early-onset dementia: A review of the literature. Clinical Practice and Epidemiology in Mental Health, 9, 88.
7. Wingo, T. S. (2012). Autosomal recessive causes likely in early-onset Alzheimer disease. Archives of Neurology, 69(1), 59-64.
No