Longitudinal Study of Sex Differences and APOE Status in White Matter Engagement Among the Elderly
Poster No:
885
Submission Type:
Abstract Submission
Authors:
Hui Zhang1, Jingrao Zhang1, Chun Liang Hsu1, Edward S. Hui2, Kai-Hei Tse3, Henry K.F. Mak4, David H.K. Shum1
Institutions:
1The Hong Kong Polytechnic University, Hong Kong, China, 2The Chinese University of Hong Kong, Hong Kong, China, 3University of Sydney, Camperdown, Australia, 4The University of Hong Kong, Hong Kong, China
First Author:
Co-Author(s):
Introduction:
With rapid global aging, the number of individuals diagnosed with Alzheimer's disease (AD) is estimated to triple worldwide by 2050. Apolipoprotein E ε4 (APOE4), age, and sex are recognized as the primary genetic risk factors for Alzheimer's disease (Jack et al., 2015). The application of diffusion tensor imaging (DTI) (Assaf & Pasternak, 2008) provides valuable insights into the integrity of white matter (WM). However, the information that each of these two imaging techniques provides is distinct and does not straightforwardly lend itself to direct integration. The WM engagement map, developed by Li's team (Li et al., 2020), offers a new way to detect and analyze WM signals within an fMRI dataset. WM BOLD signals could provide a more precise and comprehensive depiction of the brain's functional architecture. This is crucial for enhancing our understanding of how gray matter (GM) and WM together contribute to behavioral functions, such as memory. This study utilized resting-state fMRI and WM engagement mapping to analyze longitudinal data collected at three-year intervals from 173 healthy elderly participants from the Harvard Aging Brain Study (HABS) (Dagley et al., 2017). Our goal was to uncover the complex interactions between biological sex, APOE4, Default mode network-WM activity, and cognition.
Methods:
Demographic and other information, including age, sex, years of education, APOE genotyping, and neuropsychological test results (the Boston Naming Test (BNT), Digit Span Test, Free and Cued Selective Reminding Test (FCsrt), Logical Memory (LogiticMem) and Selective Reminding Test (SRT)) were collected from a dataset of n=173 healthy elderly participants from the HABS who were followed up at baseline and three years later. Further stratification was conducted to separate participants into high-risk female, low-risk female, high-risk male, and low-risk male groups, according to their biological sex.
The post-processing method for generating WM engagement maps follows the procedure outlined in Li et al.'s study (Li et al., 2020). Between-group comparisons were conducted by calculating changes in WM engagement across all brain voxels, subtracting the WM engagement maps at Visit 1 from those at Visit 2. Differences in the change of WM engagement maps between subgroups were tested with a two-sample t-test. Results were corrected for multiple comparisons using Gaussian Random Field methods, with a cluster-level p-value of < 0.05, a voxel-level p-value of < 0.01, and a minimum cluster size of more than 10 voxels. To explore the relationship between behavioral and neuroimaging findings, a partial Pearson correlation analysis was employed adjusting for years of education and age.
The post-processing method for generating WM engagement maps follows the procedure outlined in Li et al.'s study (Li et al., 2020). Between-group comparisons were conducted by calculating changes in WM engagement across all brain voxels, subtracting the WM engagement maps at Visit 1 from those at Visit 2. Differences in the change of WM engagement maps between subgroups were tested with a two-sample t-test. Results were corrected for multiple comparisons using Gaussian Random Field methods, with a cluster-level p-value of < 0.05, a voxel-level p-value of < 0.01, and a minimum cluster size of more than 10 voxels. To explore the relationship between behavioral and neuroimaging findings, a partial Pearson correlation analysis was employed adjusting for years of education and age.
Results:
We noted high-risk males exhibited improvements in various cognitive assessments (BNT and SRT measures, Table 1), there were no observed differences in DMN WM engagement compared to their low-risk counterparts. Conversely, high-risk females displayed poorer results on the FCsrt_Free call test and decreased WM engagement in the right superior longitudinal fasciculus than low-risk females, showing a positive correlation with changes in recall performance between visits (Figure 1). Within the low-risk group, sex-based comparisons revealed that males had inferior performance to females in several memory tests (LogiticMem and SRT measures, Table 1). However, high-risk males only demonstrated significant improvements in the FCsrt_Free call compared to high-risk females. No significant neuroimaging correlations with cognitive changes were observed in high-risk groups across sexes.
·Table 1
·Figure 1
Conclusions:
These findings improve our understanding of the APOE genotype and highlight the importance of sex-specific studies in deciphering the neural mechanisms responsible for the risk and development of AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Genetics:
Genetic Association Studies
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Keywords:
Aging
Cognition
FUNCTIONAL MRI
Memory
MRI
White Matter
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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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.
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Provide references using APA citation style.
Dagley, A., et al. (2017). Harvard aging brain study: dataset and accessibility. Neuroimage, 144, 255-258.
Jack, C. R., et al. (2015). Age, sex, and APOE ε4 effects on memory, brain structure, and β-amyloid across the adult life span. JAMA neurology, 72(5), 511-519.
Li, M., et al. (2020). Functional engagement of white matter in resting-state brain networks. Neuroimage, 220, 117096.