Impact of life experiences and genetic risk on brain structure and cognitive function in the elderly
Poster No:
914
Submission Type:
Abstract Submission
Authors:
Yilamujiang Abuduaini1,2, Huiying Chen1,2, Xiangzhen Kong1,2
Institutions:
1Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China, 2The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
First Author:
Yilamujiang Abuduaini
Department of Psychology and Behavioral Sciences, Zhejiang University|The State Key Lab of Brain-Machine Intelligence, Zhejiang University
Hangzhou, China|Hangzhou, China
Department of Psychology and Behavioral Sciences, Zhejiang University|The State Key Lab of Brain-Machine Intelligence, Zhejiang University
Hangzhou, China|Hangzhou, China
Co-Author(s):
Huiying Chen
Department of Psychology and Behavioral Sciences, Zhejiang University|The State Key Lab of Brain-Machine Intelligence, Zhejiang University
Hangzhou, China|Hangzhou, China
Department of Psychology and Behavioral Sciences, Zhejiang University|The State Key Lab of Brain-Machine Intelligence, Zhejiang University
Hangzhou, China|Hangzhou, China
Xiangzhen Kong, PhD Supervisor
Department of Psychology and Behavioral Sciences, Zhejiang University|The State Key Lab of Brain-Machine Intelligence, Zhejiang University
Hangzhou, China|Hangzhou, China
Department of Psychology and Behavioral Sciences, Zhejiang University|The State Key Lab of Brain-Machine Intelligence, Zhejiang University
Hangzhou, China|Hangzhou, China
Introduction:
Cognitive abilities and brain structure undergo dynamic changes across the human lifespan, with substantial individual variability, particularly during middle age and older adulthood (Fjell, 2024). This variability is considered driven by intricate interplay of genetic factors and environmental influences, such as life experiences (Smith et al., 2024; Walhovd et al., 2023). In older adults, the relationships between these factors, brain structures, and cognitive function remain poorly understood (Armstrong et al., 2020). Addressing this knowledge gap is essential for uncovering how genetic and environmental factors shape cognitive function through their effects on brain structure, providing critical insights into mechanisms of healthy aging (Lövdén et al., 2020; Mormino et al., 2016; Nyberg et al., 2021).
Methods:
Using data from 41,118 non-demented middle-aged and elderly participants in the UK Biobank, our study analyzed a range of factors, including education level, socioeconomic status (SES), apolipoprotein E (APOE) genotype, polygenic risk score for Alzheimer's disease (AD-PRS), brain imaging metrics, behavioral measures of 10 tests, and relevant covariates. Importantly, brain imaging and behavioral data were collected during the third assessment, rather than the baseline. Imaging metrics included volume, cortical thickness, and surface area of 66 cortical regions based on the Desikan-Killiany atlas (Desikan et al., 2006), as well as the bilateral volume of seven major subcortical structures. To assess cognitive ability, we selected all 10 non-pilot cognitive tests and applied principal component analysis to derive a general cognitive ability (GCA) score. Correlation analyses were conducted to explore associations among these variables, followed by mediation analyses where education, SES, APOE genotype, and AD-PRS served as predictors, brain structures as mediators, and GCA as the outcome variable. Additionally, longitudinal analyses were performed for participants with available follow-up data.
Results:
GCA showed significant associations with various brain structure measures, particularly with the volume of most cortical regions and subcortical structures (74/80, rs = 0.013 ~ 0.091). Higher GCA was strongly linked to higher educational attainment (t(31199) = 37.430, p < 2.2e-16, Cohen's d = 0.423), higher SES (r = 0.070, p < 2.2e-16), and lower AD-PRS (r = -0.038, p = 2.070e-12). Additionally, higher educational attainment was significantly associated with greater volume and cortical thickness in several frontal and temporal regions (rs = 0.013 ~ 0.028) and the volume of subcortical structures (rs = 0.014 ~ 0.037), whereas other factors showed more diffuse associations with gray matter measures.
As illustrated in Fig. 1, education enhanced GCA by increasing the volume of the bilateral superior and middle temporal gyri, precentral gyrus, and insula (βs = 0.001, ps < 0.05). The volume of the bilateral thalamus and hippocampus also mediated the effects of education, SES, and AD-PRS on GCA (βs = -0.002 ~ 0.002, ps < 0.05).
Longitudinal analyses revealed that baseline GCA was negatively associated with changes in cortical thickness and volume in the right temporal and parietal regions, and individuals with higher AD-PRS exhibited an increased GCA decline (r = 0.051, p = 0.006).
As illustrated in Fig. 1, education enhanced GCA by increasing the volume of the bilateral superior and middle temporal gyri, precentral gyrus, and insula (βs = 0.001, ps < 0.05). The volume of the bilateral thalamus and hippocampus also mediated the effects of education, SES, and AD-PRS on GCA (βs = -0.002 ~ 0.002, ps < 0.05).
Longitudinal analyses revealed that baseline GCA was negatively associated with changes in cortical thickness and volume in the right temporal and parietal regions, and individuals with higher AD-PRS exhibited an increased GCA decline (r = 0.051, p = 0.006).
Conclusions:
This study systematically investigated the relationships between GCA and brain structure in non-demented middle-aged and older adults, both cross-sectionally and longitudinally. The effects of education, SES, and genetic risks were further examined. Using large-scale data, we identified the multifaceted structural basis of cognition in older adults and uncovered specific brain regions that mediate the influence of these factors on cognitive abilities. These findings offer valuable resource for understanding how genetic and life experiences shape cognitive functions in aging, highlighting potential target brain regions and providing guidance for promoting healthy aging.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Lifespan Development:
Aging 1
Early life, Adolescence, Aging
Lifespan Development Other 2
Keywords:
Aging
Cognition
Cortex
MRI
Sub-Cortical
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Provide references using APA citation style.
2. Desikan, R. S. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.
3. Fjell, A. M. (2024). Aging Brain from a Lifespan Perspective. Springer Berlin Heidelberg.
4. Lövdén, M. et al. (2020). Education and Cognitive Functioning Across the Life Span. Psychological Science in the Public Interest, 21(1), 6–41.
5. Mormino, E. C. et al. (2016). Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology, 87(5), 481–488.
6. Nyberg, L. et al. (2021). Educational attainment does not influence brain aging. Proceedings of the National Academy of Sciences, 118(18), e2101644118.
7. Smith, E. E. et al. Nature Reviews Neurology, 20(11), 647–659.
8. Walhovd, K. B. et al. (2023). Timing of lifespan influences on brain and cognition. Trends in Cognitive Sciences, 27(10), 901–915.