Bridging the Neuroimaging Gap: Cross-Cultural Analysis of Brain Health in the UAE and Beyond
Poster No:
1828
Submission Type:
Late-Breaking Abstract Submission
Authors:
Abdalla Mohamed1, Ameen Qadi1, Aysha Hamkari1, Omnia Hassanin1, Rawand Benour1, Dana Alkalali1, Amal Salah1, Haidee Paterson1, Osama Abdullah1, Bas Rokers1
Institutions:
1New York University Abu Dhabi, Abu Dhabi, Abu Dhabi
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Introduction:
Since its founding in 1971, the United Arab Emirates (UAE) has seen rapid increases in life expectancy and urbanization. The associated environmental and lifestyle changes, in combination with genetic factors may uniquely impact brain health in the UAE. However, the Middle East lacks large-scale normative neuroimaging datasets to assess these impacts.
Methods:
The goal of the ASPIRE Research Institute (ARI) is to recruit 2,000 participants (ages 18–60), consisting of 1,000 Emirati nationals and 1,000 non-nationals. Here we compare imaging-derived phenotypes (IDPs) from an initial ARI cohort (n = 100; 3T Siemens Prisma, 64 Channel), with two multi-modal neuroimaging datasets including: the Chinese "I See Your Brain" dataset (ISYB; n = 215; 3T GE-MR750) (Gao et al., 2022) and the Dataset of Functional Connectivity during Cognitive Control for an Adult Lifespan Sample (FCC-ALS; n=157; 3T Siemens Trio, 12 Channels) (Rieck et al., 2021).
All datasets share similar protocols, and each was collected in a single site. T1-weighted scans were acquired for all participants, with additional FLAIR scans in ARI and ALS, and T2-weighted scans in ISYB. Quality control was performed using MRIQC, and all datasets were processed with Freesurfer's recon-all pipeline (v7.4.0) (Dale et al., 1999). IDPs included total gray/white matter and ventricular volumes, as well as 148 cortical and 15 subcortical regions. All measures were normalized by total brain volume and converted into percentages.
Age-related trends in regional brain volume were modeled separately for each dataset using second-degree polynomial regression, with age as the predictor. Predicted values were computed across a continuous age range, with percentile estimates (2.5th, 25th, 75th, 97.5th) derived from residual distributions to establish normative ranges, enabling cross-cohort comparisons of brain morphology and aging trajectories.
All datasets share similar protocols, and each was collected in a single site. T1-weighted scans were acquired for all participants, with additional FLAIR scans in ARI and ALS, and T2-weighted scans in ISYB. Quality control was performed using MRIQC, and all datasets were processed with Freesurfer's recon-all pipeline (v7.4.0) (Dale et al., 1999). IDPs included total gray/white matter and ventricular volumes, as well as 148 cortical and 15 subcortical regions. All measures were normalized by total brain volume and converted into percentages.
Age-related trends in regional brain volume were modeled separately for each dataset using second-degree polynomial regression, with age as the predictor. Predicted values were computed across a continuous age range, with percentile estimates (2.5th, 25th, 75th, 97.5th) derived from residual distributions to establish normative ranges, enabling cross-cohort comparisons of brain morphology and aging trajectories.
Results:
Following quality control, 463 participants (ARI: n=92, ISYB: n=214, FCC-ALS: n=157) were analyzed. FCC-ALS had the widest age range (18–80 years), while ISYB had the narrowest (18–30 years). ARI was the most ethnically diverse (Middle Eastern: 29, Asian: 38, African: 11, Latino: 2, Caucasian: 13), while FCC-ALS and ISYB were predominantly Caucasian and Asian, respectively.
Age-related trajectories of cortical and subcortical volumes were modeled. We found significant cross-dataset differences (FDR-adjusted p < 0.05; Fig 1 A-D) in most of the cortical and subcortical structures including hippocampus, amygdala, thalamus, caudate, putamen, frontal cortex, temporal cortex, and precuneus. Ventricular volume increased with age, while cortical and subcortical gray matter declined, following expected atrophy patterns, while other regions showed no differences (Fig 1 E-F). However, the rate of decline varied, with ARI showing the steepest decline, followed by FCC-ALS and ISYB, which may reflect cohort effects driven by the UAE's rapid development and lifestyle shifts across generations. Some differences may stem from MRI hardware and acquisition protocols, challenging data harmonization. These findings highlight population-specific differences in brain structure and underscore the need for diverse normative neuroimaging datasets.
Age-related trajectories of cortical and subcortical volumes were modeled. We found significant cross-dataset differences (FDR-adjusted p < 0.05; Fig 1 A-D) in most of the cortical and subcortical structures including hippocampus, amygdala, thalamus, caudate, putamen, frontal cortex, temporal cortex, and precuneus. Ventricular volume increased with age, while cortical and subcortical gray matter declined, following expected atrophy patterns, while other regions showed no differences (Fig 1 E-F). However, the rate of decline varied, with ARI showing the steepest decline, followed by FCC-ALS and ISYB, which may reflect cohort effects driven by the UAE's rapid development and lifestyle shifts across generations. Some differences may stem from MRI hardware and acquisition protocols, challenging data harmonization. These findings highlight population-specific differences in brain structure and underscore the need for diverse normative neuroimaging datasets.
Conclusions:
This study provides one of the first comparative analyses of normative brain structure across diverse populations, revealing significant variations in cortical and subcortical volumes. While age-related changes were consistent across datasets, ARI exhibited distinct volumetric differences, suggesting potential genetic, environmental, and lifestyle factors. The ARI cohort's diversity highlights the importance of representative datasets and tailored normative references for accurate brain health assessment. Residual methodological variability remains a limitation, emphasizing the need for quantitative imaging methods and advanced harmonization techniques to improve cross-cohort comparability in neuroimaging research.
Lifespan Development:
Lifespan Development Other
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Informatics Other
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
ADULTS
Aging
Modeling
NORMAL HUMAN
Open Data
STRUCTURAL MRI
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?
Which processing packages did you use for your study?
Provide references using APA citation style.
Gao, P., Dong, H. M., Liu, S. M., Fan, X. R., Jiang, C., Wang, Y. S., Margulies, D., Li, H. F., & Zuo, X. N. (2022). A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping. Scientific Data, 9(1), 286. https://doi.org/10.1038/S41597-022-01413-3
Rieck, J. R., Baracchini, G., Nichol, D., Abdi, H., & Grady, C. L. (2021). Dataset of functional connectivity during cognitive control for an adult lifespan sample. Data in Brief, 39, 107573. https://doi.org/10.1016/J.DIB.2021.107573