Poster No:
161
Submission Type:
Abstract Submission
Authors:
Tamara Jafar1, Nahian Chowdhury1, Andrei Irimia1
Institutions:
1University of Southern California, Los Angeles, CA
First Author:
Tamara Jafar
University of Southern California
Los Angeles, CA
Co-Author(s):
Introduction:
The World Health Organization estimates around 55 million people globally have dementia, with Alzheimer's disease (AD) accounting for 60–70% of cases1. Heritability estimates of 58–79% highlight a strong genetic contribution to AD risk, yet the molecular mechanisms underlying its characteristic cortical neurodegeneration remain poorly understood11. Advances in imaging transcriptomics provide a framework to uncover molecular pathways by integrating spatial gene expression data with neuroimaging phenotypes3,4. This study leverages transcriptomic data from the Allen Human Brain Atlas (AHBA) and MRI-derived gray-white contrast (GWC), a measure gray and white matter intensity differences at the cortical boundary, to identify genes and pathways associated with AD risk across two large cohorts.
Methods:
Participants included 2,286 individuals from the National Alzheimer's Coordinating Center (NACC) and 608 from the Alzheimer's Disease Neuroimaging Initiative (ADNI), comprising age- and sex-matched cognitively normal (CN) and AD groups5-7. T1-weighted MRI data were processed using FreeSurfer (v6.0) to extract cortical thickness and compute GWC as a normalized ratio of gray-to-white matter intensities at each vertex. Gene expression data from six postmortem brains from the AHBA were re-annotated using the hg38 genome reference3,4. Expression values were averaged across probes and mapped onto the cortical surface by aligning sample locations to vertices on a mid-thickness mesh with z-score standardization9. Spatial autocorrelation was addressed with spin tests using 30,000 null distributions. Statistical significance was determined via Benjamini-Hochberg FDR correction (q < 0.05). Cell-type enrichment was performed using single-cell RNA sequencing data, and gene ontology (GO) analysis was conducted to identify functional pathways23.
Results:
Analyses revealed significant associations between MRI-derived GWC and spatial gene expression patterns, with both shared and cohort-specific findings. Genes enriched in regions of altered GWC were linked to processes such as neuroinflammation, synaptic signaling, and neurovascular integrity, with notable differences between AD and CN participants. GO analysis highlighted immune response pathways in AD participants, particularly in the NACC cohort, while ADNI-specific genes were associated with synaptic plasticity and neurovascular processes. CN-specific genes were enriched in synaptic maintenance pathways, reflecting potential mechanisms of cognitive resilience. Differential correlations between gene expression and GWC were observed across cortical regions, particularly in temporal and parietal lobes vulnerable to AD-related neurodegeneration. Astrocytic and endothelial markers showed correlations with GWC in AD participants. Cell-type enrichment analyses underscored glial and vascular contributions to cortical changes, with astrocytic and endothelial markers enriched in regions exhibiting significant GWC reductions. These findings emphasize the spatial specificity of neurovascular and glial dysfunction in AD. Regional analyses further revealed expression variability across AD-associated cortical areas, with chromosomal mapping identifying gene clusters in gene-dense regions implicated in neurodegenerative processes.
Conclusions:
Using an integrative imaging transcriptomics approach, we identified spatial associations between MRI-derived GWC gene expression, identifying molecular drivers of cortical features in AD. This study revealed specific genes with distinct GWC correlations and pathways. These findings highlight the utility of GWC as a biomarker bridging structural and molecular insights, advancing our understanding of neurodegeneration and providing a foundation for targeted interventions.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Transcriptomics 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Neuroinformatics and Data Sharing:
Informatics Other
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Astrocyte
Computational Neuroscience
Cortex
Data analysis
Degenerative Disease
Informatics
Morphometrics
MRI
White Matter
Other - Alzheimer’s Disease, T1-weighted MRI, Gray-White Matter Contrast, Transcriptomics, Cell-Type Enrichment
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.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Yes, I have IRB or AUCC approval
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.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
1T
Which processing packages did you use for your study?
FSL
Free Surfer
Provide references using APA citation style.
Alexander-Bloch, A. F., et al. (2018). On testing for spatial correspondence between maps of human brain structure and function. NeuroImage, 178, 540–551.
Arnatkevic̆iūtė, A., Fulcher, B. D., & Fornito, A. (2019). A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage, 189, 353–367.
Beekly, D. L., Ramos, E. M., Lee, W. W., Deitrich, W. D., Jacka, M. E., Wu, J., et al. (2007). The National Alzheimer’s Coordinating Center (NACC) database: The uniform data set. Alzheimer Disease and Associated Disorders, 21, 249–258.
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, 968–980.
Fulcher, B. D., Arnatkeviciute, A., & Fornito, A. (2021). Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nature Communications, 12, 2669.
Koenig, S. H. (1991). Cholesterol of myelin is the determinant of gray-white contrast in MRI of brain. Magnetic Resonance in Medicine, 20, 285–291. https://doi.org/10.1002/mrm.1910200210
Morris, J. C., Weintraub, S., Chui, H. C., Cummings, J., DeCarli, C., Ferris, S., et al. (2006). The Uniform Data Set (UDS): Clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Disease and Associated Disorders, 20, 210–216.
Vidal‐Piñeiro, D., et al. (2016). Accelerated longitudinal gray/white matter contrast decline in aging in lightly myelinated cortical regions. Human Brain Mapping, 37, 3669–3684.
Weintraub, S., Besser, L., Dodge, H. H., Teylan, M., Ferris, S., Goldstein, F. C., et al. (2018). Version 3 of the Alzheimer Disease Centers’ neuropsychological test battery in the Uniform Data Set (UDS). Alzheimer Disease and Associated Disorders, 32, 10–20.
World Health Organization. (2022). Dementia. https://www.who.int/news-room/fact-sheets/detail/dementia
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