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Revisiting Spatial Patterns in Gene-Brain Associations: Insights from the Allen Human Brain Atlas

Poster No:

849 

Submission Type:

Abstract Submission 

Authors:

Chien Ming Lo1, Niall Duncan2

Institutions:

1Taipei Medical University, Taipei, Taiwan, 2Taipei Medical University, Taiwan, Taipei

First Author:

Chien Ming Lo  
Taipei Medical University
Taipei, Taiwan

Co-Author:

Niall Duncan  
Taipei Medical University
Taiwan, Taipei

Introduction:

The Allen Human Brain Atlas has been a significant tool in neuroscience, facilitating investigations into the brain's organizational principles. Studies have focused on correlated gene expression patterns and their relation to brain features, necessitating spatial correlations and specialized significance testing on the cortical sheet.

Methods:

We collected regional microarray expression data from six post-mortem brains provided by the Allen Human Brain Atlas. The data underwent preprocessing using the abagen toolbox and was then mapped onto the Schaefer-400 atlas. We used several categorical gene sets and implemented PCA to identify main spatial patterns in gene expression variations across the cortex. Our analysis also included a non-linear dimensionality reduction technique and the creation of a transcriptomic null distribution through nonparametric permutation.

Results:

The first principal components of transcriptomic covariances revealed a consistent spatial pattern across gene sets, including 'unspecific' and 'specific' sets. The brain-specific PC1 showed a strong association with the T1w/T2w map, challenging the assumption that dominant spatial patterns in AHBA data are exclusive to specific gene sets. Furthermore, our analysis indicated that the observed spatial patterns are not solely due to specific gene sets but might be a more generalized characteristic of the brain's transcriptomic organization
Supporting Image: paper_figures_final.png
Supporting Image: paper_figures_final1.png
 

Conclusions:

Our findings challenge prevailing assumptions about the specificity of gene-brain property associations and suggest a more generalized transcriptomic spatial pattern. The unique covariance characteristics within specific functional networks, particularly the Visual, Somatomotor, and Limbic networks, appear to influence these spatial transcriptomic patterns. This study prompts a critical reevaluation of methodologies and assumptions in understanding gene-brain associations, offering new insights into the complex relationships governing brain organization and gene expression patterns

Genetics:

Genetic Association Studies 1
Transcriptomics 2

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Anatomy and Functional Systems

Keywords:

Structures
Other - Transcriptomic Spatial Patterns

1|2Indicates the priority used for review

Provide references using author date format

Burt, J.B. et al. (2018), 'Hierarchy of Transcriptomic Specialization across Human Cortex Captured by Structural Neuroimaging Topography', Nature Neuroscience, vol. 21, no. 9, pp. 1251-1259.
Arnatkevičiūtė, A., Fulcher, B.D., & Fornito, A. (2019), 'A Practical Guide to Linking Brain-wide Gene Expression and Neuroimaging Data', Neuroimage, vol. 189, pp. 353-367.
Richiardi, J. et al. (2015), 'Correlated Gene Expression Supports Synchronous Activity in Brain Networks', Science, vol. 348, no. 6240, pp. 1241-1244.
Schaefer, A. et al. (2018), 'Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI', Cerebral Cortex, vol. 28, no. 9, pp. 3095-3114.
Glasser, M.F. (2016), 'A Multi-modal Parcellation of Human Cerebral Cortex', Nature, vol. 536, no. 7615, pp. 171-178.