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Related Experiment Video

Updated: Jun 13, 2026

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids

Published on: March 29, 2024

Cross-scale spatially-aware generative modeling of transcriptomic programs underlying neurodegenerative brain

Krishnakumar Vaithianathan¹

¹Department of Computer Engineering, Karaikal Polytechnic College, Karaikal, Puducherry, India.

|June 12, 2026

Summary

This study introduces a new generative model to link gene expression in the brain to neurodegenerative disease patterns. The framework accurately predicts regional brain vulnerability in Alzheimer's disease, offering insights into disease mechanisms.

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Area of Science:

Computational Neuroscience
Genomics
Neurobiology

Background:

Neurodegenerative diseases like Alzheimer’s disease show distinct regional brain vulnerability patterns.
Current imaging-transcriptomic studies often use correlational methods, limiting understanding of molecular mechanisms driving neurodegeneration.
The spatial selectivity of neurodegeneration remains poorly understood.

Purpose of the Study:

To develop a cross-scale, spatially-aware generative framework to model transcriptomic programs underlying cortical neurodegeneration.
To link regional gene expression organization with patterns of neurodegeneration.
To investigate the biological mechanisms behind spatially selective neurodegeneration.

Main Methods:

Utilized the Allen Human Brain Atlas for regional transcriptomic profiles (910 genes, 68 cortical regions).

Keywords:

Alzheimer’s disease Cross-scale brain modeling Generative neurobiology Imaging transcriptomics Spatial deep learning

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Last Updated: Jun 13, 2026

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids

Published on: March 29, 2024

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Published on: September 5, 2025

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Constructed neurodegenerative vulnerability maps from ADNI FreeSurfer cortical thickness data (comparing cognitively normal and Alzheimer's disease subjects).

Employed a variational generative architecture with graph-based spatial regularization to model gene expression-neurodegeneration links.

Main Results:

The framework achieved high prediction accuracy for regional neurodegenerative vulnerability (explained variance = 0.8604).
Demonstrated significant spatial correlation between predicted and observed cortical degeneration (r = 0.9439, p < 0.001).
Identified structured latent representations linking transcriptomic organization to disease susceptibility.

Conclusions:

Biologically constrained generative modeling can effectively bridge molecular organization and macroscale neurodegeneration.
The framework provides a foundation for spatially-aware generative neurobiology and computational neuroscience.
This approach enhances understanding of the molecular basis of spatially selective neurodegeneration.