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Related Experiment Video

Updated: Apr 2, 2026

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

Published on: May 4, 2018

Generative cerebral vasculature visualization using spatial transcriptomic data.

Ingrid Berg¹, Jiqing Wu², Viktor H Koelzer^3,4

¹Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Scientific Reports

|March 31, 2026

Summary

This summary is machine-generated.

Generative AI models can predict brain vasculature structure using spatial transcriptomics. This approach offers new insights into neurological disorders without extra tissue processing.

Keywords:

Cerebral vasculature Generative modeling Spatial transcriptomics

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Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro

Published on: January 31, 2018

Area of Science:

Neuroscience
Computational Biology
Genomics

Background:

The brain's vascular network is crucial for function and neurological disease.
Understanding neurovascular structure and function is key for disease modeling.

Purpose of the Study:

To predict the spatial organization of brain vasculature using generative AI.
To explore vascular function and its dysregulation in neurological disorders.

Main Methods:

Utilized Tera-MIND, a spatial mRNA-guided generative model.
Predicted vasculature by co-expression patterns of Cldn5 and Acta2 genes.
Analyzed cell-level resolution using learned attention maps.

Main Results:

Demonstrated generative AI's ability to capture biologically meaningful structures from spatial transcriptomic data.
Successfully predicted spatial organization of brain vasculature.
Showcased the potential of in silico systems for biological architecture simulation.

Conclusions:

Generative AI models can derive spatial insights into vascular organization from existing data.
Repurposed spatial transcriptomics datasets for new biological discoveries.
Highlighted potential for high-throughput exploration of neurological disorders.