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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Jun 23, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

BrainTACO: an explorable multi-scale multi-modal brain transcriptomic and connectivity data resource.

Florian Ganglberger^1,2, Dominic Kargl³, Markus Töpfer¹

¹Biomedical Image Informatics, VRVis Research Center, Vienna, Austria.

Communications Biology

|June 14, 2024

Summary

This summary is machine-generated.

This study introduces BrainTACO, a novel resource integrating diverse brain data for accelerated gene-circuitry exploration. It enables scientists to identify genetic drivers of brain connectivity and dysconnectivity phenotypes more efficiently.

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Last Updated: Jun 23, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

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Published on: January 24, 2025

Area of Science:

Neuroscience
Bioinformatics
Genomics

Background:

Analyzing gene-brain circuitry relationships requires integrating heterogeneous datasets (3D imaging, anatomical, network data).
Existing platforms lack comprehensive integration and exploration tools for multi-scale, multi-modal neurobiological data.
Bridging scales and modalities necessitates data fusion into a common space and effective visualization.

Purpose of the Study:

To present the Brain Transcriptomic And Connectivity Data (BrainTACO) resource for accelerated exploration of gene-brain circuitry relationships.
To provide a platform for integrating and visualizing heterogeneous, multi-scale neurobiological data.
To facilitate the identification of genetic drivers of brain connectivity and dysconnectivity phenotypes.

Main Methods:

Developed BrainTACO, a resource of spatially mapped, heterogeneous neurobiological data onto a common hierarchical reference space.
Extended the BrainTrawler web-based visual analytics framework with comparative visualizations.
Implemented a holistic data integration scheme to fuse multi-scale, multi-modal data.

Main Results:

BrainTACO offers unprecedented coverage for gene expression dissection of brain networks.
Enabled identification of potential genetic drivers of connectivity in mice and humans.
Facilitated discovery of dysconnectivity phenotypes by linking genes to brain networks.

Conclusions:

BrainTACO significantly reduces manual data aggregation time for computational neuroscience analyses.
The resource supports neuroscientists by enabling direct data leverage over preparation.
BrainTACO advances the integrated analysis of transcriptomic and connectivity data for brain research.