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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: Apr 12, 2026

Bioluminescence and Near-infrared Imaging of Optic Neuritis and Brain Inflammation in the EAE Model of Multiple Sclerosis in Mice

Bioluminescence and Near-infrared Imaging of Optic Neuritis and Brain Inflammation in the EAE Model of Multiple Sclerosis in Mice

Published on: March 1, 2017

Primer on machine learning applications in brain immunology.

Niklas Binder¹, Ashkan Khavaran¹, Roman Sankowski¹

¹Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

Frontiers in Bioinformatics

|May 2, 2025

Summary

This summary is machine-generated.

Single-cell and spatial omics reveal brain immune cell complexity and dynamics. Advanced AI methods like machine learning are key to analyzing these datasets for neurological disorder insights.

Keywords:

data integration deep learning development gene co-expression network multi-omcis perturbation prediction single-cell genomics trajectory analysis

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Last Updated: Apr 12, 2026

Bioluminescence and Near-infrared Imaging of Optic Neuritis and Brain Inflammation in the EAE Model of Multiple Sclerosis in Mice

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Published on: March 1, 2017

Precise Brain Mapping to Perform Repetitive In Vivo Imaging of Neuro-Immune Dynamics in Mice

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Published on: August 7, 2020

Author Spotlight: Studying Neuroinflammatory Pathways Through Simultaneous Isolation of All Main CNS-Resident Cell Types

Author Spotlight: Studying Neuroinflammatory Pathways Through Simultaneous Isolation of All Main CNS-Resident Cell Types

Published on: October 6, 2023

Area of Science:

Neuroscience
Immunology
Computational Biology

Background:

Single-cell and spatial technologies offer novel insights into central nervous system (CNS) immune cell heterogeneity and organization.
These technologies have uncovered complex cellular interactions and rare cell populations relevant to neurological disorders.

Purpose of the Study:

To review recent advances in single-cell omics data analysis for brain immunology.
To discuss the application of artificial intelligence (AI) and machine learning (ML) in analyzing complex single-cell datasets.
To highlight the role of these technologies in understanding brain development and neurological diseases.

Main Methods:

Application of traditional statistical techniques for cell type categorization and gene signature identification.
Utilization of machine learning, including deep learning methods (autoencoders, graph neural networks), for dimensionality reduction, data integration, and feature extraction.
Exploration of foundation models for gene expression program discovery and genetic perturbation prediction.

Main Results:

Single-cell analyses have resolved immune cell heterogeneity and temporal maturation trajectories during brain development.
Integration of single-cell and spatial omics has elucidated intricate cellular interplay within the developing brain.
These approaches identified potential therapeutic links for pathologies such as brain malignancies and neurodegeneration.

Conclusions:

Single-cell and spatial omics, powered by AI, are revolutionizing brain immunology research.
These advanced analytical methods are crucial for deciphering complex immune landscapes in the CNS.
The findings offer a concise overview for biologists on leveraging these evolving technologies for neurological research.