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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: Oct 12, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

Brain research challenges supercomputing.

Katrin Amunts¹, Thomas Lippert²

¹Institut fur Neurowissenschaften und Medizin, INM-1, Forschungszentrum Jülich, Jülich, Germany.

Science (New York, N.Y.)

|November 25, 2021

Summary

This summary is machine-generated.

Unraveling the human brain

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Area of Science:

Neuroscience
Computational Biology
Data Science

Background:

The human brain is a complex system with billions of neurons.
Advancements in neuroimaging and genetic sequencing generate vast datasets.
Analyzing this big data presents significant computational challenges.

Purpose of the Study:

To highlight the increasing processing demands of human brain big data.
To underscore the need for advanced computational methods in neuroscience.

Main Methods:

Review of current neuroimaging and data generation techniques.
Analysis of computational resource requirements for brain data processing.

Main Results:

Human brain research generates petabytes of data annually.
Existing computational infrastructure is often insufficient for real-time analysis.
Significant increases in processing power and storage are required.

Conclusions:

The scale of human brain data necessitates novel computational approaches.
Future research requires investment in high-performance computing and data management strategies.
Bridging the gap between data generation and analysis is crucial for advancing brain science.