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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: Mar 13, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Multimodal neuroimaging computing: the workflows, methods, and platforms.

Sidong Liu¹, Weidong Cai², Siqi Liu²

¹School of IT, The University of Sydney, Sydney, Australia. sliu7418@uni.sydney.edu.au.

Brain Informatics

|October 18, 2016

Summary

This summary is machine-generated.

Recent advances in noninvasive neuroimaging, particularly multimodal approaches, enhance brain research. Sophisticated computing is crucial for integrating diverse data from these powerful human brain imaging tools.

Keywords:

Medical image computing Multimodal Neuroimaging

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Area of Science:

Neuroimaging
Computational Neuroscience
Medical Imaging

Background:

Noninvasive neuroimaging technologies have rapidly advanced over the last two decades.
Multimodal neuroimaging is increasingly vital for understanding the human brain in both health and disease.
Hybrid imaging devices offer improved access to combined data streams.

Approach:

This review examines current computational workflows and methodologies for multimodal neuroimaging.
It highlights the challenges in processing and integrating data with varying spatiotemporal resolutions.
The paper demonstrates research applications using established neuroimaging computing packages and platforms.

Key Points:

Multimodal data integration presents significant computational challenges.
Addressing variations in spatiotemporal resolution is essential for accurate analysis.
Combining biophysical and biochemical information enhances neuroimaging insights.

Conclusions:

Sophisticated computing is indispensable for unlocking the full potential of multimodal neuroimaging.
Established neuroimaging computing platforms facilitate advanced research in brain science.
This work provides a guide for researchers utilizing multimodal neuroimaging data.