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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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Integration of Multimodal Data for Deciphering Brain Disorders.

Jingqi Chen^1,2,3, Guiying Dong¹, Liting Song¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; email: xmzhao@fudan.edu.cn, jffeng@fudan.edu.cn.

Annual Review of Biomedical Data Science

|September 1, 2021

Summary

Integrating multimodal human brain data reveals genetic predispositions and molecular pathways for brain disorders. This approach enhances understanding and future diagnosis and treatment strategies for neurological conditions.

Keywords:

brain disorders data integration epigenetics genetics molecular omics neuroimaging

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Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Neuroscience
Genomics
Medical Data Science

Background:

Vast multimodal human brain data are available for both healthy and diseased states.
Traditional single-dataset analyses have limitations in fully understanding complex brain disorders.

Purpose of the Study:

To review popular large human brain datasets.
To discuss the integration of multimodal datasets for brain disorder research.
To explore how data integration can illuminate disease mechanisms and inform future clinical applications.

Main Methods:

Introduction of prominent large-scale human brain datasets.
Detailed discussion on integrating diverse data types (genomics, transcriptomics, imaging).
Analysis of how multimodal data integration reveals disease-related genetic and molecular factors.

Main Results:

Multimodal data integration provides deeper insights into brain disorder mechanisms than single-dataset analyses.
Identifies genetic predispositions and abnormal molecular pathways underlying brain disorders.
Highlights the potential of integrated data for microscopic and macroscopic understanding.

Conclusions:

The integration of multimodal human brain datasets is crucial for advancing our understanding of brain disorders.
Future data integration efforts hold promise for improving the diagnosis and treatment of neurological conditions.
This review provides a framework for leveraging big data in brain research.