Journal article
Multimodal Magnetic Resonance Imaging for Brain Disorders: Advances and Perspectives
Brain imaging and behavior, Vol.2(4), pp.249-257
12/2008
DOI: 10.1007/s11682-008-9038-z
Abstract
Modern brain imaging technologies play essential roles in our understanding of brain information processing and the mechanisms of brain disorders. Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) can image the anatomy and structure of the brain. In addition, functional MRI (fMRI) can identify active regions, patterns of functional connectivities and functional networks during either tasks that are specifically related to various aspects of brain function or during the resting state. The merging of such structural and functional information obtained from brain imaging may be able to enhance our understanding of how the brain works and how its diseases can occur. In this paper, we will review advances in both methodologies and clinical applications of multimodal MRI technologies, including MRI, DTI, and fMRI. We will also give our perspectives for the future in these fields. The ultimate goal of our study is to find early biomarkers based on multimodal neuroimages and genome datasets for brain disorders. More importantly, future studies should focus on detecting exactly where and how these brain disorders affect the human brain. It would also be also very interesting to identify the genetic basis of the anatomical and functional abnormalities in the brains of people who have neurological and psychiatric disorders. We believe that we can use brain images to obtain effective biomarkers for various brain disorders with the aid of developing computational methods and models.
Details
- Title: Subtitle
- Multimodal Magnetic Resonance Imaging for Brain Disorders: Advances and Perspectives
- Creators
- Tianzi Jiang - LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing 100190 People’s Republic of ChinaYong Liu - LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing 100190 People’s Republic of ChinaFeng Shi - LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing 100190 People’s Republic of ChinaNi Shu - LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing 100190 People’s Republic of ChinaBing Liu - LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing 100190 People’s Republic of ChinaJiefeng Jiang - LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing 100190 People’s Republic of ChinaYuan Zhou - LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing 100190 People’s Republic of China
- Resource Type
- Journal article
- Publication Details
- Brain imaging and behavior, Vol.2(4), pp.249-257
- Publisher
- Springer-Verlag; New York
- DOI
- 10.1007/s11682-008-9038-z
- ISSN
- 1931-7557
- eISSN
- 1931-7565
- Language
- English
- Date published
- 12/2008
- Academic Unit
- Psychological and Brain Sciences; Iowa Neuroscience Institute
- Record Identifier
- 9984065824102771
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