Chapter title |
Brain Informatics and Health
|
---|---|
Chapter number | 29 |
Book title |
Brain Informatics and Health
|
Published in |
Lecture notes in computer science, August 2015
|
DOI | 10.1007/978-3-319-23344-4_29 |
Pubmed ID | |
Book ISBNs |
978-3-31-923343-7, 978-3-31-923344-4
|
Authors |
Li, Huang, Fang, Shiaofen, Goni, Joaquin, Contreras, Joey A, Liang, Yanhua, Cai, Chengtao, West, John D, Risacher, Shannon L, Wang, Yang, Sporns, Olaf, Saykin, Andrew J, Shen, Li, Huang Li, Shiaofen Fang, Joaquin Goni, Joey A. Contreras, Yanhua Liang, Chengtao Cai, John D. West, Shannon L. Risacher, Yang Wang, Olaf Sporns, Andrew J. Saykin, Li Shen, [Authorinst]for the ADNI |
Editors |
Yike Guo, Karl Friston, Faisal Aldo, Sean Hill, Hanchuan Peng |
Abstract |
Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 29% |
Unknown | 5 | 71% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 6 | 86% |
Scientists | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 5% |
Netherlands | 1 | 5% |
Unknown | 18 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 5 | 25% |
Student > Master | 4 | 20% |
Student > Ph. D. Student | 2 | 10% |
Other | 1 | 5% |
Professor | 1 | 5% |
Other | 3 | 15% |
Unknown | 4 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 5 | 25% |
Psychology | 3 | 15% |
Agricultural and Biological Sciences | 2 | 10% |
Neuroscience | 2 | 10% |
Business, Management and Accounting | 1 | 5% |
Other | 4 | 20% |
Unknown | 3 | 15% |