| Title |
Brain explorer for connectomic analysis
|
|---|---|
| Published in |
Brain Informatics, August 2017
|
| DOI | 10.1007/s40708-017-0071-9 |
| Pubmed ID | |
| Authors |
Huang Li, Shiaofen Fang, Joey A. Contreras, John D. West, Shannon L. Risacher, Yang Wang, Olaf Sporns, Andrew J. Saykin, Joaquín Goñi, Li Shen, for the Alzheimer’s Disease Neuroimaging Initiative |
| Abstract |
Visualization plays a vital role in the analysis of multimodal 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 anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. 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. |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 53 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 23% |
| Student > Bachelor | 5 | 9% |
| Professor > Associate Professor | 4 | 8% |
| Student > Doctoral Student | 4 | 8% |
| Student > Master | 4 | 8% |
| Other | 12 | 23% |
| Unknown | 12 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 15% |
| Computer Science | 7 | 13% |
| Neuroscience | 6 | 11% |
| Agricultural and Biological Sciences | 5 | 9% |
| Engineering | 3 | 6% |
| Other | 8 | 15% |
| Unknown | 16 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 April 2018.
All research outputs
#16,530,130
of 24,319,828 outputs
Outputs from Brain Informatics
#74
of 111 outputs
Outputs of similar age
#204,884
of 321,028 outputs
Outputs of similar age from Brain Informatics
#1
of 1 outputs
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