Name
Abstract | ||
|---|---|---|
The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using multi-modal imaging and graph theoretical approaches. Both the construction and analysis of the Brain Networks require tremendous computation. As a result, most current studies of the Brain Networks are focused on a coarse scale based on Brain Regions. Networks on this scale usually consist around 100 nodes. The more accurate and meticulous voxel-base Brain Networks, on the other hand, may consist 20K to 100K nodes. In response to the difficulties of analyzing large-scale networks, we propose an acceleration framework for voxel-base Brain Network Analysis based on Graphics Processing Unit (GPU). Our GPU implementations of Brain Network construction and modularity achieve 24x and 80x speedup respectively, compared with single-core CPU. Our work makes the processing time affordable to analyze multiple large-scale Brain Networks. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPADS.2010.105 | ICPADS |
Keywords | Field | DocType |
hardware computing,graphics processing unit,meticulous voxel-base brain networks,coarse scale,brain regions,gpu,neurophysiology,voxel based brain network,human connectome,acceleration framework,brain network construction,disease-related alterations,gpu acceleration,voxel-base brain network analysis,complex brain networks,multimodal imaging,connectivity patterns,large-scale networks,single-core cpu,brain networks,human connectome faster,human brain network,graph theory,coprocessors,graph theoretical approaches,multiple large-scale brain networks,processing time,human brain networks,brain models,gpu implementation,neural nets,human brain,hardware,correlation,algorithm design and analysis,sparse matrices,network analysis,instruction sets | Graph theory,Computer science,Instruction set,Human Connectome,Coprocessor,Graphics processing unit,Artificial neural network,Modularity,Distributed computing,Speedup | Conference |
ISSN | ISBN | Citations |
1521-9097 E-ISBN : 978-0-7695-4307-9 | 978-0-7695-4307-9 | 5 |
PageRank | References | Authors |
0.46 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Wu | 1 | 636 | 117.73 |
| Tianji Wu | 2 | 41 | 3.57 |
| Yi Shan | 3 | 253 | 15.77 |
| Yu Wang | 4 | 2279 | 211.60 |
| Yong He | 5 | 460 | 39.57 |
| Ning-Yi Xu | 6 | 563 | 36.18 |
| Huazhong Yang | 7 | 2239 | 214.90 |