Name
Abstract | ||
|---|---|---|
Study of structural and functional connectivities of the human brain has received significant interest and effort recently. A fundamental question arises when attempting to measure the structural and/or functional connectivities of specific brain networks: how to best identify possible Regions of Interests (ROIs)? In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on learned fiber shape models from multimodal task-based fMRI and diffusion tensor imaging (DTI) data. In the training stage, ROIs are identified as activation peaks in task-based fMRI data. Then, shape models of white matter fibers emanating from these functional ROIs are learned. In addition, ROIs' location distribution model is learned to be used as an anatomical constraint. In the prediction stage, functional ROIs are predicted in individual brains based on DTI data. The ROI prediction is formulated and solved as an energy minimization problem, in which the two learned models are used as energy terms. Our experiment results show that the average ROI prediction error is 3.45 mm, in comparison with the benchmark data provided by working memory task-based fMRI. Promising results were also obtained on the ADNI-2 longitudinal DTI dataset. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23629-7_6 | MICCAI (2) |
Keywords | Field | DocType |
novel roi prediction framework,individual brain,localizes rois,benchmark data,functional connectivities,roi prediction,dti data,average roi prediction error,prediction stage,functional rois,fiber shape model,functional brain rois | Computer vision,Distribution model,Mean squared prediction error,Diffusion MRI,Pattern recognition,White matter,Computer science,Working memory,Human brain,Artificial intelligence,Energy minimization | Conference |
Volume | Issue | ISSN |
14 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
2 | 0.41 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tuo Zhang | 1 | 233 | 32.92 |
| Lei Guo | 2 | 1661 | 142.63 |
| Kaiming Li | 3 | 385 | 30.92 |
| Dajing Zhu | 4 | 2 | 0.41 |
| Guangbin Cui | 5 | 43 | 2.00 |
| Tianming Liu | 6 | 1033 | 112.95 |