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| Content Provider | Springer Nature Link |
|---|---|
| Author | Wang, Qi Chen, Rong JaJa, Joseph Jin, Yu Hong, L. Elliot Herskovits, Edward H. |
| Copyright Year | 2015 |
| Abstract | Defining brain structures of interest is an important preliminary step in brain-connectivity analysis. Researchers interested in connectivity patterns among brain structures typically employ manually delineated volumes of interest, or regions in a readily available atlas, to limit the scope of connectivity analysis to relevant regions. However, most structural brain atlases, and manually delineated volumes of interest, do not take voxel-wise connectivity patterns into consideration, and therefore may not be ideal for anatomic connectivity analysis. We herein propose a method to parcellate the brain into regions of interest based on connectivity. We formulate connectivity-based parcellation as a graph-cut problem, which we solve approximately using a novel multi-class Hopfield network algorithm. We demonstrate the application of this approach using diffusion tensor imaging data from an ongoing study of schizophrenia. Compared to a standard anatomic atlas, the connectivity-based atlas supports better classification performance when distinguishing schizophrenic from normal subjects. Comparing connectivity patterns averaged across the normal and schizophrenic subjects, we note significant systematic differences between the two atlases. |
| Starting Page | 83 |
| Ending Page | 97 |
| Page Count | 15 |
| File Format | |
| ISSN | 15392791 |
| Journal | Neuroinformatics |
| Volume Number | 14 |
| Issue Number | 1 |
| e-ISSN | 15590089 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2015-10-03 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Connectivity-based parcellation Probabilistic tractography Graph-cut Multi-class Hopfield network Schizophrenia Diffusion tensor imaging Neurosciences Bioinformatics Computational Biology/Bioinformatics Computer Application in Life Sciences Neurology |
| Content Type | Text |
| Resource Type | Article |
| Subject | Neuroscience Information Systems Software |
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