Name
Title | ||
|---|---|---|
Functional Brain Areas Mapping in Patients with Glioma Based on Resting-State fMRI Data Decomposition. |
Abstract | ||
|---|---|---|
In current work we propose a three-step approach to automatic and efficient functional brain areas mapping as well demonstrate in case studies on three patients with gliomas the potential applicability of constrained source separation technique (semiblind Independent Component Analysis, ICA) to brain networks discovery and the similarity of task-based-fMRI (t-fMRI) and resting state-fMRI (rs-fMRI) results. Blind and semiblind ICA-analysis was applied for both methods t-fMRI and rs-fMRI. To measure similarity between spatial maps we used Dice coefficient, which shows the ratio of overlapping voxels and all active voxels in two compared maps for each patient Based on the analysis of Dice coefficients, there was a fairly high degree of overlap between the t-fMRI active areas, Broca and Wernicke and the language network obtained from rs-fMRI. The degree of motor areas overlap with sensorimotor network is less pronounced, but the activation sites correspond to anatomical landmarks - a complex of central gyri and supplementary motor area. In general, in comparisons of the functional brain areas obtained with t-fMRI and rs-fMRI, there is a greater specificity of semiblind ICA compared to blind ICA. RSNs of interest (motor and language) discovered by rs-fMRI highly correlate with t-fMRI reference and are located in anticipated anatomical regions. As a result, rs-fMRI maps seem as a good approximation of t-fMRI maps, especially in case of semiblind ICA decomposition. We hope that further our research of individual changes in sensorimotor and language networks based on functional rs-MRI will allow predicting the activity of neural network architectures and non-invasive mapping of functional areas for preoperative planning. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICDMW.2018.00049 | ICDM Workshops |
Keywords | Field | DocType |
Task analysis,Functional magnetic resonance imaging,Brain,Neurosurgery,Tumors,Independent component analysis,Planning | Voxel,Pattern recognition,Functional magnetic resonance imaging,Computer science,Sørensen–Dice coefficient,Resting state fMRI,Supplementary motor area,Artificial intelligence,Independent component analysis,Artificial neural network,Source separation,Machine learning | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-5386-9288-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maksim Sharaev | 1 | 0 | 2.03 |
| a i smirnov | 2 | 4 | 1.80 |
| Tatiana Melnikova-Pitskhelauri | 3 | 0 | 0.34 |
| Vyacheslav Orlov | 4 | 1 | 3.11 |
| Evgeny Burnaev | 5 | 119 | 42.78 |
| Igor Pronin | 6 | 1 | 0.72 |
| David Pitskhelauri | 7 | 0 | 0.34 |
| Alexander V. Bernstein | 8 | 9 | 7.08 |