Name
Abstract | ||
|---|---|---|
Dimensionality reduction techniques are used primarily for visualization purposes. With sophisticated visualization techniques like t-distributed Stochastic Neighbor Embedding (t-SNE), we can preserve the original neighborhood information even in lower dimensions. Taking advantage of this property, we present a post-processing technique for fMRI data, which can identify changes in the brain states in the tSNE space. The predicted brain state changes detected by such method show high temporal correlation to actual experimental paradigm. Such a technique can be used to extract additional information and better understand the temporal characteristics during task and resting-state fMRI experiments. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SSIAI49293.2020.9094599 | 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) |
Keywords | DocType | ISSN |
t-SNE,brain states,fMRI,visualization | Conference | 1550-5782 |
ISBN | Citations | PageRank |
978-1-7281-5746-7 | 1 | 0.41 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harshit S. Parmar | 1 | 1 | 0.41 |
| Sunanda Mitra | 2 | 254 | 45.17 |
| Brian Nutter | 3 | 101 | 20.24 |
| L. Rodney Long | 4 | 534 | 56.98 |
| Sameer K. Antani | 5 | 1 | 0.41 |