Systems and methods for modeling and processing functional magnetic resonance image data using full-brain vector auto-regressive model
US9454641B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 25, 2014 |
| Grant date | Sep 27, 2016 |
| Priority date | — |
| Expiry date | Feb 9, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30016
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.