Name
Abstract | ||
|---|---|---|
Functional Magnetic Resonance Imaging has become a central measuring modality to quantify functional activiation of the brain in both task and rest. Most analysis used to quantify functional activation requires supervised approaches as employed in statistical parametric mapping (SPM) to extract maps of task induced functional activations. This requires strong knowledge and assumptions on the BOLD response as a function of activitation while smoothing in general enhances the statistical power but at the cost of spatial resolution. We propose a fully unsupervised approach for the extraction of task activated functional units in multi-subject fMRI data that exploits that regions of task activation are consistent across subjects and can be more reliably inferred than regions that are not activated. We develop a non-parametric Gaussian mixture model that apriori assumes activations are smooth using a Gaussian Process prior while assuming the segmented functional maps are the same across subjects but having individual time-courses and noise variances. To improve inference we propose an enhanced split-merge procedure. We find that our approach well extracts the induced activity of a finger tapping fMRI paradigm with maps that well corresponds to a supervised group SPM analysis. We further find interesting regions that are not activated time locked to the paradigm. Demonstrating that we in a fully unsupervised manner are able to extract the task-induced activations forms a promising framework for the analysis of task fMRI and resting-state data in general where strong knowledge of how the task induces a BOLD response is missing. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MLSP.2015.7324384 | 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Functional connectivity,Gaussian Mixture Model,fMRI analysis | Pattern recognition,Functional magnetic resonance imaging,Inference,Segmentation,Computer science,Statistical parametric mapping,Smoothing,Artificial intelligence,Gaussian process,Statistical power,Machine learning,Mixture model | Conference |
ISSN | Citations | PageRank |
1551-2541 | 3 | 0.38 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rasmus E. Roge | 1 | 3 | 0.38 |
| Kristoffer Hougaard Madsen | 2 | 145 | 18.74 |
| Mikkel N. Schmidt | 3 | 277 | 26.13 |
| Morten Mørup | 4 | 704 | 51.29 |