Name
Title | ||
|---|---|---|
Automatic identification of functional clusters in FMRI data using spatial dependence. |
Abstract | ||
|---|---|---|
In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependence--mutual information--among spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TBME.2011.2167149 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
statistical dependence,functional segmentation,functional magnetic resonance imaging (fmri),mica framework,arteries,automatic identification,spatial dependence,blood vessels,image segmentation,data analysis,independent component analysis (ica),independent component analysis,fmri datasets,biomedical mri,cerebrospinal fluid,statistical hypothesis testing method,brain,multidimensional ica,brain networks,multidimensional independent component analysis (mica),large draining veins,automatic component clustering,functional clusters,ica decomposition,medical image processing,functional magnetic resonance imaging data,integrated circuit,mutual information,algorithms,magnetic resonance image,magnetic resonance imaging,cluster analysis,statistical hypothesis testing | Computer vision,Spatial dependence,Functional magnetic resonance imaging,Pattern recognition,Segmentation,Computer science,Image segmentation,Correlation,Independent component analysis,Artificial intelligence,Cluster analysis,Statistical hypothesis testing | Journal |
Volume | Issue | ISSN |
58 | 12 | 1558-2531 |
Citations | PageRank | References |
17 | 0.78 | 19 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sai Ma | 1 | 99 | 5.83 |
| Nicolle M Correa | 2 | 162 | 10.67 |
| Xi-Lin Li | 3 | 547 | 34.85 |
| Tom Eichele | 4 | 263 | 15.71 |
| Vince D Calhoun | 5 | 2769 | 268.91 |
| Tülay Adali | 6 | 1690 | 126.40 |