Name
Title | ||
|---|---|---|
Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework. |
Abstract | ||
|---|---|---|
Common fMRI data processing techniques usually minimize a temporal cost function or fit a temporal model to extract an activity map. Here, we focus on extracting a highly, spatially reproducible statistical parametric map (SPM) from fMRI data using a cost function that does not depend on a model of the subjects' temporal response. Based on a generalized version of canonical correlation analysis (gCCA), we propose a method to extract a highly reproducible map by maximizing the sum of pair-wise correlations between some maps. In a group analysis, each map is calculated from a linear combination of fMRI scans of a subset of subjects under study. The proposed method is applied to BOLD fMRI datasets without any spatial smoothing from 10 subjects performing a simple reaction time (RT) task. Using the NPAIRS split-half resampling framework with a reproducibility measure based on SPM correlations, we compare the proposed approach with canonical variate analysis (CVA) and a simple general linear model (GLM). gCCA provides statistical parametric maps with higher reproducibility than CVA and GLM with correlation reproducibilities across independent split-half SPMs of 0.78, 0.46, and 0.41, respectively. Our results show that gCCA is an efficient approach for extracting the default mode network, assessing brain connectivity, and processing event-related and resting-state datasets in which the temporal BOLD signal varies from subject to subject. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.neuroimage.2012.01.137 | NeuroImage |
Keywords | Field | DocType |
Canonical variant analysis (CVA),Functional magnetic resonance imaging (fMRI),Generalized canonical correlation analysis (gCCA),Multivariate analysis techniques,Reproducibility | Data mining,Linear combination,General linear model,Canonical correlation,Computer science,Cognitive psychology,Artificial intelligence,Resampling,Generalized canonical correlation,Default mode network,Pattern recognition,Parametric statistics,Smoothing | Journal |
Volume | Issue | ISSN |
60 | 4 | 1053-8119 |
Citations | PageRank | References |
9 | 0.50 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Babak Afshin-Pour | 1 | 13 | 1.34 |
| Gholam-ali Hossein-zadeh | 2 | 34 | 7.96 |
| Stephen C. Strother | 3 | 399 | 56.31 |
| Hamid Soltanian-Zadeh | 4 | 613 | 84.11 |