Abstract
Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 25-41 |
| Number of pages | 17 |
| Journal | NeuroImage |
| Volume | 194 |
| DOIs | |
| State | Published - 1 Jul 2019 |
Fingerprint
Keywords
- Constrained multivariate method
- Functional magnetic resonance imaging (fMRI)
- Group-level analysis
- Maximum log likelihood
- Numerical optimization
Cite this
}
Multivariate group-level analysis for task fMRI data with canonical correlation analysis. / Zhuang, Xiaowei; Yang, Zhengshi; Sreenivasan, Karthik R.; Mishra, Virendra R.; Curran, Tim; Nandy, Rajesh; Cordes, Dietmar.
In: NeuroImage, Vol. 194, 01.07.2019, p. 25-41.Research output: Contribution to journal › Article
TY - JOUR
T1 - Multivariate group-level analysis for task fMRI data with canonical correlation analysis
AU - Zhuang, Xiaowei
AU - Yang, Zhengshi
AU - Sreenivasan, Karthik R.
AU - Mishra, Virendra R.
AU - Curran, Tim
AU - Nandy, Rajesh
AU - Cordes, Dietmar
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.
AB - Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.
KW - Constrained multivariate method
KW - Functional magnetic resonance imaging (fMRI)
KW - Group-level analysis
KW - Maximum log likelihood
KW - Numerical optimization
UR - http://www.scopus.com/inward/record.url?scp=85063326925&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2019.03.030
DO - 10.1016/j.neuroimage.2019.03.030
M3 - Article
C2 - 30894332
AN - SCOPUS:85063326925
VL - 194
SP - 25
EP - 41
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -