A family of locally constrained CCA models for detecting activation patterns in fMRI
| Autor: | Zhengshi Yang, Richard H. Byrd, Dietmar Cordes, Tim Curran, Rajesh Nandy, Xiaowei Zhuang |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
Cognitive Neuroscience Models Neurological Gaussian blur Hippocampus computer.software_genre Sensitivity and Specificity Brain mapping Article 030218 nuclear medicine & medical imaging Temporal lobe 03 medical and health sciences symbols.namesake 0302 clinical medicine Voxel Image Processing Computer-Assisted medicine Null distribution Humans Sensitivity (control systems) Brain Mapping medicine.diagnostic_test business.industry Magnetic resonance imaging Pattern recognition Magnetic Resonance Imaging Data set ROC Curve Neurology Area Under Curve symbols Artificial intelligence Data mining business Functional magnetic resonance imaging computer Algorithms 030217 neurology & neurosurgery |
| Zdroj: | NeuroImage. 149:63-84 |
| ISSN: | 1053-8119 |
| DOI: | 10.1016/j.neuroimage.2016.12.081 |
| Popis: | Canonical correlation analysis (CCA) has been used in Functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection. The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques. To evaluate the performance of this cCCA model, simulated data are generated with a Signal-To-Noise Ratio of 0.25, which is realistic to the noise level contained in episodic memory fMRI data. Receiver operating characteristic (ROC) methods are used to compare the performance of different models. The cCCA model with optimum parameters (called optimum-cCCA) obtains the largest area under the ROC curve. Furthermore, a novel validation method is proposed to validate the selected optimum-cCCA parameters based on ROC from simulated data and real fMRI data. Results for optimum-cCCA are then compared with conventional fMRI analysis methods using data from an episodic memory task. Wavelet-resampled resting-state data are used to obtain the null distribution of activation. For simulated data, accuracy in detecting activation increases for the optimum-cCCA model by about 43% as compared to the single voxel analysis with comparable Gaussian smoothing. Results from the real fMRI data set indicate a significant increase in activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method. |
| Databáze: | OpenAIRE |
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