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. 2010;27(4):39-50.
doi: 10.1109/MSP.2010.936725.

Canonical Correlation Analysis for Data Fusion and Group Inferences: Examining applications of medical imaging data

Nicolle M Correa, Tülay Adali, Yi-Ou Li, Vince D Calhoun

Canonical Correlation Analysis for Data Fusion and Group Inferences: Examining applications of medical imaging data

Nicolle M Correa et al. IEEE Signal Process Mag. 2010.
No abstract available

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Figures

FIG1
FIG1
Generative models for fusion and source separation are shown in (a) and (b). To avoid overfitting, typically dimension reduced data matrices are used instead of the original high-dimensioned data X, X1, and X2 For data fusion, the spatial or temporal dimension is reduced. For group analysis, the temporal dimension is reduced.
FIG2
FIG2
Implementation steps for CCA-based fusion.
FIG3
FIG3
The fMRI component, sMRI component, and scatter plots of profiles for pair of components identified by CCA as maximally correlated. The profiles for both fMRI and sMRI are significantly different (α ≤ 0.05) between patients and controls. Patients with schizophrenia show more functional activity in motor areas and less activity in temporal areas associated with less gray matter as compared to healthy controls. The activation maps are scaled to Z values and thresholded at Z = 3.5.
FIG4
FIG4
Joint components estimated by jICA corresponding to common profile demonstrating significant difference between patients and controls. The activation maps are scaled to Z values and thresholded at Z = 3.5.
FIG5
FIG5
Data model for fusion of brain structure and function.
FIG6
FIG6
Set of associated components estimated by M-CCA that showed significantly different loading for patients versus controls.
FIG7
FIG7
Estimated mean activation maps (top left), source correlation between subjects (top), and time course (bottom) of the default mode by (a) M-CCA and (b) group ICA. The right (green circle) and left (red block) visuomotor task paradigm is overlaid onto the estimated time courses for reference.
See this image and copyright information in PMC

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