Multi-modal Brain Connectivity Study Using Deep Collaborative Learning
Functional connectivities in the brain explain how different brain regions interact with each other when conducting a specific activity. Canonical correlation analysis (CCA) based models, have been used to detect correlations and to analyze brain connectivities which further help explore how the brain works. However, the data representation of CCA lacks label related information and may be limited when applied to functional connectivity study. Collaborative regression was proposed to address the limitation of CCA by combining correlation analysis and regression. However, both prediction and correlation are sacrificed as linear collaborative regression use the same set of projections on both correlation and regression. We propose a novel method, deep collaborative learning (DCL), to address the limitations of CCA and collaborative regression. DCL improves collaborative regression by combining correlation analysis and label information using deep networks, which may lead to better performance both for classification/prediction and for correlation detection. Results demonstrated the out-performance of DCL over other conventional models in terms of classification accuracy. Experiments showed the difference of brain connectivities between different age groups may be more significant than that between different cognition groups.
KeywordsCanonical correlation Deep network fMRI Functional connectivity
The authors would like to thank the NIH (R01 GM109068, R01 MH104680, R01 MH107354, P20 GM103472, R01 REB020407, R01 EB006841) and NSF (#1539067) for the partial support.
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