Hyun-Chul Kim1, Hojin Jang1, Jong-Hwan Lee2. 1. Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea. 2. Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea. Electronic address: jonghwan_lee@korea.ac.kr.
Abstract
BACKGROUND: Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial patterns (SPs; functional networks) in resting-state fMRI (rfMRI) data. However, there has been little research on (1) the reproducibility and test-retest reliability of SPs derived from RBMs and on (2) hierarchical SPs derived from DBNs. METHODS: We applied a weight sparsity-controlled RBM and DBN to whole-brain rfMRI data from the Human Connectome Project. We evaluated the within-session reproducibility and between-session test-retest reliability of the SPs derived from the RBM approach and compared them both with those identified using independent component analysis (ICA) and with three voxel-wise statistical measures-the Hurst exponent, entropy, and kurtosis-of the rfMRI data. We also assessed the potential hierarchy of the SPs from the DBN. RESULTS: An increase in the sparsity level of the RBM weights enhanced the reproducibility of the SPs. The SPs deriving from a stringent weight sparsity level were predominantly found in the cortical gray matter and substantially overlapped with the SPs obtained from the Hurst exponent. A hierarchical representation was shown by constructed using the default-mode network obtained from the DBN. COMPARISON WITH EXISTING METHODS: The test-retest reliability of the SPs from the RBM was superior to that of the SPs from the voxel-wise statistics. CONCLUSIONS: The SPs from the RBM were reproducible within sessions and reliable across sessions. The hierarchically organized SPs from the DBN could possibly be applied to research based on rfMRI data.
BACKGROUND: Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial patterns (SPs; functional networks) in resting-state fMRI (rfMRI) data. However, there has been little research on (1) the reproducibility and test-retest reliability of SPs derived from RBMs and on (2) hierarchical SPs derived from DBNs. METHODS: We applied a weight sparsity-controlled RBM and DBN to whole-brain rfMRI data from the Human Connectome Project. We evaluated the within-session reproducibility and between-session test-retest reliability of the SPs derived from the RBM approach and compared them both with those identified using independent component analysis (ICA) and with three voxel-wise statistical measures-the Hurst exponent, entropy, and kurtosis-of the rfMRI data. We also assessed the potential hierarchy of the SPs from the DBN. RESULTS: An increase in the sparsity level of the RBM weights enhanced the reproducibility of the SPs. The SPs deriving from a stringent weight sparsity level were predominantly found in the cortical gray matter and substantially overlapped with the SPs obtained from the Hurst exponent. A hierarchical representation was shown by constructed using the default-mode network obtained from the DBN. COMPARISON WITH EXISTING METHODS: The test-retest reliability of the SPs from the RBM was superior to that of the SPs from the voxel-wise statistics. CONCLUSIONS: The SPs from the RBM were reproducible within sessions and reliable across sessions. The hierarchically organized SPs from the DBN could possibly be applied to research based on rfMRI data.