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.
| Original language | English |
|---|---|
| Article number | 108451 |
| Journal | Journal of Neuroscience Methods |
| Volume | 330 |
| DOIs | |
| Publication status | Published - 2020 Jan 15 |
Fingerprint
Keywords
- Deep belief network
- Entropy
- Hurst exponent
- Independent component analysis
- Kurtosis
- Resting-state fMRI
- Restricted Boltzmann machine
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Test–retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network. / Kim, Hyun Chul; Jang, Hojin; Lee, Jong Hwan.
In: Journal of Neuroscience Methods, Vol. 330, 108451, 15.01.2020.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Test–retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network
AU - Kim, Hyun Chul
AU - Jang, Hojin
AU - Lee, Jong Hwan
PY - 2020/1/15
Y1 - 2020/1/15
N2 - 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.
AB - 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.
KW - Deep belief network
KW - Entropy
KW - Hurst exponent
KW - Independent component analysis
KW - Kurtosis
KW - Resting-state fMRI
KW - Restricted Boltzmann machine
UR - http://www.scopus.com/inward/record.url?scp=85074409670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074409670&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2019.108451
DO - 10.1016/j.jneumeth.2019.108451
M3 - Article
C2 - 31626847
AN - SCOPUS:85074409670
VL - 330
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
M1 - 108451
ER -