• Test-retest reliability of spa...

*Result*: 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.

Title:
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.
Authors:
Kim HC; Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea., Jang H; Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea., Lee JH; Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea. Electronic address: jonghwan_lee@korea.ac.kr.
Source:
Journal of neuroscience methods [J Neurosci Methods] 2020 Jan 15; Vol. 330, pp. 108451. Date of Electronic Publication: 2019 Oct 15.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE
Imprint Name(s):
Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press.
MeSH Terms:
Neural Networks, Computer*, Brain/*physiology , Connectome/*methods , Default Mode Network/*physiology , Echo-Planar Imaging/*methods , Nerve Net/*physiology , Pattern Recognition, Automated/*methods, Brain/diagnostic imaging ; Connectome/standards ; Default Mode Network/diagnostic imaging ; Echo-Planar Imaging/standards ; Nerve Net/diagnostic imaging ; Pattern Recognition, Automated/standards ; Adult ; Humans ; Reproducibility of Results
Contributed Indexing:
Keywords: Deep belief network; Entropy; Hurst exponent; Independent component analysis; Kurtosis; Resting-state fMRI; Restricted Boltzmann machine
Entry Date(s):
Date Created: 20191019 Date Completed: 20210315 Latest Revision: 20210315
Update Code:
20260130
DOI:
10.1016/j.jneumeth.2019.108451
PMID:
31626847
Database:
MEDLINE

*Further Information*

*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.
(Copyright © 2020 Elsevier B.V. All rights reserved.)*