Network modelling methods for FMRI
- PMID: 20817103
- DOI: 10.1016/j.neuroimage.2010.08.063
Network modelling methods for FMRI
Abstract
There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution.
Copyright © 2010 Elsevier Inc. All rights reserved.
Similar articles
-
Can Patel's τ accurately estimate directionality of connections in brain networks from fMRI?Magn Reson Med. 2017 Nov;78(5):2003-2010. doi: 10.1002/mrm.26583. Epub 2017 Jan 16. Magn Reson Med. 2017. PMID: 28090665
-
Bayesian networks for fMRI: a primer.Neuroimage. 2014 Feb 1;86:573-82. doi: 10.1016/j.neuroimage.2013.10.020. Epub 2013 Oct 18. Neuroimage. 2014. PMID: 24140939 Review.
-
Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis.Neuroimage. 2010 Oct 1;52(4):1444-55. doi: 10.1016/j.neuroimage.2010.05.022. Epub 2010 Jun 1. Neuroimage. 2010. PMID: 20472076
-
Contributive sources analysis: a measure of neural networks' contribution to brain activations.Neuroimage. 2013 Aug 1;76:304-12. doi: 10.1016/j.neuroimage.2013.03.014. Epub 2013 Mar 21. Neuroimage. 2013. PMID: 23523811
-
Mapping cognitive and emotional networks in neurosurgical patients using resting-state functional magnetic resonance imaging.Neurosurg Focus. 2020 Feb 1;48(2):E9. doi: 10.3171/2019.11.FOCUS19773. Neurosurg Focus. 2020. PMID: 32006946 Free PMC article. Review.
Cited by
-
State-dependent connectivity in auditory-reward networks predicts peak pleasure experiences to music.PLoS Biol. 2024 Aug 12;22(8):e3002732. doi: 10.1371/journal.pbio.3002732. eCollection 2024 Aug. PLoS Biol. 2024. PMID: 39133721 Free PMC article.
-
A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development.bioRxiv [Preprint]. 2024 Jul 31:2024.07.31.606025. doi: 10.1101/2024.07.31.606025. bioRxiv. 2024. PMID: 39131337 Free PMC article. Preprint.
-
Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.PeerJ. 2024 Jul 30;12:e17774. doi: 10.7717/peerj.17774. eCollection 2024. PeerJ. 2024. PMID: 39099649 Free PMC article.
-
Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population.Mol Psychiatry. 2024 Jul 31. doi: 10.1038/s41380-024-02683-6. Online ahead of print. Mol Psychiatry. 2024. PMID: 39085394
-
LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.Ann Appl Stat. 2023 Jun;17(2):1307-1332. doi: 10.1214/22-aoas1670. Epub 2023 May 1. Ann Appl Stat. 2023. PMID: 39040949 Free PMC article.
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Miscellaneous