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Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data

Drakesmith, M., Caeyenberghs, K., Dutt, A., Lewis, G., David, A.S. and Jones, D.K. 2015, Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data, NeuroImage, vol. 118, pp. 313-333, doi: 10.1016/j.neuroimage.2015.05.011.

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Title Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data
Author(s) Drakesmith, M.
Caeyenberghs, K.ORCID iD for Caeyenberghs, K. orcid.org/0000-0001-7009-6843
Dutt, A.
Lewis, G.
David, A.S.
Jones, D.K.
Journal name NeuroImage
Volume number 118
Start page 313
End page 333
Total pages 21
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-09
ISSN 1053-8119
1095-9572
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Neurosciences
Neuroimaging
Radiology, Nuclear Medicine & Medical Imaging
Neurosciences & Neurology
PROBABILISTIC DIFFUSION TRACTOGRAPHY
TEST-RETEST RELIABILITY
COMPLEX BRAIN NETWORKS
RESTING-STATE FMRI
FUNCTIONAL CONNECTIVITY
SMALL-WORLD
WHITE-MATTER
ALZHEIMERS-DISEASE
CORPUS-CALLOSUM
SPHERICAL DECONVOLUTION
Summary © 2015. Graph theory (GT) is a powerful framework for quantifying topological features of neuroimaging-derived functional and structural networks. However, false positive (FP) connections arise frequently and influence the inferred topology of networks. Thresholding is often used to overcome this problem, but an appropriate threshold often relies on a priori assumptions, which will alter inferred network topologies.Four common network metrics (global efficiency, mean clustering coefficient, mean betweenness and smallworldness) were tested using a model tractography dataset. It was found that all four network metrics were significantly affected even by just one FP. Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network metrics.In a larger number (n= 248) of tractography datasets, statistics were computed across random group permutations for a range of thresholds, revealing that statistics for network metrics varied significantly more than for non-network metrics (i.e., number of streamlines and number of edges). Varying degrees of network atrophy were introduced artificially to half the datasets, to test sensitivity to genuine group differences. For some network metrics, this atrophy was detected as significant (p<. 0.05, determined using permutation testing) only across a limited range of thresholds.We propose a multi-threshold permutation correction (MTPC) method, based on the cluster-enhanced permutation correction approach, to identify sustained significant effects across clusters of thresholds. This approach minimises requirements to determine a single threshold a priori. We demonstrate improved sensitivity of MTPC-corrected metrics to genuine group effects compared to an existing approach and demonstrate the use of MTPC on a previously published network analysis of tractography data derived from a clinical population.In conclusion, we show that there are large biases and instability induced by thresholding, making statistical comparisons of network metrics difficult. However, by testing for effects across multiple thresholds using MTPC, true group differences can be robustly identified.
Language eng
DOI 10.1016/j.neuroimage.2015.05.011
Indigenous content off
Field of Research 11 Medical and Health Sciences
17 Psychology and Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30133961

Document type: Journal Article
Collections: Faculty of Health
School of Psychology
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.