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

Drakesmith, Mark ORCID: https://orcid.org/0000-0001-8574-9560, Caeyenberghs, K., Dutt, A., Lewis, G., David, A. S. and Jones, Derek ORCID: https://orcid.org/0000-0003-4409-8049 2015. Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. NeuroImage 118 , pp. 313-333. 10.1016/j.neuroimage.2015.05.011

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Cardiff University Brain Research Imaging Centre (CUBRIC) Medicine Neuroscience and Mental Health Research Institute (NMHRI) Psychology
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Additional Information:	Available online 14 May 2015
Publisher:	Elsevier
ISSN:	1053-8119
Funders:	Wellcome
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	5 May 2015
Last Modified:	04 May 2023 19:48
URI:	https://orca.cardiff.ac.uk/id/eprint/74087

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