title: Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data.
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.
authors.affiliation: Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Park Place, Cardiff CF10 3AT, UK; Neuroscience and Mental Health Research Institute (NMHRI), School of Medicine, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK. Electronic address: drakesmithm@cardiff.ac.uk.
authors.forename: M
authors.initials: M
authors.lastname: Drakesmith
authors.affiliation: School of Psychology, Faculty of Health Sciences, Australian Catholic University, 115 Victoria Parade, Melbourne, VIC 3065, Australia.
authors.forename: K
authors.initials: K
authors.lastname: Caeyenberghs
authors.affiliation: Institute of Psychiatry, King's College London, 16 De Crespigny Park, London SE5 8AF, UK.
authors.forename: A
authors.initials: A
authors.lastname: Dutt
authors.affiliation: Division of Psychiatry, Faculty of Brain Sciences, University College London, Charles Bell House, 67-73 Riding House Street, London W1W 7EJ, UK.
authors.forename: G
authors.initials: G
authors.lastname: Lewis
authors.affiliation: Institute of Psychiatry, King's College London, 16 De Crespigny Park, London SE5 8AF, UK.
authors.forename: A S
authors.initials: AS
authors.lastname: David
authors.affiliation: Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Park Place, Cardiff CF10 3AT, UK; Neuroscience and Mental Health Research Institute (NMHRI), School of Medicine, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK.
authors.forename: D K
authors.initials: DK
authors.lastname: Jones
mesh_terms: D000328:Adult; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology; D009434:Neural Pathways / Q000033:anatomy & histology; D059906:Neuroimaging / Q000379:methods*; D015203:Reproducibility of Results; D013223:Statistics as Topic; D055815:Young Adult