Estimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control benchmarks in four independent datasets associated with varying levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in independent samples of people with schizophrenia and obsessive-compulsive disorder. Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines may only be viable in low-motion data; (3) volume censoring performs well at minimising motion-related artefact but a major benefit of this approach derives from the exclusion of high-motion individuals; (4) while not as effective as volume censoring, ICA-AROMA performed well across our benchmarks for relatively low cost in terms of data loss; (5) the addition of global signal regression improved the performance of nearly all pipelines on most benchmarks, but exacerbated the distance-dependence of correlations between motion and functional connectivity; and (6) group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact.

The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.

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ObjectiveImpulsivity and compulsivity have been implicated as important transdiagnostic dimensional phenotypes with potential relevance to addiction. We aimed to develop a model that conceptualizes these constructs as overlapping dimensional phenotypes and test whether different components of this model explain the co-occurrence of addictive and related behaviors.MethodsA large sample of adults (N = 487) was recruited through Amazon’s Mechanical Turk and completed self-report questionnaires measuring impulsivity, intolerance of uncertainty, obsessive beliefs, and the severity of 6 addictive and related behaviors. Hierarchical clustering was used to organize addictive behaviors into homogenous groups reflecting their co-occurrence. Structural equation modeling was used to evaluate fit of the hypothesized bifactor model of impulsivity and compulsivity and determine the proportion of variance explained in the co-occurrence of addictive and related behaviors by each component of the model.ResultsAddictive and related behaviors clustered into 2 distinct groups: Impulse-Control Problems, consisting of harmful alcohol use, pathological gambling, and compulsive buying, and Obsessive-Compulsive-Related Problems, consisting of obsessive-compulsive symptoms, binge eating, and internet addiction. The hypothesized bifactor model of impulsivity and compulsivity provided the best empirical fit, with 3 uncorrelated factors corresponding to a general Disinhibition dimension, and specific Impulsivity and Compulsivity dimensions. These dimensional phenotypes uniquely and additively explained 39.9% and 68.7% of the total variance in Impulse-Control Problems and Obsessive-Compulsive-Related Problems.ConclusionA model of impulsivity and compulsivity that represents these constructs as overlapping dimensional phenotypes has important implications for understanding addictive and related behaviors in terms of shared etiology, comorbidity, and potential transdiagnostic treatments.