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 recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Connectivity is not distributed evenly throughout the brain. Instead, it is concentrated on a small number of highly connected neural elements that act as network hubs. Across different species and measurement scales, these hubs show dense interconnectivity, forming a core or "rich club" that integrates information across anatomically distributed neural systems. Here, we show that projections between connectivity hubs of the mouse brain are both central (i.e., they play an important role in neural communication) and costly (i.e., they extend over long anatomical distances) aspects of network organization that carry a distinctive genetic signature. Analyzing the neuronal connectivity of 213 brain regions and the transcriptional coupling, across 17,642 genes, between each pair of regions, we find that coupling is highest for pairs of connected hubs, intermediate for links between hubs and nonhubs, and lowest for connected pairs of nonhubs. The high transcriptional coupling associated with hub connectivity is driven by genes regulating the oxidative synthesis and metabolism of ATP-the primary energetic currency of neuronal communication. This genetic signature contrasts that identified for neuronal connectivity in general, which is driven by genes regulating neuronal, synaptic, and axonal structure and function. Our findings establish a direct link between molecular function and the large-scale topology of neuronal connectivity, showing that brain hubs display a tight coordination of gene expression, often over long anatomical distances, that is intimately related to the metabolic requirements of these highly active network elements.connectome | complex networks | hub | rich club | metabolism C ertain neural elements possess an unusually high degree of connectivity, designating them as putative network hubs (1). Analyses of microscale, mesoscale, and macroscale connectomes of multiple species, constructed using a variety of methods, indicate that these hubs are strongly interconnected with each other, forming a so-called "rich club" of connectivity that mediates a large fraction of communication traffic in the brain and supports the efficient integration of otherwise segregated neural systems (2-8).Hub connectivity is functionally advantageous, but it is also costly. Hub regions make more connections with other areas, and these connections often extend over long anatomical distances, thus requiring greater physical space, cellular material, and metabolic resources (3, 9). Accordingly, human neuroimaging studies have indicated that topologically central hub regions have a higher energetic demand than other brain areas (9-12), which may render them particularly vulnerable to the effects of damage or disease (10, 13). This hypothesis is supported by evidence that pathology in a broad range of disorders preferentially accumulates within highly connected brain regions (14).Hub connectivity is thus a topologically central and costly aspect of brain network organization that is conserved across species and spatial scales....

The recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.