Jonathan D. Power

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.

Prediction of Individual Brain Maturity Using fMRI

Connectivity Map of the Brain The growing appreciation that clinically abnormal behaviors in children and adolescents may be influenced or perhaps even initiated by developmental miscues has stoked an interest in mapping normal human brain maturation. Several groups have documented changes in gray and white matter using structural and functional magnetic resonance imaging (fMRI) in cross-sectional and longitudinal studies. Dosenbach et al. (p. 1358) developed an index of resting-state functional connectivity (that is, how tightly neuronal activities in distinct brain regions are correlated while the subject is at rest or even asleep) from analyses of three independent data sets (each based on fMRI scans of 150 to 200 individuals from ages 6 to 35 years old). Long-range connections increased with age and short-range connections decreased, indicating that networks become sparser and sharper with brain maturation. Multivariate pattern analysis of 5-minute brain scans provides a measure of brain maturity. Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks.

Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.

Methods to detect, characterize, and remove motion artifact in resting state fMRI

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.

Multi-task connectivity reveals flexible hubs for adaptive task control

Extensive evidence suggests that the human ability to adaptively implement a wide variety of tasks is preferentially a result of the operation of a fronto-parietal brain network (FPN). We hypothesized that this networks adaptability is made possible by flexible hubs: brain regions that rapidly update their pattern of global functional connectivity according to task demands. Using recent advances in characterizing brain network organization and dynamics, we identified mechanisms consistent with the flexible hub theory. We found that the FPNs brain-wide functional connectivity pattern shifted more than those of other networks across a variety of task states and that these connectivity patterns could be used to identify the current task. Furthermore, these patterns were consistent across practiced and novel tasks, suggesting that reuse of flexible hub connectivity patterns facilitates adaptive (novel) task performance. Together, these findings support a central role for fronto-parietal flexible hubs in cognitive control and adaptive implementation of task demands.

The development of Human Functional Brain Networks

Recent advances in MRI technology have enabled precise measurements of correlated activity throughout the brain, leading to the first comprehensive descriptions of functional brain networks in humans. This article reviews the growing literature on the development of functional networks, from infancy through adolescence, as measured by resting-state functional connectivity MRI. We note several limitations of traditional approaches to describing brain networks and describe a powerful framework for analyzing networks, called graph theory. We argue that characterization of the development of brain systems (e.g., the default mode network) should be comprehensive, considering not only relationships within a given system, but also how these relationships are situated within wider network contexts. We note that, despite substantial reorganization of functional connectivity, several large-scale network properties appear to be preserved across development, suggesting that functional brain networks, even in children, are organized in manners similar to other complex systems.

Resting-state fMRI in the Human Connectome Project

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Recent progress and outstanding issues in motion correction in resting state fMRI

The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI. In 2011, three groups reported that small head movements produced spurious but structured noise in brain scans, causing distance-dependent changes in signal correlations. This finding has prompted both methods development and the re-examination of prior findings with more stringent motion correction. Since 2011, over a dozen papers have been published specifically on motion artifact in resting state fMRI. We will attempt to distill these papers to their most essential content. We will point out some aspects of motion artifact that are easily or often overlooked. Throughout the review, we will highlight gaps in current knowledge and avenues for future research.

Evidence for Hubs in Human Functional Brain Networks

Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting-state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: (1) finding network nodes that participate in multiple subnetworks of the brain, and (2) finding spatial locations in which several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity.

Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp

We recently described a motion-related artifact in resting state functional connectivity MRI (rs-fcMRI) data that arises from subject head motion (Power et al. (2012), see also Satterthwaite et al. (2012); Van Dijk et al. (2012)). Head motion produces well-known disruptions in BOLD signal (Friston et al., 1996), and these artifactual modulations of BOLD signal, which are similar at nearby voxels, create spurious patterns in correlations in rs-fcMRI. Specifically, head motion augments short-distance correlations and weakens long-distance correlations. Thus, for instance, a higher-motion dataset would typically display weakened correlations between (distantly spaced) default mode regions but enhanced correlations between (closely spaced) visual regions in comparison to a low-motion data-set. We described this artifact in cohorts of children, adolescents, and adults, and its severity (magnitude) was related to the prevalence of motion within a cohort. Motion-related functional connectivity artifact is thus a substantial confound in the examination of single rs-fcMRI datasets and in comparisons of multiple rs-fcMRI datasets.