Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture ...present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an “intrinsic,” standard architecture of functional brain organization. Furthermore, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity—areas of neuroscientific inquiry typically considered separately.
•There is an “intrinsic” functional network architecture present across many tasks•The intrinsic architecture is highly similar to the resting-state architecture•Tasks modify the intrinsic architecture to produce “evoked” network architectures•Task-evoked changes common across tasks form a task-general network architecture
Cole et al. identify a whole-brain functional network architecture in humans that is intrinsic, as it is present across rest and dozens of tasks. Only small network modifications were observed, but many were consistent, composing a task-general evoked network architecture.
This short “how to” article describes a plot I find useful for assessing fMRI data quality. I discuss the reasoning behind the plot and how it is constructed. I create the plot in scans from several ...publicly available datasets to illustrate different kinds of fMRI signal variance, ranging from thermal noise to motion artifacts to respiratory-related signals. I also show how the plot can be used to understand the variance removed during denoising. Code to make the plot is provided with the article, and supplemental movies show plots for hundreds of additional subjects.
•Presents a plot that summarizes some important features of single fMRI scans.•Illustrates multiple kinds of noise and unwanted signals using the plot.•Illustrates signal denoising effects with the plot.•Includes software tools to make the plot.•Illustrates the plot in hundreds of subjects from several different sites.
Whole-brain fMRI signals are a subject of intense interest: variance in the global fMRI signal (the spatial mean of all signals in the brain) indexes subject arousal, and psychiatric conditions such ...as schizophrenia and autism have been characterized by differences in the global fMRI signal. Further, vigorous debates exist on whether global signals ought to be removed from fMRI data. However, surprisingly little research has focused on the empirical properties of whole-brain fMRI signals. Here we map the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans. Variance in the global fMRI signal is strongly linked to head motion, to hardware artifacts, and to respiratory patterns and their attendant physiologic changes. Many techniques used to prepare fMRI data for analysis fail to remove these uninteresting kinds of global signal fluctuations. Thus, many studies include, at the time of analysis, prominent global effects of yawns, breathing changes, and head motion, among other signals. Such artifacts will mimic dynamic neural activity and will spuriously alter signal covariance throughout the brain. Methods capable of isolating and removing global artifactual variance while preserving putative “neural” variance are needed; this paper adopts no position on the topic of global signal regression.
•Demonstrates brain-wide (global) fMRI signals, individually, in 1000+ scans from 8 sites.•Global signals often reflect artifact caused by head motion, respiration, or hardware problems.•Most existing fMRI denoising methods do not adequately remove global artifacts.•Global artifacts mimic dynamic neural activity and modulate signal correlations.•Studies reporting global fMRI signal effects must carefully account for artifact.
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.
•Reviews post-2011 research on motion artifact in resting state fMRI•Explains analyses to detect and quantify motion artifact•Presents evidence for removal of artifact by various processing strategies
Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state ...functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a “processing” system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.
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► Areal and modified voxelwise graph definitions are proposed ► Subgraphs reflect known and unknown brain systems ► Default mode, sensory, and motor systems share network properties ► Functional systems are patterned across the cortex with spatial regularities
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.
•Motion-related signal changes are varied and can persist >10s after motion ceases.•Such signal changes are often shared across almost all brain voxels.•Within-subject correction strategies can eliminate motion-related group differences.•Examines the linearity of motion's influence on resting state correlations
Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its ...impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.
•We evaluate 14 participant-level de-noising pipelines for functional connectivity.•Pipeline performance is markedly heterogeneous.•GSR minimizes the impact of motion but introduces distance dependence.•Censoring reduces motion and improves network identifiability.
In the article, the signals are hand-classified as “neural signal” or “noise”. Because there are no neural records to anchor these decisions, it is imperative to properly identify artifactual ...components, especially respiratory signals. Peak-finding is the basis of measures such as the “RVT” respiratory measure used in the target article to identify physiological noise. ...about half of the subjects have fully useable physiology data (though more would be partially useable). ...likely relatedly, the authors identify as “neural signal” several tICA components in a “sensorimotor” distribution whose amplitudes are related to subject motion (“DVARS dips” in the paper) or to sleep (which displays altered respiration).
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.
► Large changes in rs-fcMRI timecourses coincide with motion despite motion regression. ► Motion increases short-distance correlations and decreases long-distance correlations. ► Motion artifacts do not arise from, and are not fully countered by, motion regressions. ► Framewise indices of data quality and methods to remove motion artifact are proposed.
Highlights ► Surveys evidence in support of a multiple-system framework for task control. ► Summarizes the relevance of resting state MRI for studying task control. ► Highlights evidence supporting ...multiple accounts of attention and task control. ► Discusses challenges to hierarchical and conflict monitoring accounts of control.
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