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
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.
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
Abstract
A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers ...the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).
The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional ...connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.
Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain ...organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected—considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual’s systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals.
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•Single-subject areal parcellation is reproducible, valid, and convergent with task•Highly reliable correlation estimates require considerable data•Within-subject correlation is most variable in visual and somatosensory cortex•Individuals exhibit topological features distinct from group system organization
Resting state functional MRI allows non-invasive analysis of functional brain organization at multiple spatial scales. Laumann et al. report areal and system organization in a highly sampled human and demonstrate that an individual exhibits topological features distinct from group-level system organization.
Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions
...(for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping
(the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations
. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.
Resting-state functional connectivity is a powerful tool for studying human functional brain networks. Temporal fluctuations in functional connectivity, i.e., dynamic functional connectivity (dFC), ...are thought to reflect dynamic changes in brain organization and non-stationary switching of discrete brain states. However, recent studies have suggested that dFC might be attributed to sampling variability of static FC. Despite this controversy, a detailed exposition of stationarity and statistical testing of dFC is lacking in the literature. This article seeks an in-depth exploration of these statistical issues at a level appealing to both neuroscientists and statisticians.
We first review the statistical notion of stationarity, emphasizing its reliance on ensemble statistics. In contrast, all FC measures depend on sample statistics. An important consequence is that the space of stationary signals is much broader than expected, e.g., encompassing hidden markov models (HMM) widely used to extract discrete brain states. In other words, stationarity does not imply the absence of brain states. We then expound the assumptions underlying the statistical testing of dFC. It turns out that the two popular frameworks - phase randomization (PR) and autoregressive randomization (ARR) - generate stationary, linear, Gaussian null data. Therefore, statistical rejection can be due to non-stationarity, nonlinearity and/or non-Gaussianity. For example, the null hypothesis can be rejected for the stationary HMM due to nonlinearity and non-Gaussianity. Finally, we show that a common form of ARR (bivariate ARR) is susceptible to false positives compared with PR and an adapted version of ARR (multivariate ARR).
Application of PR and multivariate ARR to Human Connectome Project data suggests that the stationary, linear, Gaussian null hypothesis cannot be rejected for most participants. However, failure to reject the null hypothesis does not imply that static FC can fully explain dFC. We find that first order AR models explain temporal FC fluctuations significantly better than static FC models. Since first order AR models encode both static FC and one-lag FC, this suggests the presence of dynamical information beyond static FC. Furthermore, even in subjects where the null hypothesis was rejected, AR models explain temporal FC fluctuations significantly better than a popular HMM, suggesting the lack of discrete states (as measured by resting-state fMRI). Overall, our results suggest that AR models are not only useful as a means for generating null data, but may be a powerful tool for exploring the dynamical properties of resting-state fMRI. Finally, we discuss how apparent contradictions in the growing dFC literature might be reconciled.
•Space of stationary models bigger than expected; includes hidden Markov model (HMM).•Phase & autoregressive randomizations test for stationarity, linearity, Gaussianity.•Resting-state fMRI is mostly stationary, linear, and Gaussian.•1st order autoregressive (AR) model encodes static & one-lag FC.•1st order AR model explains sliding window correlations very well, better than HMM.
Resting State Functional Connectivity (RSFC) reveals properties related to the brain's underlying organization and function. Features related to RSFC signals, such as the locations where the patterns ...of RSFC exhibit abrupt transitions, can be used to identify putative boundaries between cortical areas (RSFC-Boundary Mapping). The locations of RSFC-based area boundaries are consistent across independent groups of subjects. RSFC-based parcellation converges with parcellation information from other modalities in many locations, including task-evoked activity and probabilistic estimates of cellular architecture, providing evidence for the ability of RSFC to parcellate brain structures into functionally meaningful units. We not only highlight a collection of these observations, but also point out several limitations and observations that mandate careful consideration in using and interpreting RSFC for the purposes of parcellating the brain's cortical and subcortical structures.
•Resting-state functional correlations (RSFC) can be used to parcellate brain areas.•RSFC parcellation is reliable across independent groups of subjects.•RSFC parcellation converges with parcellations from other modalities.•RSFC area parcellation is consistent but distinct from brain system divisions.
Humans easily and flexibly complete a wide variety of tasks. To accomplish this feat, the brain appears to subtly adjust stable brain networks. Here, we investigate what regional factors underlie ...these modifications, asking whether networks are either altered at (1) regions activated by a given task or (2) hubs that interconnect different networks. We used fMRI “functional connectivity” (FC) to compare networks during rest and three distinct tasks requiring semantic judgments, mental rotation, and visual coherence. We found that network modifications during these tasks were independently associated with both regional activation and network hubs. Furthermore, active and hub regions were associated with distinct patterns of network modification (differing in their localization, topography of FC changes, and variability across tasks), with activated hubs exhibiting patterns consistent with task control. These findings indicate that task goals modify brain networks through two separate processes linked to local brain function and network hubs.
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•Human brain networks differ between rest and task at activated and hub regions•Regions stratified by activation and hub-status show distinct FC-related attributes•Activated hubs exhibit FC attributes consistent with enacting task control•Findings suggest dissociable factors for linking brain regions in complex tasks
Gratton et al. show that, during tasks, human brain networks are subtly modified both at task-activated regions and at topologically important hubs. Classes of regions with these two properties show distinct patterns of network changes, suggesting they index dissociable factors for modifying brain networks in a task.
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