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.
Most accounts of human cognitive architectures have focused on computational accounts of cognition while making little contact with the study of anatomical structures and physiological processes. A ...renewed convergence between neurobiology and cognition is well under way. A promising area arises from the overlap between systems/cognitive neuroscience on the one side and the discipline of network science on the other. Neuroscience increasingly adopts network tools and concepts to describe the operation of collections of brain regions. Beyond just providing illustrative metaphors, network science offers a theoretical framework for approaching brain structure and function as a multi-scale system composed of networks of neurons, circuits, nuclei, cortical areas, and systems of areas. This paper views large-scale networks at the level of areas and systems, mostly on the basis of data from human neuroimaging, and how this view of network structure and function has begun to illuminate our understanding of the biological basis of cognitive architectures.
Network approaches to brain function have begun to illuminate how structural and functional connectivity support cognition and behavior. The authors present an overview of how networks inform theories of cognitive architectures and discuss future issues in the field.
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
Here, we update our 1990 Annual Review of Neuroscience article, "The Attention System of the Human Brain." The framework presented in the original article has helped to integrate behavioral, systems, ...cellular, and molecular approaches to common problems in attention research. Our framework has been both elaborated and expanded in subsequent years. Research on orienting and executive functions has supported the addition of new networks of brain regions. Developmental studies have shown important changes in control systems between infancy and childhood. In some cases, evidence has supported the role of specific genetic variations, often in conjunction with experience, that account for some of the individual differences in the efficiency of attentional networks. The findings have led to increased understanding of aspects of pathology and to some new interventions.
In recent years, some substantial advances in understanding human (and nonhuman) brain organization have emerged from a relatively unusual approach: the observation of spontaneous activity, and ...correlated patterns in spontaneous activity, in the “resting” brain. Most commonly, spontaneous neural activity is measured indirectly via fMRI signal in subjects who are lying quietly in the scanner, the so-called “resting state.” This Primer introduces the fMRI-based study of spontaneous brain activity, some of the methodological issues active in the field, and some ways in which resting-state fMRI has been used to delineate aspects of area-level and supra-areal brain organization.
Human brain organization, in health and disease, is increasingly studied by measuring spontaneous brain activity with fMRI. This Primer explains how researchers study these fMRI signals, and what these signals might reveal about brain organization.
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
Control networks and hubs Gratton, Caterina; Sun, Haoxin; Petersen, Steven E.
Psychophysiology,
March 2018, Volume:
55, Issue:
3
Journal Article
Peer reviewed
Open access
Executive control functions are associated with frontal, parietal, cingulate, and insular brain regions that interact through distributed large‐scale networks. Here, we discuss how fMRI functional ...connectivity can shed light on the organization of control networks and how they interact with other parts of the brain. In the first section of our review, we present convergent evidence from fMRI functional connectivity, activation, and lesion studies that there are multiple dissociable control networks in the brain with distinct functional properties. In the second section, we discuss how graph theoretical concepts can help illuminate the mechanisms by which control networks interact with other brain regions to carry out goal‐directed functions, focusing on the role of specialized hub regions for mediating cross‐network interactions. Again, we use a combination of functional connectivity, lesion, and task activation studies to bolster this claim. We conclude that a large‐scale network perspective provides important neurobiological constraints on the neural underpinnings of executive control, which will guide future basic and translational research into executive function and its disruption in disease.
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.
•Reveals confounds in degree-based hub detection techniques in correlation networks•Utilizes multiple methods to convergently identify hubs in correlation networks•Identifies regions and nodes that support and link different parts of brain networks•Generates differential, testable, and spatially constrained hypotheses regarding hubs
Power et al. describe methods to find influential nodes in correlation networks. These methods identify places in the human brain where lesions may disrupt many types of processing (e.g., perception, memory, attention) and regions where lesions may disrupt relatively few processes.
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.
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
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