Steven E. Petersen

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.

Toward discovery science of human brain function

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individuals “functional connectome.” Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individuals functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain–behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

A PET study of visuospatial attention

Positron emission tomography (PET) was used to identify the neural systems involved in shifting spatial attention to visual stimuli in the left or right visual field along foveofugal or foveocentric directions. Psychophysical evidence indicated that stimuli at validly cued locations were responded to faster than stimuli at invalidly cued locations. Reaction times to invalid probes were faster when they were presented in the same than in the opposite direction of an ongoing attention movement. PET evidence indicated that superior parietal and superior frontal cortex were more active when attention was shifted to peripheral locations than when maintained at the center of gaze. Both regions encoded the visual field and not the direction of an attention shift. In the right superior parietal lobe, two distinct responses were localized for attention to left and right visual field. Finally, the superior parietal region was active when peripheral locations were selected on the basis of cognitive or sensory cues independent of the execution of an overt response. The frontal region was active only when responses were made to stimuli at selected peripheral locations. These findings indicate that parietal and frontal regions control different aspects of spatial selection. The functional asymmetry in superior parietal cortex may be relevant for the pathophysiology of unilateral neglect.

Distinct brain networks for adaptive and stable task control in humans

Control regions in the brain are thought to provide signals that configure the brains moment-to-moment information processing. Previously, we identified regions that carried signals related to task-control initiation, maintenance, and adjustment. Here we characterize the interactions of these regions by applying graph theory to resting state functional connectivity MRI data. In contrast to previous, more unitary models of control, this approach suggests the presence of two distinct task-control networks. A frontoparietal network included the dorsolateral prefrontal cortex and intraparietal sulcus. This network emphasized start-cue and error-related activity and may initiate and adapt control on a trial-by-trial basis. The second network included dorsal anterior cingulate/medial superior frontal cortex, anterior insula/frontal operculum, and anterior prefrontal cortex. Among other signals, these regions showed activity sustained across the entire task epoch, suggesting that this network may control goal-directed behavior through the stable maintenance of task sets. These two independent networks appear to operate on different time scales and affect downstream processing via dissociable mechanisms.

Common blood flow changes across visual tasks: Ii. decreases in cerebral cortex

Nine previous positron emission tomography (PET) studies of human visual information processing were reanalyzed to determine the consistency across experiments of blood flow decreases during active tasks relative to passive viewing of the same stimulus array. Areas showing consistent decreases during active tasks included posterior cingulate/precuneous (Brodmann area, BA 31/7), left (BAS 40 and 39/19) and right (BA 40) inferior parietal cortex, left dorsolateral frontal cortex (BA S), left lateral inferior frontal cortex (BA 10/47), left inferior temporal gyrus @A 20), a strip of medial frontal regions running along a dorsal-ventral axis (BAs 8, 9, 10, and 32), and the right amygdala. Experiments involving language-related processes tended to show larger decreases than nonlanguage experiments. This trend mainly reflected blood flow increases at certain areas in the passive conditions of the language experiments (relative to a fixation control in which no task stimulus was present) and slight blood flow decreases in the passive conditions of the nonlanguage experiments. When the active tasks were referenced to the fixation condition, the overall size of blood flow decreases in language and nonlanguage tasks were the same, but differences were found across cortical areas. Decreases were more pronounced in the posterior cingulate/precuneous (BAS 31/7) and right inferior parietal cortex (BA 40) during language-related tasks and more pronounced in left inferior frontal cortex (BA 10/47) during nonlanguage tasks. Blood flow decreases did not generally show significant differences across the active task states within an experiment, but a verb-generation task produced larger decreases than a read task in right and left inferior parietal lobe (BA 40) and the posterior cingulate/precuneous (BA 31/7), while the read task produced larger decreases in left lateral inferior frontal cortex (BA 10/47). These effects mirrored those found between experiments in the language-nonlanguage comparison. Consistent active minus passive decreases may reflect decreased activity caused by active task processes that generalize over tasks or increased activity caused by passive task processes that are suspended during the active tasks. Increased activity during the passive condition might reflect ongoing processes, such as unconstrained verbally mediated thoughts and monitoring of the external environment, body, and emotional state.

A Common Network of Functional Areas for Attention and Eye Movements

Functional magnetic resonance imaging (fMRI) and surface-based representations of brain activity were used to compare the functional anatomy of two tasks, one involving covert shifts of attention to peripheral visual stimuli, the other involving both attentional and saccadic shifts to the same stimuli. Overlapping regional networks in parietal, frontal, and temporal lobes were active in both tasks. This anatomical overlap is consistent with the hypothesis that attentional and oculomotor processes are tightly integrated at the neural level.

Positron emission tomographic studies of the processing of singe words

PET images of blood flow change that were averaged across individuals were used to identify brain areas related to lexical (single-word) processing, A small number of discrete areas were activated during several task conditions including: modality-specific (auditory or visual) areas activated by passive word input, primary motor and premotor areas during speech output, and yet further areas during tasks making semantic or intentional demands.

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.

The maturing architecture of the brain's default network

In recent years, the brains “default network,” a set of regions characterized by decreased neural activity during goal-oriented tasks, has generated a significant amount of interest, as well as controversy. Much of the discussion has focused on the relationship of these regions to a “default mode” of brain function. In early studies, investigators suggested that, the brains default mode supports “self-referential” or “introspective” mental activity. Subsequently, regions of the default network have been more specifically related to the “internal narrative,” the “autobiographical self,” “stimulus independent thought,” “mentalizing,” and most recently “self-projection.” However, the extant literature on the function of the default network is limited to adults, i.e., after the system has reached maturity. We hypothesized that further insight into the networks functioning could be achieved by characterizing its development. In the current study, we used resting-state functional connectivity MRI (rs-fcMRI) to characterize the development of the brains default network. We found that the default regions are only sparsely functionally connected at early school age (7–9 years old); over development, these regions integrate into a cohesive, interconnected network.

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.