The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain\u27s modular network organization, which can be assessed quantitatively using computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Although the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability - which would be undetectable using past approaches - is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advance over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints

Role of the anterior insula in task-level control and focal attention

In humans, the anterior insula (aI) has been the topic of considerable research and ascribed a vast number of functional properties by way of neuroimaging and lesion studies. Here, we argue that the aI, at least in part, plays a role in domain-general attentional control and highlight studies (Dosenbach et al. 2006; Dosenbach et al. 2007) supporting this view. Additionally, we discuss a study (Ploran et al. 2007) that implicates aI in processes related to the capture of focal attention. Task-level control and focal attention may or may not reflect information processing supported by a single functional area (within the aI). Therefore, we apply a novel technique (Cohen et al. 2008) that utilizes resting state functional connectivity MRI (rs-fcMRI) to determine whether separable regions exist within the aI. rs-fcMRI mapping suggests that the ventral portion of the aI is distinguishable from more dorsal/anterior regions, which are themselves distinct from more posterior parts of the aI. When these regions are applied to functional MRI (fMRI) data, the ventral and dorsal/anterior regions support processes potentially related to both task-level control and focal attention, whereas the more posterior aI regions did not. These findings suggest that there exists some functional heterogeneity within aI that may subserve related but distinct types of higher-order cognitive processing

Spatial and temporal characteristics of error-related activity in the human brain

A number of studies have focused on the role of specific brain regions, such as the dorsal anterior cingulate cortex during trials on which participants make errors, whereas others have implicated a host of more widely distributed regions in the human brain. Previous work has proposed that there are multiple cognitive control networks, raising the question of whether error-related activity can be found in each of these networks. Thus, to examine error-related activity broadly, we conducted a meta-analysis consisting of 12 tasks that included both error and correct trials. These tasks varied by stimulus input (visual, auditory), response output (button press, speech), stimulus category (words, pictures), and task type (e.g., recognition memory, mental rotation). We identified 41 brain regions that showed a differential fMRI BOLD response to error and correct trials across a majority of tasks. These regions displayed three unique response profiles: (1) fast, (2) prolonged, and (3) a delayed response to errors, as well as a more canonical response to correct trials. These regions were found mostly in several control networks, each network predominantly displaying one response profile. The one exception to this “one network, one response profile” observation is the frontoparietal network, which showed prolonged response profiles (all in the right hemisphere), and fast profiles (all but one in the left hemisphere). We suggest that, in the place of a single localized error mechanism, these findings point to a large-scale set of error-related regions across multiple systems that likely subserve different function

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood

Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison \u27single-shot\u27 datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package

Real-time motion monitoring improves functional MRI data quality in infants

Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging

Task Control Signals in Pediatric Tourette Syndrome Show Evidence of Immature and Anomalous Functional Activity

Tourette Syndrome (TS) is a pediatric movement disorder that may affect control signaling in the brain. Previous work has proposed a dual-networks architecture of control processing involving a task-maintenance network and an adaptive control network (Dosenbach et al., 2008). A prior resting-state functional connectivity MRI (rs-fcMRI) analysis in TS has revealed functional immaturity in both putative control networks, with “anomalous” correlations (i.e., correlations outside the typical developmental range) limited to the adaptive control network (Church et al., 2009). The present study used functional MRI (fMRI) to study brain activity related to adaptive control (by studying start-cues signals), and to task-maintenance (by studying signals sustained across a task set). Two hypotheses from the previous rs-fcMRI results were tested. First, adaptive control (i.e., start-cue) activity will be altered in TS, including activity inconsistent with typical development (“anomalous”). Second, group differences found in task-maintenance (i.e., sustained) activity will be consistent with functional immaturity in TS. We examined regions found through a direct comparison of adolescents with and without TS, as well as regions derived from a previous investigation that showed differences between unaffected children and adults. The TS group showed decreased start-cue signal magnitude in regions where start-cue activity is unchanged over typical development, consistent with anomalous adaptive control. The TS group also had higher magnitude sustained signals in frontal cortex regions that overlapped with regions showing differences over typical development, consistent with immature task-maintenance in TS. The results demonstrate task-related fMRI signal differences anticipated by the atypical functional connectivity found previously in adolescents with TS, strengthening the evidence for functional immaturity and anomalous signaling in control networks in adolescents with TS

Parallel hippocampal-parietal circuits for self- and goal-oriented processing

The hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. Using individual-specific precision functional mapping of resting-state functional MRI data, we found the anterior hippocampus (head and body) to be preferentially functionally connected to the default mode network (DMN), as expected. The hippocampal tail, however, was strongly preferentially functionally connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This anterior-posterior dichotomy of resting-state functional connectivity was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the hippocampal head and body (DMN), relatively sparing the tail (PMN). The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel but distinct circuits between the hippocampus and medial parietal cortex for self- versus goal-oriented processing

A somato-cognitive action network alternates with effector regions in motor cortex

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representation