Kelly Anne Barnes

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.

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.

Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp

We recently described a motion-related artifact in resting state functional connectivity MRI (rs-fcMRI) data that arises from subject head motion (Power et al. (2012), see also Satterthwaite et al. (2012); Van Dijk et al. (2012)). Head motion produces well-known disruptions in BOLD signal (Friston et al., 1996), and these artifactual modulations of BOLD signal, which are similar at nearby voxels, create spurious patterns in correlations in rs-fcMRI. Specifically, head motion augments short-distance correlations and weakens long-distance correlations. Thus, for instance, a higher-motion dataset would typically display weakened correlations between (distantly spaced) default mode regions but enhanced correlations between (closely spaced) visual regions in comparison to a low-motion data-set. We described this artifact in cohorts of children, adolescents, and adults, and its severity (magnitude) was related to the prevalence of motion within a cohort. Motion-related functional connectivity artifact is thus a substantial confound in the examination of single rs-fcMRI datasets and in comparisons of multiple rs-fcMRI datasets.

Intact Implicit Learning of Spatial Context and Temporal Sequences in Childhood Autism Spectrum Disorder

Autism spectrum disorder (ASD) is defined by atypicalities in domains that are posited to rely on implicit learning processes such as social communication, language, and motor behavior. The authors examined 2 forms of implicit learning in 14 children with high-functioning ASD (10 of whom were diagnosed with Aspergers syndrome) and 14 control children, learning of spatial context known to be mediated by the medial temporal lobes (using the contextual cueing task) and of sequences known to be mediated by frontal-striatal and frontal-cerebellar circuits (using the alternating serial reaction time task). Both forms of learning were unimpaired in ASD. Spatial contextual implicit learning was spared in ASD despite slower visual search of spatial displays. The present findings provide evidence for the integrity of learning processes dependent on integration of spatial and sequential contextual information in high-functioning children with ASD.

Identifying basal ganglia divisions in individuals using resting-state functional connectivity MRI

Studies in non-human primates and humans reveal that discrete regions (henceforth, “divisions”) in the basal ganglia are intricately interconnected with regions in the cerebral cortex. However, divisions within basal ganglia nuclei (e.g., within the caudate) are difficult to identify using structural MRI. Resting-state functional connectivity MRI (rs-fcMRI) can be used to identify putative cerebral cortical functional areas in humans (Cohen et al., 2008). Here, we determine whether rs-fcMRI can be used to identify divisions in individual human adult basal ganglia. Putative basal ganglia divisions were generated by assigning basal ganglia voxels to groups based on the similarity of whole-brain functional connectivity correlation maps using modularity optimization, a network analysis tool. We assessed the validity of this approach by examining the spatial contiguity and location of putative divisions and whether divisions’ correlation maps were consistent with previously reported patterns of anatomical and functional connectivity. Spatially constrained divisions consistent with the dorsal caudate, ventral striatum, and dorsal caudal putamen could be identified in each subject. Further, correlation maps associated with putative divisions were consistent with their presumed connectivity. These findings suggest that, as in the cerebral cortex, subcortical divisions can be identified in individuals using rs-fcMRI. Developing and validating these methods should improve the study of brain structure and function, both typical and atypical, by allowing for more precise comparison across individuals.

Functional Connectivity of the Inferior Frontal Cortex Changes with Age in Children with Autism Spectrum Disorders: A fcMRI Study of Response Inhibition

Unmasking the neural basis of neurodevelopmental disorders, such as autism spectrum disorders (ASD), requires studying functional connectivity during childhood when cognitive skills develop. A functional connectivity magnetic resonance imaging (fcMRI) analysis was performed on data collected during Go/NoGo task performance from 24 children ages 8-12 years (12 with ASD; 12 controls matched on age and intellectual functioning). We investigated the connectivity of the left and right inferior frontal cortex (IFC; BA 47), key regions for response inhibition, with other active regions in frontal, striatal, and parietal cortex. Groups did not differ on behavioral measures or functional connectivity of either IFC region. A trend for reduced connectivity in the right IFC for the ASD group was revealed when controlling for age. In the ASD group, there was a significant negative correlation between age and 2 right IFC correlation pairs: right IFC-bilateral presupplementary motor area (BA 6) and right IFC-right caudate. Compared with typical controls, children with ASD may not have gross differences in IFC functional connectivity during response inhibition, which contrasts with an adult study of ASD that reported reduced functional connectivity. This discrepancy suggests an atypical developmental trajectory in ASD for right IFC connectivity with other neural regions supporting response inhibition.

The fMRI Success Rate of Children and Adolescents: Typical Development, Epilepsy, Attention Deficit/Hyperactivity Disorder, and Autism Spectrum Disorders

Functional magnetic resonance imaging (fMRI) in children is increasingly used in clinical application and in developmental research; however, little is known how pediatric patient and typically developing populations successfully complete studies. We examined pediatric success rates with epilepsy, attention deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), and typically developing children (TYP). We also examined the affect of age, and, for ADHD populations, medication status on success rates. We defined a successful fMRI individual run when the data were interpretable and included in group statistics. For unsuccessful runs, datasets with excessive motion or floor task performance were categorized when possible. All clinical groups scanned less successfully than controls; medication status did not affect ADHD success (epilepsy, 80%; ADHD (off methylphenidate), 77%; ADHD (on methylphenidate), 81%; ASD, 70%; TYP, 87%). Ten to 18‐year‐old had a significantly greater scan success rate than 4‐ to 6‐year‐old; adolescents (13‐ to 18‐year‐old) demonstrated greater scan success rates than 7‐ to 9‐year‐old. Success rate for completing an entire battery of experimental runs (n = 2–6), varied between 50–59% for patient populations and 69% for TYP (79% when excluding 4‐ to 6‐year‐old). Success rate for completing one run from a battery was greater than 90% for all groups, except for ASD (81%). These data suggest 20–30% more children should be recruited in these patient groups, but only 10–20% for TYP for research studies. Studies with 4‐ to 6‐year‐olds may require 20–40% additional participants; studies with 10‐ to 18‐year‐olds may require 10–15% additional participants. Hum Brain Mapp, 2009.

Two Forms of Implicit Learning in Childhood ADHD

Attention deficit hyperactivity disorder (ADHD) is characterized by inattention, impulsivity, and hyperactivity mediated by frontal-striatal-cerebellar dysfunction. These circuits support implicit learning of perceptual-motor sequences but not visual-spatial context. ADHD and control children performed the Alternating Serial Reaction Time (ASRT) task, a measure of sequence learning, and the Contextual Cueing (CC) task, a measure of spatial contextual learning. Relative to controls, children with ADHD showed inconsistent ASRT learning but did not differ on CC learning. Thus, implicit sequence learning, a cognitive process mediated by frontal-striatal-cerebellar circuitry that is not under executive control, was atypical in ADHD.

Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism.

Significance Few individuals diagnosed with autism spectrum disorder (ASD) go on to achieve high levels of independence or what are considered “very good” outcomes. As such, there is a need to identify predictors of outcomes to improve treatment and services for these individuals. Although behavioral and cognitive variables can predict substantial variance in outcomes, the majority of the variance remains unexplained. In this study, we investigated whether a measure of intrinsic functional brain connectivity [resting-state functional connectivity MRI (rs-fcMRI)] could add meaningful predictive power. Indeed, we found that connectivity involving several brain networks previously implicated in ASD could predict improvements in adaptive behaviors several years after the scan with a high degree of sensitivity. Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome—adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement.

Functional neuroimaging study in identical twin pairs discordant for regular cigarette smoking

Despite the tremendous public health and financial burden of cigarette smoking, relatively little is understood about brain mechanisms that subserve smoking behavior. This study investigated the effect of lifetime regular smoking on brain processing in a reward guessing task using functional magnetic resonance imaging and a co‐twin control study design in monozygotic (MZ) twin pairs that maximally controls for genetic and family background factors. Young adult (24–34 years) MZ female twin pairs (n = 15 pairs), discordant for regular smoking defined using Centers for Disease Control criteria as having smoked ≥100 cigarettes in their lifetime, were recruited from an ongoing genetic epidemiological longitudinal study of substance use and psychopathology. We applied hypothesis‐driven region of interest (ROI) and whole‐brain analyses to investigate the effect of regular smoking on reward processing. Reduced response to reward and punishment in regular compared with never‐regular smokers was seen in hypothesis‐driven ROI analysis of bilateral ventral striatum. Whole‐brain analysis identified bilateral reward‐processing regions that showed activation differences in response to winning or losing money but no effect of regular smoking; and frontal/parietal regions, predominantly in the right hemisphere, that showed robust effect of regular smoking but no effect of winning or losing money. Altogether, using a study design that maximally controls for group differences, we found that regular smoking had modest effects on striatal reward processing regions but robust effects on cognitive control/attentional systems.