John R. Pruett

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.

Early brain development in infants at high risk for autism spectrum disorder

Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life. These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6–12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.

Network inefficiencies in autism spectrum disorder at 24 months.

Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral symptoms that emerge during the first years of life. Associated with these symptoms are differences in the structure of a wide array of brain regions, and in the connectivity between these regions. However, the use of cohorts with large age variability and participants past the generally recognized age of onset of the defining behaviors means that many of the reported abnormalities may be a result of cascade effects of developmentally earlier deviations. This study assessed differences in connectivity in ASD at the age at which the defining behaviors first become clear. There were 113 24-month-old participants at high risk for ASD, 31 of whom were classified as ASD, and 23 typically developing 24-month-old participants at low risk for ASD. Utilizing diffusion data to obtain measures of the length and strength of connections between anatomical regions, we performed an analysis of network efficiency. Our results showed significantly decreased local and global efficiency over temporal, parietal and occipital lobes in high-risk infants classified as ASD, relative to both low- and high-risk infants not classified as ASD. The frontal lobes showed only a reduction in global efficiency in Broca’s area. In addition, these same regions showed an inverse relation between efficiency and symptom severity across the high-risk infants. The results suggest delay or deficits in infants with ASD in the optimization of both local and global aspects of network structure in regions involved in processing auditory and visual stimuli, language and nonlinguistic social stimuli.

Developmental changes in the organization of functional connections between the basal ganglia and cerebral cortex

The basal ganglia (BG) comprise a set of subcortical nuclei with sensorimotor, cognitive, and limbic subdivisions, indicative of functional organization. BG dysfunction in several developmental disorders suggests the importance of the healthy maturation of these structures. However, few studies have investigated the development of BG functional organization. Using resting-state functional connectivity MRI (rs-fcMRI), we compared human child and adult functional connectivity of the BG with rs-fcMRI-defined cortical systems. Because children move more than adults, customized preprocessing, including volume censoring, was used to minimize motion-induced rs-fcMRI artifact. Our results demonstrated functional organization in the adult BG consistent with subdivisions previously identified in anatomical tracing studies. Group comparisons revealed a developmental shift in bilateral posterior putamen/pallidum clusters from preferential connectivity with the somatomotor “face” system in childhood to preferential connectivity with control/attention systems (frontoparietal, ventral attention) in adulthood. This shift was due to a decline in the functional connectivity of these clusters with the somatomotor face system over development, and no change with control/attention systems. Applying multivariate pattern analysis, we were able to reliably classify individuals as children or adults based on BG–cortical system functional connectivity. Interrogation of the features driving this classification revealed, in addition to the somatomotor face system, contributions by the orbitofrontal, auditory, and somatomotor hand systems. These results demonstrate that BG–cortical functional connectivity evolves over development, and may lend insight into developmental disorders that involve BG dysfunction, particularly those involving motor systems (e.g., Tourette syndrome).

Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age

Functional brain imaging of 6-month-old infants with a high familial risk for autism predicts a diagnosis of autism at 24 months of age. Predicting the future with brain imaging In a new study, Emerson et al. show that brain function in infancy can be used to accurately predict which high-risk infants will later receive an autism diagnosis. Using machine learning techniques that identify patterns in the brain’s functional connections, Emerson and colleagues were able to predict with greater than 96% accuracy whether a 6-month-old infant would develop autism at 24 months of age. These findings must be replicated, but they represent an important step toward the early identification of individuals with autism before its characteristic symptoms develop. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% [95% confidence interval (CI), 62.9 to 100], correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified [specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3)]. These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD.

Analysis of Cortical Shape in Children with Simplex Autism

We used surface-based morphometry to test for differences in cortical shape between children with simplex autism (n = 34, mean age 11.4 years) and typical children (n = 32, mean age 11.3 years). This entailed testing for group differences in sulcal depth and in 3D coordinates after registering cortical midthickness surfaces to an atlas target using 2 independent registration methods. We identified bilateral differences in sulcal depth in restricted portions of the anterior-insula and frontal-operculum (aI/fO) and in the temporoparietal junction (TPJ). The aI/fO depth differences are associated with and likely to be caused by a shape difference in the inferior frontal gyrus in children with simplex autism. Comparisons of average midthickness surfaces of children with simplex autism and those of typical children suggest that the significant sulcal depth differences represent local peaks in a larger pattern of regional differences that are below statistical significance when using coordinate-based analysis methods. Cortical regions that are statistically significant before correction for multiple measures are peaks of more extended, albeit subtle regional differences that may guide hypothesis generation for studies using other imaging modalities.

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Highlights • SVMs classified 6 versus 12 month-old infants above chance based on fcMRI data alone.• We carefully accounted for the effects of fcMRI motion artifact.• These results coincide with a period of dramatic change in infant development.• Two interpretations about connections supporting this age categorization are given.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers

Abstract Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social‐communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain‐behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development.

The hemodynamic response in children with Simplex Autism.

BACKGROUND Numerous functional magnetic resonance imaging (fMRI) studies of the brain-bases of autism have demonstrated altered cortical responses in subjects with autism, relative to typical subjects, during a variety of tasks. These differences may reflect altered neuronal responses or altered hemodynamic response. This study searches for evidence of hemodynamic response differences by using a simple visual stimulus and elementary motor actions, which should elicit similar neuronal responses in patients and controls. METHODS We acquired fMRI data from two groups of 16 children, a typical group and a group with Simplex Autism, during a simple visuomotor paradigm previously used to assess this question in other cross-group comparisons. A general linear model estimated the blood-oxygen-level-dependent (BOLD) signal time course, and repeated-measures analysis of variance tested for potential cross-group differences in the BOLD signal. RESULTS The hemodynamic response in Simplex Autism is similar to that found in typical children. Although the sample size was small for a secondary analysis, medication appeared to have no effect on the hemodynamic response within the Simplex Autism group. CONCLUSIONS When fMRI studies show BOLD response differences between autistic and typical subjects, these results likely reflect between-group differences in neural activity and not an altered hemodynamic response.

The Emergence of Network Inefficiencies in Infants With Autism Spectrum Disorder

BACKGROUND Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral features that emerge during the first years of life. Research indicates that abnormalities in brain connectivity are associated with these behavioral features. However, the inclusion of individuals past the age of onset of the defining behaviors complicates interpretation of the observed abnormalities: they may be cascade effects of earlier neuropathology and behavioral abnormalities. Our recent study of network efficiency in a cohort of 24-month-olds at high and low familial risk for ASD reduced this confound; we reported reduced network efficiencies in toddlers classified with ASD. The current study maps the emergence of these inefficiencies in the first year of life. METHODS This study uses data from 260 infants at 6 and 12 months of age, including 116 infants with longitudinal data. As in our earlier study, we use diffusion data to obtain measures of the length and strength of connections between brain regions to compute network efficiency. We assess group differences in efficiency within linear mixed-effects models determined by the Akaike information criterion. RESULTS Inefficiencies in high-risk infants later classified with ASD were detected from 6 months onward in regions involved in low-level sensory processing. In addition, within the high-risk infants, these inefficiencies predicted 24-month symptom severity. CONCLUSIONS These results suggest that infants with ASD, even before 6 months of age, have deficits in connectivity related to low-level processing, which contribute to a developmental cascade affecting brain organization and eventually higher-level cognitive processes and social behavior.