Mark D. Shen

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.

Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness

Resting state functional magnetic resonance imaging (rs-fMRI) in infants enables important studies of functional brain organization early in human development. However, rs-fMRI in infants has universally been obtained during sleep to reduce participant motion artifact, raising the question of whether differences in functional organization between awake adults and sleeping infants that are commonly attributed to development may instead derive, at least in part, from sleep. This question is especially important as rs-fMRI differences in adult wake vs. sleep are well documented. To investigate this question, we compared functional connectivity and BOLD signal propagation patterns in 6, 12, and 24 month old sleeping infants with patterns in adult wakefulness and non-REM sleep. We find that important functional connectivity features seen during infant sleep closely resemble those seen during adult sleep, including reduced default mode network functional connectivity. However, we also find differences between infant and adult sleep, especially in thalamic BOLD signal propagation patterns. These findings highlight the importance of considering sleep state when drawing developmental inferences in infant rs-fMRI.

Walking, Gross Motor Development, and Brain Functional Connectivity in Infants and Toddlers

Abstract Infant gross motor development is vital to adaptive function and predictive of both cognitive outcomes and neurodevelopmental disorders. However, little is known about neural systems underlying the emergence of walking and general gross motor abilities. Using resting state fcMRI, we identified functional brain networks associated with walking and gross motor scores in a mixed cross-sectional and longitudinal cohort of infants at high and low risk for autism spectrum disorder, who represent a dimensionally distributed range of motor function. At age 12 months, functional connectivity of motor and default mode networks was correlated with walking, whereas dorsal attention and posterior cingulo-opercular networks were implicated at age 24 months. Analyses of general gross motor function also revealed involvement of motor and default mode networks at 12 and 24 months, with dorsal attention, cingulo-opercular, frontoparietal, and subcortical networks additionally implicated at 24 months. These findings suggest that changes in network-level brain–behavior relationships underlie the emergence and consolidation of walking and gross motor abilities in the toddler period. This initial description of network substrates of early gross motor development may inform hypotheses regarding neural systems contributing to typical and atypical motor outcomes, as well as neurodevelopmental disorders associated with motor dysfunction.

Decreased Axon Caliber Underlies Loss of Fiber Tract Integrity, Disproportional Reductions in White Matter Volume, and Microcephaly in Angelman Syndrome Model Mice

Angelman syndrome (AS) is a debilitating neurodevelopmental disorder caused by loss of function of the maternally inherited UBE3A allele. It is currently unclear how the consequences of this genetic insult unfold to impair neurodevelopment. We reasoned that by elucidating the basis of microcephaly in AS, a highly penetrant syndromic feature with early postnatal onset, we would gain new insights into the mechanisms by which maternal UBE3A loss derails neurotypical brain growth and function. Detailed anatomical analysis of both male and female maternal Ube3a-null mice reveals that microcephaly in the AS mouse model is primarily driven by deficits in the growth of white matter tracts, which by adulthood are characterized by densely packed axons of disproportionately small caliber. Our results implicate impaired axon growth in the pathogenesis of AS and identify noninvasive structural neuroimaging as a potentially valuable tool for gauging therapeutic efficacy in the disorder. SIGNIFICANCE STATEMENT People who maternally inherit a deletion or nonfunctional copy of the UBE3A gene develop Angelman syndrome (AS), a severe neurodevelopmental disorder. To better understand how loss of maternal UBE3A function derails brain development, we analyzed brain structure in a maternal Ube3a knock-out mouse model of AS. We report that the volume of white matter (WM) is disproportionately reduced in AS mice, indicating that deficits in WM development are a major factor underlying impaired brain growth and microcephaly in the disorder. Notably, we find that axons within the WM pathways of AS model mice are abnormally small in caliber. This defect is associated with slowed nerve conduction, which could contribute to behavioral deficits in AS, including motor dysfunction.

Development of white matter circuitry in infants with fragile x syndrome

Importance Fragile X syndrome (FXS) is a genetic neurodevelopmental disorder and the most common inherited cause of intellectual disability in males. However, there are no published data on brain development in children with FXS during infancy. Objective To characterize the development of white matter at ages 6, 12, and 24 months in infants with FXS compared with that of typically developing controls. Design, Setting, and Participants Longitudinal behavioral and brain imaging data were collected at 1 or more time points from 27 infants with FXS and 73 typically developing controls between August 1, 2008, and June 14, 2016, at 2 academic medical centers. Infants in the control group had no first- or second-degree relatives with intellectual or psychiatric disorders, including FXS and autism spectrum disorder. Main Outcomes and Measures Nineteen major white matter pathways were defined in common atlas space based on anatomically informed methods. Diffusion parameters, including fractional anisotropy, were compared between groups using linear mixed effects modeling. Fiber pathways showing group differences were subsequently examined in association with direct measures of verbal and nonverbal development. Results There were significant differences in the development of 12 of 19 fiber tracts between the 27 infants with FXS (22 boys and 5 girls) and the 73 infants in the control group (46 boys and 27 girls), with lower fractional anisotropy in bilateral subcortical-frontal, occipital-temporal, temporal-frontal, and cerebellar-thalamic pathways, as well as 4 of 6 subdivisions of the corpus callosum. For all 12 of these pathways, there were significant main effects between groups but not for the interaction of age × group, indicating that lower fractional anisotropy was present and stable from age 6 months in infants with FXS. Lower fractional anisotropy values in the uncinate fasciculi were correlated with lower nonverbal developmental quotient in the FXS group (left uncinate, F = 10.06; false discovery rate–corrected P = .03; right uncinate, F = 21.8; P = .004). Conclusions and Relevance The results substantiate in human infants the essential role of fragile X gene expression in the early development of white matter. The findings also suggest that the neurodevelopmental effects of FXS are well established at 6 months of age.

Extra-axial cerebrospinal fluid in high-risk and normal-risk children with autism aged 2–4 years: a case-control study

BACKGROUND We previously showed, in two separate cohorts, that high-risk infants who were later diagnosed with autism spectrum disorder had abnormally high extra-axial cerebrospinal fluid (CSF) volume from age 6-24 months. The presence of increased extra-axial CSF volume preceded the onset of behavioural symptoms of autism and was predictive of a later diagnosis of autism spectrum disorder. In this study, we aimed to establish whether increased extra-axial CSF volume is found in a large, independent sample of children diagnosed with autism spectrum disorder, whether extra-axial CSF remains abnormally increased beyond infancy, and whether it is present in both normal-risk and high-risk children with autism. METHODS In this case-control MRI study, children with autism spectrum disorder or with typical development aged 2-4 years were recruited from the community to the UC Davis MIND Institute Autism Phenome Project, based in Sacramento, CA, USA. The autism spectrum disorder group comprised children with autism spectrum disorder who were either normal risk (ie, from simplex families) or high risk (ie, from multiplex families). Measurements of extra-axial CSF volume, brain volume, head circumference, sleep problems, and familial risk status were derived from MRI and behavioural assessments. We applied a previously validated machine learning algorithm based on extra-axial CSF volume, brain volume, age, and sex to the current dataset. FINDINGS Between July 20, 2007, and Dec 13, 2012, 159 children with autism spectrum disorder (132 male, 27 female) and 77 with typical development (49 male, 28 female) underwent MRI scans. The autism spectrum disorder group had an average of 15·1% more extra-axial CSF than controls after accounting for differences in brain volume, weight, age, and sex (least-squares mean 116·74 cm3 [SE 3·33] in autism group vs 101·40 cm3 [3·93] in typical development group; p=0·007; Cohens d = 0·39). Subgroups of normal-risk (n=132) and high-risk (n=27) children with autism spectrum disorder had nearly identical extra-axial CSF volumes (p=0·78), and both subgroups had significantly greater volumes than controls. Both extra-axial CSF volume (p=0·004) and brain volume (p<0·0001) uniquely contributed to enlarged head circumference in the autism spectrum disorder group (p=0·04). Increased extra-axial CSF volume was associated with greater sleep disturbances (p=0·03) and lower non-verbal ability (p=0·04). The machine learning algorithm correctly predicted autism spectrum disorder diagnosis with a positive predictive value of 83% (95% CI 76·2-88·3). INTERPRETATION Increased extra-axial CSF volume is a reliable brain anomaly that has now been found in three independent cohorts, comprising both high-risk and normal-risk children with autism spectrum disorder. Increased extra-axial CSF volume is detectable using conventional structural MRI scans from infancy through to age 3 years. These results suggest that increased extra-axial CSF volume could be an early stratification biomarker of a biologically based subtype of autism that might share a common underlying pathophysiology. FUNDING US National Institutes of Health.

A novel framework for the local extraction of extra-axial cerebrospinal fluid from MR brain images

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain developmental. Extr a-axial fluid (EA-CSF), which is characterized by the CSF in the subarachnoid space, is promising in the early detection of children at risk for neurodevelopmental disorders. Currently, though, there is no tool to extract local EA-CSF measurements in a way that is suitable for localized analysis. In this paper, we propose a novel framework for the localized, cortical surface based analysis of EA-CSF. In our proposed processing, we combine probabilistic brain tissue segmentation, cortical surface reconstruction as well as streamline based local EA-CSF quantification. For streamline computation, we employ the vector field generated by solving a Laplacian partial differential equation (PDE) between the cortical surface and the outer CSF hull. To achieve sub-voxel accuracy while minimizing numerical errors, fourth-order Runge-Kutta (RK4) integration was used to generate the streamlines. Finally, the local EA-CSF is computed by integrating the CSF probability along the generated streamlines. The proposed local EA-CSF extraction tool was used to study the early postnatal brain development in typically developing infants. The results show that the proposed localized EA-CSF extraction pipeline can produce statistically significant regions that are not observed in previous global approach.