Dissecting the heterogeneous cortical anatomy of autism spectrum disorder using normative models

International audienceBACKGROUNDThe neuroanatomical basis of autism spectrum disorder (ASD) has remained elusive, mostly owing to high biological and clinical heterogeneity among diagnosed individuals. Despite considerable effort toward understanding ASD using neuroimaging biomarkers, heterogeneity remains a barrier, partly because studies mostly employ case-control approaches, which assume that the clinical group is homogeneous.METHODS:Here, we used an innovative normative modeling approach to parse biological heterogeneity in ASD. We aimed to dissect the neuroanatomy of ASD by mapping the deviations from a typical pattern of neuroanatomical development at the level of the individual and to show the necessity to look beyond the case-control paradigm to understand the neurobiology of ASD. We first estimated a vertexwise normative model of cortical thickness development using Gaussian process regression, then mapped the deviation of each participant from the typical pattern. For this, we employed a heterogeneous cross-sectional sample of 206 typically developing individuals (127 males) and 321 individuals with ASD (232 males) (6-31 years of age).RESULTS:We found few case-control differences, but the ASD cohort showed highly individualized patterns of deviations in cortical thickness that were widespread across the brain. These deviations correlated with severity of repetitive behaviors and social communicative symptoms, although only repetitive behaviors survived corrections for multiple testing.CONCLUSIONS:Our results 1) reinforce the notion that individuals with ASD show distinct, highly individualized trajectories of brain development and 2) show that by focusing on common effects (i.e., the "average ASD participant"), the case-control approach disguises considerable interindividual variation crucial for precision medicine

Elevated glutamatergic compounds in pregenual anterior cingulate in pediatric autism spectrum disorder demonstrated by 1H MRS and 1H MRSI.

Recent research in autism spectrum disorder (ASD) has aroused interest in anterior cingulate cortex and in the neurometabolite glutamate. We report two studies of pregenual anterior cingulate cortex (pACC) in pediatric ASD. First, we acquired in vivo single-voxel proton magnetic resonance spectroscopy ((1)H MRS) in 8 children with ASD and 10 typically developing controls who were well matched for age, but with fewer males and higher IQ. In the ASD group in midline pACC, we found mean 17.7% elevation of glutamate + glutamine (Glx) (p<0.05) and 21.2% (p<0.001) decrement in creatine + phosphocreatine (Cr). We then performed a larger (26 subjects with ASD, 16 controls) follow-up study in samples now matched for age, gender, and IQ using proton magnetic resonance spectroscopic imaging ((1)H MRSI). Higher spatial resolution enabled bilateral pACC acquisition. Significant effects were restricted to right pACC where Glx (9.5%, p<0.05), Cr (6.7%, p<0.05), and N-acetyl-aspartate + N-acetyl-aspartyl-glutamate (10.2%, p<0.01) in the ASD sample were elevated above control. These two independent studies suggest hyperglutamatergia and other neurometabolic abnormalities in pACC in ASD, with possible right-lateralization. The hyperglutamatergic state may reflect an imbalance of excitation over inhibition in the brain as proposed in recent neurodevelopmental models of ASD

Uncovering the Social Deficits in the Autistic Brain. A Source-Based Morphometric Study

Autism is a neurodevelopmental disorder that mainly affects social interaction and communication. Evidence from behavioral and functional MRI studies supports the hypothesis that dysfunctional mechanisms involving social brain structures play a major role in autistic symptomatology. However, the investigation of anatomical abnormalities in the brain of people with autism has led to inconsistent results. We investigated whether specific brain regions, known to display functional abnormalities in autism, may exhibit mutual and peculiar patterns of covariance in their gray-matter concentrations. We analyzed structural MRI images of 32 young men affected by autistic disorder (AD) and 50 healthy controls. Controls were matched for sex, age, handedness. IQ scores were also monitored to avoid confounding. A multivariate Source-Based Morphometry (SBM) was applied for the first time on AD and controls to detect maximally independent networks of gray matter. Group comparison revealed a gray-matter source that showed differences in AD compared to controls. This network includes broad temporal regions involved in social cognition and high-level visual processing, but also motor and executive areas of the frontal lobe. Notably, we found that gray matter differences, as reflected by SBM, significantly correlated with social and behavioral deficits displayed by AD individuals and encoded via the Autism Diagnostic Observation Schedule scores. These findings provide support for current hypotheses about the neural basis of atypical social and mental states information processing in autism

Machine Learning Based Autism Detection Using Brain Imaging

Autism Spectrum Disorder (ASD) is a group of heterogeneous developmental disabilities that manifest in early childhood. Currently, ASD is primarily diagnosed by assessing the behavioral and intellectual abilities of a child. This behavioral diagnosis can be subjective, time consuming, inconclusive, does not provide insight on the underlying etiology, and is not suitable for early detection. Diagnosis based on brain magnetic resonance imaging (MRI)—a widely used non- invasive tool—can be objective, can help understand the brain alterations in ASD, and can be suitable for early diagnosis. However, the brain morphological findings in ASD from MRI studies have been inconsistent. Moreover, there has been limited success in machine learning based ASD detection using MRI derived brain features. In this thesis, we begin by demonstrating that the low success in ASD detection and the inconsistent findings are likely attributable to the heterogeneity of brain alterations in ASD. We then show that ASD detection can be significantly improved by mitigating the heterogeneity with the help of behavioral and demographics information. Here we demonstrate that finding brain markers in well-defined sub-groups of ASD is easier and more insightful than identifying markers across the whole spectrum. Finally, our study focused on brain MRI of a pediatric cohort (3 to 4 years) and achieved a high classification success (AUC of 95%). Results of this study indicate three main alterations in early ASD brains: 1) abnormally large ventricles, 2) highly folded cortices, and 3) low image intensity in white matter regions suggesting myelination deficits indicative of decreased structural connectivity. Results of this thesis demonstrate that the meaningful brain markers of ASD can be extracted by applying machine learning techniques on brain MRI data. This data-driven technique can be a powerful tool for early detection and understanding brain anatomical underpinnings of ASD

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 its relationship to the appearance of behavioral symptoms is unknown. Retrospective head circumference and longitudinal brain volume studies of 2 year olds followed up at age 4 years, have provided evidence that increased brain volume may emerge early in development.1, 2 Studies of infants at high familial risk for autism can provide insight into the early development of autism and have found that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life3,4. These observations suggest that prospective brain imaging studies of infants at high familial risk for ASD might identify early post-natal changes in brain volume occurring before the emergence of 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 cortical surface area hyper-expansion between 6-12 months of age precedes brain volume overgrowth observed between 12-24 months in the 15 high-risk infants diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep learning algorithm primarily using surface area information from brain MRI at 6 and 12 months of age predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81%, sensitivity of 88%). These findings demonstrate that early brain changes unfold during the period in which autistic behaviors are first emerging

Prenatal Neurogenesis in Autism Spectrum Disorders.

An ever-increasing body of literature describes compelling evidence that a subset of young children on the autism spectrum show abnormal cerebral growth trajectories. In these cases, normal cerebral size at birth is followed by a period of abnormal growth and starting in late childhood often by regression compared to unaffected controls. Recent work has demonstrated an abnormal increase in the number of neurons of the prefrontal cortex suggesting that cerebral size increase in autism is driven by excess neuronal production. In addition, some affected children display patches of abnormal laminar positioning of cortical projection neurons. As both cortical projection neuron numbers and their correct layering within the developing cortex requires the undisturbed proliferation of neural progenitors, it appears that neural progenitors lie in the center of the autism pathology associated with early brain overgrowth. Consequently, autism spectrum disorders associated with cerebral enlargement should be viewed as birth defects of an early embryonic origin with profound implications for their early diagnosis, preventive strategies, and therapeutic intervention

Gyrification brain abnormalities as predictors of outcome in anorexia nervosa.

Gyrification brain abnormalities are considered a marker of early deviations from normal developmental trajectories and a putative predictor of poor outcome in psychiatric disorders. The aim of this study was to explore cortical folding morphology in patients with anorexia nervosa (AN). A MRI brain study was conducted on 38 patients with AN, 20 fully recovered patients, and 38 healthy women. Local gyrification was measured with procedures implemented in FreeSurfer. Vertex-wise comparisons were carried out to compare: (1) AN patients and healthy women; (2) patients with a full remission at a 3-year longitudinal follow-up assessment and patients who did not recover. AN patients exhibited significantly lower gyrification when compared with healthy controls. Patients with a poor 3-year outcome had significantly lower baseline gyrification when compared to both healthy women and patients with full recovery at follow-up, even after controlling for the effects of duration of illness and gray matter volume. No significant correlation has been found between gyrification, body mass index, amount of weight loss, onset age, and duration of illness. Brain gyrification significantly predicted outcome at follow-up even after controlling for the effects of duration of illness and other clinical prognostic factors. Although the role of starvation in determining our findings cannot be excluded, our study showed that brain gyrification might be a predictor of outcome in AN. Further studies are needed to understand if brain gyrification abnormalities are indices of early neurodevelopmental alterations, the consequence of starvation, or the interaction between both factors