2020Alycia Halladay
Which came first, ASD or sleep problems?
The current study examined the relationship between quantitative measures of reward and punishment sensitivity, features of autism spectrum disorder (ASD), and resting and functional pupil response metrics across a clinically heterogeneous sample. Scores on a parent-report measure of punishment and reward sensitivity were correlated with ASD features. We also assessed whether pupil measurements could be used as a physiologic correlate of reward sensitivity and predictor of ASD diagnosis. In a logistic regression model, pupil dilation metrics, sex, and IQ, correctly classified 86.3% of participants as having an ASD diagnosis versus not. This research highlights individual differences of reward sensitivity associated with ASD features. Results support the use of pupil metrics and other patient-level variables as predictors of ASD diagnostic status.
Keywords: Autism spectrum disorder; Individual differences; Motivation; Punishment sensitivity; Pupillometry; Reward.
Humans are innately social creatures and the social environment strongly influences brain development. As such, the human brain is primed for and sensitive to social information even in the absence of explicit task or instruction. In this study, we examined the influence of different levels of interpersonal proximity on resting state brain activity and its association with social cognition. We measured EEG in pairs of 13 typically developing (TD) adults seated in separate rooms, in the same room back-to-back, and in the same room facing each other. Interpersonal proximity modulated broadband EEG power from 4-55 Hz and individual differences in self-reported social cognition modulated these effects in the beta and gamma frequency bands. These findings provide novel insight into the influence of social environment on brain activity and its association with social cognition through dual-brain EEG recording and demonstrate the importance of using interactive methods to study the human brain.
Keywords: EEG; autism spectrum disorder; dual brain; interactive social neuroscience; resting state; social cognition.
Background: Autism spectrum disorder (“autism”) is a highly heterogeneous neurodevelopmental condition with few effective treatments for core and associated features. To make progress we need to both identify and validate neural markers that help to parse heterogeneity to tailor therapies to specific neurobiological profiles. Atypical hemispheric lateralization is a stable feature across studies in autism, but its potential as a neural stratification marker has not been widely examined.
Methods: In order to dissect heterogeneity in lateralization in autism, we used the large EU-AIMS (European Autism Interventions-A Multicentre Study for Developing New Medications) Longitudinal European Autism Project dataset comprising 352 individuals with autism and 233 neurotypical control subjects as well as a replication dataset from ABIDE (Autism Brain Imaging Data Exchange) (513 individuals with autism, 691 neurotypical subjects) using a promising approach that moves beyond mean group comparisons. We derived gray matter voxelwise laterality values for each subject and modeled individual deviations from the normative pattern of brain laterality across age using normative modeling.
Results: Individuals with autism had highly individualized patterns of both extreme right- and leftward deviations, particularly in language, motor, and visuospatial regions, associated with symptom severity. Language delay explained most variance in extreme rightward patterns, whereas core autism symptom severity explained most variance in extreme leftward patterns. Follow-up analyses showed that a stepwise pattern emerged, with individuals with autism with language delay showing more pronounced rightward deviations than individuals with autism without language delay.
Conclusions: Our analyses corroborate the need for novel (dimensional) approaches to delineate the heterogeneous neuroanatomy in autism and indicate that atypical lateralization may constitute a neurophenotype for clinically meaningful stratification in autism.
Keywords: Autism spectrum disorder; Brain asymmetry; Hemispheric specialization; Heterogeneity; Language delay; Normative modeling.
We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n = 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.
Keywords: autism spectrum disorder; cell type; cytoskeleton; excitatory neurons; excitatory-inhibitory balance; exome sequencing; genetics; inhibitory neurons; liability; neurodevelopment.
Significant heterogeneity across aetiologies, neurobiology and clinical phenotypes have been observed in individuals with autism spectrum disorder (ASD). Neuroimaging-based neuroanatomical studies of ASD have often reported inconsistent findings which may, in part, be attributable to an insufficient understanding of the relationship between factors influencing clinical heterogeneity and their relationship to brain anatomy. To this end, we performed a large-scale examination of cortical morphometry in ASD, with a specific focus on the impact of three potential sources of heterogeneity: sex, age and full-scale intelligence (FIQ). To examine these potentially subtle relationships, we amassed a large multi-site dataset that was carefully quality controlled (yielding a final sample of 1327 from the initial dataset of 3145 magnetic resonance images; 491 individuals with ASD). Using a meta-analytic technique to account for inter-site differences, we identified greater cortical thickness in individuals with ASD relative to controls, in regions previously implicated in ASD, including the superior temporal gyrus and inferior frontal sulcus. Greater cortical thickness was observed in sex specific regions; further, cortical thickness differences were observed to be greater in younger individuals and in those with lower FIQ, and to be related to overall clinical severity. This work serves as an important step towards parsing factors that influence neuroanatomical heterogeneity in ASD and is a potential step towards establishing individual-specific biomarkers
Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both “constant” and “temporal-predominant” eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.
Keywords: BrainVar; DLPFC; LOC101926933 RP11-298I3.1 AL132780.1 ENSG00000257285; PsychENCODE; RHEBL1; dorsolateral prefrontal cortex; fetal transition; mTOR; prenatal eQTL
Background: The lack of robust and reliable clinical biomarkers in Fragile X Syndrome (FXS), the most common inherited form of intellectual disability, has limited the successful translation of bench-to-bedside therapeutics. While numerous drugs have shown promise in reversing synaptic and behavioral phenotypes in mouse models of FXS, none have demonstrated clinical efficacy in humans. Electroencephalographic (EEG) measures have been identified as candidate biomarkers as EEG recordings of both adults with FXS and mouse models of FXS consistently exhibit alterations in resting state and task-related activity. However, the developmental timing of these EEG differences is not known as thus far EEG studies have not focused on young children with FXS. Further, understanding how EEG differences are associated with core symptoms of FXS is crucial to successful use of EEG as a biomarker, and may improve our understanding of the disorder.
Methods: Resting-state EEG was collected from FXS boys with full mutation of Fmr1 (2.5-7 years old, n = 11) and compared with both age-matched (n = 12) and cognitive-matched (n = 12) typically developing boys. Power spectra (including aperiodic and periodic components) were compared using non-parametric cluster-based permutation testing. Associations between 30 and 50 Hz gamma power and cognitive, language, and behavioral measures were evaluated using Pearson correlation and linear regression with age as a covariate.
Results: FXS participants showed increased power in the beta/gamma range (~ 25-50 Hz) across multiple brain regions. Both a reduction in the aperiodic (1/f) slope and increase in beta/gamma periodic activity contributed to the significant increase in high-frequency power. Increased gamma power, driven by the aperiodic component, was associated with better language ability in the FXS group. No association was observed between gamma power and parent report measures of behavioral challenges, sensory hypersensitivities, or adaptive behaviors.
Limitations: The study sample size was small, although comparable to other human studies in rare-genetic disorders. Findings are also limited to males in the age range studied.
Conclusions: Resting-state EEG measures from this study in young boys with FXS identified similar increases in gamma power previously reported in adults and mouse models. The observed positive association between resting state aperiodic gamma power and language development supports hypotheses that alterations in some EEG measures may reflect ongoing compensatory mechanisms.
Keywords: Biomarker; E:I ratio; Electroencephalography; Fragile X Syndrome; Gamma; Language; Outcome measures.
Background: Autism spectrum disorder (ASD) is highly familial, with a positively skewed male-to-female ratio that is purported to arise from the so-called female protective effect. A serious implication of a female protective effect is that familial ASD liability would be expected to aggregate asymptomatically in sisters of affected probands, who would incur elevated rates of ASD among their offspring. Currently, there exist no data on second-generation recurrence rates among families affected by ASD.
Methods: We analyzed data from the Swedish National Patient Register and the Multi-Generation Register for a cohort of children born between 2003 and 2012. ASD was ascertained in both the child and parental generations.
Results: Among 847,732 children, 13,103 (1.55%) children in the cohort were diagnosed with ASD. Among their maternal/paternal aunts and uncles, 1744 (0.24%) and 1374 (0.18%) were diagnosed with ASD, respectively. Offspring of mothers with a sibling(s) diagnosed with ASD had higher rates of ASD than the general population (relative risk, 3.05; 95% confidence interval, 2.52-3.64), but not more than would be predicted for second-degree relatives within a generation, and only slightly more than was observed for fathers with siblings with ASD (relative risk, 2.08; 95% confidence interval, 1.53-2.67). Models adjusting for temporal trends and for psychiatric history in the parental generation did not alter the results.
Conclusions: These findings establish a robust general estimate of ASD transmission risk for siblings of individuals affected by ASD, the first ever reported. Our findings do not suggest female protective factors as the principal mechanism underlying the male sex bias in ASD.
Keywords: Autism; Epidemiology; Female protective effect; Population-based; Psychiatry; Sex bias.