Recognizing and Responding to the Needs of Future Child and Adult Neurology Care Through the Evolution of Residency Training.

Recent insights into the frequency of occurrence and the genetic and mechanistic basis of nervous system disease have demonstrated that neurologic disorders occur as a spectrum across all ages. To meet future needs of patients with neurologic disease of all ages and prepare for increasing implementaton of precision therapies, greater integration of child and adult neurology residency training is needed. ANN NEUROL 2023;94:1005-1007.

Associations between thalamocortical functional connectivity and sensory over-responsivity in infants at high likelihood for ASD.

Despite growing evidence implicating thalamic functional connectivity atypicalities in autism spectrum disorder (ASD), it remains unclear how such alterations emerge early in human development. Because the thalamus plays a critical role in sensory processing and neocortical organization early in life, its connectivity with other cortical regions could be key for studying the early onset of core ASD symptoms. Here, we investigated emerging thalamocortical functional connectivity in infants at high (HL) and typical (TL) familial likelihood for ASD in early and late infancy. We report significant thalamo-limbic hyperconnectivity in 1.5-month-old HL infants, and thalamo-cortical hypoconnectivity in prefrontal and motor regions in 9-month-old HL infants. Importantly, early sensory over-responsivity (SOR) symptoms in HL infants predicted a direct trade-off in thalamic connectivity whereby stronger thalamic connectivity with primary sensory regions and basal ganglia was inversely related to connectivity with higher order cortices. This trade-off suggests that ASD may be characterized by early differences in thalamic gating. The patterns reported here could directly underlie atypical sensory processing and attention to social vs. nonsocial stimuli observed in ASD. These findings lend support to a theoretical framework of ASD whereby early disruptions in sensorimotor processing and attentional biases early in life may cascade into core ASD symptomatology.

Dual orexin receptor antagonists for insomnia in youth with neurodevelopmental disorders: a case series and review.

Insomnia is a common, impairing, and difficult-to-treat comorbidity in children with neurodevelopmental disorders (NDDs). Behavioral interventions can be challenging because of developmental and behavioral features that interfere with treatment. Medication management also can be difficult due to a high burden of side effects, a high rate of paradoxical responses, and frequent treatment resistance. Therefore, new treatment options for insomnia in children with NDDs are needed. Dual orexin receptor antagonists (DORAs) are a relatively new class of pharmacotherapeutics that induce sleep by inhibiting the orexin signaling pathway. To date, there is little safety or efficacy data on the use of DORAs in children with NDDs. We present four patients with NDDs and insomnia that we treated with the DORA, suvorexant. We found that patients had a wide range of responses, with one patient displaying a robust improvement in sleep onset and maintenance, while another had significant improvement in insomnia symptoms on combination therapy with trazodone. Our final two patients had mild or no benefit from suvorexant therapy. Further research is necessary to establish the safety and efficacy of DORAs in this population and to identify predictive factors, such as specific neurogenetic diagnoses or clinical features, of a positive treatment response.

Atypical cerebellar functional connectivity at 9 months of age predicts delayed socio-communicative profiles in infants at high and low risk for autism.

BACKGROUND: While the cerebellum is traditionally known for its role in sensorimotor control, emerging research shows that particular subregions, such as right Crus I (RCrusI), support language and social processing. Indeed, cerebellar atypicalities are commonly reported in autism spectrum disorder (ASD), a neurodevelopmental disorder characterized by socio-communicative impairments. However, the cerebellum's contribution to early socio-communicative development remains virtually unknown. METHODS: Here, we characterized functional connectivity within cerebro-cerebellar networks implicated in language/social functions in 9-month-old infants who exhibit distinct 3-year socio-communicative developmental profiles. We employed a data-driven clustering approach to stratify our sample of infants at high (n = 82) and low (n = 37) familial risk for ASD into three cohorts-Delayed, Late-Blooming, and Typical-who showed unique socio-communicative trajectories. We then compared the cohorts on indices of language and social development. Seed-based functional connectivity analyses with RCrusI were conducted on infants with fMRI data (n = 66). Cohorts were compared on connectivity estimates from a-priori regions, selected on the basis of reported coactivation with RCrusI during language/social tasks. RESULTS: The three trajectory-based cohorts broadly differed in social communication development, as evidenced by robust differences on numerous indices of language and social skills. Importantly, at 9 months, the cohorts showed striking differences in cerebro-cerebellar circuits implicated in language/social functions. For all regions examined, the Delayed cohort exhibited significantly weaker RCrusI connectivity compared to both the Late-Blooming and Typical cohorts, with no significant differences between the latter cohorts. CONCLUSIONS: We show that hypoconnectivity within distinct cerebro-cerebellar networks in infancy predicts altered socio-communicative development before delays overtly manifest, which may be relevant for early detection and intervention. As the cerebellum is implicated in prediction, our findings point to probabilistic learning as a potential intermediary mechanism that may be disrupted in infancy, cascading into alterations in social communication.

Altered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder.

Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6 weeks predicted atypical social development between 9 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD.

Lack of neural evidence for implicit language learning in 9-month-old infants at high risk for autism.

Word segmentation is a fundamental aspect of language learning, since identification of word boundaries in continuous speech must occur before the acquisition of word meanings can take place. We previously used functional magnetic resonance imaging (fMRI) to show that youth with autism spectrum disorder (ASD) are less sensitive to statistical and speech cues that guide implicit word segmentation. However, little is known about the neural mechanisms underlying this process during infancy and how this may be associated with ASD risk. Here, we examined early neural signatures of language-related learning in 9-month-old infants at high (HR) and low familial risk (LR) for ASD. During natural sleep, infants underwent fMRI while passively listening to three speech streams containing strong statistical and prosodic cues, strong statistical cues only, or minimal statistical cues to word boundaries. Compared to HR infants, LR infants showed greater activity in the left amygdala for the speech stream containing statistical and prosodic cues. While listening to this same speech stream, LR infants also showed more learning-related signal increases in left temporal regions as well as increasing functional connectivity between bilateral primary auditory cortex and right anterior insula. Importantly, learning-related signal increases at 9 months positively correlated with expressive language outcome at 36 months in both groups. In the HR group, greater signal increases were additionally associated with less severe ASD symptomatology at 36 months. These findings suggest that early differences in the neural networks underlying language learning may predict subsequent language development and altered trajectories associated with ASD risk.

Beyond Baby Siblings-Expanding the Definition of "High-Risk Infants" in Autism Research.

PURPOSE OF REVIEW: Much of our understanding of early development in children with autism spectrum disorder (ASD) comes from studies of children with a family history of autism. We reviewed the current literature on neurodevelopmental profiles and autism prevalence from other high-risk infant groups to expose gaps and inform next steps. We focused on infants with early medical risk (e.g., preterm birth) and genetic risk (tuberous sclerosis complex [TSC]). RECENT FINDINGS: About 7% of very preterm infants are later diagnosed with ASD. Prospective studies of early development outside of familial-risk infants are rare; however, recent work within preterm and TSC infants suggests interesting similarities and differences from infants with a family history of ASD. It is essential that we extend our knowledge of early markers of ASD beyond familial-risk infants to expand our knowledge of autism as it emerges in order to develop better, more individualized early interventions.

Early concerns in parents of infants at risk for autism.

AIM: To examine parental concerns about children at increased familial risk (i.e. high risk) of developing autism spectrum disorder (ASD) in early infancy. METHOD: ASD-related and general parental concerns were prospectively collected for 76 infants at ages 1.5, 3, 6, 9, 12, and 18 months. Outcome classification was determined at 36 months. Analyses included generalized linear mixed models and qualitative evaluation of parental concerns in relation to risk status (high vs low risk) and outcome classification within the high-risk group (atypically developing vs typically developing) over time. RESULTS: Most parents had no concerns at 1.5 (high risk 71%, low risk 87%) and 3 months (high risk 77%, low risk 86%). Beginning at 6 months, parents of high-risk infants reported more ASD-related (p<0.001) and general concerns (p=0.003) than parents of low-risk infants. Beginning at 12 months, parents of high-risk atypically developing infants reported more ASD-related concerns than parents of high-risk typically developing infants (p=0.013). INTERPRETATION: Clinicians should elicit parental concerns and provide support, as parents are worried about their high-risk infants by age 6 months. Additionally, parents' abilities to identify concerns that are suggestive of ASD by age 12 months may aid in earlier screening and intervention. What this paper adds Most parents did not report concerns during early infancy. By 6 months, parents of high-risk infants reported autism spectrum disorder (ASD)-related and general concerns. By 12 months, parents of high-risk atypically developing infants identified ASD-related concerns.

Electrophysiological signatures of visual statistical learning in 3-month-old infants at familial and low risk for autism spectrum disorder.

Visual statistical learning (VSL) refers to the ability to extract associations and conditional probabilities within the visual environment. It may serve as a precursor to cognitive and social communication development. Quantifying VSL in infants at familial risk (FR) for Autism Spectrum Disorder (ASD) provides opportunities to understand how genetic predisposition can influence early learning processes which may, in turn, lay a foundation for cognitive and social communication delays. We examined electroencephalography (EEG) signatures of VSL in 3-month-old infants, examining whether EEG correlates of VSL differentiated FR from low-risk (LR) infants. In an exploratory analysis, we then examined whether EEG correlates of VSL at 3 months relate to cognitive function and ASD symptoms at 18 months. Infants were exposed to a continuous stream of looming shape pairs with varying probability that the shapes would occur in sequence (high probability-deterministic condition; low probability-probabilistic condition). EEG was time-locked to shapes based on their transitional probabilities. EEG analysis examined group-level characteristics underlying specific components, including the late frontal positivity (LFP) and N700 responses. FR infants demonstrated increased LFP and N700 response to the probabilistic condition, whereas LR infants demonstrated increased LFP and N700 response to the deterministic condition. LFP at 3 months predicted 18-month visual reception skills and not ASD symptoms. Our findings thus provide evidence for distinct VSL processes in FR and LR infants as early as 3 months. Atypical pattern learning in FR infants may lay a foundation for later delays in higher level, nonverbal cognitive skills, and predict ASD symptoms well before an ASD diagnosis is made.

Altered lateralization of dorsal language tracts in 6-week-old infants at risk for autism.

Altered structural connectivity has been identified as a possible biomarker of autism spectrum disorder (ASD) risk in the developing brain. Core features of ASD include impaired social communication and early language delay. Thus, examining white matter tracts associated with language may lend further insight into early signs of ASD risk and the mechanisms that underlie language impairments associated with the disorder. Evidence of altered structural connectivity has previously been detected in 6-month-old infants at high familial risk for developing ASD. However, as language processing begins in utero, differences in structural connectivity between language regions may be present in the early infant brain shortly after birth. Here we investigated key white matter pathways of the dorsal language network in 6-week-old infants at high (HR) and low (LR) risk for ASD to identify atypicalities in structural connectivity that may predict altered developmental trajectories prior to overt language delays and the onset of ASD symptomatology. Compared to HR infants, LR infants showed higher fractional anisotropy (FA) in the left superior longitudinal fasciculus (SLF); in contrast, in the right SLF, HR infants showed higher FA than LR infants. Additionally, HR infants showed more rightward lateralization of the SLF. Across both groups, measures of FA and lateralization of these pathways at 6 weeks of age were related to later language development at 18 months of age as well as ASD symptomatology at 36 months of age. These findings indicate that early differences in the structure of language pathways may provide an early predictor of future language development and ASD risk.

Brain connectivity in autism spectrum disorder.

PURPOSE OF REVIEW: Many studies have reported that individuals with autism spectrum disorder (ASD) have different brain connectivity patterns compared with typically developing individuals. However, the results of more recent studies do not unanimously support the traditional view in which individuals with ASD have lower connectivity between distant brain regions and increased connectivity within local brain regions. In this review, we discuss different methods for measuring brain connectivity and how the use of different metrics may contribute to the lack of convergence of investigations of connectivity in ASD. RECENT FINDINGS: The discrepancy in brain connectivity results across studies may be due to important methodological factors, such as the connectivity measure applied, the age of patients studied, the brain region(s) examined, and the time interval and frequency band(s) in which connectivity was analyzed. SUMMARY: We conclude that more sophisticated electroencephalography analytic approaches should be utilized to more accurately infer causation and directionality of information transfer between brain regions, which may show dynamic changes of functional connectivity in the brain. Moreover, further investigations of connectivity with respect to behavior and clinical phenotype are needed to probe underlying brain networks implicated in core deficits of ASD.

Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders.

PURPOSE OF REVIEW: The heterogeneity in clinical presentation and outcome in neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD) autism spectrum disorder (ASD) necessitates the identification and validation of biomarkers that can guide diagnosis, predict developmental outcomes, and monitor treatment response. Electrophysiology holds both practical and theoretical advantages as a clinical biomarker in neurodevelopmental disorders, and considerable effort has been invested in the search for electroencephalography (EEG) biomarkers in ADHD and ASD. RECENT FINDINGS: Here, we discuss the major themes in the evaluation of biomarkers and then review studies that have applied EEG to better inform diagnosis, focusing on the controversy surrounding the theta:beta ratio in ADHD; prediction of risk, highlighting recent studies of infants at high risk for ASD; and treatment monitoring, presenting new efforts in the redefinition of outcome measures in clinical trials of ASD treatment. SUMMARY: We conclude that insights gained from EEG studies will contribute significantly to a more mechanistic understanding of these disorders and to the development of biomarkers that can assist with diagnosis, prognosis, and intervention. There is a need, however, to utilize approaches that accommodate, rather than ignore, diagnostic heterogeneity and individual differences.

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function.

Impaired language pathways in tuberous sclerosis complex patients with autism spectrum disorders.

The purpose of this study was to examine the relationship between language pathways and autism spectrum disorders (ASDs) in patients with tuberous sclerosis complex (TSC). An advanced diffusion-weighted magnetic resonance imaging (MRI) was performed on 42 patients with TSC and 42 age-matched controls. Using a validated automatic method, white matter language pathways were identified and microstructural characteristics were extracted, including fractional anisotropy (FA) and mean diffusivity (MD). Among 42 patients with TSC, 12 had ASD (29%). After controlling for age, TSC patients without ASD had a lower FA than controls in the arcuate fasciculus (AF); TSC patients with ASD had even a smaller FA, lower than the FA for those without ASD. Similarly, TSC patients without ASD had a greater MD than controls in the AF; TSC patients with ASD had even a higher MD, greater than the MD in those without ASD. It remains unclear why some patients with TSC develop ASD, while others have better language and socio-behavioral outcomes. Our results suggest that language pathway microstructure may serve as a marker of the risk of ASD in TSC patients. Impaired microstructure in language pathways of TSC patients may indicate the development of ASD, although prospective studies of language pathway development and ASD diagnosis in TSC remain essential.

Autism and Epilepsy.

Epilepsy is one of the most common comorbidities in individuals with autism spectrum disorders (ASDs). Risk factors include the presence of developmental delay/intellectual disability, female sex, age, and an underlying genetic condition. Due to higher prevalence of epilepsy in ASD, it is important to have a high index of suspicion for seizures and refer to a neurologist if there are concerns. Genetic testing is recommended for all children with ASD but it becomes more high yield in children with epilepsy and ASD.

Measurement of Developmental and Behavioral Concerns in Toddlers With Tuberous Sclerosis Complex.

BACKGROUND: The TAND (Tuberous Sclerosis Complex [TSC]-Associated Neuropsychiatric Disorders) Checklist was developed as a clinical screener for neurodevelopmental disorders in TSC. Most studies have described patterns in older children and adults. This study sought to better understand behavioral concerns as measured by the TAND Checklist in young children with TSC. METHODS: We examined patterns of caregiver responses to the TAND Checklist in 90 toddlers with TSC (12 to 23 months n = 60; 24 to 36 months n = 30) through data collected during baseline visits across two TSC early intervention studies. RESULTS: Over 90% of caregivers reported at least one behavioral concern related to TAND. The number of concerns increased with age. Delayed language was the most frequently reported concern across ages (12 to 23 months: 58.3%, 24 to 36 months: 86.7%). Questions related to behavioral concerns were largely relevant in this age range, but questions in other areas, such as neuropsychological or academic function, were not. CONCLUSIONS: TAND symptoms are very common in toddlers with TSC, and these symptoms may increase with age. The TAND Checklist is a useful tool for identifying behavioral concerns efficiently, but several items and sections are not suited to younger children. Results support the development of an abbreviated form of the TAND Checklist for toddlers.

Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity.

BACKGROUND: Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD. METHODS: EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate. RESULTS: Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found. CONCLUSIONS: The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism.

Concomitant medication use in children with autism spectrum disorder: Data from the Autism Biomarkers Consortium for Clinical Trials.

LAY ABSTRACT: Children with autism spectrum disorder are prescribed a variety of medications that affect the central nervous system (psychotropic medications) to address behavior and mood. In clinical trials, individuals taking concomitant psychotropic medications often are excluded to maintain homogeneity of the sample and prevent contamination of biomarkers or clinical endpoints. However, this choice may significantly diminish the clinical representativeness of the sample. In a recent multisite study designed to identify biomarkers and behavioral endpoints for clinical trials (the Autism Biomarkers Consortium for Clinical Trials), school-age children with autism spectrum disorder were enrolled without excluding for medications, thus providing a unique opportunity to examine characteristics of psychotropic medication use in a research cohort and to guide future decisions on medication-related inclusion criteria. The aims of the current analysis were (1) to quantify the frequency and type of psychotropic medications reported in school-age children enrolled in the ABC-CT and (2) to examine behavioral features of children with autism spectrum disorder based on medication classes. Of the 280 children with autism spectrum disorder in the cohort, 42.5% were taking psychotropic medications, with polypharmacy in half of these children. The most commonly reported psychotropic medications included melatonin, stimulants, selective serotonin reuptake inhibitors, alpha agonists, and antipsychotics. Descriptive analysis showed that children taking antipsychotics displayed a trend toward greater overall impairment. Our findings suggest that exclusion of children taking concomitant psychotropic medications in trials could limit the clinical representativeness of the study population, perhaps even excluding children who may most benefit from new treatment options.

Evaluation of clinical assessments of social abilities for use in autism clinical trials by the autism biomarkers consortium for clinical trials.

Clinical trials in autism spectrum disorder (ASD) often rely on clinician rating scales and parent surveys to measure autism-related features and social behaviors. To aid in the selection of these assessments for future clinical trials, the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) directly compared eight common instruments with respect to acquisition rates, sensitivity to group differences, equivalence across demographic sub-groups, convergent validity, and stability over a 6-week period. The sample included 280 children diagnosed with ASD (65 girls) and 119 neurotypical children (36 girls) aged from 6 to 11 years. Full scale IQ for ASD ranged from 60 to 150 and for neurotypical ranged from 86 to 150. Instruments measured clinician global assessment and autism-related behaviors, social communication abilities, adaptive function, and social withdrawal behavior. For each instrument, we examined only the scales that measured social or communication functioning. Data acquisition rates were at least 97.5% at T1 and 95.7% at T2. All scales distinguished diagnostic groups. Some scales significantly differed by participant and/or family demographic characteristics. Within the ASD group, most clinical instruments exhibited weak (≥ |0.1|) to moderate (≥ |0.4|) intercorrelations. Short-term stability was moderate (ICC: 0.5-0.75) to excellent (ICC: >0.9) within the ASD group. Variations in the degree of stability may inform viability for different contexts of use, such as identifying clinical subgroups for trials versus serving as a modifiable clinical outcome. All instruments were evaluated in terms of their advantages and potential concerns for use in clinical trials.