Autism Spectrum Disorder Subgroups Research Articles

Cognitive inflexibility and immunome biomarkers in children with autism spectrum disorder

Cognitive inflexibility is a transdiagnostic endophenotype that presents across a range of disorders, including autism spectrum disorder (ASD), which is maintained through adulthood, and encompasses both cognitive and behavioral rigidity. There is evidence for immune dysfunction in a subgroup of ASD, including systemic inflammation, cytokine dysregulation and anti-brain autoantibodies. Although immunome pathways are involved in ASD pathophysiology, there is little known about how they relate to symptom domains or symptom severity, and whether such biomarkers may be useful in optimizing future clinical trials. We correlated baseline clinical measures of cognitive inflexibility and resultant irritability with immunome biomarker levels in children with ASD, aged 5–18 years with ABC-I scores ≥18, CGI-S scores ≥4 and SRS-2 scores ≥66T, at Albert Einstein College of Medicine (AECOM). Non-parametric Spearman correlations and estimated multivariable regression analyses adjusting for potential confounders, including other clinical variables, race, sex, and age were completed. Strong positive correlations (rs > .70) were found between the Montefiore Einstein Rigidity Scale – Revised (MERS-R) Total Score and the pro-inflammatory cytokines IL-6 (rs=.80), granulocyte-colony stimulating factor (GCSF; rs =.72), macrophage inflammatory protein-1 alpha (MIP-1a; rs =.71). The MERS-R subscales also had moderate and strong correlations with the immunome biomarkers. The MERS-R Total Rigidity Subscale Score had a strong positive relationship with IL-6 (rs = 0.7856). Using multivariable regression analyses significant relationships were found between the MERS-R Total Rigidity Subscale Score and proinflammatory cytokine IL-18 (p = 0.02), and a nonsignificant trend was found between it and IFN-alpha2 (β = −4.982, p = 0.058). The ABC-I was significantly correlated with pro-inflammatory cytokine IL-18 (p = .013), IFN-alpha2 (p = 0.039), the anti-inflammatory cytokine IL-10 (p = 0.02), and adaptive immunity cytokine IL-2 (p = 0.041). This preliminary data is the first to examine the relationship of clinical measures of cognitive inflexibility and immunome biomarkers in children with ASD, and may provide a framework for better understanding the relationship between immunome mechanisms, cognitive inflexibility, and ASD symptomatology. Clinicaltrials.gov: NCT03202303.

Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study

ObjectiveHow abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology. MethodsA total of 83 ASD and typically developing (TD) individuals participated in this study. Participants’ interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio. ResultsASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017). ConclusionsDifferential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation. SignificanceBrain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.

Associations Between Joint Attention, Supported Joint Engagement and Language in TD Children and Children with ASD: Potential Sources of Individual and Group Differences in Language Outcomes

ABSTRACT Joint Attention (JA) and Supported Joint Engagement (Supported JE) have each been reported to predict later language development in typically developing (TD) children and children with Autism Spectrum Disorder (ASD). In this longitudinal study including 33 TD children (20 months at V1) and 30 children with ASD (33 months at V1), the contributions of JA and Supported JE to later language, assessed via standardized tests and spontaneous speech, were directly compared. Frequency and durations of JA and Supported JE episodes were coded from 30-minute interactions with caregivers; subsequent language skills were assessed two years later. JA duration in the ASD group significantly predicted later standardized and spontaneous language, most strongly in the low-verbal ASD subgroup. Supported JE measures did not positively predict later language in either group. These findings suggest that JA played a larger role with children with ASD with low-verbal abilities, but not with children with ASD with high-verbal abilities nor with the TD children. The current study adds to existing literature by providing further support for studying children with ASD as two subgroups based on their verbal abilities (high vs low), as well as directly comparing the effects of JA and Supported JE on later language development in such groups. Implications for further research are discussed.

Identification of Immune Infiltration and Iron Metabolism-Related Subgroups in Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with a broad spectrum of symptoms and prognoses. Effective therapy requires understanding this variability. ASD children's cognitive and immunological development may depend on iron homoeostasis. This study employs a machine learning model that focuses on iron metabolism hub genes to identify ASD subgroups and describe immune infiltration patterns. A total of 97 control and 148 ASD samples were obtained from the GEO database. Differentially expressed genes (DEGs) and an iron metabolism gene collection achieved the intersection of 25 genes. Unsupervised cluster analysis determined molecular subgroups in individuals with ASD based on 25 genes related to iron metabolism. We assessed gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, gene set variation analysis (GSVA), and immune infiltration analysis to compare iron metabolism subtype effects. We employed machine learning to identify subtype-predicting hub genes and utilized both training and validation sets to assess gene subtype prediction accuracy. ASD can be classified into two iron-metabolizing molecular clusters. Metabolic enrichment pathways differed between clusters. Immune infiltration showed that clusters differed immunologically. Cluster 2 had better immunological scores and more immune cells, indicating a stronger immune response. Machine learning screening identified SELENBP1 and CAND1 as important genes in ASD's iron metabolism signaling pathway. These genes express in the brain and have AUC values over 0.8, implying significant predictive power. The present study introduces iron metabolism signaling pathway indicators to predict ASD subtypes. ASD is linked to immune cell infiltration and iron metabolism disorders.

Assessing cortical features in early stage ASD children.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental disorder largely investigated in the neurologic field. Recently, neuroimaging studies have been conducted in order to investigate cerebral morphologic alterations in ASD patients, demonstrating an atypical brain development before the clinical manifestations of the disorder. Cortical Thickness (CT) and Local Gyrification Index (LGI) distribution for ASD children were investigated in this study, with the aim to evaluate possible relationship between brain measures and individual characteristics (i.e., IQ and verbal ability). 3D T1-w sequences from 129 ASD and 58 age-matched Healthy Controls (HC) were acquired and processed in order to assess CT and LGI for each subject. Intergroup differences between ASD and HC were investigated, including analyses of 2 ASD subgroups, split according to patient verbal ability and IQ. When compared to HC, ASD showed increased CT and LGI within several brain areas, both as an overall group and as verbal ability an IQ subgroups. Moreover, when comparing language characteristics of the ASD subjects, those patients with verbal ability exhibit significant CT and LGI increase was found within the occipital lobe of right hemisphere. No significant results occurred when comparing ASD patients according to their IQ value. These results support the hypothesis of abnormal brain maturation in ASD since early childhood with differences among clinical subgroups suggesting different anatomical substrates underlying an aberrant connectivity.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis.

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

Ophthalmologic diagnoses in youths with autism spectrum disorder: Prevalence and clinical correlates.

Autism spectrum disorder (ASD) is associated with a high prevalence of visual dysfunction. This study aimed to investigate the rates of amblyopia, refractive errors, and strabismus, as well as their clinical correlates in ASD. This population-based matched-cohort study used data from the Taiwan National Health Insurance Research Database. A total of 3,551 youths with ASD and 35,510 non-autistic control participants matched by age and sex were included. All the participants were followed-up until they were 18 years old. The prevalence of amblyopia, refractive errors, and strabismus was compared between the ASD and control groups. Effect modifiers, including sex, ASD subgroup, and co-diagnosis of intelligence disability, were examined. Compared to the control group, youths with ASD had a significantly increased risk of amblyopia (adjusted odds ratio [aOR] = 1.75), anisometropia (aOR = 1.66), astigmatism (aOR = 1.51), hypermetropia (aOR = 2.08), exotropia (aOR = 2.86), and esotropia (aOR = 2.63), but a comparable likelihood of myopia according to age. Males with ASD had a significantly lower likelihood of exotropia, but a higher likelihood of myopia than females with ASD. The autism subgroup had a higher OR for hypermetropia, but a lower OR for myopia than the other ASD subgroups. ASD youths with intelligence disabilities demonstrated significantly higher ORs for amblyopia, hypermetropia, and all types of strabismus and lower OR for myopia than those without intelligence disabilities. In conclusion, the rates of amblyopia, refractive errors, and strabismus were higher in youths with ASD. Ocular abnormalities in youths with ASD require a comprehensive assessment and management.

Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI

Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations. This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n=415 ASD patients (males n=357), and n=574 typical development (TD) controls (males n=410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model. All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ~75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%). The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

Phenotypically driven subgroups of ASD display distinct metabolomic profiles

Autism Spectrum Disorder (ASD) is a heterogeneous condition that includes a broad range of characteristics and associated comorbidities; however, the biology underlying the variability in phenotypes is not well understood. As ASD impacts approximately 1 in 100 children globally, there is an urgent need to better understand the biological mechanisms that contribute to features of ASD. In this study, we leveraged rich phenotypic and diagnostic information related to ASD in 2001 individuals aged 4 to 17 years from the Simons Simplex Collection to derive phenotypically driven subgroups and investigate their respective metabolomes. We performed hierarchical clustering on 40 phenotypes spanning four ASD clinical domains, resulting in three subgroups with distinct phenotype patterns. Using global plasma metabolomic profiling generated by ultrahigh-performance liquid chromatography mass spectrometry, we characterized the metabolome of individuals in each subgroup to interrogate underlying biology related to the subgroups. Subgroup 1 included children with the least maladaptive behavioral traits (N = 862); global decreases in lipid metabolites and concomitant increases in amino acid and nucleotide pathways were observed for children in this subgroup. Subgroup 2 included children with the highest degree of challenges across all phenotype domains (N = 631), and their metabolome profiles demonstrated aberrant metabolism of membrane lipids and increases in lipid oxidation products. Subgroup 3 included children with maladaptive behaviors and co-occurring conditions that showed the highest IQ scores (N = 508); these individuals had increases in sphingolipid metabolites and fatty acid byproducts. Overall, these findings indicated distinct metabolic patterns within ASD subgroups, which may reflect the biological mechanisms giving rise to specific patterns of ASD characteristics. Our results may have important clinical applications relevant to personalized medicine approaches towards managing ASD symptoms.

Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder.

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.

Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms.

Abnormal gray matter (GM) asymmetry has been verified in autism spectrum disorder (ASD), which is characterized by high heterogeneity. ASD is distinguished by three core symptom domains. Previous neuroimaging studies have offered support for divergent neural substrates of different core symptom domains in ASD. However, no previous study has explored GM asymmetry alterations underlying different core symptom domains. This study sought to clarify atypical GM asymmetry patterns underlying three core symptom domains in ASD with a large sample of 230 minors with ASD (ages 7-18 years) and 274 matched TD controls from the Autism Brain Imaging Data Exchange I (ABIDE I) repository. To this end, the scores of the revised autism diagnostic interview (ADI-R) subscales were normalized for grouping ASD into three core-symptom-defined subgroups: social interaction (SI), verbal communication (VA), and restricted repetitive behaviors (RRB). We investigated core-symptom-related GM asymmetry alterations in ASD resulting from advanced voxel-based morphometry (VBM) by general linear models. We also examined the relationship between GM asymmetry and age and between GM asymmetry and symptom severity assessed by the Autism Diagnostic Observation Schedule (ADOS). We found unique GM asymmetry alterations underlying three core-symptom-defined subgroups in ASD: more rightward asymmetry in the thalamus for SI, less rightward asymmetry in the superior temporal gyrus, anterior cingulate and caudate for VA, and less rightward asymmetry in the middle and inferior frontal gyrus for RRB. Furthermore, the asymmetry indexes in the thalamus were negatively associated with ADOS_SOCIAL scores in the general ASD group. We also showed significant correlations between GM asymmetry and age in ASD and TD individuals. Our results support the theory that each core symptom domain of ASD may have independent etiological and neurobiological underpinnings, which is essential for the interpretation of heterogeneity and the future diagnosis and treatment of ASD.

Catecholaminergic and cholinergic neuromodulation in autism spectrum disorder: A comparison to attention-deficit hyperactivity disorder.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by social impairments and restricted, repetitive behaviors. Treatment of ASD is notoriously difficult and might benefit from identification of underlying mechanisms that overlap with those disturbed in other developmental disorders, for which treatment options are more obvious. One example of the latter is attention-deficit hyperactivity disorder (ADHD), given the efficacy of especially stimulants in treatment of ADHD. Deficiencies in catecholaminergic systems [dopamine (DA), norepinephrine (NE)] in ADHD are obvious targets for stimulant treatment. Recent findings suggest that dysfunction in catecholaminergic systems may also be a factor in at least a subgroup of ASD. In this review we scrutinize the evidence for catecholaminergic mechanisms underlying ASD symptoms, and also include in this analysis a third classic ascending arousing system, the acetylcholinergic (ACh) network. We complement this with a comprehensive review of DA-, NE-, and ACh-targeted interventions in ASD, and an exploratory search for potential treatment-response predictors (biomarkers) in ASD, genetically or otherwise. Based on this review and analysis we propose that (1) stimulant treatment may be a viable option for an ASD subcategory, possibly defined by genetic subtyping; (2) cerebellar dysfunction is pronounced for a relatively small ADHD subgroup but much more common in ASD and in both cases may point toward NE- or ACh-directed intervention; (3) deficiency of the cortical salience network is sizable in subgroups of both disorders, and biomarkers such as eye blink rate and pupillometric data may predict the efficacy of targeting this underlying deficiency via DA, NE, or ACh in both ASD and ADHD.

A family study of symbolic learning and synaptic plasticity in autism spectrum disorder.

The current study presents a male with autism spectrum disorder (ASD) and a 3q29 deletion, and three healthy first-degree relatives. Our magnetic resonance imaging (MRI) dataset included a healthy control subset. We describe a comprehensive multimodal approach, including equivalence class formation, neurocognitive testing, MRI, and electroencephalography (EEG)-based cortical plasticity, which can provide new insights into socio-communicative and learning impairments and neural underpinnings in ASD. On neurocognitive testing, the proband showed reduced processing speed, attending behavior, and executive function. He required more training trials in equivalence class training compared with family members and exhibited impaired priming of words compared with priming with images. The proband had smaller intracranial volume and surface area and a larger visual evoked potential (VEP) C1 amplitude than family members and intact long-term potentiation (LTP)-like visual cortex plasticity. Together, these results suggest that 3q29 deletion-related ASD is associated with impaired problem-solving strategies in complex socio-communicative and learning tasks, smaller intracranial and surface area, altered VEP amplitude, and normal LTP-like visual cortex plasticity. Further studies are needed to clarify whether this multimodal approach can be used to identify ASD subgroups with distinct neurobiological alterations and to uncover mechanisms underlying socio-communicative and learning impairments. Lay Summary: We studied learning, brain activity, and brain structure in a person with autism and a genetic aberration, and his close relatives. Compared with relatives, the person with autism required more training for learning, and visual learning was better than verbal learning. This person had some changes in the activity of the visual cortex, and the size and the surface area of the brain were reduced. Knowledge about learning and brain mechanisms is valuable for the development of training programs for individuals with autism.

Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder

Defining different genetic subtypes of autism spectrum disorder (ASD) can enable the prediction of developmental outcomes. Based on minor physical and major congenital anomalies, we categorize 325 Canadian children with ASD into dysmorphic and nondysmorphic subgroups. We develop a method for calculating a patient-level, genome-wide rare variant score (GRVS) from whole-genome sequencing (WGS) data. GRVS is a sum of the number of variants in morphology-associated coding and non-coding regions, weighted by their effect sizes. Probands with dysmorphic ASD have a significantly higher GRVS compared to those with nondysmorphic ASD (P = 0.03). Using the polygenic transmission disequilibrium test, we observe an over-transmission of ASD-associated common variants in nondysmorphic ASD probands (P = 2.9 × 10−3). These findings replicate using WGS data from 442 ASD probands with accompanying morphology data from the Simons Simplex Collection. Our results provide support for an alternative genomic classification of ASD subgroups using morphology data, which may inform intervention protocols.

Cluster Analysis of Short Sensory Profile Data Reveals Sensory-Based Subgroups in Autism Spectrum Disorder.

Autism spectrum disorder is a common, heterogeneous neurodevelopmental disorder lacking targeted treatments. Additional features include restricted, repetitive patterns of behaviors and differences in sensory processing. We hypothesized that detailed sensory features including modality specific hyper- and hypo-sensitivity could be used to identify clinically recognizable subgroups with unique underlying gene variants. Participants included 378 individuals with a clinical diagnosis of autism spectrum disorder who contributed Short Sensory Profile data assessing the frequency of sensory behaviors and whole genome sequencing results to the Autism Speaks' MSSNG database. Sensory phenotypes in this cohort were not randomly distributed with 10 patterns describing 43% (162/378) of participants. Cross comparison of two independent cluster analyses on sensory responses identified six distinct sensory-based subgroups. We then characterized subgroups by calculating the percent of patients in each subgroup who had variants with a Combined Annotation Dependent Depletion (CADD) score of 15 or greater in each of 24,896 genes. Each subgroup exhibited a unique pattern of genes with a high frequency of variants. These results support the use of sensory features to identify autism spectrum disorder subgroups with shared genetic variants.

Multimorbidity patterns and subgroups among autistic adults with intellectual disability: Results from the EFAAR study.

Multimorbidity relates to having multiple chronic health conditions. It is a risk factor for poor health and reduces life expectancy. Autistic people have multiple chronic health conditions and die prematurely, especially if they have an intellectual disability (autism spectrum disorder and intellectual disability). Certain pathophysiological processes observed in autism spectrum disorder are common to those related to the genesis and/or maintenance of multimorbidity. Furthermore, multimorbidity could be helpful in better identifying patient subgroups in autism spectrum disorder. It is therefore essential to better characterize multimorbidity and its consequences in the subgroup of autism spectrum disorder + intellectual disability individuals to offer them personalized care. We conducted a preliminary study of 63 autism spectrum disorder + intellectual disability adults to classify them according to their multimorbidity and search for a specific combination of chronic health conditions. We observed high and early multimorbidity in this sample and identified four classes of participants, distinguished by their multimorbidity status, independence and number of treatments. In addition, we observed a dominant combination of multimorbidity in our sample, combining immune dysfunction and gastrointestinal disorders, neurological and joint diseases. These findings support the hypothesis that an altered gut-brain relationship is involved in the risk of autism spectrum disorder, its outcome, and its association with chronic health conditions. Although larger studies are needed, our results suggest that subgroups of autism spectrum disorder + intellectual disability individuals can be identified based on their multimorbidity and potentially different ageing trajectories. A more comprehensive and personalized approach is needed to reduce the burden of multimorbidity and increase the quality of life and life expectancy in autism spectrum disorder/ intellectual disability.

Stratifying the autistic phenotype using electrophysiological indices of social perception.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heterogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD.

Understanding the nature of face processing in early autism: A prospective study.

Dimensional approaches to psychopathology interrogate the core neurocognitive domains interacting at the individual level to shape diagnostic symptoms. Embedding this approach in prospective longitudinal studies could transform our understanding of the mechanisms underlying neurodevelopmental disorders. Such designs require us to move beyond traditional group comparisons and determine which domain-specific alterations apply at the level of the individual, and whether they vary across distinct phenotypic subgroups. As a proof of principle, this study examines how the domain of face processing contributes to the emergence of autism spectrum disorder (ASD). We used an event-related potentials (ERPs) task in a cohort of 8-month-old infants with (n = 148) and without (n = 68) an older sibling with ASD, and combined traditional case-control comparisons with machine-learning techniques for prediction of social traits and ASD diagnosis at 36 months, and Bayesian hierarchical clustering for stratification into subgroups. A broad profile of alterations in the time-course of neural processing of faces in infancy was predictive of later ASD, with a strong convergence in ERP features predicting social traits and diagnosis. We identified two main subgroups in ASD, defined by distinct patterns of neural responses to faces, which differed on later sensory sensitivity. Taken together, our findings suggest that individual differences between infants contribute to the diffuse pattern of alterations predictive of ASD in the first year of life. Moving from group-level comparisons to pattern recognition and stratification can help to understand and reduce heterogeneity in clinical cohorts, and improve our understanding of the mechanisms that lead to later neurodevelopmental outcomes.

Potential Cross Talk between Autism Risk Genes and Neurovascular Molecules: A Pilot Study on Impact of Blood Brain Barrier Integrity.

Autism Spectrum Disorder (ASD) is a common pediatric neurobiological disorder with up to 80% of genetic etiologies. Systems biology approaches may make it possible to test novel therapeutic strategies targeting molecular pathways to alleviate ASD symptoms. A clinical database of autism subjects was queried for individuals with a copy number variation (CNV) on microarray, Vineland, and Parent Concern Questionnaire scores. Pathway analyses of genes from pathogenic CNVs yielded 659 genes whose protein–protein interactions and mRNA expression mapped 121 genes with maximal antenatal expression in 12 brain regions. A Research Domain Criteria (RDoC)-derived neural circuits map revealed significant differences in anxiety, motor, and activities of daily living skills scores between altered CNV genes and normal microarrays subjects, involving Positive Valence (reward), Cognition (IQ), and Social Processes. Vascular signaling was identified as a biological process that may influence these neural circuits. Neuroinflammation, microglial activation, iNOS and 3-nitrotyrosine increase in the brain of Semaphorin 3F- Neuropilin 2 (Sema 3F-NRP2) KO, an ASD mouse model, agree with previous reports in the brain of ASD individuals. Signs of platelet deposition, activation, release of serotonin, and albumin leakage in ASD-relevant brain regions suggest possible blood brain barrier (BBB) deficits. Disruption of neurovascular signaling and BBB with neuroinflammation may mediate causative pathophysiology in some ASD subgroups. Although preliminary, these data demonstrate the potential for developing novel therapeutic strategies based on clinically derived data, genomics, cognitive neuroscience, and basic neuroscience methods.

Vitamin D Status in Children with Autism Spectrum Disorders: Determinants and Effects of the Response to Probiotic Supplementation.

A relationship between the presence of clinical symptoms and gastrointestinal (GI) disturbances associated with nutritional deficiencies, including vitamin D (25(OH)D) deficiency, has been observed in autism spectrum disorder (ASD). The aim was to evaluate 25(OH)D levels according to the annual rhythm cycle, gender, the severity of autism, nutritional or clinical status, inflammatory and metabolic biomarkers, GI symptoms, and the clinical response to probiotic/placebo supplementation in preschooler children with ASD. Eighty-one ASD preschoolers (67 males) were assessed with standardized tools for ASD severity (ADOS score) and GI symptoms (by GI-Index at six-items and at nine-items, the latter defined as the Total GI-Index). The 25(OH)D levels were compared among different ASD subgroups according to metabolic and inflammatory biomarkers (leptin, insulin, resistin, PAI-1, MCP-1, TNF-alfa, and IL-6), gender, and the presence or absence of: (i) GI symptoms, (ii) the response to probiotic supplementation (the improvement of GI symptomatology), (iii) the response to probiotic supplementation (improvement of ASD severity). Only 25% of the ASD children presented an adequate 25(OH)D status (≥30 ng/mL according to the Endocrine Society guidelines). All the 25(OH)D levels falling in the severe deficiency range (<10 ng/mL) were observed in the male subgroup. A significant inverse correlation between 25(OH)D and leptin was observed (R = −0.24, p = 0.037). An inverse correlation was found between 25(OH)D levels and the GI Index 6-Items and Total GI-Index (R = −0.25, p = 0.026; −0.27, = 0.009) and a direct relationship with the probiotic response (R = 0.4, p = 0.05). The monitoring of 25(OH)D levels and the co-administration of 25(OH)D and probiotic supplementation could be considered in ASD from early ages.