Gregory Neal Barnes

Autism spectrum disorder (ASD) prevalence continues to rise despite no recent changes to screening or diagnostic criteria. A complete understanding of the

Background and Objectives: Autism spectrum disorder (ASD) is a prevalent and largely idiopathic developmental disorder with relatively widespread etiology. Currently, there are no validated diagnostic or screening biomarkers for ASD, besides addressing the associated comorbidities. ASD is primarily diagnosed based on behavioral, motor, and cognitive characteristics. Until recently, although the cerebellum was particularly implicated in motor control, it was under-researched for its potential role in the development of ASD. However, cerebellar circuitry is altered in ASD, impacting its brain interconnections, affecting brain development, as well as social and behavioral outcomes associated with ASD. Methods: We reviewed the potential role of the cerebellum in ASD, particularly how its dysfunction during development or its early postnatal injury may impact on the maturation of other connected circuits and play a role in the development of core ASD symptoms. Results: Based on the literature, we addressed cerebellar changes that may alter synaptic pruning, immune cells’ function, neurotransmitters, blood–brain barrier permeability, and potential signaling pathways involved in ASD, and how these changes may interplay to contribute to ASD pathophysiology. Conclusions: Further research is needed to understand these interactions that may provide novel therapeutic options specifically targeted at the cerebellum.

Autism spectrum disorder (ASD) is a prevalent and largely idiopathic developmental disorder with relatively widespread etiology. Currently there are no validated diagnostic or screening biomarkers for ASD, besides addressing the associated comorbidities. ASD is primarily diagnosed based on behavioral motor and cognitive characteristics. Until recently, the cerebellum had been particularly implicated in motor control, and under-researched for its potential role in the development of ASD. However, cerebellar circuitry is altered in ASD, impacting its brain interconnections, affecting brain development, and social and behavioral outcomes associated with ASD. We review the potential role of the cerebellum in ASD, how its dysfunction during development or its early postnatal injury may impact the maturation of other connected circuits, and play a role in the development of core ASD symptoms. We address cerebellar changes that may alter synaptic pruning, immune cells’ function, neurotransmitters, blood brain barrier permeability, and potential signaling pathways involved in ASD and how all these changes interplay may contribute to ASD pathophysiology. Understanding of these interactions, may provide novel therapeutic options specifically targeted to the cerebellum.

Introduction: The prevalence of Autism Spectrum Disorder (ASD) has drastically risen over the last two decades and is currently estimated to affect 1 in 36 children in the U.S., according to the center for disease control (CDC). This heterogenous neurodevelopmental disorder is characterized by impaired social interactions, communication deficits, and repetitive behaviors plus restricted interest. Autistic individuals also commonly present with a myriad of comorbidities, such as attention deficit hyperactivity disorder, anxiety, and seizures. To date, a pharmacological intervention for the treatment of core autistic symptoms has not been identified. Cannabidiol (CBD), the major nonpsychoactive constituent of Cannabis sativa, is suggested to have multiple therapeutic applications, but its effect(s) on idiopathic autism is unknown. We hypothesized that CBD will effectively attenuate the autism-like behaviors and autism-associated comorbid behaviors in BTBR T⁺Itpr3^tf/J (BTBR) mice, an established mouse model of idiopathic ASD.Methods: Male BTBR mice were injected intraperitoneally with either vehicle, 20 mg/kg CBD or 50 mg/kg CBD daily for two weeks beginning at postnatal day 21 ± 3. On the final treatment day, a battery of behavioral assays were used to evaluate the effects of CBD on the BTBR mice, as compared to age-matched, vehicle-treated C57BL/6 J mice.Results: High dose (50 mg/kg) CBD treatment attenuated the elevated repetitive self-grooming behavior and hyperlocomotion in BTBR mice. The social deficits exhibited by the control BTBR mice were rescued by the 20 mg/kg CBD treatment.Discussion: Our data indicate that different doses for CBD are needed for treating specific ASD-like behaviors. Together, our results suggest that CBD may be an effective drug to ameliorate repetitive/restricted behaviors, social deficits, and autism-associated hyperactivity.

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.

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.

Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview–Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.

Alzheimer’s disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10–15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder’s effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease originating from combined genetic and environmental factors. Post‐mortem human studies and some animal ASD models have shown brain neuroinflammation, oxidative stress, and changes in blood–brain barrier (BBB) integrity. However, the signaling pathways leading to these inflammatory findings and vascular alterations are currently unclear. The BBB plays a critical role in controlling brain homeostasis and immune response. Its dysfunction can result from developmental genetic abnormalities or neuroinflammatory processes. In this review, we explore the role of the Sonic Hedgehog/Wingless‐related integration site (Shh/Wnt) pathways in neurodevelopment, neuroinflammation, and BBB development. The balance between Wnt‐β‐catenin and Shh pathways controls angiogenesis, barriergenesis, neurodevelopment, central nervous system (CNS) morphogenesis, and neuronal guidance. These interactions are critical to maintain BBB function in the mature CNS to prevent the influx of pathogens and inflammatory cells. Genetic mutations of key components of these pathways have been identified in ASD patients and animal models, which correlate with the severity of ASD symptoms. Disruption of the Shh/Wnt crosstalk may therefore compromise BBB development and function. In turn, impaired Shh signaling and glial activation may cause neuroinflammation that could disrupt the BBB. Elucidating how ASD‐related mutations of Shh/Wnt signaling could cause BBB leaks and neuroinflammation will contribute to our understanding of the role of their interactions in ASD pathophysiology. These observations may provide novel targeted therapeutic strategies to prevent or alleviate ASD symptoms while preserving normal developmental processes. image Cover Image for this issue: https://doi.org/10.1111/jnc.15081