Translational approaches to the biology of Autism: false dawn or a new era?

Links between brain structure and function in children with autism spectrum disorder by parallel independent component analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder accompanied by structural and functional changes in the brain. However, the relationship between brain structure and function in children with ASD remains largely obscure. In the current study, parallel independent component analysis (pICA) was performed to identify inter-modality associations by drawing on information from different modalities. Structural and resting-state functional magnetic resonance imaging data from 105 children with ASD and 102 typically developing children (obtained from the open-access Autism Brain Imaging Data Exchange database) were combined through the pICA framework. Features of structural and functional modalities were represented by the voxel-based morphometry (VBM) and amplitude of low-frequency fluctuations (ALFF), respectively. The relationship between the structural and functional components derived from the pICA was investigated by Pearson's correlation analysis, and between-group differences in these components were analyzed through the two-sample t-test. Finally, multivariate support vector regression analysis was used to analyze the relationship between the structural/functional components and Autism Diagnostic Observation Schedule (ADOS) subscores in the ASD group. This study found a significant association between VBM and ALFF components in ASD. Significant between-group differences were detected in the loading coefficients of the VBM component. Furthermore, the ALFF component loading coefficients predicted the subscores of communication and repetitive stereotypic behaviors of the ADOS. Likewise, the VBM component loading coefficients predicted the ADOS communication subscore in ASD. These findings provide evidence of a link between brain function and structure, yielding new insights into the neural mechanisms of ASD.

A quantitative weight-of-evidence review of preclinical studies examining the potential developmental neurotoxicity of acetaminophen

Acetaminophen [paracetamol; N-acetyl-para-aminophenol (APAP)] is an antipyretic/analgesic commonly used in the treatment of fever and mild to moderate pain, headache, myalgia, and dysmenorrhea. Recent literature has questioned the safety of acetaminophen use during pregnancy, with an emphasis on whether exposure to the developing nervous system results in behavioral changes consistent with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and/or other cognitive deficits in the offspring. No previous review has used a fully detailed, quantitative weight-of-evidence (QWoE) approach to critically examine the preclinical acetaminophen data with regards to potential developmental neurotoxicity (DNT). Following regulatory guidance, a QWoE framework using prespecified scoring criteria was developed consistent with previous approaches to characterize potential adverse DNT outcomes with considerations for biological relevance of the response to adverse outcomes (outcome score) and the strength of methods and study design (methods score). Considerations for the methods score included (1) experimental design, (2) details/reliability of measurement(s), (3) data transparency, and (4) translational/methodological relevance. Considerations for the outcome score included response-related (1) statistical significance, (2) dose-response, (3) relevance/reliability/magnitude, (4) plausibility, and (5) translational relevance, including consideration of systemic toxicity/hepatotoxicity and therapeutic and/or non-systemically toxic doses and durations of use. Application of this QWoE framework to the 34 in vivo studies identified that assess the potential DNT of acetaminophen resulted in 188 QWoE entries documented across 11 DNT endpoints: social behavior, stereotypic behavior, behavioral rigidity, attention/impulsivity, hyperactivity, anxiety-like behavior, sensorimotor function, spatial learning/memory, nonspatial learning/memory, neuroanatomy, and neurotransmission. For each endpoint, the mean outcome score and methods score were calculated for total entries and for entries segregated by sex to assist in determining data quality and potential adversity. Informed by all 188 entries, the QWoE analysis demonstrated data of moderate quality showing no consistent evidence of DNT in male and female rodents following exposure to acetaminophen at therapeutic and/or nonsystemically toxic doses. Although some of the DNT endpoints (behavioral rigidity, attention/impulsivity, spatial learning/memory, neuroanatomy, and neurotransmission) generally displayed a more limited dataset and/or relatively lower data quality, similar conclusions were drawn based on results indicating a lack of biological relevance and reliability of reported adverse effects. Overall, this QWoE analysis on the preclinical in vivo data demonstrates no consistent evidence of adverse effects following developmental exposure to acetaminophen at therapeutic and/or non-systemically toxic doses on the structure and function of the nervous system, including neuroanatomical, neurotransmission, and behavioral endpoints.

Mapping the structure of biomarkers in autism spectrum disorder: a review of the most influential studies

BACKGROUND

Autism spectrum disorder is a distinctive developmental condition which is caused by an interaction between genetic vulnerability and environmental factors. Biomarkers play a crucial role in understanding disease characteristics for diagnosis, prognosis, and treatment. This study employs bibliometric analysis to identify and review the 100 top-cited articles' characteristics, current research hotspots and future directions of autism biomarkers.

METHODS

A comprehensive search of autism biomarkers studies was retrieved from the Web of Science Core Collection database with a combined keyword search strategy. A comprehensive analysis of the top 100 articles was conducted with CiteSpace, VOSviewer, and Excel, including citations, countries, authors, and keywords.

RESULTS

The top 100 cited studies were published between 1988 and 2021, with the United States led in productivity. Core biomarkers such as genetics, children, oxidative stress, and mitochondrial dysfunction are well-established. Potential trends for future research may include brain studies, metabolomics, and associations with other psychiatric disorders.

CONCLUSION

This pioneering bibliometric analysis provides a comprehensive compilation of the 100 most-cited studies on autism, which not only offers a valuable resource for doctors, and researchers but shedding insights into current shortcomings and future endeavors. Future research should prioritize the application of emerging technologies for biomarkers, longitudinal study of biomarkers, and specificity of autism biomarkers to advance the precision of ASD diagnosis and treatment.

Low Plasma Levels of Contactin-Associated Protein-Like 2 in Children with Autism Spectrum Disorder: Links to Neural Development

Background

Autism spectrum disorder (ASD) is a condition of atypical neurodevelopment and is characterized by social communication problems and repetitive patterns of behavior. Early diagnosis and intervention are decisive for managing symptoms and improving outcomes. Contactin-associated protein-like 2 (CNTNAP2) protein is implicated in neural development and plays a role in brain connectivity and synapse formation. Genetic research has shown a possible link between CNTNAP2 and ASD.

Aim

We aimed to discover the blood plasma levels of CNTNAP2 in children with ASD and explore the potential association between CNTNAP2 concentrations and ASD severity.

Methodology

This case-control study included children with ASD (n=40) and aged-matched healthy controls (n=40). Blood plasma levels of CNTNAP2 were measured using enzyme-linked immunosorbent assay (ELISA). The Children Autism Rating Scale (CARS) and Social Responsiveness Scale (SRS) were used to assess the severity of the ASD. Spearman correlation coefficient (r) was used to correlate the variables.

Results

Children with severe ASD had significantly lower CNTNAP2 levels (0.31 (0.14) ng/mL, p=0.003) compared to normal controls (0.47 (0.24) ng/mL). However, CNTNAP2 levels of children with mild autism (0.44(0.22), ng/mL, p=0.77) were not significantly different as compared to normal controls (0.47 (0.24) ng/mL). Furthermore, a significant difference was found between CNTNAP2 levels, by comparing the mild and severe groups based on the CARS (p= 0.05). Furthermore, no significant correlation between CNTNAP2 levels, and severity scores (CARS and SRS), was obtained. However, a significant correlation between CNTNAP2 and age was observed.

Conclusion

The low CNTNAP2 plasma level in children with ASD indicated that it might be involved in the pathophysiology of ASD. Nevertheless, these results should be interpreted with care till more studies are achieved using a larger population to decide whether the reduction in CNTNAP2 plasma level is a mere outcome of ASD or it plays a pathogenic role in the disease.

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

Hidden regulators: the emerging roles of lncRNAs in brain development and disease

Long non-coding RNAs (lncRNAs) have emerged as critical players in brain development and disease. These non-coding transcripts, which once considered as "transcriptional junk," are now known for their regulatory roles in gene expression. In brain development, lncRNAs participate in many processes, including neurogenesis, neuronal differentiation, and synaptogenesis. They employ their effect through a wide variety of transcriptional and post-transcriptional regulatory mechanisms through interactions with chromatin modifiers, transcription factors, and other regulatory molecules. Dysregulation of lncRNAs has been associated with certain brain diseases, including Alzheimer's disease, Parkinson's disease, cancer, and neurodevelopmental disorders. Altered expression and function of specific lncRNAs have been implicated with disrupted neuronal connectivity, impaired synaptic plasticity, and aberrant gene expression pattern, highlighting the functional importance of this subclass of brain-enriched RNAs. Moreover, lncRNAs have been identified as potential biomarkers and therapeutic targets for neurological diseases. Here, we give a comprehensive review of the existing knowledge of lncRNAs. Our aim is to provide a better understanding of the diversity of lncRNA structure and functions in brain development and disease. This holds promise for unravelling the complexity of neurodevelopmental and neurodegenerative disorders, paving the way for the development of novel biomarkers and therapeutic targets for improved diagnosis and treatment.

CRISPR-Cas9-generated PTCHD1 2489T>G stem cells recapitulate patient phenotype when undergoing neural induction

An estimated 3.5%-5.9% of the global population live with rare diseases, and approximately 80% of these diseases have a genetic cause. Rare genetic diseases are difficult to diagnose, with some affected individuals experiencing diagnostic delays of 5-30 years. Next-generation sequencing has improved clinical diagnostic rates to 33%-48%. In a majority of cases, novel variants potentially causing the disease are discovered. These variants require functional validation in specialist laboratories, resulting in a diagnostic delay. In the interim, the finding is classified as a genetic variant of uncertain significance (VUS) and the affected individual remains undiagnosed. A VUS (PTCHD1 c. 2489T>G) was identified in a child with autistic behavior, global developmental delay, and hypotonia. Loss of function mutations in PTCHD1 are associated with autism spectrum disorder and intellectual disability; however, the molecular function of PTCHD1 and its role in neurodevelopmental disease is unknown. Here, we apply CRISPR gene editing and induced pluripotent stem cell (iPSC) neural disease modeling to assess the variant. During differentiation from iPSCs to neural progenitors, we detect subtle but significant gene signatures in synaptic transmission and muscle contraction pathways. Our work supports the causal link between the genetic variant and the child's phenotype, providing evidence for the variant to be considered a pathogenic variant according to the American College of Medical Genetics and Genomics guidelines. In addition, our study provides molecular data on the role of PTCHD1 in the context of other neurodevelopmental disorders.

Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis

Narrative reviews have described various resting-state EEG power differences in autism across all five canonical frequency bands, with increased power for low and high frequencies and reduced power for middle frequencies. However, these differences have yet to be quantified using effect sizes and probed robustly for consistency, which are critical next steps for clinical translation. Following PRISMA guidelines, we conducted a systematic review of published and gray literature on resting-state EEG power in autism. We performed 10 meta-analyses to synthesize and quantify differences in absolute and relative resting-state delta, theta, alpha, beta, and gamma EEG power in autism. We also conducted moderator analyses to determine whether demographic characteristics, methodological details, and risk-of-bias indicators might account for heterogeneous study effect sizes. Our literature search and study selection processes yielded 41 studies involving 1,246 autistic and 1,455 neurotypical individuals. Meta-analytic models of 135 effect sizes demonstrated that autistic individuals exhibited reduced relative alpha (g = -0.35) and increased gamma (absolute: g = 0.37, relative: g = 1.06) power, but similar delta (absolute: g = 0.06, relative: g = 0.10), theta (absolute: g = -0.03, relative: g = -0.15), absolute alpha (g = -0.17), and beta (absolute: g = 0.01, relative: g = 0.08) power. Substantial heterogeneity in effect sizes was observed across all absolute (I2: 36.1-81.9%) and relative (I2: 64.6-84.4%) frequency bands. Moderator analyses revealed that age, biological sex, IQ, referencing scheme, epoch duration, and use of gold-standard autism diagnostic instruments did not moderate study effect sizes. In contrast, resting-state paradigm type (eyes-closed versus eyes-open) moderated absolute beta, relative delta, and relative alpha power effect sizes, and resting-state recording duration moderated relative alpha power effect sizes. These findings support further investigation of resting-state alpha and gamma power as potential biomarkers for autism.

CNTNAP2 intracellular domain (CICD) generated by γ-secretase cleavage improves autism-related behaviors

As the most prevalent neurodevelopmental disorders in children, autism spectrum disorders (ASD) are characterized by deficits in language development, social interaction, and repetitive behaviors or inflexible interests. Contactin associated protein like 2 (CNTNAP2), encoding a single transmembrane protein (CNTNAP2) with 1331 amino acid residues, is a widely validated ASD-susceptible gene. Cntnap2-deficient mice also show core autism-relevant behaviors, including the social deficits and repetitive behavior. However, the cellular mechanisms underlying dysfunction CNTNAP2 and ASD remain elusive. In this study, we found a motif within the transmembrane domain of CNTNAP2 was highly homologous to the γ-secretase cleavage site of amyloid-β precursor protein (APP), suggesting that CNTNAP2 may undergo proteolytic cleavage. Further biochemical analysis indicated that CNTNAP2 is cleaved by γ-secretase to produce the CNTNAP2 intracellular domain (CICD). Virally delivery of CICD to the medial prefrontal cortex (mPFC) in Cntnap2-deficient (Cntnap2^-/-) mice normalized the deficit in the ASD-related behaviors, including social deficit and repetitive behaviors. Furthermore, CICD promoted the nuclear translocation of calcium/calmodulin-dependent serine protein kinase (CASK) to regulate the transcription of genes, such as Prader Willi syndrome gene Necdin. Whereas Necdin deficiency led to reduced social interaction in mice, virally expression of Necdin in the mPFC normalized the deficit in social preference of Cntnap2^-/- mice. Our results thus reveal a critical function of CICD and highlight a role of the CNTNAP2-CASK-Necdin signaling pathway in ASD.

Association of GABRG3, GABRB3, HTR2A Gene Variants with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental and neurobehavioral disorder characterized by impaired social communication, repetitive and restricted patterns of behavior, activity, or interest, and altered emotional processing. Reported prevalence is 4 times higher in men and it has increased in recent years. Immunological, environmental, epigenetic, and genetic factors play a role in the pathophysiology of autism. Many neurochemical pathways and neuroanatomical events are effective in determining the disease. It is still unclear how the main symptoms of autism occur because of this complex and heterogeneous situation. In this study, we focused on gamma amino butyric acid (GABA) and serotonin, which are thought to contribute to the etiology of autism; it is aimed to elucidate the mechanism of the disease by investigating variant changes in the GABA receptor subunit genes GABRB3, GABRG3 and the HTR2A gene, which encodes one of the serotonin receptors. 200 patients with ASD between the ages of 3-9 and 100 healthy volunteers were included in the study. Genomic DNA isolation was performed from peripheral blood samples taken from volunteers. Genotyping was performed using the RFLP method with PCR specific for specific variants. Data were analyzed with SPSS v25.0 program. According to the data obtained in our study; In terms of HTR2A (rs6313 T102C) genotypes, the homozygous C genotype carrying frequency in the patient group and the homozygous T genotype carrying frequency in the GABRG3 (rs140679 C/T) genotypes were found to be significantly higher in the patient group compared to the control group (*p: 0.0001, p: 0.0001). It was determined that the frequency of individuals with homozygous genotype was significantly higher in the patient group compared to the control group and having homozygous genotypes increased the disease risk approximately 1.8 times. In terms of GABRB3 (rs2081648 T/C) genotypes, it was determined that there was no statistically significant difference in the frequency of carrying homozygous C genotype in the patient group compared to the control group (p: 0.36). According to the results of our study, we think that the HTR2A (rs6313 T102C) polymorphism is effective in modulating the empathic and autistic characteristics of individuals, and that the HTR2A (rs6313 T102C) polymorphism is more distributed in the post-synaptic membranes in individuals with a higher number of C alleles. We believe that this situation can be attributed to the spontaneous stimulatory distribution of the HTR2A gene in the postsynaptic membranes because of T102C transformation. In genetically based autism cases, carrying the point mutation in the rs6313 variant of the HTR2A gene and the C allele and the point mutation in the rs140679 variant of the GABRG3 gene and accordingly carrying the T allele provide a predisposition to the disease.

Cognitive theories of autism based on the interactions between brain functional networks

Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5-14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.

Social Deficits or Interactional Differences? Interrogating Perspectives on Social Functioning in Autism

Social dysfunction is a key characteristic of autism. Determining and treating autism-related social deficits have been challenging. The medical model views interpersonal difficulties in autism as a localized set of deficits to be managed, whereas the neurodiversity movement calls for the accommodation of differences by the larger community. One common assumption underlying these perspectives is a misalignment in social behaviors between autistic individuals and neurotypicals. This paper reviews and interrogates current perspectives on social functioning in autism to uncover the intricacies of such a notion. Even though extant literature has alluded to a misalignment in social behaviors between autistic and neurotypical individuals, it is uncertain where this disparity lies. Implications for future research and practice are discussed.

Objective frequency analysis of transient visual evoked potentials in autistic children

Visual evoked potentials (VEPs) provide a means to examine neural mechanisms in autism with high temporal resolution. Conventional VEP analysis relies on subjective inspection of a few points (peaks and troughs) in the time-domain waveform. The current study applied power spectral analysis and magnitude-squared coherence (MSC) statistics (frequency-domain measures) to VEPs recorded during 1-minute runs and with a recently developed short-duration technique that allow for objective examination of the responses (Zemon & Gordon, European Journal of Neuroscience, 2018, 48, 1765-1788) from nonautistic and autistic children. Results indicate that, for both groups, early time-domain measures (P₆₀ , N₇₅ , P₁₀₀ ) are highly correlated with middle- and high-frequency (14-28 and 30-48 Hz, respectively) mechanisms, and late measures are highly correlated with a low-frequency (6-12 Hz) mechanism. One frequency-domain measure (power in the middle-frequency band) is capable of predicting the key amplitude measure (N₇₅ -P₁₀₀ ) with high accuracy. MSC and power measures were combined to yield separate measures of signal and noise strength to evaluate alternate hypotheses in autism. Linear mixed-effects modeling demonstrated selective differences in early time-domain and middle-to-high frequency-domain measures in autistic children as compared to nonautistic children given both recording techniques, implicating weaker excitatory input to the cortex. Receiver-operating-characteristic curve analysis showed predictive diagnostic accuracy for middle- and high-frequency bands based on MSC. These findings support the value of frequency analysis measures (power spectral analysis and MSC) in the objective examination of neural differences in autism. LAY SUMMARY: Visual evoked potentials (VEPs) are used to assess neural mechanisms. Typically, VEPs are analyzed by subjective examination of time-series waveforms; but here objective techniques were applied to quantify VEP frequency components to investigate neural differences between autistic and nonautistic children. The objective measures demonstrate group differences in brain function that point to weaker excitatory input to the cortex in autism.

The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI

A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks' time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models' parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal's application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain's right to the left hemisphere is different between ASD and controls.

A white paper on a neurodevelopmental framework for drug discovery in autism and other neurodevelopmental disorders

In the last decade there has been a revolution in terms of genetic findings in neurodevelopmental disorders (NDDs), with many discoveries critical for understanding their aetiology and pathophysiology. Clinical trials in single-gene disorders such as fragile X syndrome highlight the challenges of investigating new drug targets in NDDs. Incorporating a developmental perspective into the process of drug development for NDDs could help to overcome some of the current difficulties in identifying and testing new treatments. This paper provides a summary of the proceedings of the 'New Frontiers Meeting' on neurodevelopmental disorders organised by the European College of Neuropsychopharmacology in conjunction with the Innovative Medicines Initiative-sponsored AIMS-2-TRIALS consortium. It brought together experts in developmental genetics, autism, NDDs, and clinical trials from academia and industry, regulators, patient and family associations, and other stakeholders. The meeting sought to provide a platform for focused communication on scientific insights, challenges, and methodologies that might be applicable to the development of CNS treatments from a neurodevelopmental perspective. Multidisciplinary translational consortia to develop basic and clinical research in parallel could be pivotal to advance knowledge in the field. Although implementation of clinical trials for NDDs in paediatric populations is widely acknowledged as essential, safety concerns should guide each aspect of their design. Industry and academia should join forces to improve knowledge of the biology of brain development, identify the optimal timing of interventions, and translate these findings into new drugs, allowing for the needs of users and families, with support from regulatory agencies.

Age-related differences in white matter diffusion measures in autism spectrum condition

BACKGROUND

Autism spectrum condition (ASC) is accompanied by developmental differences in brain anatomy and connectivity. White matter differences in ASC have been widely studied with diffusion imaging but results are heterogeneous and vary across the age range of study participants and varying methodological approaches. To characterize the neurodevelopmental trajectory of white matter maturation, it is necessary to examine a broad age range of individuals on the autism spectrum and typically developing controls, and investigate age × group interactions.

METHODS

Here, we employed a spatially unbiased tract-based spatial statistics (TBSS) approach to examine age-related differences in white matter connectivity in a sample of 41 individuals with ASC, and 41 matched controls between 7-17 years of age.

RESULTS

We found significant age-related differences between the ASC and control group in widespread brain regions. This included age-related differences in the uncinate fasciculus, corticospinal tract, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, anterior thalamic radiation, superior longitudinal fasciculus and forceps major. Measures of fractional anisotropy (FA) were significantly positively associated with age in both groups. However, this relationship was significantly stronger in the ASC group relative to controls. Measures of radial diffusivity (RD) were significantly negatively associated with age in both groups, but this relationship was significantly stronger in the ASC group relative to controls.

LIMITATIONS

The generalisability of our findings is limited by the restriction of the sample to right-handed males with an IQ > 70. Furthermore, a longitudinal design would be required to fully investigate maturational processes across this age group.

CONCLUSIONS

Taken together, our findings suggest that autistic males have an altered trajectory of white matter maturation relative to controls. Future longitudinal analyses are required to further characterize the extent and time course of these differences.

Dysregulation of neuron differentiation in an autistic savant with exceptional memory

Autism spectrum disorder (ASD) is a heterogeneous group of complex neurodevelopmental disorders without a unique or definite underlying pathogenesis. Although savant syndrome is common in ASD, few models are available for studying the molecular and cellular mechanisms of this syndrome. In this study, we generated urinary induced pluripotent stem cells (UiPSCs) from a 13-year-old male autistic savant with exceptional memory. The UiPSC-derived neurons of the autistic savant exhibited upregulated expression levels of ASD genes/learning difficulty-related genes, namely PAX6, TBR1 and FOXP2, accompanied by hypertrophic neural somas, enlarged spines, reduced spine density, and an increased frequency of spontaneous excitatory postsynaptic currents. Although this study involved only a single patient and a single control because of the rarity of such cases, it provides the first autistic savant UiPSC model that elucidates the potential cellular mechanisms underlying the condition.

Bridging the species gap in translational research for neurodevelopmental disorders

The prevalence and societal impact of neurodevelopmental disorders (NDDs) continue to increase despite years of research in both patient populations and animal models. There remains an urgent need for translational efforts between clinical and preclinical research to (i) identify and evaluate putative causes of NDD, (ii) determine their underlying neurobiological mechanisms, (iii) develop and test novel therapeutic approaches, and (iv) translate basic research into safe and effective clinical practices. Given the complexity behind potential causes and behaviors affected by NDDs, modeling these uniquely human brain disorders in animals will require that we capitalize on unique advantages of a diverse array of species. While much NDD research has been conducted in more traditional animal models such as the mouse, ultimately, we may benefit from creating animal models with species that have a more sophisticated social behavior repertoire such as the rat (Rattus norvegicus) or species that more closely related to humans, such as the rhesus macaque (Macaca mulatta). Here, we highlight the rat and rhesus macaque models for their role in previous psychological research discoveries, current efforts to understand the neurobiology of NDDs, and focus on the convergence of behavior outcome measures that parallel features of human NDDs.

Decreased Expression of Synaptophysin 1 (SYP1 Major Synaptic Vesicle Protein p38) and Contactin 6 (CNTN6/NB3) in the Cerebellar Vermis of reln Haplodeficient Mice

Reeler heterozygous mice (reln+/-) are seemingly normal but haplodeficient in reln, a gene implicated in autism. Structural/neurochemical alterations in the reln+/- brain are subtle and difficult to demonstrate. Therefore, the usefulness of these mice in translational research is still debated. As evidence implicated several synapse-related genes in autism and the cerebellar vermis is structurally altered in the condition, we have investigated the expression of synaptophysin 1 (SYP1) and contactin 6 (CNTN6) within the vermis of reln+/- mice. Semi-thin plastic sections of the vermis from adult mice of both sexes and different genotypes (reln+/- and reln+/+) were processed with an indirect immunofluorescence protocol. Immunofluorescence was quantified on binary images and statistically analyzed. Reln+/- males displayed a statistically significant reduction of 11.89% in the expression of SYP1 compared to sex-matched wild-type animals, whereas no differences were observed between reln+/+ and reln+/- females. In reln+/- male mice, reductions were particularly evident in the molecular layer: 10.23% less SYP1 than reln+/+ males and 5.84% < reln+/+ females. In reln+/- females, decrease was 9.84% versus reln+/+ males and 5.43% versus reln+/+ females. Both reln+/- males and females showed a stronger decrease in CNTN6 expression throughout all the three cortical layers of the vermis: 17-23% in the granular layer, 24-26% in the Purkinje cell layer, and 9-14% in the molecular layer. Altogether, decrease of vermian SYP1 and CNTN6 in reln+/- mice displayed patterns compatible with the structural modifications of the autistic cerebellum. Therefore, these mice may be a good model in translational studies.

Neonatal social isolation increases the proportion of the immature spines in the layer 2/3 pyramidal neurons of the somatosensory cortex

Social isolation during the juvenile period is postulated to leave specific sequelae, such as attention deficits and emotion recognition. Miswiring of the cortical neuronal circuit during postnatal development may underlie such behavioral impairments, but the details of the circuit-level impairment associated with social isolation have not yet been clarified. In this study, we evaluated the possibility that environmental factors may induce alternation in spine characteristics and dynamics. We isolated mice from the mother and siblings from postnatal day 7 to 11 for 6 h per day. Both dynamics and structural properties of spines in the layer 2/3 pyramidal neurons of the somatosensory cortex were measured at postnatal 3 weeks by in vivo two-photon microscopy. We found decrease in the ratio of PSD-95-positive dendritic spines in the mice after social isolation. These mice did not show alteration in spine dynamics. Those results suggest that the neonatal social isolation results in less mature spines, with normal rate of their turnover, which is distinct from spine phenotype seen in multiple models of autism spectrum disorders.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Nano-scale analysis of synapse morphology in an autism mouse model with 15q11-13 copy number variation using focused ion beam milling and scanning electron microscopy

Circuit-level alternations in patients of autism spectrum disorder (ASD) is under active investigation and detailed characterization of synapse morphology in ASD model mice should be informative. We utilized focused ion beam milling and scanning electron microscopy (FIB-SEM) to obtain three-dimensional images of synapses in the layer 2/3 of the somatosensory cortex from a mouse model for ASD with human 15q11-13 chromosomal duplication (15q dup mice). We found a trend of higher spine density and a higher fraction of astrocytic contact with both spine and shaft synapses in 15q dup mice. Measurement of spine synapse structure indicated that the size of the post-synaptic density (PSD), spine head volume, spine head width and spine neck width were smaller in 15q dup mice. Categorization of spine synapses into five classes suggested a trend of less frequent mushroom spines in 15q dup mice. These results suggest relative increase in excitatory synapses with immature morphology but more astrocytic contacts in 15q dup mice, which may be linked to enhanced synapse turnover seen in ASD mouse models.

Neural gain control measured through cortical gamma oscillations is associated with sensory sensitivity

Gamma oscillations facilitate information processing by shaping the excitatory input/output of neuronal populations. Recent studies in humans and nonhuman primates have shown that strong excitatory drive to the visual cortex leads to suppression of induced gamma oscillations, which may reflect inhibitory-based gain control of network excitation. The efficiency of the gain control measured through gamma oscillations may in turn affect sensory sensitivity in everyday life. To test this prediction, we assessed the link between self-reported sensitivity and changes in magneto-encephalographic gamma oscillations as a function of motion velocity of high-contrast visual gratings. The induced gamma oscillations increased in frequency and decreased in power with increasing stimulation intensity. As expected, weaker suppression of the gamma response correlated with sensory hypersensitivity. Robustness of this result was confirmed by its replication in the two samples: neurotypical subjects and people with autism, who had generally elevated sensory sensitivity. We conclude that intensity-related suppression of gamma response is a promising biomarker of homeostatic control of the excitation-inhibition balance in the visual cortex.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

From bedside to bench and back: Translating ASD models

Autism spectrum disorders (ASD) represent a heterogeneous group of disorders defined by deficits in social interaction/communication and restricted interests, behaviors, or activities. Models of ASD, developed based on clinical data and observations, are used in basic science, the "bench," to better understand the pathophysiology of ASD and provide therapeutic options for patients in the clinic, the "bedside." Translational medicine creates a bridge between the bench and bedside that allows for clinical and basic science discoveries to challenge one another to improve the opportunities to bring novel therapies to patients. From the clinical side, biomarker work is expanding our understanding of possible mechanisms of ASD through measures of behavior, genetics, imaging modalities, and serum markers. These biomarkers could help to subclassify patients with ASD in order to better target treatments to a more homogeneous groups of patients most likely to respond to a candidate therapy. In turn, basic science has been responding to developments in clinical evaluation by improving bench models to mechanistically and phenotypically recapitulate the ASD phenotypes observed in clinic. While genetic models are identifying novel therapeutics targets at the bench, the clinical efforts are making progress by defining better outcome measures that are most representative of meaningful patient responses. In this review, we discuss some of these challenges in translational research in ASD and strategies for the bench and bedside to bridge the gap to achieve better benefits to patients.

Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% [95% confidence interval (CI), 62.9 to 100], correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified [specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3)]. These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD.

Association Between Magnetoencephalographic Interictal Epileptiform Discharge and Cognitive Function in Young Children With Typical Development and With Autism Spectrum Disorders

Electroencephalograms of individuals with autism spectrum disorders (ASD) show higher rates of interictal epileptiform discharges (IEDs), which are known to have an inverse association with cognitive function in typically developed (TD) children. Nevertheless, that phenomenon has not been investigated adequately in children with ASD. From university and affiliated hospitals, 163 TD children (84 male, 79 female, aged 32-89 months) and 107 children (85 male, 22 female, aged 36-98 months) with ASD without clinical seizure were recruited. We assessed their cognitive function using the Kaufman Assessment Battery for Children (K-ABC) and recorded 10 min of MEG. Original waveforms were visually inspected. Then a linear regression model was applied to evaluate the association between the IED frequency and level of their cognitive function. Significantly higher rates of IEDs were found in the ASD group than in the TD group. In the TD group, we found significant negative correlation between mental processing scale scores (MPS) and the IED frequency. However, for the ASD group, we found significant positive correlation between MPS scores and the IED frequency. In terms of the achievement scale, correlation was not significant in either group. Although we found a correlative rather than a causal effect, typically developed children with higher IED frequency might better be followed up carefully. Furthermore, for children with ASD without clinical seizure, clinicians might consider IEDs as less harmful than those observed in TD children.

Increased Left Inferior Temporal Gyrus Was Found in Both Low Function Autism and High Function Autism

Previous neuroimaging studies of autism spectrum disorder (ASD) have focused on subjects with IQ > 70 or ASD without considering IQ levels. It remains unclear whether differences in brain anatomy in this population are associated with variations in clinical phenotype. In this study, 19 children with low functioning autism (LFA) and 19 children with high functioning autism (HFA) were compared with 27 healthy controls (HC). We found increased gray matter volume (GMV) in the left inferior temporal gyrus in subjects with both HFA and LFA and increased GMV of left middle temporal gyrus BA21 was found only in the LFA group. A significant negative correlation was found between the left inferior temporal gyrus (LITG) and the score of repetitive behavior in the HFA group.

Modulation of Neocortical Development by Early Neuronal Activity: Physiology and Pathophysiology

Animal and human studies revealed that patterned neuronal activity is an inherent feature of developing nervous systems. This review summarizes our current knowledge about the mechanisms generating early electrical activity patterns and their impact on structural and functional development of the cerebral cortex. All neocortical areas display distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, intermittent spontaneous activity is synchronized within small neuronal networks, becoming more complex with further development. This transition is accompanied by a gradual shift from electrical to chemical synaptic transmission, with a particular role of non-synaptic tonic currents before the onset of phasic synaptic activity. In this review article we first describe functional impacts of classical neurotransmitters (GABA, glutamate) and modulatory systems (e.g., acetylcholine, ACh) on early neuronal activities in the neocortex with special emphasis on electrical synapses, nonsynaptic and synaptic currents. Early neuronal activity influences probably all developmental processes and is crucial for the proper formation of neuronal circuits. In the second part of our review, we illustrate how specific activity patterns might interfere with distinct neurodevelopmental processes like proliferation, migration, axonal and dendritic sprouting, synapse formation and neurotransmitter specification. Finally, we present evidence that transient alterations in neuronal activity during restricted perinatal periods can lead to persistent changes in functional connectivity and therefore might underlie the manifestation of neurological and neuropsychiatric diseases.

Advances in nonhuman primate models of autism: Integrating neuroscience and behavior

Given the prevalence and societal impact of autism spectrum disorders (ASD), there is an urgent need to develop innovative preventative strategies and treatments to reduce the alarming number of cases and improve core symptoms for afflicted individuals. Translational efforts between clinical and preclinical research are needed to (i) identify and evaluate putative causes of ASD, (ii) determine the underlying neurobiological mechanisms, (iii) develop and test novel therapeutic approaches and (iv) ultimately translate basic research into safe and effective clinical practices. However, modeling a uniquely human brain disorder, such as ASD, will require sophisticated animal models that capitalize on unique advantages of diverse species including drosophila, zebra fish, mice, rats, and ultimately, species more closely related to humans, such as the nonhuman primate. Here we discuss the unique contributions of the rhesus monkey (Macaca mulatta) model to ongoing efforts to understand the neurobiology of the disorder, focusing on the convergence of brain and behavior outcome measures that parallel features of human ASD.

Screening of autism based on task-free fMRI using graph theoretical approach

Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, findings have not been established as a general diagnostic tool yet. In this regard, current study proposed an automatic screening method for recognition of ASDs from healthy controls (HCs) based on their brain functional abnormalities. In this paradigm, brain functional networks of 60 adolescent and young adult males (29 ASDs and 31 HCs) were estimated from subjects' task-free fMRI data. Then, autism screening was developed based on characteristics of the functional networks using the following steps: A) local and global parameters of the brain functional network were calculated using graph theory. B) network parameters of the ASDs were statistically compared to the HCs. C) significantly altered parameters were used as input features of the screening system. D) performance of the system was verified using various classification techniques. The support vector machine showed superiority to others with an accuracy of 92%. Subsequently, reliability of the results was examined using an independent dataset including 20 ASDs and 20 HCs. Our findings suggest that local parameters of the brain functional network, despite the individual variability, can potentially be used for autism screening.

Shifting brain inhibitory balance and connectivity of the prefrontal cortex of adults with autism spectrum disorder

Currently, there are no effective pharmacologic treatments for the core symptoms of autism spectrum disorder (ASD). There is, nevertheless, potential for progress. For example, recent evidence suggests that the excitatory (E) glutamate and inhibitory (I) GABA systems may be altered in ASD. However, no prior studies of ASD have examined the 'responsivity' of the E-I system to pharmacologic challenge; or whether E-I modulation alters abnormalities in functional connectivity of brain regions implicated in the disorder. Therefore, we used magnetic resonance spectroscopy ([1H]MRS) to measure prefrontal E-I flux in response to the glutamate and GABA acting drug riluzole in adult men with and without ASD. We compared the change in prefrontal 'Inhibitory Index'-the GABA fraction within the pool of glutamate plus GABA metabolites-post riluzole challenge; and the impact of riluzole on differences in resting-state functional connectivity. Despite no baseline differences in E-I balance, there was a significant group difference in response to pharmacologic challenge. Riluzole increased the prefrontal cortex inhibitory index in ASD but decreased it in controls. There was also a significant group difference in prefrontal functional connectivity at baseline, which was abolished by riluzole within the ASD group. Our results also show, for we believe the first time in ASD, that E-I flux can be 'shifted' with a pharmacologic challenge, but that responsivity is significantly different from controls. Further, our initial evidence suggests that abnormalities in functional connectivity can be 'normalised' by targeting E-I, even in adults.

The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting

Autism spectrum disorder is a complex neurodevelopmental disorder, which is accompanied by differences in brain anatomy, functioning and brain connectivity. Due to its neurodevelopmental character, and the large phenotypic heterogeneity among individuals on the autism spectrum, the neurobiology of autism spectrum disorder is inherently difficult to describe. Nevertheless, significant progress has been made in characterizing the neuroanatomical underpinnings of autism spectrum disorder across the human life span, and in identifying the molecular pathways that may be affected in autism spectrum disorder. Moreover, novel methodological frameworks for analyzing neuroimaging data are emerging that make it possible to characterize the neuroanatomy of autism spectrum disorder on the case level, and to stratify individuals based on their individual phenotypic make up. While these approaches are increasingly more often employed in the research setting, their applicability in the clinical setting remains a vision for the future. The aim of the current review is to (1) provide a general overview of recent structural neuroimaging findings examining the neuroanatomy of autism spectrum disorder across the human life span, and in males and females with the condition, (2) highlight potential neuroimaging (bio)markers that may in the future be used for the stratification of autism spectrum disorder individuals into biologically homogeneous subgroups and (3) inform treatment and intervention strategies.

Neuroanatomical Alterations in High-Functioning Adults with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental condition, affecting cognition and behavior throughout the life span. With recent advances in neuroimaging techniques and analytical approaches, a considerable effort has been directed toward identifying the neuroanatomical underpinnings of ASD. While gray-matter abnormalities have been found throughout cortical, subcortical, and cerebellar regions of affected individuals, there is currently little consistency across findings, partly due to small sample-sizes and great heterogeneity among participants in previous studies. Here, we report voxel-based morphometry of structural magnetic resonance images in a relatively large sample of high-functioning adults with ASD (n = 66) and matched typically-developing controls (n = 66) drawn from multiple studies. We found decreased gray-matter volume in posterior brain regions, including the posterior hippocampus and cuneus, as well as increased gray-matter volume in frontal brain regions, including the medial prefrontal cortex, superior and inferior frontal gyri, and middle temporal gyrus in individuals with ASD. We discuss our results in relation to findings obtained in previous studies, as well as their potential clinical implications.

Excitatory/inhibitory imbalance in autism spectrum disorders: Implications for interventions and therapeutics

OBJECTIVES

Imbalance between excitation and inhibition and increased excitatory-inhibitory (E-I) ratio is a common mechanism in autism spectrum disorders (ASD) that is responsible for the learning and memory, cognitive, sensory, motor deficits, and seizures occurring in these disorders. ASD are very heterogeneous and better understanding of E-I imbalance in brain will lead to better diagnosis and treatments.

METHODS

We perform a critical literature review of the causes and presentations of E-I imbalance in ASD.

RESULTS

E-I imbalance in ASD is due primarily to abnormal glutamatergic and GABAergic neurotransmission in key brain regions such as neocortex, hippocampus, amygdala, and cerebellum. Other causes are due to dysfunction of neuropeptides (oxytocin), synaptic proteins (neuroligins), and immune system molecules (cytokines). At the neuropathological level E-I imbalance in ASD is presented as a "minicolumnopathy". E-I imbalance alters the manner by which the brain processes information and regulates behaviour. New developments for investigating E-I imbalance such as optogenetics and transcranial magnetic stimulation (TMS) are presented. Non-invasive brain stimulation methods such as TMS for treatment of the core symptoms of ASD are discussed.

CONCLUSIONS

Understanding E-I imbalance has important implications for developing better pharmacological and behavioural treatments for ASD, including TMS, new drugs, biomarkers and patient stratification.

PX-RICS-deficient mice mimic autism spectrum disorder in Jacobsen syndrome through impaired GABAA receptor trafficking

Jacobsen syndrome (JBS) is a rare congenital disorder caused by a terminal deletion of the long arm of chromosome 11. A subset of patients exhibit social behavioural problems that meet the diagnostic criteria for autism spectrum disorder (ASD); however, the underlying molecular pathogenesis remains poorly understood. PX-RICS is located in the chromosomal region commonly deleted in JBS patients with autistic-like behaviour. Here we report that PX-RICS-deficient mice exhibit ASD-like social behaviours and ASD-related comorbidities. PX-RICS-deficient neurons show reduced surface γ-aminobutyric acid type A receptor (GABAAR) levels and impaired GABAAR-mediated synaptic transmission. PX-RICS, GABARAP and 14-3-3ζ/θ form an adaptor complex that interconnects GABAAR and dynein/dynactin, thereby facilitating GABAAR surface expression. ASD-like behavioural abnormalities in PX-RICS-deficient mice are ameliorated by enhancing inhibitory synaptic transmission with a GABAAR agonist. Our findings demonstrate a critical role of PX-RICS in cognition and suggest a causal link between PX-RICS deletion and ASD-like behaviour in JBS patients.

Annual Research Review: The role of the environment in the developmental psychopathology of autism spectrum condition

BACKGROUND

Although autism spectrum condition (ASC) is strongly genetic in origin, accumulating evidence points to the critical roles of various environmental influences on its emergence and subsequent developmental course.

METHODS

A developmental psychopathology framework was used to synthesise literature on environmental factors associated with the onset and course of ASC (based on a systematic search of the literature using PubMed, PsychInfo and Google Scholar databases). Particular emphasis was placed on gene-environment interplay, including gene-environment interaction (G × E) and gene-environment correlation (rGE).

RESULTS

Before conception, advanced paternal and maternal ages may independently enhance offspring risk for ASC. Exogenous prenatal risks are evident (e.g. valproate and toxic chemicals) or possible (e.g. selective serotonin reuptake inhibitors), and processes endogenous to the materno-foeto-placental unit (e.g. maternal diabetes, enhanced steroidogenic activities and maternal immune activation) likely heighten offspring vulnerability to ASC. Folate intake is a prenatal protective factor, with a particular window of action around 4 weeks preconception and during the first trimester. These prenatal risks and protective mechanisms appear to involve G × E and potentially rGE. A variety of perinatal risks are related to offspring ASC risk, possibly reflecting rGE. Postnatal social factors (e.g. caregiver-infant interaction, severe early deprivation) during the first years of life may operate through rGE to influence the likelihood of manifesting a full ASC phenotype from a 'prodromal' phase (a proposal distinct to the discredited and harmful 'refrigerator mother hypothesis'); and later postnatal risks, after the full manifestation of ASC, shape life span development through transactions mediated by rGE. There is no evidence that vaccination is a postnatal risk for ASC.

CONCLUSIONS

Future investigations should consider the specificity of risks for ASC versus other atypical neurodevelopmental trajectories, timing of risk and protective mechanisms, animal model systems to study mechanisms underlying gene-environment interplay, large-sample genome-envirome designs to address G × E and longitudinal studies to elucidate how rGE plays out over time. Clinical and public health implications are discussed.

How can clinicians detect and treat autism early? Methodological trends of technology use in research

We reviewed original research papers that used quantifiable technology to detect early autism spectrum disorder (ASD) and identified 376 studies from 34 countries from 1965 to 2013. Publications have increased significantly since 2000, with most coming from the USA. Electroencephalogram, magnetic resonance imaging and eye tracking were the most frequently used technologies.

CONCLUSION

The use of quantifiable technology to detect early ASD has increased in recent decades, but has had limited impact on early detection and treatment. Further scientific developments are anticipated, and we hope that they will increasingly be used in clinical practice for early ASD screening, diagnosis and intervention.

Characteristics of Brains in Autism Spectrum Disorder: Structure, Function and Connectivity across the Lifespan

Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors (RRBs). Over the past decade, neuroimaging studies have provided considerable insights underlying neurobiological mechanisms of ASD. In this review, we introduce recent findings from brain imaging studies to characterize the brains of ASD across the human lifespan. Results of structural Magnetic Resonance Imaging (MRI) studies dealing with total brain volume, regional brain structure and cortical area are summarized. Using task-based functional MRI (fMRI), many studies have shown dysfunctional activation in critical areas of social communication and RRBs. We also describe several data to show abnormal connectivity in the ASD brains. Finally, we suggest the possible strategies to study ASD brains in the future.

Decreased centrality of cortical volume covariance networks in autism spectrum disorders

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions characterized by atypical structural and functional brain connectivity. Complex network analysis has been mainly used to describe altered network-level organization for functional systems and white matter tracts in ASD. However, atypical functional and structural connectivity are likely to be also linked to abnormal development of the correlated structure of cortical gray matter. Such covariations of gray matter are particularly well suited to the investigation of the complex cortical pathology of ASD, which is not confined to isolated brain regions but instead acts at the systems level. In this study, we examined network centrality properties of gray matter networks in adults with ASD (n = 84) and neurotypical controls (n = 84) using graph theoretical analysis. We derived a structural covariance network for each group using interregional correlation matrices of cortical volumes extracted from a surface-based parcellation scheme containing 68 cortical regions. Differences between groups in closeness network centrality measures were evaluated using permutation testing. We identified several brain regions in the medial frontal, parietal and temporo-occipital cortices with reductions in closeness centrality in ASD compared to controls. We also found an association between an increased number of autistic traits and reduced centrality of visual nodes in neurotypicals. Our study shows that ASD are accompanied by atypical organization of structural covariance networks by means of a decreased centrality of regions relevant for social and sensorimotor processing. These findings provide further evidence for the altered network-level connectivity model of ASD.

Neuroanatomy of Individual Differences in Language in Adult Males with Autism

One potential source of heterogeneity within autism spectrum conditions (ASC) is language development and ability. In 80 high-functioning male adults with ASC, we tested if variations in developmental and current structural language are associated with current neuroanatomy. Groups with and without language delay differed behaviorally in early social reciprocity, current language, but not current autistic features. Language delay was associated with larger total gray matter (GM) volume, smaller relative volume at bilateral insula, ventral basal ganglia, and right superior, middle, and polar temporal structures, and larger relative volume at pons and medulla oblongata in adulthood. Despite this heterogeneity, those with and without language delay showed significant commonality in morphometric features when contrasted with matched neurotypical individuals (n = 57). In ASC, better current language was associated with increased GM volume in bilateral temporal pole, superior temporal regions, dorsolateral fronto-parietal and cerebellar structures, and increased white matter volume in distributed frontal and insular regions. Furthermore, current language-neuroanatomy correlation patterns were similar across subgroups with or without language delay. High-functioning adult males with ASC show neuroanatomical variations associated with both developmental and current language characteristics. This underscores the importance of including both developmental and current language as specifiers for ASC, to help clarify heterogeneity.

Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan

Over the past decade, in-vivo MRI studies have provided many invaluable insights into the neural substrates underlying autism spectrum disorder (ASD), which is now known to be associated with neurodevelopmental variations in brain anatomy, functioning, and connectivity. These systems-level features of ASD pathology seem to develop differentially across the human lifespan so that the cortical abnormalities that occur in children with ASD differ from those noted at other stages of life. Thus, investigation of the brain in ASD poses particular methodological challenges, which must be addressed to enable the comparison of results across studies. Novel analytical approaches are also being developed to facilitate the translation of findings from the research to the clinical setting. In the future, the insights provided by human neuroimaging studies could contribute to biomarker development for ASD and other neurodevelopmental disorders, and to new approaches to diagnosis and treatment.

Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers). Morphometry analysis revealed increased cortical thickness in ASD participants, relative to typical controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex, and right precuneus, and reduced cortical thickness in right cuneus and right precentral gyrus. ASD adults also had reduced fractional anisotropy (FA) and increased radial diffusivity (RD) for two clusters on the forceps minor of the corpus callosum, revealed by DTI analyses. 1H-MRS results showed a reduction in the N-acetylaspartate/Creatine ratio in dorsal anterior cingulate cortex (dACC) in ASD participants. A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors. Examining the same cohort of adults with ASD and their TD peers, this study found alterations in cortical thickness, white matter (WM) connectivity, and neurochemical concentration in ASD. These findings underscore the potential for multimodal imaging to better inform on the neural characteristics most relevant to the disorder.

Hypersensitive pupillary light reflex in infants at risk for autism

Background

Post mortem brain tissue data and animal modeling work indicate cholinergic disruptions in autism. Moreover, the cholinergic system plays a key role in the early neurodevelopmental processes believed to be derailed early in life in individuals with the disorder. Yet, there is no data from human infants supporting a developmentally important role of this neurotransmitter system. Because the pupillary light reflex depends largely on cholinergic synaptic transmission, we assessed this reflex in a sample of infants at risk for autism as well as infants at low (average) risk.

Methods

Ten-month-old infants with an older sibling with autism (n = 29, 16 females), and thus a genetic predisposition to developing the disorder themselves, were presented with white flashes on a computer monitor, and pupillary responses were captured using eye tracking. A control group matched on age and developmental level (n = 15, seven females) was also tested.

Results

The siblings of children with autism had a faster and stronger pupillary light reflex compared to control infants. Baseline pupil diameter was equal in the two groups, ruling out tonic autonomic imbalance as an explanation for these differences.

Conclusions

This study establishes that infant siblings of children with autism have hypersensitive pupillary light reflexes, a result which supports the view that altered sensory processing in infancy is associated with elevated autism risk. Moreover, the study indicates that individual differences in autism susceptibility are linked to differences in the cholinergic system during an early developmental period.

Left-hemispheric microstructural abnormalities in children with high-functioning autism spectrum disorder

Current theories of the neurobiological basis of autism spectrum disorder (ASD) posit an altered pattern of connectivity in large-scale brain networks. Here we used diffusion tensor imaging to investigate the microstructural properties of the white matter (WM) that mediates interregional connectivity in 36 high-functioning children with ASD (HF-ASD) as compared with 37 controls. By employing an atlas-based analysis using large deformation diffeometric morphic mapping registration, a widespread but left-lateralized pattern of abnormalities was revealed. The mean diffusivity (MD) of water in the WM of HF-ASD children was significantly elevated throughout the left hemisphere, particularly in the outer-zone cortical WM. Across diagnostic groups, there was a significant effect of age on left-hemisphere MD, with a similar reduction in MD during childhood in both typically developing and HF-ASD children. The increased MD in children with HF-ASD suggests hypomyelination and may reflect increased short-range cortico-cortical connections subsequent to early WM overgrowth. These findings also highlight left-hemispheric connectivity as relevant to the pathophysiology of ASD and indicate that the spatial distribution of microstructural abnormalities in HF-ASD is widespread and left-lateralized. This altered left-hemispheric connectivity may contribute to deficits in communication and praxis observed in ASD.

Autistic-like syndrome in mu opioid receptor null mice is relieved by facilitated mGluR4 activity

The etiology of Autism Spectrum Disorders (ASDs) remains largely unknown. Identifying vulnerability genes for autism represents a major challenge in the field and allows the development of animal models for translational research. Mice lacking the mu opioid receptor gene (Oprm1(-/-)) were recently proposed as a monogenic mouse model of autism, based on severe deficits in social behavior and communication skills. We confirm this hypothesis by showing that adult Oprm1(-/-) animals recapitulate core and multiple comorbid behavioral symptoms of autism and also display anatomical, neurochemical, and genetic landmarks of the disease. Chronic facilitation of mGluR4 signaling, which we identified as a novel pharmacological target in ASDs in these mice, was more efficient in alleviating behavioral deficits than the reference molecule risperidone. Altogether, our data provide first evidence that disrupted mu opioid receptor signaling is sufficient to trigger a comprehensive autistic syndrome, maybe through blunted social reward processes, and this mouse model opens promising avenues for therapeutic innovation.