Cannabidiol is a behavioral modulator in BTBR mouse model of idiopathic autism.

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+Itpr3tf/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.

Venous Thromboembolism Among People With HIV: Design, Implementation, and Findings of a Centralized Adjudication System in Clinical Care Sites Across the United States.

BACKGROUND: People with HIV (PWH) are at increased risk for venous thromboembolism (VTE). We conducted this study to characterize VTE including provoking factors among PWH in the current treatment era. METHODS: We included PWH with VTE between 2010 and 2020 at 6 sites in the CFAR Network of Integrated Clinical Systems cohort. We ascertained for possible VTE using diagnosis, VTE-related imaging, and VTE-related procedure codes, followed by centralized adjudication of primary data by expert physician reviewers. We evaluated sensitivity and positive predictive value of VTE ascertainment approaches. VTEs were classified by type and anatomic location. Reviewers identified provoking factors such as hospitalizations, infections, and other potential predisposing factors such as smoking. RESULTS: We identified 557 PWH with adjudicated VTE: 239 (43%) had pulmonary embolism with or without deep venous thrombosis, and 318 (57%) had deep venous thrombosis alone. Ascertainment with clinical diagnoses alone missed 6% of VTEs identified with multiple ascertainment approaches. DVTs not associated with intravenous lines were most often in the proximal lower extremities. Among PWH with VTE, common provoking factors included recent hospitalization (n = 134, 42%), infection (n = 133, 42%), and immobilization/bed rest (n = 78, 25%). Only 57 (10%) PWH had no provoking factor identified. Smoking (46%), HIV viremia (27%), and injection drug use (22%) were also common. CONCLUSIONS: We conducted a robust adjudication process that demonstrated the benefits of multiple ascertainment approaches followed by adjudication. Provoked VTEs were more common than unprovoked events. Nontraditional and modifiable potential predisposing factors such as viremia and smoking were common.

A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework.

Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study.

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.

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

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

The Role of Structure MRI in Diagnosing Autism.

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results.

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.

A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI).

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.

Potential crosstalk between sonic hedgehog-WNT signaling and neurovascular molecules: Implications for blood-brain barrier integrity in autism spectrum disorder.

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-ß-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. Cover Image for this issue: https://doi.org/10.1111/jnc.15081.

A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI.

PURPOSE: Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment. METHODS: To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard-Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K-means clustering technique is performed on such significant brain areas. Informative blood oxygen level-dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step. RESULTS: Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%). CONCLUSION: The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns.

Altered functional connectivity patterns play an important role in explaining autism spectrum disorder related impairments. In order to examine such connectivity, resting state functional MRI is the most commonly used technique. To date, the majority of works in this area examine a whole time series of brain activation as a discrete stationary process. This study proposes a more detailed analysis of how functional connectivity fluctuates over time and how it is used to quantify instances demonstrating overconnectivity or underconnectivity. Non-parametric surrogates test identifies the areas where underconnectivity or overconnectivity correlate with the Autism Diagnosis Observation Schedule. In addition, this study shows how the areas identified affect the subjects behaviors. Our ultimate goal is a personalized autism diagnosis and treatment CAD system, where each subject impairments are distinctly mapped so they can be addressed with targeted treatments.

Dynamic glial response and crosstalk in demyelination-remyelination and neurodegeneration processes.

Multiple sclerosis is an autoimmune disease in which the immune system attacks the myelin sheath in the central nervous system. It is characterized by blood-brain barrier dysfunction throughout the course of multiple sclerosis, followed by the entry of immune cells and activation of local microglia and astrocytes. Glial cells (microglia, astrocytes, and oligodendrocyte lineage cells) are known as the important mediators of neuroinflammation, all of which play major roles in the pathogenesis of multiple sclerosis. Network communications between glial cells affect the activities of oligodendrocyte lineage cells and influence the demyelination-remyelination process. A finely balanced glial response may create a favorable lesion environment for efficient remyelination and neuroregeneration. This review focuses on glial response and neurodegeneration based on the findings from multiple sclerosis and major rodent demyelination models. In particular, glial interaction and molecular crosstalk are discussed to provide insights into the potential cell- and molecule-specific therapeutic targets to improve remyelination and neuroregeneration.

Disruption of essential metal homeostasis in the brain by cadmium and high-fat diet.

Analyses of human cohort data support the roles of cadmium and obesity in the development of several neurocognitive disorders. To explore the effects of cadmium exposure in the brain, mice were subjected to whole life oral cadmium exposure. There were significant increases in cadmium levels with female animals accumulating more metal than males (p < 0.001). Both genders fed a high fat diet showed significant increases in cadmium levels compared to low fat diet fed mice (p < 0.001). Cadmium and high fat diet significantly affected the levels of several essential metals, including magnesium, potassium, chromium, iron, cobalt, copper, zinc and selenium. Additionally, these treatments resulted in increased superoxide levels within the cortex, amygdala and hippocampus. These findings support a model where cadmium and high fat diet affect the levels of redox-active, essential metal homeostasis. This phenomenon may contribute to the underlying mechanism(s) responsible for the development of neurocognitive disorders.

Neuroprotective Effects of Adenosine A1 Receptor Signaling on Cognitive Impairment Induced by Chronic Intermittent Hypoxia in Mice.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a breathing disorder associated with cognitive impairment. However, the mechanisms leading to cognitive deficits in OSAHS remain uncertain. In this study, a mouse model of chronic intermittent hypoxia (CIH) exposures were applied for simulating the deoxygenation-reoxygenation events occurring in OSAHS. The conventional adenosine A1 receptor gene (A1R) knockout mice and the A1R agonist CCPA- or antagonist DPCPX-administrated mice were utilized to determine the precise function of A1R signaling in the process of OSAHS-relevant cognitive impairment. We demonstrated that CIH induced morphological changes and apoptosis in hippocampal neurons. Further, CIH blunted hippocampal long-term potentiation (LTP) and resulted in learning/memory impairment. Disruption of adenosine A1R exacerbated morphological, cellular, and functional damage induced by CIH. In contrast, activation of adenosine A1R signaling reduced morphological changes and apoptosis of hippocampal neurons, promoted LTP, and enhanced learning and memory. A1Rs may up-regulate protein kinase C (PKC) and its subtype PKC-ζ through the activation of Gα(i) improve spatial learning and memory disorder induced by CIH in mice. Taken together, A1R signaling plays a neuroprotective role in CIH-induced cognitive dysfunction and pathological changes in the hippocampus.

Platelet-Activating Factor Deteriorates Lysophosphatidylcholine-Induced Demyelination Via Its Receptor-Dependent and -Independent Effects.

Accumulating evidence suggests that platelet-activating factor (PAF) increases the inflammatory response in demyelinating diseases such as multiple sclerosis. However, PAF receptor (PAFR) antagonists do not show therapeutic efficacy for MS, and its underlying mechanisms remain poorly understood. In the present study, we investigated the effects of PAF on an ex vivo demyelination cerebellar model following lysophosphatidylcholine (LPC, 0.5 mg/mL) application using wild-type and PAFR conventional knockout (PAFR-KO) mice. Demyelination was induced in cerebellar slices that were cultured with LPC for 18 h. Exogenous PAF (1 µM) acting on cerebellar slices alone did not cause demyelination but increased the severity of LPC-induced demyelination in both wild-type and PAFR-KO mice. LPC inhibited the expression of PAF-AH, MBP, TNF-α, and TGF-ß1 but facilitated the expression of IL-1ß and IL-6 in wild-type preparations. Of note, exogenous PAF stimulated microglial activation in both wild-type and PAFR-KO mice. The subsequent inflammatory cytokines TNFα, IL-1ß, and IL-6 as well as the anti-inflammatory cytokine TGF-ß1 demonstrated a diverse transcriptional profile with or without LPC treatment. PAF promoted TNF-α expression and suppressed TGF-ß1 expression indiscriminately in wild-type and knockout slices; however, transcription of IL-1ß and IL-6 was not significantly affected in both slices. The syntheses of IL-1ß and IL-6 were significantly increased in LPC-induced demyelination preparations without PAF but showed a redundancy in PAF-treated wild-type and knockout slices. These data suggest that PAF can play a detrimental role in LPC-induced demyelination probably due to a redundant response of PAFR-dependent and PAFR-independent effects on inflammatory cytokines.

A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI.

Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired social abilities and communication difficulties. The golden standard for autism diagnosis in research rely on behavioral features, for example, the autism diagnosis observation schedule, the Autism Diagnostic Interview-Revised. In this study we introduce a computer-aided diagnosis system that uses features from structural MRI (sMRI) and resting state functional MRI (fMRI) to help predict an autism diagnosis by clinicians. The proposed system is capable of parcellating brain regions to show which areas are most likely affected by autism related abnormalities and thus help in targeting potential therapeutic interventions. When tested on 18 data sets (n = 1060) from the ABIDE consortium, our system was able to achieve high accuracy (sMRI 0.75-1.00; fMRI 0.79-1.00), sensitivity (sMRI 0.73-1.00; fMRI 0.78-1.00), and specificity (sMRI 0.78-1.00; fMRI 0.79-1.00). The proposed system could be considered an important step toward helping physicians interpret results of neuroimaging studies and personalize treatment options. To the best of our knowledge, this work is the first to combine features from structural and functional MRI, use them for personalized diagnosis and achieve high accuracies on a relatively large population.

Factors Associated With Seizure Onset in Children With Autism Spectrum Disorder.

BACKGROUND AND OBJECTIVES: Children with autism spectrum disorder (ASD) have a higher prevalence of epilepsy compared with general populations. In this pilot study, we prospectively identified baseline risk factors for the development of seizures in individuals with ASD and also identified characteristics sensitive to seizure onset up to 6 years after enrollment in the Autism Speaks Autism Treatment Network. METHODS: Children with ASD and no history of seizures at baseline who either experienced onset of seizures after enrollment in the Autism Treatment Network or remained seizure free were included in the analysis. RESULTS: Among 472 qualifying children, 22 (4.7%) experienced onset of seizures after enrollment. Individuals who developed seizures after enrollment exhibited lower scores at baseline on all domains of the Vineland Adaptive Behavior Scales, greater hyperactivity on the Aberrant Behavior Checklist (25.4 ± 11.8 vs 19.2 ± 11.1; P = .018), and lower physical quality of life scores on the Pediatric Quality of Life Inventory (60.1 ± 24.2 vs 76.0 ± 18.2; P < .001). Comparing change in scores from entry to call-back, adjusting for age, sex, length of follow-up, and baseline Vineland II composite score, individuals who developed seizures experienced declines in daily living skills (-8.38; 95% confidence interval -14.50 to -2.50; P = .005). Adjusting for baseline age, sex, and length of follow-up, baseline Vineland II composite score was predictive of seizure development (risk ratio = 0.95 per unit Vineland II composite score, 95% confidence interval 0.92 to 0.99; P = .007). CONCLUSIONS: Individuals with ASD at risk for seizures exhibited changes in adaptive functioning and behavior.

Publisher Correction: Cadmium and High-Fat Diet Disrupt Renal, Cardiac and Hepatic Essential Metals.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dynamic response of microglia/macrophage polarization following demyelination in mice.

BACKGROUND: The glial response in multiple sclerosis (MS), especially for recruitment and differentiation of oligodendrocyte progenitor cells (OPCs), predicts the success of remyelination of MS plaques and return of function. As a central player in neuroinflammation, activation and polarization of microglia/macrophages (M/M) that modulate the inflammatory niche and cytokine components in demyelination lesions may impact the OPC response and progression of demyelination and remyelination. However, the dynamic behaviors of M/M and OPCs during demyelination and spontaneous remyelination are poorly understood, and the complex role of neuroinflammation in the demyelination-remyelination process is not well known. In this study, we utilized two focal demyelination models with different dynamic patterns of M/M to investigate the correlation between M/M polarization and the demyelination-remyelination process. METHODS: The temporal and spatial features of M/M activation/polarization and OPC response in two focal demyelination models induced by lysolecithin (LPC) and lipopolysaccharide (LPS) were examined in mice. Detailed discrimination of morphology, sensorimotor function, diffusion tensor imaging (DTI), inflammation-relevant cytokines, and glial responses between these two models were analyzed at different phases. RESULTS: The results show that LPC and LPS induced distinctive temporal and spatial lesion patterns. LPS produced diffuse demyelination lesions, with a delayed peak of demyelination and functional decline compared to LPC. Oligodendrocytes, astrocytes, and M/M were scattered throughout the LPS-induced demyelination lesions but were distributed in a layer-like pattern throughout the LPC-induced lesion. The specific M/M polarization was tightly correlated to the lesion pattern associated with balance beam function. CONCLUSIONS: This study elaborated on the spatial and temporal features of neuroinflammation mediators and glial response during the demyelination-remyelination processes in two focal demyelination models. Specific M/M polarization is highly correlated to the demyelination-remyelination process probably via modulations of the inflammatory niche, cytokine components, and OPC response. These findings not only provide a basis for understanding the complex and dynamic glial phenotypes and behaviors but also reveal potential targets to promote/inhibit certain M/M phenotypes at the appropriate time for efficient remyelination.