Autism and Epilepsy: Exploring the Relationship Using Experimental Models.
Ontology highlight
ABSTRACT:
SUBMITTER: Stafstrom CE
PROVIDER: S-EPMC4532234 | biostudies-other | 2015 Jul-Aug
REPOSITORIES: biostudies-other
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Project description:Elevated anxiety symptoms are one of the most common forms of psychopathology to co-occur with autism spectrum disorders (ASDs). The purpose of this study was to explore the association between anxiety and ASD symptoms, particularly the degree to which the relationship is explained by insistence on sameness (IS) behaviors and/or cognitive ability. The sample included 1429 individuals aged 5:8-18:0 years who participated in the Simons Simplex Collection, a genetic consortium study of ASD. Child Behavior Checklist Anxiety Problems T-scores and Autism Diagnostic Interview-Revised "IS" item raw totals were treated as both categorical and continuous measures of anxiety and IS, respectively. Chronological age, verbal intelligence quotient (IQ), and a variety of ASD phenotype-related and other behavioral variables were assessed for potential association with anxiety and IS. Anxiety and IS continuous variables were minimally, although significantly, associated with each other and with chronological age and verbal IQ. Neither anxiety nor IS was associated with other core autism diagnostic scores. Anxiety was associated with a variety of other psychiatric and behavioral symptoms in ASD, including irritability, attention problems, and aggression, while IS was not. Anxiety and IS appear to function as distinct constructs, each with a wide range of expression in children with ASD across age and IQ levels. Thus, both variables could be of use in ASD behavioral research or in dimensional approaches to genetic exploration. Unlike IS, however, anxiety is related to non-ASD-specific behavioral symptoms.
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Project description:Research in genetics of epilepsy represents an area of great interest both for clinical purposes and for understanding the basic mechanisms of epilepsy. Most mutations in epilepsies without structural brain abnormalities have been identified in ion channel genes, but an increasing number of genes involved in a diversity of functional and developmental processes are being recognized through whole exome or genome sequencing. Targeted molecular diagnosis is now available for different forms of epilepsy. The identification of epileptogenic mutations in patients before epilepsy onset and the possibility of developing therapeutic strategies tested in experimental models may facilitate experimental approaches that prevent epilepsy or decrease its severity. Functional analysis is essential for better understanding pathogenic mechanisms and gene interactions. In vitro experimental systems are either cells that usually do not express the protein of interest or neurons in primary cultures. In vivo/ex vivo systems are organisms or preparations obtained from them (e.g., brain slices), which should better model the complexity of brain circuits and actual pathophysiological conditions. Neurons differentiated from induced pluripotent stem cells generated from the skin fibroblasts of patients have recently allowed the study of mutations in human neurons having the genetic background of a given patient. However, there is remarkable complexity underlying epileptogenesis in the clinical dimension, as reflected by the fact that experimental models have not provided yet results having clinical translation and that, with a few exceptions concerning rare conditions, no new curative treatment has emerged from any genetic finding in epilepsy.
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Project description:Neonatal shock and necrotizing enterocolitis (NEC) are leading causes of morbidity and mortality in premature infants. NEC is a life-threatening gastrointestinal illness, the precise etiology of which is not well understood, but is characterized by an immaturity of the intestinal barrier, altered function of the adaptive immune system, and intestinal dysbiosis. The complexities of NEC and shock in the neonatal population necessitate relevant clinical modeling using newborn animals that mimic the disease in human neonates to better elucidate the pathogenesis and provide an opportunity for the discovery of potential therapeutics. A wide variety of animal species-including rats, mice, piglets, and primates-have been used in developing experimental models of neonatal diseases such as NEC and shock. This review aims to highlight the immunologic differences in neonates compared with adults and provide an assessment of the advantages and drawbacks of established animal models of both NEC and shock using enteral or intraperitoneal induction of bacterial pathogens. The selection of a model has benefits unique to each type of animal species and provides individual opportunities for the development of targeted therapies. This review discusses the clinical and physiologic relevance of animal models and the insight they contribute to the complexities of the specific neonatal diseases: NEC and shock.
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Project description:Modern functional neuroimaging provides opportunities to visualize activity of the entire brain, making it an indispensable diagnostic tool for epilepsy. Various forms of noninvasive functional neuroimaging are now also being performed as research tools in animal models of epilepsy and provide opportunities for parallel animal/human investigations into fundamental mechanisms of epilepsy and identification of epilepsy biomarkers. Recent animal studies of epilepsy using positron emission tomography, tractography, and functional magnetic resonance imaging were reviewed. Epilepsy is an abnormal emergent property of disturbances in neuronal networks which, even for epilepsies characterized by focal seizures, involve widely distributed systems, often in both hemispheres. Functional neuroimaging in animal models now provides opportunities to examine neuronal disturbances in the whole brain that underlie generalized and focal seizure generation as well as various types of epileptogenesis. Tremendous advances in understanding the contribution of specific properties of widely distributed neuronal networks to both normal and abnormal human behavior have been provided by current functional neuroimaging methodologies. Successful application of functional neuroimaging of the whole brain in the animal laboratory now permits investigations during epileptogenesis and correlation with deep brain electroencephalography (EEG) activity. With the continuing development of these techniques and analytical methods, the potential for future translational research on epilepsy is enormous. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.
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Project description:ObjectiveThe relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD.MethodsCaregivers of ASD (n = 144) and typically developing (TD) (n = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety.ResultsPrevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly (p < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly (p < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety.ConclusionCaregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD.
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Project description:Alongside positive blood oxygenation level-dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography-functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.
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Project description:Epilepsy is a common neurological disorder in domestic dogs. However, its complex mechanism involves multiple genetic and environmental factors that make it challenging to identify the real pathogenic factors contributing to epilepsy, particularly for idiopathic epilepsy. Conventional genome-wide association studies (GWASs) can detect various genes associated with epilepsy, although they primarily detect the effects of single-site mutations in epilepsy while ignoring their interactions. In this study, we used a systems genetics method combining both GWAS and gene interactions and obtained 26 significantly mutated subnetworks. Among these subnetworks, seven genes were reported to be involved in neurological disorders. Combined with gene ontology enrichment analysis, we focused on 4 subnetworks that included traditional GWAS-neglected genes. Moreover, we performed a drug enrichment analysis for each subnetwork and identified significantly enriched candidate anti-epilepsy drugs using a hypergeometric test. We discovered 22 potential drug combinations that induced possible synergistic effects for epilepsy treatment, and one of these drug combinations has been confirmed in the Drug Combination database (DCDB) to have beneficial anti-epileptic effects. The method proposed in this study provides deep insight into the pathogenesis of canine epilepsy and implications for anti-epilepsy drug discovery.
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Project description:The association of epilepsy, autism spectrum disorders (ASD), and intellectual disability (ID) is well recognized. There is a wide range of social-cognitive deficits that can be identified in epilepsy over the life-span, from ASD in infants with an epileptic encephalopathy, to social-cognitive impairments affecting social interaction and comprehension in those with normal nonsocial cognitive function. Identifying ASD and social-cognitive deficits is an important aspect of comprehensive epilepsy care. There are behavioral and educational interventions that exist to treat ASD and social-cognitive deficits. These behavioral, communication, and educational interventions, in conjunction with medications to treat the seizures, should be considered an integral part of the comprehensive management of epilepsy throughout the life-span. The following are the key points of this review: Autism spectrum disorders and social-cognitive deficits are associated with epilepsy throughout the life-span, and identification of these deficits is an important part of epilepsy care.Children with an epileptic encephalopathy such as infantile spasms are at high risk for developing ASD, and the social-cognitive deficits that precede ASD may be recognized in the first year of life.In epilepsy, the likelihood of developing autism spectrum disorders is highest in those with ID, but there is a wide spectrum of manifestations, from ASD in children with epilepsy and ID, to social-cognitive impairments affecting social interaction and comprehension in those with normal nonsocial cognitive function.Implementation of behavioral, communication, and educational interventions that exist to treat ASD and social-cognitive deficits, along with medications to treat the seizures, should be considered an important part of the comprehensive management of epilepsy throughout the life-span.
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