scholarly journals Neonatal valproic acid exposure produces altered gyrification related to increased parvalbumin-immunopositive neuron density with thickened sulcal floors

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250262
Author(s):  
Kazuhiko Sawada ◽  
Shiori Kamiya ◽  
Ichio Aoki
Keyword(s):  
Autism Spectrum Disorder ◽  
Valproic Acid ◽  
Late Stage ◽  
Ex Vivo ◽  
Early Stage ◽  
Autism Spectrum ◽  
Cortical Neurogenesis ◽  
Neuron Density ◽  
Positive Neuron ◽  
Gyrification Index

Valproic acid (VPA) treatment is associated with autism spectrum disorder in humans, and ferrets can be used as a model to test this; so far, it is not known whether ferrets react to developmental VPA exposure with gyrencephalic abnormalities. The current study characterized gyrification abnormalities in ferrets following VPA exposure during neonatal periods, corresponding to the late stage of cortical neurogenesis as well as the early stage of sulcogyrogenesis. Ferret pups received intraperitoneal VPA injections (200 μg/g of body weight) on postnatal days (PD) 6 and 7. BrdU was administered simultaneously at the last VPA injection. Ex vivo MRI-based morphometry demonstrated significantly lower gyrification index (GI) throughout the cortex in VPA-treated ferrets (1.265 ± 0.027) than in control ferrets (1.327 ± 0.018) on PD 20, when primary sulcogyrogenesis is complete. VPA-treated ferrets showed significantly smaller sulcal-GIs in the rostral suprasylvian sulcus and splenial sulcus but a larger lateral sulcus surface area than control ferrets. The floor cortex of the inner stratum of both the rostral suprasylvian and splenial sulci and the outer stratum of the lateral sulcus showed a relatively prominent expansion. Parvalbumin-positive neuron density was significantly greater in the expanded cortical strata of sulcal floors in VPA-treated ferrets, regardless of the BrdU-labeled status. Thus, VPA exposure during the late stage of cortical neurogenesis may alter gyrification, primarily in the frontal and parietotemporal cortical divisions. Altered gyrification may thicken the outer or inner stratum of the cerebral cortex by increasing parvalbumin-positive neuron density.

scholarly journals Preliminary Results Regarding Sleep in a Zebrafish Model of Autism Spectrum Disorder

2021 ◽  
Vol 11 (5) ◽  
pp. 556
Author(s):  
Madalina Andreea Robea ◽  
Alin Ciobica ◽  
Alexandrina-Stefania Curpan ◽  
Gabriel Plavan ◽  
Stefan Strungaru ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Valproic Acid ◽  
Social Behavior ◽  
Antiepileptic Drug ◽  
Alternative Model ◽  
Neurological Diseases ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Zebrafish Model ◽  
Developmental Defects

Autism spectrum disorder (ASD) is one of the most salient developmental neurological diseases and remarkable similarities have been found between humans and model animals of ASD. A common method of inducing ASD in zebrafish is by administrating valproic acid (VPA), which is an antiepileptic drug that is strongly linked with developmental defects in children. In the present study we replicated and extended the findings of VPA on social behavior in zebrafish by adding several sleep observations. Juvenile zebrafish manifested hyperactivity and an increase in ASD-like social behaviors but, interestingly, only exhibited minimal alterations in sleep. Our study confirmed that VPA can generate specific ASD symptoms, indicating that the zebrafish is an alternative model in this field of research.

scholarly journals A Longitudinal Study of Local Gyrification Index in Young Boys With Autism Spectrum Disorder

2018 ◽  
Vol 29 (6) ◽  
pp. 2575-2587 ◽  
Author(s):  
Lauren E Libero ◽  
Marie Schaer ◽  
Deana D Li ◽  
David G Amaral ◽  
Christine Wu Nordahl
Keyword(s):  
Autism Spectrum Disorder ◽  
Longitudinal Study ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Gyrification Index ◽  
Local Gyrification Index

scholarly journals What We Have Learned about Autism Spectrum Disorder from Valproic Acid

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Taylor Chomiak ◽  
Nathanael Turner ◽  
Bin Hu
Keyword(s):  
Autism Spectrum Disorder ◽  
Valproic Acid ◽  
Animal Model ◽  
Neurodevelopmental Disorders ◽  
Significant Risk ◽  
Autism Spectrum ◽  
Research Literature ◽  
Spectrum Disorder ◽  
Epidemiological Evidence ◽  
Growing Body

Two recent epidemiological investigations in children exposed to valproic acid (VPA) treatment in utero have reported a significant risk associated with neurodevelopmental disorders and autism spectrum disorder (ASD) in particular. Parallel to this work, there is a growing body of animal research literature using VPA as an animal model of ASD. In this focused review we first summarize the epidemiological evidence linking VPA to ASD and then comment on two important neurobiological findings linking VPA to ASD clinicopathology, namely, accelerated or early brain overgrowth and hyperexcitable networks. Improving our understanding of how the drug VPA can alter early development of neurological systems will ultimately improve our understanding of ASD.

Social-Emotional Correlates of Early Stage Social Information Processing Skills in Children With and Without Autism Spectrum Disorder

2015 ◽  
Vol 8 (5) ◽  
pp. 486-496 ◽  
Author(s):  
Nicole M. Russo-Ponsaran ◽  
Clark McKown ◽  
Jason K. Johnson ◽  
Adelaide W. Allen ◽  
Bernadette Evans-Smith ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Information Processing ◽  
Social Information Processing ◽  
Early Stage ◽  
Social Information ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Social Emotional

scholarly journals Autism spectrum disorder and molecular imaging following environmental stress: Functional aspects of the risk using multi-omics

2021 ◽  
Vol 16 (1) ◽  
pp. 027-030
Author(s):  
Sandeep K Reddy ◽  
Bandar E Almansouri ◽  
Rehab A Alshammari ◽  
Nishat Anwar ◽  
Diane E Heck ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Autism Spectrum Disorder ◽  
Environmental Risk ◽  
Value Chain ◽  
Prediction Models ◽  
Early Stage ◽  
Disease Onset ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Molecular Determinants

Autism Spectrum Disorder (ASD) is characterized by complicated phenotypic symptoms, including intervention with social activity, communication, and unusually behavioral abnormality. ASD is a lifelong developmental condition affecting one in 88 children and is considered one of today's most urgent public health challenges. Individuals with ASD tend to respond inappropriately in conversation and may struggle to build relationships. Currently, the prime cause of ASD remains unclear, even though emerging findings emphasize the role of genetic and environmental factors in the development of autistic behavior could be examined. At present, risks such as exposure to unknown chemicals as an environmental factor in ASD are less appreciated. This review will discuss potential risks include air pollution and particle matters in alignment with detection strategies, like multidimensional Omics and the transcriptomic approach, which may empower the capability of predicting potential risk from gene expression to phenotype level as a hallmark of transformation outcome. In addition, this genomic-driven validation process saves time and quality of accuracy in the process of finding molecular determinants in the early stage of disease onset. Currently, the genomics era brings prediction models with various algorithms, and its intervention alternatives speed up to analyze the environmental risk of chemical stressors, such as hazardous chemicals, air pollutants, and/or nanoparticles, in compliance with regulatory measures of exploring molecular determinants associated with chronic disease and metabolic disorders. The value chain of disease prevention along with surveillance platform closely interacts with the prediction of risk assessment using a molecular-based platform. Efficacy of a sequential workout, including exploring, monitoring, and the translational application process in cellular or in vitro systems, could crosstalk with a transgenic animal model. Targeting molecule implication, such as gain- or loss-of-functional reverse genetic technology to verify its functional analysis, multi-dimensional omics could be beneficial in the field of environmental risk assessment, including safety evaluation: food and drug screening in ASD combined with imaging technology.

scholarly journals Diffusion kurtosis imaging of gray matter in young adults with autism spectrum disorder

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Faye McKenna ◽  
Laura Miles ◽  
Jeffrey Donaldson ◽  
F. Xavier Castellanos ◽  
Mariana Lazar
Keyword(s):  
Autism Spectrum Disorder ◽  
Young Adults ◽  
Gray Matter ◽  
Ex Vivo ◽  
Mean Diffusivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Diffusion Kurtosis Imaging ◽  
Adults With Autism

AbstractPrior ex vivo histological postmortem studies of autism spectrum disorder (ASD) have shown gray matter microstructural abnormalities, however, in vivo examination of gray matter microstructure in ASD has remained scarce due to the relative lack of non-invasive methods to assess it. The aim of this work was to evaluate the feasibility of employing diffusional kurtosis imaging (DKI) to describe gray matter abnormalities in ASD in vivo. DKI data were examined for 16 male participants with a diagnosis of ASD and IQ>80 and 17 age- and IQ-matched male typically developing (TD) young adults 18–25 years old. Mean (MK), axial (AK), radial (RK) kurtosis and mean diffusivity (MD) metrics were calculated for lobar and sub-lobar regions of interest. Significantly decreased MK, RK, and MD were found in ASD compared to TD participants in the frontal and temporal lobes and several sub-lobar regions previously associated with ASD pathology. In ASD participants, decreased kurtosis in gray matter ROIs correlated with increased repetitive and restricted behaviors and poor social interaction symptoms. Decreased kurtosis in ASD may reflect a pathology associated with a less restrictive microstructural environment such as decreased neuronal density and size, atypically sized cortical columns, or limited dendritic arborizations.

ML-PASD

2021 ◽  
pp. 82-93
Author(s):  
Vishal Jagota ◽  
Vinay Bhatia ◽  
Luis Vives ◽  
Arun B. Prasad
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Artificial Neural Network ◽  
Early Stage ◽  
Age Groups ◽  
Autism Spectrum ◽  
Screening Procedures ◽  
Artificial Neural Network Ann

Autism spectrum disorder (ASD) is growing faster than ever before. Autism detection is costly and time intensive with screening procedures. Autism can be detected at an early stage by the development of artificial intelligence and machine learning (ML). While a number of experiments using many approaches were conducted, these studies provided no conclusion as to the prediction of autism characteristics in various age groups. This chapter is therefore intended to suggest an accurate MLASD predictive model based on the ML methodology to prevent ASD for people of all ages. It is a method for prediction. This survey was conducted to develop and assess ASD prediction in an artificial neural network (ANN). AQ-10 data collection was used to test the proposed pattern. The findings of the evaluation reveal that the proposed prediction model has improved results in terms of consistency, specificity, sensitivity, and dataset accuracy.

scholarly journals A Deep learning of Autism Spectrum Disorder using Naïve Bayes, IBk and J48 classifiers

2019 ◽  
Vol 8 (2) ◽  
pp. 1428-1432
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Naive Bayes ◽  
Early Stage ◽  
Confusion Matrix ◽  
Autism Spectrum ◽  
Naïve Bayes ◽  
Spectrum Disorder ◽  
The Right ◽  
Fold Cross Validation

Deciding the right classification algorithm to classify and predict the disease is more important in the health care field. The eminence of prediction depends on the accuracy of the dataset and the machine learning method used to classify the dataset. Predicting autism behaviors through laboratory or image tests is very time consuming and expensive. With the advancement of machine learning (ML), autism can be predicted in the early stage. The main objective of the paper is to analyze the three classifiers Naïve Bayes, J48 and IBk (k-NN). An Autism Spectrum Disorder (ASD) diagnosis dataset with 21 attributes is obtained from the UCI machine learning repository. The attributes have experimented with the three classifiers using WEKA tool. 10-fold cross validation is used in all three classifiers. In the analysis, J48 shows the best accuracy compared with the other two classifiers. The architecture diagram is shown to depict the flow of the analysis. The Confusion matrix with other relevant results and figures are shown.

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