Introduction: Early recognition of autism spectrum disorder (ASD) provides opportunities for early intervention and you can also achieve better development results. The use of infant electroencephalography (EEG) has shown promise in predicting the subsequent diagnosis of ASD and elucidating the neural mechanisms underlying the disorder. Given the high comorbidity of speech disorders, we and others hypothesized that language acquisition, including phonetic discrimination, is impaired in children subsequently diagnosed with ASD. Phonetic learning occurs rapidly in infancy, so the altered neural matrix in the first year of life may be a rapid and accurate indicator for the later diagnosis of autism. Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder with core functions such as communication and social disturbance and stereotyped behavior (Chase et al., 2006). Currently, the incidence of ASD is increasing rapidly worldwide. Currently, the cause of ASD is unclear, and diagnosis is made primarily on the basis of subjective behavioral ratings and observational scales. Therefore, it is important to find objective indicators for a correct assessment.
Methods: As you have noticed just now, the junction action potentials recorded from the excitation electrode can be approximated very well by a Gaussian distribution. The standard interpretation of the Gaussian distribution is that it represents the result of a homogeneous process dependent on the parameters of the normal distribution. For example, the shape of the complex action potential of the sciatic nerve shows that very few neurons with high conduction velocities respond to the electrical activity represented by the first part of the complex action potential curve and neurons with very slow conduction velocities. . It is responsible for the electrical activity shown in the last part of the curve. The large-amplitude central portion of the complex action potential "peak" indicates that most neurons in the fixation nerve have conduction velocities between the two extremes, with most conduction velocities concentrated in the middle range, but fewer and more "extreme" ones. On the other hand, autism is associated with childhood seizures, but an association with TSC is not the only result. In 1981, Rijkkonen and Amnell studied 192 children with a diagnosis of (provoked) infantile seizures and found a prevalence of 12.5% according to established criteria. Using the same criteria, Hunt and Dennis (19) found that 58% of children with TSD and a history of childhood seizures had autism. Hunt and Shepherd suggested that the higher incidence of autism in TSC reflects a more fundamental relationship between the disorders. However, there appears to be a difference in the autistic symptom profile of individuals with TSD compared to idiopathic autism.
Results: The identification of the TSC1 and TSC2 genes and the first characterization of protein products and their roles represents the significant advances in our understanding of TSC that have been achieved in the past decade. The different disease risks associated with TSC1 and TSC2 need to be clarified. Especially since prenatal diagnostic decisions can be based on them. The important functions of Hamatin and Tuberin seem to require the interaction of the two proteins, thus explaining the difference in TSC1 and TSC2 disease severity. Presumably, disease severity is actually determined by the rate of somatic mutations at the two loci. If this ratio is higher in his TSC2 site than his TSC1 site with TSC2, then he has a higher rate of second stroke and we predict he has more hamartomas and malformations with TSC2 than TSC1. These are all associated with Laplace's first and second error curves (based on Gaussian distribution)
Conclusion: These curves may have the same mean and the same variance. This is to be expected when they are all based on the same underlying process. The only difference is that the peak amplitude occurs when the same event is performed at three different "intensities". For example, for complex action potentials, alpha rhythm and power spectrum shape: different amplitudes may reflect more (or less) active neurons, better (or worse) recording technique, and so on.
Keywords: Gaussian distribution- autism- TSC- power spectrum shape- alpha rhythm