Autism is a neuro-developmental disorder that retards the normal cognitive development of an affected person. It is prevalent in children below the age of five and is generally identified through the symptoms exhibited by them while they interact with the environment. This work focuses on the extraction of texture features for autistic and control subjects and validation is done using the neural classifiers, Learning Vector Quantization (LVQ) and Support Vector Machines (SVM). Six texture features namely, energy, entropy, contrast, inverse differential moment, directional moment, correlation and homogeneity were extracted for 15 autistic and 15 control groups through Gray Level Co-occurrence Matrix (GLCM). The system has been trained by subjecting these texture features using the LVQ and SVM. In order to ensure correctness of this mechanism, the validation has been done by employing the same techniques, where in LVQ gave a classification accuracy of 87.7% and SVM accounted 97.8% of classification accuracy.
Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task i.e., “rest.” Adults with ASD have been found to show weaker connectivity relative to controls. This work focuses on analyzing the brain activation for autistic subjects, measured by fMRI during rest, relative to the control group using interhemispherical analysis. Though both groups activated similarly in cortical areas, indications of under connectivity were exhibited by the autistic group measured by Granger Causality and Conditional Granger Causality. Results show that as connectivity decreases, GC and CGC values also get decreased. The left hemisphere exhibits depreciation in the connectivity in comparison to that of right hemisphere for the autistic individuals whose GC and CGC values keeps decreasing in the left hemisphere seed regions. Finally, the results provide an approach for analyzing the cortical underconnectivity, in clinical relevance for diagnosing autism in children.
Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.
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