Altered Structural Connectivity in Autism Spectrum Disorder
R. Geetha Ramani1, R. Sahayamary Jabarani2

1*R.GeethaRamani, Department of Information Science and Technology, College of Engineering, Guindy, Anna university, Chennai, (Tamil Nadu), India.
2R,Sahayamary Jabarani, Department of Information Science and Technology, College of Engineering, Guindy, Anna university, Chennai, (Tamil Nadu), India.
Manuscript received on November 28, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3792-3801  | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3889129219/2019©BEIESP | DOI: 10.35940/ijeat.B3889.129219
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Research in Neurological field has been in great trend in recent days, since the need of detection and treatment of various neuropsychological disorders are in increasing order. Automated approaches for the detection are possible by various technological methods. Autism Spectrum Disorder (ASD) is a one such serious disorder which can be diagnosed in early ages of children. The Emerging technology had contributed the neuro imaging techniques to understand the various basic features and characteristics that cause the disorder. This neuro imaging had lead to a better perspective called connectome analysis which deals network structures (connectome) derived from the neuro images and are used in detection and treatment of the disorder. For these analysis functional and structural connectomes / network of brain are utilized. In this work structural connectomes derived from the Diffusion Tensor Imaging of Typically Developing and Autism Spectrum Disordered had been considered . This connectome / network consists of 264 regions (based on PowerNeuron_264 atlas) and thus 69696 connectivity features (connection between regions). Using the structural connectomes, average connectome analysis had been done and 91 connections had been identified as altered in ASD. There are 112 distinct regions involved in these altered connections and are having varied number of altered connections from one to six. 15 regions among them found to have much alteration since more number of (More than 2) altered connectivity are involved with these regions. To prove the finding , Data mining technique, Support Vector Machine was applied over 42 connectivity features (0.06% of original) out of 91 and are involved with the 15 regions filtered and the classification is done (with 82% accuracy) . Classifier rules are utilized in the diagnosis of ASD . The 15 regions extracted through thisprocess are found to be altered in ASD. These altered regions are related to sensory(touch and taste), memory, movements control, Lexical processing, Consciousness and sleep. This proposed system surely have effective use in the process of high dimensional and complex brain data and the identification of typically developed and autism spectrum disordered brain .This methodology can also be used in detection of other diseases, Role of various Regions, influential regions, etc.
Keywords:  Brain, Connectome, Diseases, Neuro images.