Graph theory based classification of brain connectivity network for autism spectrum disorder

Ertan Tolan, Zerrin Isik

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Connections in the human brain can be examined efficiently using brain imaging techniques such as Diffusion Tensor Imaging (DTI), Resting-State fMRI. Brain connectivity networks are constructed by using image processing and statistical methods, these networks explain how brain regions interact with each other. Brain networks can be used to train machine learning models that can help the diagnosis of neurological disorders. In this study, two types (DTI, fMRI) of brain connectivity networks are examined to retrieve graph theory based knowledge and feature vectors of samples. The classification model is developed by integrating three machine learning algorithms with a naïve voting scheme. The evaluation of the proposed model is performed on the brain connectivity samples of patients with Autism Spectrum Disorder. When the classification model is compared with another state-of-the-art study, it is seen that the proposed method outperforms the other one. Thus, graph-based measures computed on brain connectivity networks might help to improve diagnostic capability of in-silico methods. This study introduces a graph theory based classification model for diagnostic purposes that can be easily adapted for different neurological diseases.
Original languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings
EditorsI. Rojas, F. Ortuno
PublisherSpringer Verlag
Pages520-530
ISBN (Print)9783319787220
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event6th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2018 - Granada, Spain
Duration: 25 Apr 201827 Apr 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2018
Country/TerritorySpain
CityGranada
Period25/04/1827/04/18

Funding

E. Tolan is supported by the 100/2000 CoHE Doctoral Scholar-

FundersFunder number
100/2000 CoHE

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