An evolutionary approach for fMRI big data classification

Amirhessam Tahmassebi, Amir H. Gandomi, Ian McCann, Mieke Hj Schulte, Lianne Schmaal, Anna E. Goudriaan, Anke Meyer-Baese

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

Abstract

Resting-state function magnetic resonance imaging (fMRI) images allow us to see the level of activity in a patient's brain. We consider fMRI of patients before and after they underwent a smoking cessation treatment. Two classes of patients have been studied here, that one took the drug N-acetylcysteine and the ones took a placebo. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-treatment based on their fMRI scans. The image slices of brain are used as the variable and as results here we deal with a big data problem with about 240,000 inputs. To handle this problem, the data had to be reduced and the first process in doing that was to create a mask to apply to all images. The mask was created by averaging the before images for all patients and selecting the top 40% of voxels from that average. This mask was then applied to all fMRI images for all patients. The average of the difference in the before treatment and after fMRI images for each patient were found and these were flattened to one dimension. Then a matrix was made by stacking these 1D arrays on top of each other and a data reduction algorithm was applied on it. Lastly, this matrix was fed into some machine learning and Genetic Programming algorithms and leave-one-out cross-validation was used to test the accuracy. Out of all the data reduction machine learning algorithms used, the best accuracy was obtained using Principal Component Analysis along with Genetic Programming classifier. This gave an accuracy of 74%, which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.

Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1029-1036
Number of pages8
ISBN (Electronic)9781509046010
DOIs
Publication statusPublished - 5 Jul 2017
Externally publishedYes
Event2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain
Duration: 5 Jun 20178 Jun 2017

Publication series

Name2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings

Conference

Conference2017 IEEE Congress on Evolutionary Computation, CEC 2017
CountrySpain
CityDonostia-San Sebastian
Period5/06/178/06/17

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