Title: Analysis of resting state functional magnetic resonance images for evaluating the changes in brain function depression

Authors: Hao Yu; Ye Yuan; Ashutosh Sharma; Abolfazl Mehbodniya; Mohammad Shabaz

Addresses: Changchun Medical College, Changchun, 130031, China ' Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Fei, 230022, China ' Southern Federal University, Russia ' Department of Electronics and Communications Engineering, Kuwait College of Science and Technology (KCST), Doha Area, 7th Ring Road, Kuwait ' Department of Computer Science and Engineering, Model Institute of Engineering and Technology, Jammu, J&K, 181123, India

Abstract: Prolonged emotions of sadness are habitually considered as major depressive disorder (MDD) that has parallel signs like other mental illnesses. These parallel indicative features can frequently lead to suffering of depression and other psychological conditions and therefore involve experts to predict such symptoms and use the timely treatment of MDD in order to evade the adverse effects. Magnetic resonance imaging (MRI) is involved as a vital role in deducing the pathologies related to MDD. This paper deals with the application of data collection for the characteristics of spontaneous brain activity in the basic state of depression patients using resting state functional magnetic resonance images (fMRI), and discusses the changes in the brain function during a depression stage. In this paper, 16 patients with depression underwent 5 minutes and 12 seconds of brain functional MRI scan, and the Hamilton Depression Scale was used to evaluate the severity of the condition. The ReHo software was used to examine local brain regions on the image data. It is revealed that the resting brain fMRI-ReHo method found that the abnormal brain function area of patients with depression included: left thalamus, left temporal lobe, left cerebellum, occipital lobe, and the spontaneous activity consistency of patients in these areas was reduced. This work is done by SVM approach that utilises AUC value of 0.885 for prediction, and it outperforms the state-of-the-art methods in a brain abnormality prediction by a maximum improvement of 22.24% and minimum improvement of 13.75%.

Keywords: data collection; MRI; magnetic resonance imaging; fMRI; functional magnetic resonance images; resting state function; depression; brain function; HAMD; Hamilton depression; DMN; default mode network.

DOI: 10.1504/IJNT.2023.134018

International Journal of Nanotechnology, 2023 Vol.20 No.5/6/7/8/9/10, pp.644 - 665

Received: 31 Jul 2021
Accepted: 09 Dec 2021
Published online: 10 Oct 2023 *

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