Brain Activity Map Extraction from Multiple Sclerosis Patients Using Resting-State fMRI Data Based on Amplitude of Low Frequency Fluctuations and Regional Homogeneity Analysis

Document Type : Conference Proceedings


1 Department of Biomedical Engineering, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.

2 Department of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.


Introduction: Multiple Sclerosis (MS) is the most common non-traumatic neurological diseases of young adults. MS often reported during ages 20-62. MS affects the various anatomical parts of the central nervous system. Up to 65% of multiple sclerosis patients MS patients suffer from various problems, such as fatigue, depression, pain and sleep disorders. Unlike MRI, that only shows anatomical data of the brain, the fMRI technique can reveal functional relationship and brain activity map. Analysis of fMRI data in patients with MS can provide valuable information on post-traumatic neurological changes, so it leads to understanding the pathophysiology of the disease as well as the better choice of treatments methods. Many studies have been done on patient’s whit MS using fMRI data but unfortunately the results of the studies are not compatible. It means that changes in fMRI data which is done based on the disease are not specified completely.
Materials and Method: Based on a neurologist's diagnosis, resting-state fMRI imaging was done on 20 patients with MS and 20 normal persons. . In order to analyze the data, the Amplitude of Low Frequency Fluctuations (ALFF) and Regional Homogeneity (ReHo) methods were used to study the activity of brain regions. To do that, the time series related to each three-dimensional volume voxels were extracted. After selecting the desired clusters in standard space and registering functional images on structural images and then on standard atlas between two groups, the results were analyzed statistically. All steps of this study were done using the Resting- State fMRI Data Analysis Toolkit in MATLAB software.
Results: By comparing the statistical data of the two groups of testing and controlling, the results of collective analysis of low frequency oscillations in resting state indicated increased activity in motor and cerebellar parts and decreased activity in central parts. Also, the results of the statistical analysis of functional relationships show a significant level of negative correlation in low frequency oscillations between some of the motor regions and basal nuclei, which are observed only in MS subjects, and there is only a significant level of positive correlation within the motor regions or the core nucleus in normal subjects.
Conclusion: MS might be related to reduction activity of thalamus and increase activity of lateral ventricular volume of the brain.