Biomedical Image Processing / Medical Image Processing
Amirhossein Chalechale; Ali Khadem
Volume 14, Issue 1 , May 2020, , Pages 31-42
Abstract
The well-timed and correct diagnosis of Bipolar Disorder (BD) followed by proper treatment is vital for avoiding the progress of the illness. Although using resting-state functional magnetic resonance imaging (rs-fMRI) data and the features extracted from them may have an important role in diagnosing ...
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The well-timed and correct diagnosis of Bipolar Disorder (BD) followed by proper treatment is vital for avoiding the progress of the illness. Although using resting-state functional magnetic resonance imaging (rs-fMRI) data and the features extracted from them may have an important role in diagnosing this kind of brain disorder, few researches have been conducted on this illness and the obtained results are not accurate. In this research we used a new approach to diagnose BD I. By using seed-based correlation we used the following 4 regions of interest in order to extract the connectivity maps for each subject: the posterior cingulate cortex (PCC) to probe the default mode network (DMN), the amygdala and the subgenual cingulate cortex (sgACC) to probe the salience network (SN) and the dorsolateral prefrontal cortex (dlPFC) to probe the frontoparietal network (FPN). After computing the connectivity maps for each subject we extracted the most important connectivities using different threshold on the t-value from the t-test that we applied on them and then we used a support vector machine (SVM) using only four combined features and a leave one out cross-validation (LOOCV) method to classify the two groups. The proposed method was done on rs-fMRI data from 49 healthy control subjects and 34 BD I patients and an accuracy of higher than 90% was obtained in differentiating the two groups from each other. Also there were no hyper-connectivity between the 4 ROIs and the rest of the brain regions for the BD I groups in relation with the healthy controls. The regions that had most of the hypo-connectivity with the 4 ROI’s that we used were: the angular gyrus (Ag) and the orbitofrontal cortex (OFC) with the PCC, the anterior cingulate cortex with the amygdala and the dlPFC and the inferior temporal gyrus (ITG) with the sgACC.
Biomedical Imaging / Medical Imaging
Neda Sardaripour; Alireza Sedghi; Ali Yoonessi; Ali Khadem; Hamid Abrishami Moghaddam
Volume 12, Issue 4 , January 2019, , Pages 299-315
Abstract
During vision process, the information produced by rod and cone photoreceptors is compressed in retina and then is transmitted by three separated pathways of ganglion cells, Magno, Parvo and Konio, to the upper level processing centers. There are electrophysiological and psychophysical evidences that ...
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During vision process, the information produced by rod and cone photoreceptors is compressed in retina and then is transmitted by three separated pathways of ganglion cells, Magno, Parvo and Konio, to the upper level processing centers. There are electrophysiological and psychophysical evidences that these three pathways show characteristic patterns of malfunction in multiple sclerosis (MS) patients. Although fMRI can provide accurate localization of the neural activities in these pathways, there is no fMRI study on malfunctions of these pathwyas in MS yet. So by employing the differences in structure and function of these cells, we generated three different visual stimuli with different spatial and temporal frequencies to stimulate each pathway separately. These stimuli were shown to the subject inside MRI scanner by a calibrated projector located outside of scanner room. The fMRI data were acquired from two groups of normal and MS subjects (each including 5 subjects) by using a standard protocol. Finally, the activation results in visual lobe and LGN were analyzed in within-group and between-group levels. The group analysis of fMRI data was performed by using general linear modeling (GLM) and fixed-effect method via FSL software and results showed patterns of malfunctions in visual cortex and LGN in MS group. Also, among Magno, Parvo, and Konio cellular pathways in LGN, just the activation of Magno cellular pathway showed significant malfunction in MS group.
