Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Tahereh Taleei; Ali Motie Nasrabadi
Volume 15, Issue 4 , March 2022, , Pages 341-353
Abstract
To interact with such an ever-changing environment in which we live, our brain requires to continuously generate and update expectations about relevant upcoming events and their estimation for the corresponding sensory and motor responses. The goal of this study is to investigate the connectivity in ...
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To interact with such an ever-changing environment in which we live, our brain requires to continuously generate and update expectations about relevant upcoming events and their estimation for the corresponding sensory and motor responses. The goal of this study is to investigate the connectivity in time perception in the two predictable and unpredictable conditions. The data needed for the study from EEG signals recorded from the existing database that included an experiment was conducted on 29 healthy subjects in the two predictable and unpredictable conditions and in 4 delays of 83, 150, 400, 800 ms for each person was done. To estimate the functional connectivity between brain regions, we used the phase lag index method. This method is used to detect time perception in two conditions, predictable and unpredictable events. Initially, by comparing the two conditions in 4 delays was shown that more of the differences were in the gamma, beta, and theta bands. Also, the significant difference between the delays in the predictable condition was greater than the unpredictable condition. Then, the difference between the two conditions in each delay was discussed. The results showed a significant difference in all delays. The alpha band in the unpredictable condition in 400-ms delay, the number of connectivity between occipital and temporal regions was increased and stronger, and also the mean of the unpredictable connectivity was higher than predictable condition. In the delta band for 150, 400 and 800-ms delays, there was connectivity between the central and frontal regions, whereas in 83-ms-delay there was stronger connectivity between the central and prefrontal regions. The right hemisphere of the prefrontal is important in time perception. At the longest delay (800 ms), in three bands, delta, theta, and beta, connectivity decreased in both conditions compared to the other delays.
Biomedical Imaging / Medical Imaging
Farzaneh Keyvanfard; Alireza Rahiminasab; Abbas Nasiraei Moghaddam
Volume 15, Issue 3 , December 2021, , Pages 211-220
Abstract
In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure ...
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In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure and function. Investigation of brain variations in this disease has commonly been based on voxel-wise analysis or region-based studies. The aim of this study is to evaluate brain structure and function alterations in schizophrenia patients comparing to healthy control from the brain connectivity perspective. For this purpose, using the statistical test method, a comparison was made between all the structural and functional connections in the brain of 92 healthy individuals and 37 schizophrenia patients obtained from diffusion tensor imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) respectively. The findings of this study indicate that the number of altered edges in the brain functional network of patients is about 4 times more than the number of varied structural connections, which indicates the high impact of this disorder on brain function. Also, examination of the number of altered edges connected to each node, the affected areas in this disease were identified and it was shown that the schizophrenia patients’ brain has changed in parts of the brain subnetworks related to the default mode network (DMN), attention, somatomotor and vision networks. It was also shown that the altered brain structural connections of patients are involved in the areas of the superior frontal gyrus, temporal gyrus and part of the occipital cortex which are mostly shown relative increasing of the structural connectivity weights. The results of this study indicate the widespread effect of this disorder on the brain and suggest that the occurrence of some abnormal behaviors in schizophrenia patients may be due to some increased structural connectivity weights.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hessam Ahmadi; Emad Fatemizadeh; Alimotie Nasrabadi
Volume 14, Issue 3 , October 2020, , Pages 235-249
Abstract
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique for analyzing the brain functions through low-frequency fluctuations called the Blood-Oxygen-Level-Dependent (BOLD) signals. Measurement of the functional connectivity in brain networks is usually done by the fMRI time-series ...
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Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique for analyzing the brain functions through low-frequency fluctuations called the Blood-Oxygen-Level-Dependent (BOLD) signals. Measurement of the functional connectivity in brain networks is usually done by the fMRI time-series through Pearson Correlation Coefficients (PCC). As the PCC shows linear dependencies, in this study, non-linear relationships in the fMRI signals of the patients with Alzheimer's Disease (AD) were investigated using the kernel trick method. Kernel trick approach maps the input information into a higher dimension space and implements the linear calculations in a new space that is proportionate to the non-linear relationships in the primary space. After generating the weighted undirected brain graphs based on the Automated Anatomical Labeling (AAL) atlas, different kernel functions with different parameters were applied. Then the graph global measures including degree, strength, small-worldness, modularity, and efficiencies features were computed and the non-parametric permutation test was performed. According to the results, the kernel trick method showed more significant differences with AD and healthy subjects in comparison with the simple PCC and it could be because of the non-linear correlations that are not captured by the PCC. Among different kernel functions, the Polynomial function had the best performance. Applying this kernel, the classification was done by the Support Vector Machine (SVM) classifier. The achieved accuracy was equal to 98.68±0.79%. The Occipital and Temporal lobes and also the Default Mode Network (DMN) were analyzed and the kernel trick method showed more significant differences in all of them. It is worthwhile to mention that the right and left Angular areas of DMN showed no significant changes in none of the methods and it could be concluded that the AD does not affect this areas effectively.
Biomedical Image Processing / Medical Image Processing
Mahdie Ghasemi; Ali Mahloojifar; Mehdi Omidi
Volume 8, Issue 3 , September 2014, , Pages 261-275
Abstract
Functional changes in the brain motor network are responsible for the major clinical features of Parkinson’s disease (PD). Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. ...
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Functional changes in the brain motor network are responsible for the major clinical features of Parkinson’s disease (PD). Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. In this paper, we examine changes of functional dependency between brain regions of interest associated with known anatomical pathology in Parkinson Disease (PD) using copula theory on resting state fMRI. Five types of copulas were tested: Gaussian and t (Euclidean), Clayton, Gumbel and Frank (Archimedean). We used an efficient maximum likelihood procedure for estimating copula parameters. Goodness of fits was tested using root mean square error (RMSE) and kulback-leibler divergence between each copula function and joint empirical cumulative distribution. Control vs PD group comparison was also done on dependency parameter using parametric and nonparametric tests. The results show that functional dependency between cerebellum and basal ganglia is much stronger in PD than in control. In this paper, we proposed for the first time that joint distribution characteristics could potentially provide information on discriminative features for functional connectivity analysis between healthy and patients.
