Search for: brain-networks
0.006 seconds

    Functional brain networks in parkinson's disease

    , Article 24th Iranian Conference on Biomedical Engineering and 2017 2nd International Iranian Conference on Biomedical Engineering, ICBME 2017, 30 November 2017 through 1 December 2017 ; 2018 ; 9781538636091 (ISBN) Akbari, S ; Fatemizadeh, E ; Reza Deevband, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Parkinson's disease (PD) is the second most common and progressive neurological disorder. Parkinson's signs are caused by dysfunction in PD patient's brain network. Newly, resting state functional magnetic resonance imaging has been utilized to assess the altered functional connectivity in PD patients. In this study, we investigated the properties of the brain network topology in 19 PD patients compared to 17 normal healthy group by means of graph theory. In addition, we used four different graph formation methods to explore linear and nonlinear relationships between fMRI signals. Each correlation measure created a weighted graph for each subject. Different graph characteristics have been... 

    Brain Connectivity Analysis Using Multiple Partial Least Square on fMRI Signals

    , M.Sc. Thesis Sharif University of Technology Hosseini Naghavi, Nader (Author) ; Fatemizadeh, Emad (Supervisor)
    Nowadays studying the brain's function in different Mental States like Resting-State or performing cognitive tasks is a very important component of research areas such as Biomedical Engineering, Neuroscience and Cognitive Sciences. The applications of studying the brain's function can be divided into two principal groups. In the first group of applications, the goal is understanding how the brain processes and response to external stimuli (like visual or audio stimuli) and internal states (like emotions). In these kinds of applications, particularly healthy subjects participate since the goal of these studies is finding healthy brain function in different states and stimuli. However, in the... 

    Synchronizability of EEG-based functional networks in early alzheimer's disease

    , Article IEEE Transactions on Neural Systems and Rehabilitation Engineering ; Volume 20, Issue 5 , 2012 , Pages 636-641 ; 15344320 (ISSN) Tahaei, M. S ; Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    IEEE  2012
    Recently graph theory and complex networks have been widely used as a mean to model functionality of the brain. Among different neuroimaging techniques available for constructing the brain functional networks, electroencephalography (EEG) with its high temporal resolution is a useful instrument of the analysis of functional interdependencies between different brain regions. Alzheimer's disease (AD) is a neurodegenerative disease, which leads to substantial cognitive decline, and eventually, dementia in aged people. To achieve a deeper insight into the behavior of functional cerebral networks in AD, here we study their synchronizability in 17 newly diagnosed AD patients compared to 17 healthy... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; 2020 Ahmadi, H ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2020
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    EEG-based functional networks in schizophrenia

    , Article Computers in Biology and Medicine ; Volume 41, Issue 12 , 2011 , Pages 1178-1186 ; 00104825 (ISSN) Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the... 

    Transcranial DC stimulation modifies functional connectivity of large-scale brain networks in abstinent methamphetamine users

    , Article Brain and Behavior ; Volume 8, Issue 3 , 2018 ; 21623279 (ISSN) Shahbabaie, A ; Ebrahimpoor, M ; Hariri, A ; Nitsche, M. A ; Hatami, J ; Fatemizadeh, E ; Oghabian, M. A ; Ekhtiari, H ; Sharif University of Technology
    John Wiley and Sons Ltd  2018
    Background: Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation tool suited to alter cortical excitability and activity via the application of weak direct electrical currents. An increasing number of studies in the addiction literature suggests that tDCS modulates subjective self-reported craving through stimulation of dorsolateral prefrontal cortex (DLPFC). The major goal of this study was to explore effects of bilateral DLPFC stimulation on resting state networks (RSNs) in association with drug craving modulation. We targeted three large-scale RSNs; the default mode network (DMN), the executive control network (ECN), and the salience network (SN). Methods:...