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    Synchronization Analysis of EEG-Based Brain Functional Network

    , M.Sc. Thesis Sharif University of Technology Alamfard, Vahid (Author) ; Manzuri, Mohammad Taghi (Supervisor)
    Abstract
    It is believed that the synchronized activity of different brain areas, is the main cause of information binding inside the brain. Tis is definitely one of the most exciting challenges in modelling modern complex systems. Brain disorders such as schizophrenia,Alzheimer’s disease, epilepsy, autism and Parkinson’s disease are associated with abnormal synchronization abilities of neural networks. Functional connections can be assessed indirectly by measuring the electrophysiological criteria of ynchronization.Traditionally, in the study of neurophysiological, synchronizations are assessed by analyzing the coherence of frequency-domain characteristics of time series in standard methods for... 

    EEG Brain Functional Network Analysis in Cortex Level

    , M.Sc. Thesis Sharif University of Technology Pedrood, Bahman (Author) ; Jalili, Mahdi (Supervisor)
    Abstract
    Complex networks science have received tremendous attention in recent years and the brain is one of the systems to which graph theoretical tools have been applied. Alzheimer’s disease (AD) is a neurodegenerative disease affecting many of elderly population. AD changes the anatomy of the brain, which subsequently results in changes in its functions. These changes have been frequently reported in signals recorded from the brain (such as MEG, fMRI and EEG). Among these neuroimaging techniques EEG is one of the most aproprate methods for extracting functional connectivites according to high temporal resolution. In this thesis, we aimed at analyzing the properties of EEG-based functional networks... 

    Constructing EEG-Based Brain Functional Connectome Using Network-based Statistics

    , M.Sc. Thesis Sharif University of Technology Barzegaran, Elham (Author) ; Jalili, Mahdi (Supervisor)
    Abstract
    In recent years, there have been increasing attempts to study brain connectivity. Among a number of brain mapping techniques, Electroencephalography is an easy to use and cheap method that can be used in the study of brain function. One way of understanding the intricate wiring pattern and functions of brain is to consider it as a complex network. In this approach, a graph of brain functions, based on the functional relation of recorded electric signals, is constructed and then the network is evaluated with a number of network metrics that measure its different aspect of structure. Different neurological and psychological diseases can affect the connectivity power within the brain; as a... 

    Analysis of Functional Connectivity Among Brain Networks Using FMRI

    , M.Sc. Thesis Sharif University of Technology Rahmati Kargar, Behnam (Author) ; Vosughi Vahdat, Bijan (Supervisor) ; Amini, Arash (Supervisor)
    Abstract
    Development of the fMRI imaging method gives the scientists the opportunity to record functional images from the brain with high spatial resolution and several researches were conducted on this field. Autistic people’s brain has functional differences with normal people. In this paper these differences have been studied. At first fMRI datasets from autistic subjects and control have been recorded and preprocessed. Then the independent components from these datasets have been extracted using group ICA method. Any independent component is an image depicting a brain network. There is a time series for each image which shows the temporal variations of each component. In the next step, the... 

    Evaluation of Functional and Structural Networks of Healthy Macaque Monkey Brains and Comparison with Macaque Monkeys with Parkinson’s in Previous Research

    , M.Sc. Thesis Sharif University of Technology Yousef Abadi, Matin (Author) ; Rabiee, Hamid Reza (Supervisor)
    Abstract
    The brain is one of the most critical parts of the body with a lot of complexity. The treatment of brain diseases has always been in an aura of uncertainty due to its high sensitivity. In the meantime, Parkinson's disease has become the second most frequent brain disease after Alzheimer's, involving more than two percent of the population over 65 years of age. One of the biggest questions in this field is how the Parkinson's process is formed. This question has already received much attention from the pathophysiological point of view but has not been answered from the functional and structural brain network's point of view. This research compares healthy macaque monkeys' functional and... 

    EEG-based functional brain networks: Hemispheric differences in males and females

    , Article Networks and Heterogeneous Media ; Volume 10, Issue 1 , March , 2015 , Pages 223-232 ; 15561801 (ISSN) Jalili, M ; Sharif University of Technology
    American Institute of Mathematical Sciences  2015
    Abstract
    Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks whose nodes are brain regions and edges correspond to functional links between them are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using... 

    Analyzing Directed Functional Brain Networks Based On Electroencephalogram Data

    , M.Sc. Thesis Sharif University of Technology Afshari, Saeedeh (Author) ; Rabiei, Hamid Reza (Supervisor)
    Abstract
    Over the past few years, various studies have demonstrated that the complex networks can be used to model the structure and functions of human brain. Some of these studies indi- cated that diseases such as Alzheimer, Epilepsy, and Schizophrenia can cause changes in this network. The main idea behind the methods proposed to analyze human brain’s behav- ior, is to identify regions of the brain with specific tasks. Recent studies show that multiple regions of human brain are involved in complex activities, so it’s important to detect their interactions. Using functional high resolution multichannel neurophysiological signals, like electroencephalographic (EEG) and magnetoencephalographic... 

    Multilayer Network Approach to Brain Connectivity Analysis in Cognitive Disorder

    , M.Sc. Thesis Sharif University of Technology Talezade Lari, Emran (Author) ; Rabiee, Hamid Reza (Supervisor) ; Manzori, Mohammad Taghi (Supervisor)
    Abstract
    Brain is the most complex part of the human body. This three pound organ acting as seed of intelligence, database of memories, interpreter of the senses, and managing our movement. Network neuroscience plays an important role in revealing hidden aspects of brain functions. Recently, multilayer network models have been proposed to achieve a deeper analysis on the brain networks. Multilayer network is a framework that can represent multiple relations between nodes. In a single layer brain network, different shared information methods can be used to find connection between Regions of Interests (ROIs), but in a multilayer approach, ROIs can have multiple connections in different domains such as... 

    Failure tolerance of motif structure in biological networks

    , Article PLoS ONE ; Volume 6, Issue 5 , May , 2011 ; 19326203 (ISSN) Mirzasoleiman, B ; Jalili, M ; Sharif University of Technology
    2011
    Abstract
    Complex networks serve as generic models for many biological systems that have been shown to share a number of common structural properties such as power-law degree distribution and small-worldness. Real-world networks are composed of building blocks called motifs that are indeed specific subgraphs of (usually) small number of nodes. Network motifs are important in the functionality of complex networks, and the role of some motifs such as feed-forward loop in many biological networks has been heavily studied. On the other hand, many biological networks have shown some degrees of robustness in terms of their efficiency and connectedness against failures in their components. In this paper we... 

    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
    Abstract
    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... 

    Constructing brain functional networks from EEG: Partial and unpartial correlations

    , Article Journal of Integrative Neuroscience ; Volume 10, Issue 2 , 2011 , Pages 213-232 ; 02196352 (ISSN) Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    Abstract
    We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial...