Search for: functional-connectivity
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    Identifying brain functional connectivity alterations during different stages of alzheimer’s disease

    , Article International Journal of Neuroscience ; 2020 Ahmadi, H ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
    Taylor and Francis Ltd  2020
    Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated... 

    Brain Connectivity Analysis from EEG Signals using Entropy based Measures

    , M.Sc. Thesis Sharif University of Technology Saboksayr, Saman (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Even in the simplest of activities in the brain such as resting condition, there are connections in between different regions of the brain so that the whole system functions consistently in harmony. Studies related to brain connectivity provides an opportunity to better understand how the brain works. To assess these connectivities an estimation is usually conducted based on brain signals. Among different estimation methods, quantities of information theory are in general more practical due to avoiding any assumptions toward the system model and the ability to recognize linear and non-linear connectivity. One of the main quantities related to the information theory is in fact, entropy.... 

    Functional Connectivity Detection in Resting-State Brain using functional Magnetic Resonance Imaging

    , M.Sc. Thesis Sharif University of Technology Ramezani, Mahdi (Author) ; Fatemizadeh, Emadeddin (Supervisor) ; Soltanianzadeh, Hamid (Supervisor)
    The functional network of the human brain is altered in many neurological and psychiatric disorders. Characterizing brain activity in terms of functionally segregated regions does not reveal anything about the communication among different brain regions and how such inter-communication could influence neural activity in each local region. The aim of this project is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the simulated, realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral... 

    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)
    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... 

    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
    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... 

    The olfactory bulb modulates entorhinal cortex oscillations during spatial working memory

    , Article Journal of Physiological Sciences ; Volume 71, Issue 1 , 2021 ; 18806546 (ISSN) Salimi, M ; Tabasi, F ; Nazari, M ; Ghazvineh, S ; Salimi, A ; Jamaati, H ; Raoufy, M. R ; Sharif University of Technology
    BioMed Central Ltd  2021
    Cognitive functions such as working memory require integrated activity among different brain regions. Notably, entorhinal cortex (EC) activity is associated with the successful working memory task. Olfactory bulb (OB) oscillations are known as rhythms that modulate rhythmic activity in widespread brain regions during cognitive tasks. Since the OB is structurally connected to the EC, we hypothesized that OB could modulate EC activity during working memory performance. Herein, we explored OB–EC functional connectivity during spatial working memory performance by simultaneous recording local field potentials when rats performed a Y-maze task. Our results showed that the coherence of delta,... 

    Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data

    , Article Cognitive Neurodynamics ; Volume 14, Issue 4 , 2020 , Pages 457-471 Ghahari, S ; Farahani, N ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Springer  2020
    Investigating human brain activity during expressing emotional states provides deep insight into complex cognitive functions and neurological correlations inside the brain. To be able to resemble the brain function in the best manner, a complex and natural stimulus should be applied as well, the method used for data analysis should have fewer assumptions, simplifications, and parameter adjustment. In this study, we examined a functional magnetic resonance imaging dataset obtained during an emotional audio-movie stimulus associated with human life. We used Jackknife Correlation (JC) method to derive a representation of time-varying functional connectivity. We applied different binary measures... 

    Analysis of Functional Brain Connectivity Using EEG Signals for Classification of Brain States

    , M.Sc. Thesis Sharif University of Technology Ghodsi, Saeed (Author) ; Karbalai Aghajan, Hamid (Supervisor) ; Mohamadzadeh, Hoda ($item.subfieldsMap.e)
    Different perceptual, cognitive, and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brain response to the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective, and functional, s.t. the first one referes to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional connectivity between... 

    Drug Effect on Brain Functional Connectivity Using EEG Signals

    , M.Sc. Thesis Sharif University of Technology Karimi, Sajjad (Author) ; Shamsollahi, Mohammad Bagher (Supervisor) ; Molaee-Ardekani, Behnam (Co-Advisor)
    In this study Donepezil effect on the brain functional connectivity investigated. In order to construct the brain functional network, EEG artifacts must firstly be removed because this step has important effects on the final interpretation of the results. Therefor, a new artifact removing method is proposed and better performance of the proposed method compared to other existing methods is stated using quantitative evaluations. After artifact removal, the functional brain network is extracted using conventional methods that were applied in the similar previous studies. The reasons for using conventional methods are their simplicity and reliability. Furtheremore, to study the recent... 

    Temporal Analysis of Functional Brain Connectivity Using EEG Signals

    , M.Sc. Thesis Sharif University of Technology Khazaei, Ensieh (Author) ; Mohammadzadeh, Narges Hoda (Supervisor)
    Human has different emotions such as happiness, sadness, anger, etc. Recognizing these emotions plays an important role in human-machine interface. Emotion recognition can be divided into approaches, physiological and non-physiological signals. Non-physiological signals include facial expressions, body gesture, and voice, and physiological signals include electroencephalograph (EEG), electrocardiograph (ECG), and functional magnetic resonance imaging (fMRI). EEG signal has been absorbed a lot of attention in emotion recognition because recording of EEG signal is easy and it is non-invasive. Analysis of connectivity and interaction between different areas of the brain can provide useful... 

    Evaluation Auditory Attention Using Eeg Signals when Performing Motion and Visual Tasks

    , M.Sc. Thesis Sharif University of Technology Bagheri, Sara (Author) ; Hajipour, Sepideh (Supervisor)
    Attention is one of the important aspects of brain cognitive activities, which has been widely discussed in psychology and neuroscience and is one of the main fields of research in the education field. The human sense of hearing is very complex, impactful and crucial in many processes such as learning. Human body always does several tasks and uses different senses simultaneously. For example, a student who listens to his/her teacher in the class, at the same time pays attention to the teacher, looks at a text or image, and sometimes writes a note.Using the electroencephalogram (EEG) signal for attention assessment and other cognitive activities is considered because of its facile recording,... 

    Dynamic Functional Connectivity in Autism Spectrum Disorder Using Resting-State fMRI

    , M.Sc. Thesis Sharif University of Technology Jalil Piran, Fardin (Author) ; Fatemizadeh, Emadeddin (Supervisor)
    Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders that cause repetitive behaviors and social and communication skills abnormalities. Autistic Disorder(AD) is one of the disorders in ASD that is being investigated in this study. There has been an increase in research about AD in recent years due to the increasing AD prevalence and the high autistic living costs. The dynamic functional connectivity between healthy and autistic groups has been analyzed by using graph theory. The brain is modeled as a dynamic graph using resting-state fMRI. The graph theory metric is calculated in the dynamic graph of each subject, and the distinction of the two groups is checked using... 

    Brain Connectivity Based on the MVAR Model and their Relationship to each other

    , M.Sc. Thesis Sharif University of Technology Abbaskhah, Ahmad (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    During the time that the simplest action (rest) of the human brain is active, and for integration and coordination of the brain, different parts of it are in connection with each other. This connection can be directional and directionless, which are called effective connectivity and functional connectivity, respectively. It is clear that effective connectivity shows brain function better than other connectivity due to its directionality.One of the most common ways to define effective connectivity is the use of the Multivariate Autoregressive (MVAR). The MVAR model provides the time Cause of different signals on each other, meaning that the influence of the past of a variable on other... 

    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... 

    Functional Connectivity Network in Rest-State fMRI Baseline in High Functioning Autism Disorder

    , M.Sc. Thesis Sharif University of Technology Akbarian Aghdam, Amir (Author) ; Fatemizadeh, Emad (Supervisor)
    Autism spectrum disorders (ASD) have been defined as developmental disorders characterized by abnormalities in social interaction, communication skills, and behavioral flexibility. Over the past decades, studies using various genetic, neurobiological, cognitive and behavioral approaches have sought a single explanation for the heterogeneous manifestations of ASD, but no consensus on the etiology of ASD has emerged. Further studies aim to clarify the mechanism of disease.
    Functional Magnetic Resonance Imaging (fMRI) is a new way of imaging which evaluates activity of brain by measuring magnetic difference caused by oscillation in blood oxygen level. fMRI has been widely used in recent... 

    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... 

    Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

    , Article Biomedizinische Technik ; Volume 65, Issue 1 , 2020 , Pages 23-32 Rajabioun, M ; Motie Nasrabadi, A ; Shamsollahi, M. B ; Coben, R ; Sharif University of Technology
    De Gruyter  2020
    Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities... 

    Automated detection of autism spectrum disorder using a convolutional neural network

    , Article Frontiers in Neuroscience ; Volume 13 , 2020 Sherkatghanad, Z ; Akhondzadeh, M ; Salari, S ; Zomorodi Moghadam, M ; Abdar, M ; Acharya, U. R ; Khosrowabadi, R ; Salari, V ; Sharif University of Technology
    Frontiers Media S.A  2020
    Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD... 

    Allergic rhinitis impairs working memory in association with drop of hippocampal – Prefrontal coupling

    , Article Brain Research ; Volume 1758 , 2021 ; 00068993 (ISSN) Salimi, M ; Ghazvineh, S ; Nazari, M ; Dehdar, K ; Garousi, M ; Zare, M ; Tabasi, F ; Jamaati, H ; Salimi, A ; Barkley, V ; Mirnajafi Zadeh, J ; Raoufy, M. R ; Sharif University of Technology
    Elsevier B.V  2021
    Allergic rhinitis (AR) is a chronic inflammatory disease frequently associated with a deficit in learning and memory. Working memory is an important system for decision making and guidance, which depends on interactions between the ventral hippocampus (vHipp) and the prelimbic prefrontal cortex (plPFC). It is still unclear whether AR influences the activity and coupling of these brain areas, which consequently may impair working memory. The current study aimed to examine alterations of the vHipp-plPFC circuit in a rat model of AR. Our results show decreased working memory performance in AR animals, accompanied by a reduction of theta and gamma oscillations in plPFC. Also, AR reduces...