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    Functional neuroimaging for addiction medicine: From mechanisms to practical considerations

    , Article Progress in Brain Research ; Volume 224 , 2016 , Pages 129-153 ; 00796123 (ISSN) ; 9780444637161 (ISBN) Ekhtiari, H ; Faghiri, A ; Oghabian, M. A ; Paulus, M. P ; Ekhtiari H. E ; Paulus M. P ; Sharif University of Technology
    Elsevier  2016
    During last 20 years, neuroimaging with functional magnetic resonance imaging (fMRI) in people with drug addictions has introduced a wide range of quantitative biomarkers from brain's regional or network level activities during different cognitive functions. These quantitative biomarkers could be potentially used for assessment, planning, prediction, and monitoring for "addiction medicine" during screening, acute intoxication, admission to a program, completion of an acute program, admission to a long-term program, and postgraduation follow-up. In this chapter, we have briefly reviewed main neurocognitive targets for fMRI studies associated with addictive behaviors, main study types using... 

    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... 

    An entropy based method for activation detection of functional MRI data using independent component analysis

    , Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 14 March 2010 through 19 March 2010 ; March , 2010 , Pages 2014-2017 ; 15206149 (ISSN) ; 9781424442966 (ISBN) Akhbari, M ; Babaie Zadeh, M ; Fatemizadeh, E ; Jutten, C ; Sharif University of Technology
    Independent Component Analysis (ICA) can be used to decompose functional Magnetic Resonance Imaging (fMRI) data into a set of statistically independent images which are likely to be the sources of fMRI data. After applying ICA, a set of independent components are produced, and then, a "meaningful" subset from these components must be identified, because a large majority of components are non-interesting. So, interpreting the components is an important and also difficult task. In this paper, we propose a criterion based on the entropy of time courses to automatically select the components of interest. This method does not require to know the stimulus pattern of the experiment  

    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... 

    Affecting on Brain Activation by Transcranial Direct Current Stimulation

    , M.Sc. Thesis Sharif University of Technology Mohseni Salehi Monfared, Sadegh (Author) ; Vosoughi Vahdat, Bijan (Supervisor) ; Oghabian, Mohammad Ali (Co-Advisor)
    Transcranial direct current stimulation (tDCS) over the different brain regions has been documented in clinical and laboratory experiments. Anodal tDCS on the dorsolateral prefrontal cortex (DLPFC) has shown promising effects in enhancing cognition. Furthermore, such stimulations have been proposed in treatment of several neurological and psychological disorders. Investigations have verified the positive effect of such stimulations on drug addicts by diminishing their drug craving after stimulation. In spite of the extended research in this field, the effect of tDCS on different brain region and brain networks has yet not been studied through computational models. In this study, we evaluated... 

    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... 

    Functional Mapping of Regions Involved In Addiction Using Magnetic Resonance Imaging and Proposing a New Measure for Multivariate Methods

    , M.Sc. Thesis Sharif University of Technology Faghiri, Ashkan (Author) ; Vosughi Vahdat, Bijan (Supervisor) ; Ekhtiari, Hamed (Co-Advisor)
    Methamphetamine (meth) abuse and addiction (MA), with its serious medical, psychiatric and social complications, is a growing national disaster in Iran. Response control deficit during exposure to drug related cues is one of the main neurocognitive cores in MA and results in continued drug use and treatment failure. There have been many studies focused on cue exposure, but most of their paradigms required subjects to passively view the cues; this aspect of these paradigms cause a wide gap between reality and experimental studies. Developing a functional and structural neuroimaging protocol to map realistic brain circuits that are involved in craving among meth users is of importance.... 

    Semi-spatiotemporal fMRI brain decoding

    , Article Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 ; 2013 , Pages 182-185 ; 9780769550619 (ISBN) Kefayati, M. H ; Sheikhzadeh, H ; Rabiee, H. R ; Soltani Farani, A ; Sharif University of Technology
    Functional behavior of the brain can be captured using functional Magnetic Resonance Imaging (fMRI). Even though fMRI signals have temporal and spatial structures, most studies have neglected the temporal structure when inferring mental states (brain decoding). This has two main side effects: 1. Degradation in brain decoding performance due to lack of temporal information in the model, 2. Inability to provide temporal interpretability. Few studies have targeted this issue but have had less success due to the burdening challenges related to high feature-to-instance ratio. In this study, a novel model for incorporating temporal information while maintaining a low feature-to-instance ratio, is... 

    Application of independent component analysis for activation detection in functional magnetic resonance imaging (fMRI) data

    , Article IEEE Workshop on Statistical Signal Processing Proceedings, 31 August 2009 through 3 September 2009, Cardiff ; 2009 , Pages 129-132 ; 9781424427109 (ISBN) Akhbari, M ; Fatemizadeh, E ; Sharif University of Technology
    In this extended summary, our aim is analyzing functional magnetic resonance imaging (fMRI) data by independent component analysis (ICA) in order to find regions of brain which were activated by neural activity in human brain. We employ the minimum description length (MDL) criterion to reduce the dimension of the data and estimate the number of components, which makes ICA work more efficiently. We also use a simple oscillating index method to select automatically the components of interest. MDL and oscillating index criteria have not already been used in applying ICA for analyzing fMRI data. In order to investigate the advantage of using MDL and oscillating index, we perform some experiments... 

    Activation Detection in fMRI Using Nonlinear Time Series Analysis

    , M.Sc. Thesis Sharif University of Technology Taalimi, Ali (Author) ; Fatemizadeh, Emadeddin (Supervisor)
    Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. To obtain these goals the analysis of fMRI is the first condition which should be met. First methods were linear and assumed the... 

    Blind Source Separation Analysis of brain fMRI for Activation Detection

    , M.Sc. Thesis Sharif University of Technology Akhbari, Mahsa (Author) ; Fatemizadeh, Emadeddin (Supervisor) ; Babaiezadeh, Massoud (Co-Advisor)
    Functional Magnetic Resonance Imaging (fMRI) is one of the imaging techniques that are used to study human brain function and neurological disease diagnosis. Popular techniques in fMRI utilize the blood oxygenation level dependent (BOLD) contrast, which is based on the differing magnetic properties of oxygenated (diamagnetic) and deoxygenated (paramagnetic) blood. In order to analyze fMRI data, hypothesis-driven or data-driven methods can be used. Among data-driven techniques, Independent Component Analysis (ICA) provides a powerful method for the exploratory analysis of fMRI data. In this thesis, we use ICA on fMRI data for detecting active regions in brain, without a-priori knowledge of... 

    Effect of Obesity on Spinal Loads during Various Activities: A Combined in Vivo-Modeling Approach

    , M.Sc. Thesis Sharif University of Technology Kazemi, Hossein (Author) ; Arjmand, Navid (Supervisor) ; Parnianpour, Mohammad (Supervisor)
    Obesity is a worldwide growing health challenge affecting ~30% of the world's population. Increased rate of disc degeneration and herniation, low back pain and surgery has been reported in obese individuals. Although obesity-related low back diseases have multifactorial etiology, presumably greater mechanical loads on the spine of heavier individuals during their daily activities may be considered as a risk factor. Likely larger trunk muscle sizes, disc sizes and thus passive stiffness in heavier individuals may however partly or fully offset the effect of their additional body weight on the spinal loads. In absence of in vivo approaches, the present study aims to construct subject-specific... 

    Joint Analysis of fMRI Multi-subject Data to Extract Common Spatial and Temporal Sources

    , M.Sc. Thesis Sharif University of Technology Pakravan, Mansooreh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering.In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this thesis, this source model is referred to as joint/partiallyjoint/individual multiple datasets unidimensional (JpJI-MDU), and accordingly, a source extraction method is developed.Method: We present a deflation-based algorithm utilizing higher order cumulants to analyze the JpJI-MDU source model. The algorithm maximizes a cost function which leads to an eigenvalue problem solved with thin-SVD (singular value decomposition) factorization.... 

    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... 

    Designing EEG-based Deep Neural Network for Analysis of Functional and Effective Brain Connectivity

    , M.Sc. Thesis Sharif University of Technology Shoushtari, Shirin (Author) ; Mohammadzadeh, Hoda (Supervisor) ; Amini, Arash (Supervisor)
    Brain states analysis during consciousness is emerging research in brain-computer interface(BCI). Emotion recognition can be applied to learn brain states and stages of neural activities. Therefore, emotion recognition is crucial to the analysis of brain functioning. Electrical signals such as electroencephalogram (EEG), electrocardiogram (ECG) and functional magnetic resonance imaging(fMRI) are frequently used in emotion recognition researches. Convenience in recording, non-invasive nature and high temporal resolution are the factors that have made EEG popular in brain researches. EEG can be used to identify brain region activity solely or the connectivity of various regions in time in the... 

    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... 

    Effect of Reward Training on Visual Representation of Objects in the Brain

    , M.Sc. Thesis Sharif University of Technology Sharifi, Kiomars (Author) ; Ghazizadeh, Ali (Supervisor)
    Sight is probably our most important sense. Every day, humans are exposed to many visual stimuli in their surroundings. The human brain is able to identify and prioritize important and valuable stimuli and memorize them. Identifying and remembering these valuable stimuli is vital to meeting the needs and maintaining survival. The aim of the proposed research is to find the effect of reward learning on the coding of visual objects in the human brain. Previous results have shown that long-term reward-object association make valuable objects more recognizable behaviorally. Studies have also shown that visual stimuli and the pattern of activity of primary visual cortex neurons are closely... 

    Graph Learning for Brain Connectivity Map Based on fMRI Data

    , M.Sc. Thesis Sharif University of Technology Sharafi, Omid (Author) ; Fatemizadeh, Emadeddin (Supervisor) ; Amini, Arash (Co-Supervisor)
    In recent years, due to the structural need of most medical data for graphic models such as the graphic model of patients and the loss of data correlation in previous methods, graphic methods have been designed and developed. On the other hand, with the growing presence of magnetic resonance imaging devices in various medical centers, a large amount of functional magnetic resonance images of healthy and sick people have become available to researchers. In this study, our goal is to use a new method in the field of graphic modeling so that we can extract functional connectivity graphs from functional magnetic resonance images and measure the performance of these graphs in different groups of... 

    Disease Classification Based on Graph Learning using fMRI Datasets

    , M.Sc. Thesis Sharif University of Technology Arasteh, Ali (Author) ; Amini, Arash (Supervisor)
    In the past few years, the available knowledge in graph-based processing has made significant progress, and as a result, powerful tools have been created. In this regard, graph learning with the assumption of data smoothness on the final result can be considered a successful example. Briefly, in graph learning, to describe the relationship between the problem components, a graph is learned using the available data whose nodes represent the problem components, and its edges represent how much these components are connected. The usefulness of this method lies in the possibility of using the obtained graph as the input to currently known methods of classification and achieving better results... 

    Investigation of brain default network's activation in autism spectrum disorders using group independent component analysis

    , Article 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014 ; 2014 , p. 177-180 Alizadeh, A ; Fatemizadeh, E ; Deevband, M. R ; Sharif University of Technology
    Autism Spectrum Disorders (ADS), with unknown etiology, is one of the most understudy fields of research worldwide that requires complicated and delicate analytical study methods. The purpose of this study was to compare active regions of Brain Default Mode Network (DMN) using Group Independent Component Analysis (6ICA) among resting state patients with Autism Disorder and healthy subjects. Default Mode Network consists of posterior cingulate cortex (PCC), lateral parietal cortex/angular gyrus retrosplenial cortex, medial prefrontal cortex, superior frontal gyrus, parahippocampal gyrus and temporal lobe shows more prominent activity in passive resting conditions. The diagnosis of autism...