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    Non-invasive auditory brain stimulation for gamma-band entrainment in dementia patients: An EEG dataset

    , Article Data in Brief ; Volume 41 , 2022 ; 23523409 (ISSN) Lahijanian, M ; Sedghizadeh, M. J ; Aghajan, H ; Vahabi, Z ; Sharif University of Technology
    Elsevier Inc  2022
    Abstract
    Gamma entrainment has been shown to enhance beta amyloid (Aβ) uptake in mouse models of Alzheimer's disease (AD) as well as improve cognitive symptoms of dementia in both humans and mice. Similar improvements have been reported for both invasive and non-invasive brain stimulation in the gamma oscillatory band, with 40 Hz auditory and visual sensory stimulants employed in non-invasive approaches. Non-invasive stimulation techniques possess the clear advantage of not requiring surgical procedures and can hence be applicable to a wider set of patients. The dataset introduced here was acquired with the aim of examining the network-level mechanisms governing the production of the brain's... 

    Brain tumor segmentation based on 3D neighborhood features using rule-based learning

    , Article 11th International Conference on Machine Vision, ICMV 2018, 1 November 2018 through 3 November 2018 ; Volume 11041 , 2019 ; 0277786X (ISSN); 9781510627482 (ISBN) Barzegar, Z ; Jamzad, M ; Sharif University of Technology
    SPIE  2019
    Abstract
    In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

    Brain tumor segmentation based on 3D neighborhood features using rule-based learning

    , Article 11th International Conference on Machine Vision, ICMV 2018, 1 November 2018 through 3 November 2018 ; Volume 11041 , 2019 ; 0277786X (ISSN) ; 9781510627482 (ISBN) Barzegar, Z ; Jamzad, M ; Sharif University of Technology
    SPIE  2019
    Abstract
    In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

    Numerical and experimental evaluation of ultrasound-assisted convection enhanced delivery to transfer drugs into brain tumors

    , Article Scientific Reports ; Volume 12, Issue 1 , 2022 ; 20452322 (ISSN) Boroumand, A ; Mehrarya, M ; Ghanbarzadeh Dagheyan, A ; Ahmadian, M. T ; Sharif University of Technology
    Nature Research  2022
    Abstract
    Central Nervous System (CNS) malignant tumors are a leading cause of death worldwide with a high mortality rate. While numerous strategies have been proposed to treat CNS tumors, the treatment efficacy is still low mainly due to the existence of the Blood–Brain Barrier (BBB). BBB is a natural cellular layer between the circulatory system and brain extracellular fluid, limiting the transfer of drug particles and confining the routine treatment strategies in which drugs are released in the blood. Consequently, direct drug delivery methods have been devised to bypass the BBB. However, the efficiency of these methods is not enough to treat deep and large brain tumors. In the study at hand, the... 

    An Investigation of Resting-State Eeg Biomarkers Derived from Graph of Brain Connectivity for Diagnosis of Depressive Disorder

    , M.Sc. Thesis Sharif University of Technology Arabpour, Mohammad Reza (Author) ; Hajipour, Sepideh (Supervisor)
    Abstract
    Among the most costly diseases that affect a person's quality of life throughout his or her life, mental disorders (excluding sleep disorders) affect up to 25 percent of people in any community. One of the most common types of these disorders in Iran is depressive disorder, which according to official statistics, 13% of Iranians have some symptoms of it. Until now, the diagnosis of this disease has been traditionally done in clinics with interviews and questionnaires tests based on behavioral psychology and using symptom assessment. Therefore, there is a relatively low accuracy in the treatment process. Nowadays, with the help of functional brain imaging such as electroencephalogram (EEG)... 

    Speed control of a digital servo system using brain emotional learning based intelligent controller

    , Article PEDSTC 2013 - 4th Annual International Power Electronics, Drive Systems and Technologies Conference ; 2013 , Pages 311-314 ; 9781467344845 (ISBN) Jafari, M ; Shahri, A. M ; Shuraki, S. B ; Sharif University of Technology
    2013
    Abstract
    In this paper, a biologically motivated controller based on a mammalian limbic system called Brain Emotional Learning Based Intelligent Controller (BELBIC) is used for speed control of a Digital Servo System. The proposed controller is applied experimentally to a laboratory Digital Servo System 1 via MATLAB external mode. Comparing results of the proposed controller with conventional PID controller shows satisfactory performance including faster response and lower overshoot  

    Traumatic brain injury caused by +Gz acceleration

    , Article ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 21 August 2016 through 24 August 2016 ; Volume 3 , 2016 ; 9780791850138 (ISBN) Shafiee, A ; Ahmadian, M.T ; Hoviattalab, M ; Computers and Information in Engineering Division; Design Engineering Division ; Sharif University of Technolgy
    American Society of Mechanical Engineers (ASME) 
    Abstract
    Traumatic brain injury (TBI) has long been known as one of the most anonymous reasons for death around the world. This phenomenon has been under study for many years and yet it remains a question due to physiological, geometrical and computational complexity. Although the modeling facilities for soft tissue have improved, the precise CT-imaging of human head has revealed novel details of the brain, skull and meninges. In this study a 3D human head including the brain, skull, and meninges is modeled using CT-scan and MRI data of a 30-year old human. This model is named "Sharif University of Technology Head Trauma Model (SUTHTM)". By validating SUTHTM, the model is then used to study the... 

    The most descriptive surprise definition for brain's EEG response to visual and auditory oddball tasks

    , Article 30th International Conference on Electrical Engineering, ICEE 2022, 17 May 2022 through 19 May 2022 ; 2022 , Pages 267-271 ; 9781665480871 (ISBN) Kiani, M. M ; Mousavi, Z ; Aghajan, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2022
    Abstract
    The human brain continuously tries to predict sensory input in order to prepare for responding to new events. The brain develops a model for the incoming sensory information and updates it as new inputs arrive. It is hypothesized that the brain deduces a distribution for the input which is made more accurate with new observations. A notable question is how the brain perceives and reacts to new information. The oddball paradigm task is a simple experiment that can reveal the brain's ability in predicting the incoming input. We analyzed the EEG response of the brain recorded during oddball visual and auditory tasks in order to characterize its response to surprising instances embedded in a... 

    Toward epileptic brain region detection based on magnetic nanoparticle patterning

    , Article Sensors (Switzerland) ; Volume 15, Issue 9 , September , 2015 , Pages 24409-24427 ; 14248220 (ISSN) Pedram, M. Z ; Shamloo, A ; Alasty, A ; Ghafar Zadeh, E ; Sharif University of Technology
    MDPI AG  2015
    Abstract
    Resection of the epilepsy foci is the best treatment for more than 15% of epileptic patients or 50% of patients who are refractory to all forms of medical treatment. Accurate mapping of the locations of epileptic neuronal networks can result in the complete resection of epileptic foci. Even though currently electroencephalography is the best technique for mapping the epileptic focus, it cannot define the boundary of epilepsy that accurately. Herein we put forward a new accurate brain mapping technique using superparamagnetic nanoparticles (SPMNs). The main hypothesis in this new approach is the creation of super-paramagnetic aggregates in the epileptic foci due to high electrical and... 

    A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months

    , Article Acute and Critical Care ; Volume 37, Issue 1 , 2022 , Pages 45-52 ; 25866052 (ISSN) Nourelahi, M ; Dadboud, F ; Khalili, H ; Niakan, A ; Parsaei, H ; Sharif University of Technology
    Korean Society of Critical Care Medicine  2022
    Abstract
    Background: Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. Methods: In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI... 

    Brain activity modeling in general anesthesia: Enhancing local mean-field models using a slow adaptive firing rate

    , Article Physical Review E - Statistical, Nonlinear, and Soft Matter Physics ; Volume 76, Issue 4 , 2007 ; 15393755 (ISSN) Molaee Ardekani, B ; Senhadji, L ; Shamsollahi, M. B ; Vosoughi Vahdat, B ; Wodey, E ; Sharif University of Technology
    American Physical Society  2007
    Abstract
    In this paper, an enhanced local mean-field model that is suitable for simulating the electroencephalogram (EEG) in different depths of anesthesia is presented. The main building elements of the model (e.g., excitatory and inhibitory populations) are taken from Steyn-Ross and Bojak and Liley mean-field models and a new slow ionic mechanism is included in the main model. Generally, in mean-field models, some sigmoid-shape functions determine firing rates of neural populations according to their mean membrane potentials. In the enhanced model, the sigmoid function corresponding to excitatory population is redefined to be also a function of the slow ionic mechanism. This modification adapts the... 

    Optimum recovery time for cyclic compression tests on bovine brain tissue

    , Article Scientia Iranica ; Volume 26, Issue 4A , 2019 , Pages 2203-2211 ; 10263098 (ISSN) Mohajery, M ; Ahmadian, M. T ; Sharif University of Technology
    Sharif University of Technology  2019
    Abstract
    In conducting mechanical tests on the brain tissue, it is preferred to perform multiple tests on the same sample. In this study, we investigated the behavior of the bovine brain tissue in repeated compression tests wit h 0 recovery periods (namely, 10, 60, 120, 180, 240, and 300 s). Compression tests were performed on cylindrical samples with average diameter and height of 18.0 mm and 15.0 mm, respectively. Two testing protocols were employed; t he first one comprised experiments wit h 5, 25, and 125 mm/min loading speeds up to 33% strain and the second one consisted of tests with 25 and 125 mm/min loading speeds up to 17% strain. Each experiment was conducted in two cycles separated by a... 

    Optimum recovery time for cyclic compression tests on bovine brain tissue

    , Article Scientia Iranica ; Volume 26, Issue 4A , 2019 , Pages 2203-2211 ; 10263098 (ISSN) Mohajery, M ; Ahmadian, M. T ; Sharif University of Technology
    Sharif University of Technology  2019
    Abstract
    In conducting mechanical tests on the brain tissue, it is preferred to perform multiple tests on the same sample. In this study, we investigated the behavior of the bovine brain tissue in repeated compression tests wit h 0 recovery periods (namely, 10, 60, 120, 180, 240, and 300 s). Compression tests were performed on cylindrical samples with average diameter and height of 18.0 mm and 15.0 mm, respectively. Two testing protocols were employed; t he first one comprised experiments wit h 5, 25, and 125 mm/min loading speeds up to 33% strain and the second one consisted of tests with 25 and 125 mm/min loading speeds up to 17% strain. Each experiment was conducted in two cycles separated by a... 

    Source Localization of EEG in Early Alzheimer’s Disease

    , M.Sc. Thesis Sharif University of Technology Salami, Mohsen (Author) ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
    Abstract
    Localization of electrical activity in the brain is one of the major problems in cognitive science and neuroscience. Indeed, Source localization is the inverse processing procedure on brain signals to estimate the location and position of resources in the human brain. Current technics for neurological imaging is included fMRI، PET، MEG and ERP. These methods is not appropriated to answer the question that when does each of different components of the brain begin their activity. The EEG signals could be useful to eliminate some of limitations of above methods. The problem with EEG signals collected from the skull is that they don’t refer directly to the location of active neurons. The... 

    Modelling and analysis of the effect of angular velocity and acceleration on brain strain field in traumatic brain injury

    , Article ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) ; Volume 3 A , 2013 ; 9780791856215 (ISBN) Hoursan, H ; Ahmadian, M. T ; Barari, A ; Beidokhti, H. N ; Sharif University of Technology
    Abstract
    Traumatic brain injury (TBI) has long been known as one of the most anonymous reasons for death around the world. A presentation of a model of what happens in the process has been under study for many years; and yet it remains a question due to physiological, geometrical and computational complications. Although the facilities for soft tissue modeling have improved and the precise CT-imaging of human head has revealed novel details of brain, skull and the interface (the meninges), a comprehensive FEM model of TBI is still being studied. This study aims to present an optimized model of human head including the brain, skull, and the meninges after a comprehensive study of the previous models.... 

    EEG-based functional brain networks: does the network size matter?

    , Article PloS one ; Volume 7, Issue 4 , 2012 ; 19326203 (ISSN) Joudaki, A ; Salehi, N ; Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    PLOS  2012
    Abstract
    Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of... 

    Effect of mozart music on hippocampal content of BDNF in postnatal rats

    , Article Basic and Clinical Neuroscience ; Volume 2, Issue 3 , 2011 , Pages 21-26 ; 2008126X (ISSN) Marzban, M ; Shahbazi, A ; Tondar, M ; Soleimani, M ; Bakhshayesh, M ; Moshkforoush, A ; Sadati, M ; Zendehrood, S. A ; Joghataei, M. T ; Sharif University of Technology
    2011
    Abstract
    Introduction: It has shown that listening to Mozart music can potentiate spatial tasks in human; and reduce seizure attacks in epileptic patients. A few studies have reported the effects of prenatal plus postpartum exposure of mice to the Mozart music on brain-drived neurotrophic factor (BDNF) in the hippocampus. Here we investigated the effect of postpartum exposure to The Mozart music on BDNF concentration in the hippocampus of rat. Methods: Thirty male one day old newborn Wistar rats divided randomly in two equal experimental and control groups. Experimental group exposed to slow rhythm Mozart music (Mozart Sonata for two pianos KV 448, 6 hour per day; sound pressure levels, between 80... 

    Resiliency of cortical neural networks against cascaded failures

    , Article NeuroReport ; Volume 26, Issue 12 , 2015 , Pages 718-722 ; 09594965 (ISSN) Jalili, M ; Sharif University of Technology
    Lippincott Williams and Wilkins  2015
    Abstract
    Network tools have been extensively applied to study the properties of brain functional and anatomical networks. In this paper, resiliency of Caenorhabditis elegans cortical networks against cascaded failures is studied. To this end, directed network formed by chemical connections and undirected network formed by electrical couplings through gap junctions are considered. Furthermore, two types of C. elegans networks are studied: the whole cortical network of the hermaphrodite type and the network of the posterior cortex in male C. elegans. The results show that resiliency of hermaphrodite and male networks is different. The male cortical network of chemical synapses shows extensively weaker... 

    Superparamagnetic nanoparticles for epilepsy detection

    , Article World Congress on Medical Physics and Biomedical Engineering, 2015, 7 June 2015 through 12 June 2015 ; Volume 51 , June , 2015 , Pages 1237-1240 ; 16800737 (ISSN) ; 9783319193878 (ISBN) Pedram, M. Z ; Shamloo, A ; Alasty, A ; Ghafar Zadeh, E ; Jaffray D. A ; Sharif University of Technology
    Springer Verlag  2015
    Abstract
    Epilepsy is the most common neurological disorder that is known with uncontrolled seizure. Around 30% of patients with epilepsy resist to all forms of medical treatments and therefore, the removal of epileptic brain tissue is the only solution to get these patients free from chronical seizures. The precise detection of an epileptic zone is key to its treatment. In this paper, we propose a method of epilepsy detection using brain magnetic field. The possibility of superparamagnetic nanoparticle (SPMN) as sensors for the detection of the epileptic area inside the brain is investigated. The aggregation of nanoparticles in the weak magnetic field of epileptic brain is modeled using potential... 

    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...