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

    Sensing of alzheimer's disease and multiple sclerosis using nano-bio interfaces

    , Article Journal of Alzheimer's Disease ; Volume 59, Issue 4 , 2017 , Pages 1187-1202 ; 13872877 (ISSN) Hajipour, M. J ; Ghasemi, F ; Aghaverdi, H ; Raoufi, M ; Linnef, U ; Atyabi, F ; Nabipour, I ; Azhdarzadeh, M ; Derakhshankhah, H ; Lotfabadi, A ; Bargahi, A ; Alekhamis, Z ; Aghaie, A ; Hashemi, E ; Tafakhori, A ; Aghamollaii, V ; Mashhadi, M. M ; Sheibani, S ; Vali, H ; Mahmoudi, M ; Sharif University of Technology
    It is well understood that patients with different diseases may have a variety of specific proteins (e.g., type, amount, and configuration) in their plasmas. When nanoparticles (NPs) are exposed to these plasmas, the resulting coronas may incorporate some of the disease-specific proteins. Using gold (Au) NPs with different surface properties and corona composition, we have developed a technology for the discrimination and detection of two neurodegenerative diseases, Alzheimer's disease (AD) and multiple sclerosis (MS). Applying a variety of techniques, including UV-visible spectra, colorimetric response analyses and liquid chromatography-tandem mass spectrometry, we found the corona-NP... 

    Designing a new multifunctional peptide for metal chelation and Aβ inhibition

    , Article Archives of Biochemistry and Biophysics ; Volume 653 , 2018 , Pages 1-9 ; 00039861 (ISSN) Shamloo, A ; Asadbegi, M ; Khandan, V ; Amanzadi, A ; Sharif University of Technology
    Academic Press Inc  2018
    According to the Amyloid hypothesis, as the foremost scientific explanation for Alzheimer Disease (AD), the neuropathology of AD is related to toxic fragments of amyloid beta (Aβ) protein. Based on this hypothesis, an attractive therapeutic approach was demonstrated to identify multifunctional peptides able to modulate Aβ pathologies as the source of AD. On this premise, a bifunctional polypeptide based on the iAβ5p lead compound, was designed to inhibit Aβ aggregation and free metal ions. Herein, the efficacy of this novel drug in Zn2+ and Cd2+ ion chelation was examined through an integrated technique comprising combined Docking, QM, and MD simulations. MD relaxation of a set of probable... 

    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
    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... 

    Interdisciplinary challenges and promising theranostic effects of nanoscience in Alzheimer's disease

    , Article RSC Advances ; Volume 2, Issue 12 , 2012 , Pages 5008-5033 ; 20462069 (ISSN) Laurent, S ; Ejtehadi, M. R ; Rezaei, M ; Kehoe, P. G ; Mahmoudi, M ; Sharif University of Technology
    During the last decade, reports show that the incidence and prevalence of Alzheimer's disease (AD) and other dementias have significantly increased. AD poses an enormous escalating threat to health services and resources. Early diagnosis of AD is recognized as one of the major challenges and primary aims in scientific communities. With the arrival of nanoscience and nanotechnology to medicine, hopes for early diagnosis and treatment of AD have considerably increased. To this end, nanobioresearchers are focused on three major areas consisting of early detection and recognition, biological markers and diagnosis, and pharmacotherapy. Several efforts are in progress for the discovery of new... 

    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... 

    Discriminating early stage AD patients from healthy controls using synchronization analysis of EEG

    , Article 2011 6th International Conference on Digital Information Management, ICDIM 2011 ; 2011 , Pages 282-287 ; 9781457715389 (ISBN) Jalili, M ; Sharif University of Technology
    In this paper we study how the meso-scale and micro-scale electroencephalography (EEG) synchronization measures can be used for discriminating patients suffering from Alzheimer's disease (AD) from normal control subjects. To this end, two synchronization measures, namely power spectral density and multivariate phase synchronization, are considered and the topography of the changes in patients vs. Controls is shown. The AD patients showed increased power spectral density in the frontal area in theta band and widespread decrease in the higher frequency bands. It was also characterized with decreased multivariate phase synchronization in the left fronto-temporal and medial regions, which was... 

    Well-posedness of Two Mathematical Models for Alzheimer's Disease

    , M.Sc. Thesis Sharif University of Technology Yarmohammadi, Parisa (Author) ; Hesaaraki, Mahmoud (Supervisor)
    In season 1, we introduce a mathematical model of the in vivo progression of Alzheimer’s disease with focus on the role of prions in memory impairment. Our model consists of differential equations that describe the dynamic formation of Aβ -amyloid plaques based on the concentrations of Aβ oligomers, PrPC proteins, and the Aβ-×-PrPC complex, which are hypothesized to be responsible for synaptic tox- icity. We prove the well posedness of the model and provided stability results for its unique equilibrium, when the polymerization rate of β-amyloid is constant and also when it is described by a power law. In seson 2, We consider the existence and uniqueness of solutions of an initial-boundary... 

    Directed functional networks in Alzheimer's disease: disruption of global and local connectivity measures

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 21, Issue 4 , 2017 , Pages 949-955 ; 21682194 (ISSN) Afshari, S ; Jalili, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    Techniques available in graph theory can be applied to signals recorded from human brain. In network analysis of EEG signals, the individual nodes are EEG sensor locations and the edges correspond to functional relations between them that are extracted from EEG time series. In this paper, we study EEG-based directed functional networks in Alzheimer's disease (AD). To this end, directed connectivity matrices of 25 AD patients and 26 healthy subjects are processed and a number of meaningful graph theory metrics are studied. Our data show that functional networks of AD brains have significantly reduced global connectivity in alpha and beta bands (P < 0.05). The AD brains have significantly... 

    Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 6 , 2013 , Pages 1039-1045 ; 21682194 (ISSN) Aghajani, H ; Zahedi, E ; Jalili, M ; Keikhosravi, A ; Vahdat, B. V ; Sharif University of Technology
    In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta... 

    Protein fibrillation and nanoparticle interactions: Opportunities and challenges

    , Article Nanoscale ; Volume 5, Issue 7 , Jan , 2013 , Pages 2570-2588 ; 20403364 (ISSN) Mahmoudi, M ; Kalhor, H. R ; Laurent, S ; Lynch, I ; Sharif University of Technology
    Due to their ultra-small size, nanoparticles (NPs) have distinct properties compared with the bulk form of the same materials. These properties are rapidly revolutionizing many areas of medicine and technology. NPs are recognized as promising and powerful tools to fight against the human brain diseases such as multiple sclerosis or Alzheimer's disease. In this review, after an introductory part on the nature of protein fibrillation and the existing approaches for its investigations, the effects of NPs on the fibrillation process have been considered. More specifically, the role of biophysicochemical properties of NPs, which define their affinity for protein monomers, unfolded monomers,... 

    Parallel nonlinear analysis of weighted brain's gray and white matter images for Alzheimer's dementia diagnosis

    , Article 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, 31 August 2010 through 4 September 2010, Buenos Aires ; 2010 , Pages 5573-5576 ; 9781424441235 (ISBN) Razavian, S. M. J ; Torabi, M ; Kim, K ; Sharif University of Technology
    In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2- weighted Magnetic Resonance Images (MRI's) of brain. Since T1-weighted images and T2-weighted images have inherent physical differences, obviously each of them has its own particular medical data and hence, we extracted some specific features from each. Then the variations of the relevant eigenvalues of the extracted features were tracked to pick up the most informative ones. The final features were assigned to two parallel systems to be nonlinearly categorized. Considering the fact that AD defects the white and gray regions... 

    Identification of a novel multifunctional ligand for simultaneous inhibition of amyloid-beta (aβ42) and chelation of zinc metal ion

    , Article ACS Chemical Neuroscience ; Volume 10, Issue 11 , 2019 , Pages 4619-4632 ; 19487193 (ISSN) Asadbegi, M ; Shamloo, A ; Sharif University of Technology
    American Chemical Society  2019
    Zinc binding to β-amyloid structure could promote amyloid-β aggregation, as well as reactive oxygen species (ROS) production, as suggested in many experimental and theoretical studies. Therefore, the introduction of multifunctional drugs capable of chelating zinc metal ion and inhibiting Aβ aggregation is a promising strategy in the development of AD treatment. The present study has evaluated the efficacy of a new bifunctional peptide drug using molecular docking and molecular dynamics (MD) simulations. This drug comprises two different domains, an inhibitor domain, obtained from the C-terminal hydrophobic region of Aβ, and a Zn2+ chelating domain, derived from rapeseed meal, merge with a... 

    fMRI functional connectivity analysis via kernel graph in Alzheimers 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... 

    Development of Alzheimer's disease recognition using semiautomatic analysis of statistical parameters based on frequency characteristics of medical images

    , Article 2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007, Dubai, 14 November 2007 through 27 November 2007 ; 2007 , Pages 868-871 ; 9781424412365 (ISBN) Torabi, M ; Moradzadeh, H ; Vaziri, R ; Razavian, S. M. J ; Dehestani Ardekani, R ; Rahmandoust, M ; Taalimi, A ; Fatemizadeh, E ; Sharif University of Technology
    The paper presents an effective algorithm to analyze MR-images in order to recognize Alzheimer's Disease (AD) which appeared in patient's brain. The features of interest are categorized in Features of the Spatial Domain (FSD's) and Features of the Frequency Domain (FFD's) which are based on the first four statistic moments of the wavelet transform. Extracted features have been classified by a multi-layer perceptron Artificial Neural Network (ANN). Before ANN, the number of features is reduced from 44 to 12 to optimize and eliminate any correlation between them. The contribution of this paper is to demonstrate that by using the wavelet transform number of features needed for AD diagnosis has... 

    Evaluating the multifunctionality of a new modulator of zinc-induced Aβ aggregation using a novel computational approach

    , Article Journal of Chemical Information and Modeling ; Volume 61, Issue 3 , 2021 , Pages 1383-1401 ; 15499596 (ISSN) Asadbegi, M ; Shamloo, A ; Sharif University of Technology
    American Chemical Society  2021
    The high concentration of zinc metal ions in Aβ aggregations is one of the most cited hallmarks of Alzheimer's disease (AD), and several substantial pieces of evidence emphasize the key role of zinc metal ions in the pathogenesis of AD. In this study, while designing a multifunctional peptide for simultaneous targeting Aβ aggregation and chelating the zinc metal ion, a novel and comprehensive approach is introduced for evaluating the multifunctionality of a multifunctional drugs based on computational methods. The multifunctional peptide consists of inhibitor and chelator domains, which are included in the C-terminal hydrophobic region of Aβ, and the first four amino acids of human albumin.... 

    Alzheimers disease early diagnosis using manifold-based semi-supervised learning

    , Article Brain Sciences ; Volume 7, Issue 8 , 2017 ; 20763425 (ISSN) Khajehnejad, M ; Habibollahi Saatlou, F ; Mohammadzade, H ; Sharif University of Technology
    Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests, therefore, an efficient approach for accurate prediction of the... 

    Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series

    , Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    IOP Publishing Ltd  2020
    Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

    Discrimination between Alzheimer's disease and control group in MR-images based on texture analysis using artificial neural network

    , Article ICBPE 2006 - 2006 International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11 December 2006 through 14 December 2006 ; 2006 , Pages 79-83 ; 8190426249 (ISBN); 9788190426244 (ISBN) Torabi, M ; Ardekani, R. D ; Fatemizadeh, E ; Sharif University of Technology
    In this study, we have proposed a novel method investigates MR-Images for normal and abnormal brains which effected by Alzheimer's Disease (AD) to extract 336 number of different features based on texture analysis. Before applying this algorithm, we have to use a registration method because of variety in size of normal and abnormal images. Consequently, the output of Texture Analysis System (TAS) is a vector containing 336 elements that are features extracted from texture. This vector is considered as the input of the Artificial Neural Network (ANN) which is feed-forward one. The features extracted from the Gray-level Co-occurrence Matrix (GLCM) have been interpreted and compared with normal... 

    Behavior of olfactory-related frontal lobe oscillations in Alzheimer's disease and MCI: A pilot study

    , Article International Journal of Psychophysiology ; Volume 175 , 2022 , Pages 43-53 ; 01678760 (ISSN) Fatemi, S. N ; Aghajan, H ; Vahabi, Z ; Afzal, A ; Sedghizadeh, M. J ; Sharif University of Technology
    Elsevier B.V  2022
    Slow-gamma (35-45 Hz) phase synchronization and the coupling between slow-gamma and low-frequency theta oscillations (4–8 Hz) are closely related to memory retrieval and cognitive functions. In this pilot study, we assess the Phase Amplitude Coupling (PAC) between theta and slow-gamma oscillatory bands and the quality of synchronization in slow-gamma oscillations using Phase Locking Value (PLV) on EEG data from healthy individuals and patients diagnosed with amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD) during an oddball olfactory task. Our study indicates noticeable differences between the PLV and PAC values corresponding to olfactory stimulation in the three groups...