Loading...
Search for:
brain-regions
0.009 seconds
Interview based connectivity analysis of EEG in order to detect deception
, Article Medical Hypotheses ; Volume 136 , 2020 ; Motie NasrAbadi, A ; sharifi, A ; Bagher Shamsollahi, M ; Sharif University of Technology
Churchill Livingstone
2020
Abstract
Deception is mentioned as an expression or action which hides the truth and deception detection as a concept to uncover the truth. In this research, a connectivity analysis of Electro Encephalography study is presented regarding cognitive processes of an instructed liar/truth-teller about identity during an interview. In this survey, connectivity analysis is applied because it can provide unique information about brain activity patterns of lying and interaction among brain regions. The novelty of this paper lies in applying an open-ended questions interview protocol during EEG recording. We recruited 40 healthy participants to record EEG signal during the interview. For each subject,...
Interictal EEG noise cancellation: GEVD and DSS based approaches versus ICA and DCCA based methods
, Article IRBM ; Volume 36, Issue 1 , 2015 , Pages 20-32 ; 19590318 (ISSN) ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
Elsevier Masson SAS
2015
Abstract
Denoising is an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications. For this purpose, Blind Source Separation (BSS) methods, such as Independent Component Analysis (ICA) and Decorrelated and Colored Component Analysis (DCCA), are commonly used. Although ICA and DCCA-based methods are powerful tools to extract sources of interest, the procedure of eliminating the effect of sources of non-interest is usually manual. It should be noted that some methods for automatic selection of artifact sources after BSS methods exist, although they imply a training supervised step. On the other hand, in cases where there are some a prioriinformation about the subspace of...
A new mathematical approach for detection of active area in human brain fMRI using nonlinear model
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 22, Issue 5 , 2010 , Pages 409-418 ; 10162372 (ISSN) ; Fatemizadeh, E ; Sharif University of Technology
Abstract
Functional magnetic resonance imaging (fMRI) is widely-used for detection of the brain's neural activity. The signals and images acquired through this imaging technique demonstrate the human brain's response to pre-scheduled tasks. Several studies on blood oxygenation level-dependent (BOLD) signal responses demonstrate nonlinear behavior in response to a stimulus. In this paper we propose a new mathematical approach for modeling BOLD signal activity, which is able to model nonlinear and time variant behaviors of this physiological system. We employ the Nonlinear Auto Regressive Moving Average (NARMA) model to describe the mathematical relationship between output signals and predesigned...
Classifying Brain Activities by Deep Methods Over Graphs
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor) ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
In recent years, the spread of neurological disorders worldwide has been increasing, especially in developing countries. Due to the unknown function, complexity, and high importance of the brain, such disorders have been pervasive, severe, prolonged, and impose enormous costs on the individual, the family, and the community. Thus, increasing the knowledge about the brain and its areas in various activities is too vital and can facilitate the diagnosis and treatment of many different and unknown neuro- logical disorders. Different kinds of research have been done to automatically process and find the active and vital areas in various states and brain activities. The problem with most of these...
2D computational fluid dynamic modeling of human ventricle system based on fluid-solid interaction and pulsatile flow
, Article Basic and Clinical Neuroscience ; Volume 4, Issue 1 , 2013 , Pages 64-75 ; 2008126X (ISSN) ; Framanzad, F ; Zamanian, B ; Seddighi, A. S ; Moosavi, M. H ; Najarian, S ; Bastani, D ; Sharif University of Technology
2013
Abstract
Many diseases are related to cerebrospinal fluid (CSF) hydrodynamics. Therefore, understanding the hydrodynamics of CSF flow and intracranial pressure is helpful for obtaining deeper knowledge of pathological processes and providing better treatments. Furthermore, engineering a reliable computational method is promising approach for fabricating in vitro models which is essential for inventing generic medicines. A Fluid-Solid Interaction (FSI)model was constructed to simulate CSF flow. An important problem in modeling the CSF flow is the diastolic back flow. In this article, using both rigid and flexible conditions for ventricular system allowed us to evaluate the effect of surrounding brain...
Psychogenic seizures and frontal disconnection: EEG synchronisation study
, Article Journal of Neurology, Neurosurgery and Psychiatry ; Volume 82, Issue 5 , 2011 , Pages 505-511 ; 00223050 (ISSN) ; Jalili, M ; Frackowiak, R. S ; Rossetti, A. O ; Sharif University of Technology
2011
Abstract
Objective Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. Methods The authors analysed the whole-head...
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 ; Motie Nasrabadi, A ; Shamsollahi, M. B ; Coben, R ; Sharif University of Technology
De Gruyter
2020
Abstract
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...
Allergic rhinitis impairs working memory in association with drop of hippocampal – Prefrontal coupling
, Article Brain Research ; Volume 1758 , 2021 ; 00068993 (ISSN) ; 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
Abstract
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...
Modeling the Parkinson's tremor and its treatments
, Article Journal of Theoretical Biology ; Volume 236, Issue 3 , 2005 , Pages 311-322 ; 00225193 (ISSN) ; Sarbaz, Y ; Gharibzadeh, S ; Sharif University of Technology
2005
Abstract
In this paper, we discuss modeling issues of the Parkinson's tremor. Through the work we have employed physiological structure as well as functioning of the parts in brain that are involved in the disease. To obtain more practical similarity, random behaviors of the connection paths are also considered. Medication or treatment of the disease both by drug prescription and electrical signal stimulation are modeled based on the same model introduced for the disease itself. Two new medication strategies are proposed based on the model to reduce the side effects caused by the present drug prescription. © 2005 Elsevier Ltd. All rights reserved
Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions
, Article PLoS Computational Biology ; Volume 18, Issue 7 , 2022 ; 1553734X (ISSN) ; Fakharian, M. A ; Ghazizadeh, A ; Sharif University of Technology
Public Library of Science
2022
Abstract
Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as...
Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain
, Article 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010, 15 June 2010 through 17 June 2010 ; 2010 ; 9781424466238 (ISBN) ; Heidari, A ; Fatemizadeh, E ; Soltanianzadeh, H ; Sharif University of Technology
Abstract
The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the 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 decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known...
Deep sparse graph functional connectivity analysis in AD patients using fMRI data
, Article Computer Methods and Programs in Biomedicine ; Volume 201 , 2021 ; 01692607 (ISSN) ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Elsevier Ireland Ltd
2021
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to...
Deep sparse graph functional connectivity analysis in AD patients using fMRI data
, Article Computer Methods and Programs in Biomedicine ; Volume 201 , 2021 ; 01692607 (ISSN) ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Elsevier Ireland Ltd
2021
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to...