Sharif Digital Repository

Human brain is one of the most wonderful complex machine which is designed for ever. A huge complex network, composed of neurons as tiny biological and chemical processors which are distributed and work together as a super parallel system to do control and vital activities of human body. Today the main secrecies of operation mechanism in individual neurons as fundamental elements of brain are reasonably understood, but network interactions of this wonderful processors and full understanding of information coding in brain seems elusive and remains as a big challenge in many interdisciplinary fields of science, from biology to cognitive science and engineering.
Thus human brain learning... 

Complex networks science have received tremendous attention in recent years and the brain is one of the systems to which graph theoretical tools have been applied. Alzheimer’s disease (AD) is a neurodegenerative disease affecting many of elderly population. AD changes the anatomy of the brain, which subsequently results in changes in its functions. These changes have been frequently reported in signals recorded from the brain (such as MEG, fMRI and EEG). Among these neuroimaging techniques EEG is one of the most aproprate methods for extracting functional connectivites according to high temporal resolution. In this thesis, we aimed at analyzing the properties of EEG-based functional networks... 

In recent years, there have been increasing attempts to study brain connectivity. Among a number of brain mapping techniques, Electroencephalography is an easy to use and cheap method that can be used in the study of brain function. One way of understanding the intricate wiring pattern and functions of brain is to consider it as a complex network. In this approach, a graph of brain functions, based on the functional relation of recorded electric signals, is constructed and then the network is evaluated with a number of network metrics that measure its different aspect of structure. Different neurological and psychological diseases can affect the connectivity power within the brain; as a... 

Parkinson's disease is a degenerative disorder of the central nervous system. The motor symptoms of Parkinson's disease result from the death of dopamine-generating cells in the substantia nigra. The cause of this cell death is unknown. Early in the course of the disease, the most obvious symptoms are movement-related. Some of the symptoms are: shaking, rigidity, slowness of movement and difficulty with walking and gait. Modern treatments are effective at managing the early motor symptoms of the disease, mainly through the use of levodopa and dopamine agonists. As the disease progresses these drugs eventually become ineffective and produce a complication called dyskinesia, marked by... 

The main idea in this project is investigation of multidimensional recording brain signal (ECoG) for estimation of 3D arm trajectory. First we introduce a general structure with variable blocks, in this structure we have many ways to estimate hand trajectory and obtain different result. By statistical test we find the best state of this structure and apply it on other dtae set trials. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes and have been shown to contain reliable information about the direction of arm Trajectory and movements. We using... 

Memory and recalling process has always been a basic question. Decoding the Long-Term_Memory is one of the first steps in answering this question. Since various experiments in the field of human long-term memory, was conducted. This research is motivated by a trial that in which, the Mgntvansfalvgram (MEG) has been recorded while recalling the color and orientation of a grading which is associated with an object, after the object has been shown. High accuracy in Decoding the mentioned color and direction, will be decoding the long-term memory. In order to enhance memory decoding, the research studies different classifiers such as sparse based classifiers and other popular one. It has also... 

Fish robot has attracted many attentions in the last decade due to several types of its applications. Many researches have been done recently on this type of robot. Dynamic modeling and control of a fish robot are important and challenging problems. This issue has a great influence on design, manufacturing, efficiency and control of the fish robot. According to the literature review, it seems that no exact analytical model exists for predicting the propulsive forces which are used in controlling and simulating dynamic behavior of the robot. Fortunately, Lighthill has proposed an analytical relation to predict propulsive forces of carangiform fishes in his research. Large amplitude elongated... 

Attention to the Brain Computer interfaces (BCI) because of their potentials in improving, enhancing and substituting daily task, especially in people who suffer from diseases, has been increasing in the recent years. Such systems, receive brain activities and by extracting suitable features, try to interpret the brain commands. The aim of this project is to explore the ability of electroencephalogram (EEG) signal for silent communication by means of decoding imagined speech in brain activities. The previous research results show that imagining a word in the mind causes changes in the brain signals. These changes are interchangeable among different words. As a result, discriminating between... 

It is believed that the synchronized activity of different brain areas, is the main cause of information binding inside the brain. Tis is definitely one of the most exciting challenges in modelling modern complex systems. Brain disorders such as schizophrenia,Alzheimer’s disease, epilepsy, autism and Parkinson’s disease are associated with abnormal synchronization abilities of neural networks. Functional connections can be assessed indirectly by measuring the electrophysiological criteria of ynchronization.Traditionally, in the study of neurophysiological, synchronizations are assessed by analyzing the coherence of frequency-domain characteristics of time series in standard methods for... 

Over the past few years, various studies have demonstrated that the complex networks can be used to model the structure and functions of human brain. Some of these studies indi- cated that diseases such as Alzheimer, Epilepsy, and Schizophrenia can cause changes in this network. The main idea behind the methods proposed to analyze human brain’s behav- ior, is to identify regions of the brain with specific tasks. Recent studies show that multiple regions of human brain are involved in complex activities, so it’s important to detect their interactions. Using functional high resolution multichannel neurophysiological signals, like electroencephalographic (EEG) and magnetoencephalographic... 

Self-organized critical phenomena are interesting phenomena which are ubiquitous in nature. Examples include mountain ranges , coastlines and also activities in the hu-man's brain. In these processes, without fine-tuning of any external parameter such as the temperature, the system exhibits critical behavior. In other words, the dynamics of the system, drives it towards an state in which long range correlations in space and scaling behaviors can be seen.The first successful model which could characterize such systems was BTW model, introduced by Bak , Tang and Wiesenfeld in 1987. This model, later named Abelian sandpile model, was very simple and because of this simplicity, a large amount of... 

Conventional MRI has been used to diagnose different types of brain injuries. However these methods have generally failed to diagnose mild types of injury. We are working on the specification of mild traumatic brain injury, using diffusion MRI data, based on a multi compartment simulation of white matter tissue. This effort is essential for better understanding of underlying tissue micro-structure changes in patients with trauma. Some studies have been used in similar data fitting approaches in order to estimate axon diameter distribution. Specifically, a comparative study between different Compartment Models has shown that “ActiveAx” model has the best agreement with underlying tissue... 

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.... 

Attention is a cognitive process in which the mind reacts to certain stimuli or stimuli of the environment while other environmental stimuli are ignored. Attention might be an overt or covert process. Overt attention is a process in which based on the purpose, we selectively choose an object or place among other objects and places to focus on and we are aware of it. However, the covert attention originates from hidden source, and we are not aware of it. In fact, the covert attention causes a clear and rapid movement of the eye toward the stimulus or space to be taken into consideration and the time when the movement of the eye it means overt attention has occurred. Visual attention is given... 

In the thesis , we present a universal model of brain tissue microstructure that dynamically links osmosis and diffusion with geometrical parameters of brain extracellular space (ECS) . In the first part , we investigate the biological aspects of the model , and in the second , we analysis the model in the mathematical framework . The first part : Our model robustly describes and predicts the nonlinear time dependency of tortuosity ($\lambda = \sqrt{D/{D^{*}}}$) changes with very high precision in various media with uniform and nonuniform osmolarity distribution , as demonstrated by previously published experimental data ($D$ = free diffusion coefficient , $D^{*}$ = effective... 

FE simulations have been widely used to investigate the response of the brain tissue in various circumstances. The accuracy of the material models critically affects the results of the numerical simulation. However, despite the numerous studies aiming for the material modeling of the brain, there is still divergence between the results reported in the literature. A part of this discrepancy is due to the inherent difference between samples. Nonetheless, another part of it is resulted from the differences between testing protocols used by researchers. In some protocols multiple mechanical tests are performed on each sample. Enough recovery time should be considered between consecutive test... 

The brain-computer interface is an advanced technology in human-machine interaction. The Speller system is a typical use of BCI, in which the target stimulation is detected by the induced signal in the brain. The most commonly speller system, the matrix Speller, has a major disadvantage, and it is Gaze-dependent. Research has proven that target-character selection in the matrix Speller is dependent on eye movement, or as referred to in technical terminology, it is gaze dependent. Therefore, the Speller matrix is not usable for users suffering from unimpaired oculomotor control. Many researchers attempted to overcome this issue, and their results led to two solutions; 1) changing the type of... 

Diffuse tensor imaging or tractography is a useful method for tracking the axonal tracts pathways within the brain white matter by monitoring the movements of water molecules along the axons. The higher the level of the tissue anisotropy, the more accurate the pathways can be estimated. But in the swelling regions around an edematous tumor, the excess of watery fluid disrupts the directional movement of water molecules, and consequently, the diffuse tensor imaging is unable to track the pathways. This impairment should be resolved by predicting the axontal tracts arrangement in the blind regions of the images using the numerical modeling. To this end, a finite element model of the human... 

Brain is the most complex part of the human body. This three pound organ acting as seed of intelligence, database of memories, interpreter of the senses, and managing our movement. Network neuroscience plays an important role in revealing hidden aspects of brain functions. Recently, multilayer network models have been proposed to achieve a deeper analysis on the brain networks. Multilayer network is a framework that can represent multiple relations between nodes. In a single layer brain network, different shared information methods can be used to find connection between Regions of Interests (ROIs), but in a multilayer approach, ROIs can have multiple connections in different domains such as... 

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. Due to limitation in experimental study on human head, the finite element human head model with precise geometric characteristics and mechanical properties is essential. In this study, the visco-hyperelastic parameters of bovine brain extracted from experimental data and finite element simulations which validated by experimental results. Then a 3D human head including brain, skull, and the meninges is modeled using CT-scan and MRI data...