[Show abstract][Hide abstract] ABSTRACT: Introduction: Loss of inhibitory output from purkinje cells leads to hyperexcitability of the deep cerebellar nuclei (DCN), which results in cerebellar ataxia. Also, inhibition of small-conductance calcium-activated potassium (SK) channel increases firing rate of DCN, which could cause cerebellar ataxia. Therefore, SK channel activators can be effective in reducing the symptoms of this disease, and used for the treatment of cerebellar ataxia. In this regard, we hypothesized that blockade of SK channels in different compartments of DCN would increasefiring rate with different value. The location of these channels has different effects on increasing firing rate.Methods: In this study, multi-compartment computational model of DCN was used. This computational stimulation allowed us to study the changes in the firing activity of dcn neuron without concerns about interfering parameters in the experiment. Results: The simulation results demonstrated that blockade of somatic and dendritic SK channel increased the firing rate of DCN. In addition, after hyperpolarization (AHP) amplitude increased with blocking SK channel, and its regularity and resting potential changed. However, action potentials amplitude and duration had no significant changes. The simulation results illustrated a more significant contribution of sk channelson the dendritic tree to the DCN firing rate. SK channels in the proximal dendrites have more impact on firing rate compared to distal dendrites. Discussion: Therefore, inhibition of sk channel in DCN can cause cerebellar ataxia, and SK channel openers can have a therapeutic effect on cerebellar ataxia. In addition, the location of SKchannels could be important in therapeutic goals. Dendritic SK channels can be a more effective target compared to somatic SK channels.
[Show abstract][Hide abstract] ABSTRACT: Parkinson's disease (PD), one of the most common movement disorders, is caused by damage to the central nervous system. Despite all of the studies on PD, the formation mechanism of its symptoms remained unknown. It is still not obvious why damage only to the substantia nigra pars compacta, a small part of the brain, causes a wide range of symptoms. Moreover, the causes of brain damages remain to be fully elucidated. Exact understanding of the brain function seems to be impossible. On the other hand, some engineering tools are trying to understand the behavior and performance of complex systems. Modeling is one of the most important tools in this regard. Developing quantitative models for this disease has begun in recent decades. They are very effective not only in better understanding of the disease, offering new therapies, and its prediction and control, but also in its early diagnosis. Modeling studies include two main groups: black-box models and gray-box models. Generally, in the black-box modeling, regardless of the system information, the symptom is only considered as the output. Such models, besides the quantitative analysis studies, increase our knowledge of the disorders behavior and the disease symptoms. The gray-box models consider the involved structures in the symptoms appearance as well as the final disease symptoms. These models can effectively save time and be cost-effective for the researchers and help them select appropriate treatment mechanisms among all possible options. In this review paper, first, efforts are made to investigate some studies on PD quantitative analysis. Then, PD quantitative models will be reviewed. Finally, the results of using such models are presented to some extent.
No preview · Article · Nov 2015 · Medical & Biological Engineering
[Show abstract][Hide abstract] ABSTRACT: Multiple Sclerosis (MS) is one of the most common neurological diseases that it is often progressive and disabling. Its main cause is destruction of myelin sheaths by the immune system. Myelin damage seriously affects people’s physical activities, such as postural impairments. Early detection of the disease is very important in disease management. Unfortunately, currently there is no definite test for MS diagnosis. Of course, there are some tests that help to confirm the diagnosis in advanced stages of the disease butnone of them can independently confirm the disease and have some restrictions and errors. It seems that quantitative analysis of movement disorders especially postural disorders can be helpful in diagnosis of MS even in its early stages. In this study, posturalimpairment was studied. First postural disorders were extracted, then obtained signals were processed quantitatively sovariance and proper frequency features were extracted. At the end, using statistical tests it was shown that these features were significantly different. Therefore based on the results it is possible to design a classifier that can be a firm basis for presenting Decision Support System (DSS) for multiple sclerosis diagnosis.
[Show abstract][Hide abstract] ABSTRACT: Since gait is a semi-periodic behaviour which is disturbed in Pakinson's Disease (PD), it seems that analyzing gait behaviour may present good information about the involved systems in its production. In this study, we investigated swing and stand signals of gait in 16 healthy and 14 PD cases and tried to understand the role of basal ganglia (BG) in different parts of gait. We hypothesize that BG are directly involved in stand time interval regulation and control its pattern. In swing regulation however, BG have little effect and other parts of the brain may be involved. We think that an available proper route for swing improvement is rehabilitation.
No preview · Article · Jun 2014 · Journal of Medical Imaging and Health Informatics
[Show abstract][Hide abstract] ABSTRACT: Huntington's disease (HD) is one of the neural diseases with movement disorders like chorea, ballism, and athetosis. These symptoms play an important role on gait asymmetry, thus gait recordings are considered important resources for studying this disease. Diagnosing HD in its early stages is important and critical. We aimed to discover differences between HD and normal behavior. As the gait is semi-periodic, analyzing frequency could reveal disorders. We made an attempt to extract some proper features using power spectra density. Studies revealed that HD adds high frequencies to the spectrum on all gait phases. Statistical analysis of the gait features showed significant differences between normal and HD groups. At the end, we tried to separate the patients and healthy individuals using a new intelligent mathematical system. An artificial neural network classifier was used for this reason,and our best separation accuracy was 96.6%. This study could be the basis of designing a practical decision support system. This system can diagnose patients at the first stages of the disease, and it also can recommend suspected persons to the specialist.
No preview · Article · Feb 2014 · Journal of Mechanics in Medicine and Biology
[Show abstract][Hide abstract] ABSTRACT: Purkinje neurons are the sole output neuron of the cerebellar cortex, and they generate high-frequency action potentials. Electrophysiological dysfunction of Purkinje neurons causes cerebellar ataxia. Mutant med mice have the lack of expression of the Scn8a gene. This gene encodes the NaV1.6 protein. In med Purkinje neurons, regular high-frequency firing is slowed, and med mice are ataxic. The aim of this study was to propose the neuroprotective drugs which could be useful for ataxia treatment in med mice, and to investigate the neuroprotective effects of these drugs by simulation. Simulation results showed that Kv4 channel inhibitors and BK channel activators restored the normal electrophysiological properties of the med Purkinje neurons. 4-Aminopyridine (4-AP) and acetazolamide (ACTZ) were proposed as neuroprotective drugs for Kv4 channel inhibitor and BK channel activator, respectively.
No preview · Article · Nov 2013 · Computer methods and programs in biomedicine
[Show abstract][Hide abstract] ABSTRACT: The Lotka-Volterra or predator-prey models contain a pair of first order, non-linear, differential equations, which describe the dynamics of two species interaction in biological systems. Hence, accurate simulation strategies development for mentioned equations is crucial. In this paper, first, the presented model equations are simulated by ARX, ARMAX and BJ parametric models of the Identification Toolbox in MATLAB software. Afterwards, this simulation has been done in the Neural Network Toolbox by Feed-Forward and Elman networks with equal number of neurons, layers and same transfer functions. Finally, the results of these two simulations have been compared to introduce the best simulation methodology. It is shown that more accurate results are achieved by Elman network. In addition, the paper demonstrates that the simulation error can be decreased by simply increasing the number of these neural networks’ neurons.
[Show abstract][Hide abstract] ABSTRACT: Parkinson's disease (PD) is a widespread progressive neural degenerative disease. Patients encounter problems in carrying out ordinary voluntary movements such as gaiting and writing. Effects of tremor, rigidity, and bradykinesia are seen in writing. Since the diagnosis of PD is based on the presence of cardinal symptoms, therefore voluntary movements like writing could be helpful as an indicator in this process. Handwriting analysis can be a suitable method to separate patients from normal individuals. We recorded handwriting data from 17 normal and 13 PD subjects, and then used mathematical analysis in order to extract proper features. At the end of the study, we used these selected features in a classifier. We achieved 93.89% accuracy in the test phase. Hence, this tool may be aid in the diagnosing of both PD and suspected PD individuals in the early stages of the disease.
No preview · Article · May 2013 · Journal of Mechanics in Medicine and Biology
[Show abstract][Hide abstract] ABSTRACT: This work proposes feature extraction of lung sounds
using wavelet coefficients and their classification by neural
network and support vector machines. The lung sounds were
classified into 6 classes. The results stated the advantages of a
support vector machines for the classification of normal and
abnormal lung sounds, and indicated that SVMs are a highly
successful classifier with accuracy about 93.51 - 100 for
classification of lung sounds.
[Show abstract][Hide abstract] ABSTRACT: Objectives: Early exact diagnosis of Parkinson's disease (PD) is still limited. We presented a new method for testing the gait in order to find the difference of normal and PD patients. Patients and Methods: We chose the walking pattern a circular path instead of a direct
path. However, for considering both body sides of the participants, we combined two circles to create an “8” like path. We recorded the gait acceleration in seven normal and seven PD persons (for extracting stride time intervals). In order to eliminate the effect of participant's
attention, we wanted the subjects to count their paces. Results: We used t-test to determine the difference between the variance of stride time intervals in normal and PD groups. P values were 0.0269 for left foot. The Spearman correlation between severity and the variance
of stride yielded R = 0 7709. Conclusion: Our results show that paying attention to variance of stride time intervals can be a proper valuable test for early diagnosis as well as for staging the PD. Since gait recording in an “8” shaped path is a simple inexpensive
clinical test, we propose to do this test for all persons who are prone to PD and for staging this disease.
No preview · Article · Mar 2013 · Journal of Medical Imaging and Health Informatics
[Show abstract][Hide abstract] ABSTRACT: Some previous studies have focused on chaotic properties of Parkinson's disease (PD). It seems that considering PD from dynamical systems perspective is a relevant method that may lead to better understanding of the disease. There is some ambiguity about chaotic nature in PD symptoms and normal behaviour. Some studies claim that normal gait has somehow a chaotic behaviour and disturbed gait in PD has decreased chaotic nature. However, it is worth noting that the basis of this idea is the difference of fractal behaviour in gait of normal and PD patients, which is concluded from Long Range Correlation (LRC) indices. Our primary calculations show that a large number of normal persons and patients have similar LRC. It seems that chaotic studies on PD need a different view. Because of short time recording of symptoms, accurate calculation of chaotic features is tough. On the other hand, long time recording of symptoms is experimentally difficult. In this research, we have first designed a physiologically plausible model for normal and PD gait. Then, after validating the model with neural network classifier, we used the model for extracting long time simulation of stride in normal and PD persons. These long time simulations were then used for calculating the chaotic features of gait. According to change of phase space behaviour and alteration of three largest lyapunov exponents, it was observed that simulated normal persons act as chaotic systems in stride production, but simulated PD does not have chaotic dynamics and is stochastic. Based on our results, it may be claimed that normal gait has chaotic nature which is disturbed in PD state. Surely, long time real recordings from gait signal in normal persons and PD patients are necessary to warranty this hypothesis.
No preview · Article · May 2012 · Journal of Theoretical Biology
[Show abstract][Hide abstract] ABSTRACT: Freezing of gait (FOG) is a disabling symptom of Parkinson's disease (PD). In this study, we used the model of PD gait behavior for comparing normal and PD persons in order to simulate FOG and find its pathophysiology and probable treatments. We observed in the adapted model that the dopaminergic weights were reduced and the amount of dopaminergic bias was increased. These findings show that the aggravation of the disease and severe resistance of neurons to dopamine agonists may be the main cause of the FOG. Based on our model three therapeutic strategies may be proposed: decreasing the cortex signal to basal ganglia, using high dose glutamate antagonist, and using less glutamate antagonist with some amounts of gabapentin.
Full-text · Article · Nov 2011 · Medical Hypotheses
[Show abstract][Hide abstract] ABSTRACT: In this study, we present a model for the gait of normal and Parkinson's disease (PD) persons. Gait is semi-periodic and has fractal properties. Sine circle map (SCM) relation has a sinusoidal term and can show chaotic behaviour. Therefore, we used SCM as a basis for our model structure. Moreover, some similarities exist between the parameters of this relation and basal ganglia (BG) structure. This relation can explain the complex behaviours and the complex structure of BG. The presented model can simulate the BG behaviour globally. A model parameter, Ω, has a key role in the model response. We showed that when Ω is between 0.6 and 0.8, the model simulates the behaviour of normal persons; the amounts greater or less than this range correspond to PD persons. Our statistical tests show that there is a significant difference between the Ω of normal and PD patients. We conclude that Ω can be introduced as a parameter to distinguish normal and PD persons. Additionally, our results showed that Spearman correlation between the Ω and the severity of PD is 0.586. This parameter may be a good index of PD severity.
Full-text · Article · Nov 2011 · Neuroscience Letters
[Show abstract][Hide abstract] ABSTRACT: In this study, we focused on the gait of Parkinson's disease (PD) and presented a gray box model for it. We tried to present a model for basal ganglia structure in order to generate stride time interval signal in model output for healthy and PD states. Because of feedback role of dopamine neurotransmitter in basal ganglia, this part is modelled by "Elman Network", which is a neural network structure based on a feedback relation between each layer. Remaining parts of the basal ganglia are modelled with feed-forward neural networks. We first trained the model with a healthy person and a PD patient separately. Then, in order to extend the model generality, we tried to generate the behaviour of all subjects of our database in the model. Hence, we extracted some features of stride signal including mean, variance, fractal dimension and five coefficients from spectral domain. With adding 10% tolerance to above mentioned neural network weights and using genetic algorithm, we found proper parameters to model every person in the used database. The following points may be regarded as clues for the acceptability of our model in simulating the stride signal: the high power of the network for simulating normal and patient states, high ability of the model in producing the behaviour of different persons in normal and patient cases, and the similarities between the model and physiological structure of basal ganglia.