Hareket başlatma ve derin beyin uyarımına ilişkin bir bazal çekirdek devresi hesaplamalı modeli
A computational model of basal ganglia for action initiation and deep brain stimulation
Yazar: MUSTAFA YASİR ÖZDEMİR
Danışman: PROF. NESLİHAN SERAP ŞENGÖR
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı / Biyomedikal Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2017
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering
Dizin: Bazal ganglia = Basal ganglia ; Bazal ganglia hastalıkları = Basal ganglia diseases ; Parkinson hastalığı = Parkinson disease

Özet:
Sinir sisteminin keşfi ile ortaya çıkan sinirbilim kavramı günümüze gelene kadar birçok alanı da içine alarak büyümüş ve genişlemiştir. Bunların içerisinde matematiksel ve fiziksel metotları kullanarak iş yapmaya çalışan hesaplamalı sinirbilim birçok konuya açıklama getirmeyi amaçlamaktadır. Parkinson Hastalığı gibi birçok motor hareket bozukluğuna sebep olan hastalıklar bu alanda sıkça çalışılan konulardan olmuştur. Bu tez çalışmasının konusu Bazal çekirdeklerde hareketin nasıl başlatıldığını ve Derin Beyin Uyarımının (DBS) etkilerini hesapsal bir model üzerinde göstermektir. Bunu gerçekleştirebilmek için öncelikle Bazal Çekirdek devresinin hesapsal bir modeli kurulmuş ve üzerinde hareket başlatmanın nasıl gerçekleştiği gösterilmiştir. Sonrasında Parkinson Hastalığı modeli aynı model üzerinden oluşturulmuş ve oluşturulan bu Parkinson Hastalığı modeline Derin Beyin Uyarımı (DBS) uygulanarak model üzerinde iyileşmenin nasıl gerçekleştiği gösterilmiştir.
Bunların yapılabilmesi için önerilen tüm devrenin hesapsal modeli,  Ateşleyen Sinir Hücreleri (Spiking Neural Network) modeli kullanılarak oluşturulmuştur. Ateşleyen Sinir Hücreleri modeli hücre bazında kurulan bir modeldir ve tanımlanan tek hücre modelleri biyolojik hücrelerin zar potansiyeli davranışlarını temsil ederler. Daha üst düzeydeki nöral alt yapıların davranışları bu hücrelerin davranışları ve birbirlerine olan etkilerinin doğal bir sonucu olarak ortaya çıkar. Bu sayede tek hücre ve bağlantıların modellenmesi ile grup davranışları ve daha üst düzeyde hareket başlatma gibi etkiler gözlemlenebilir.
Bu çalışmada tek hücre modellerinin kurulması için Izhikevich sinir hücresi modeli kullanılmıştır. Bazal Çekirdek belirlenen nöral alt yapılar ile temsil edilmiş ve bu nöral alt yapıların her biri farklı tek hücre modelleri ile kurgulanmıştır. Her bir nöral alt yapıyı temsil eden tek hücreler modellenirken o bölgede baskın olan hücrelerin davranışları esas alınmıştır. Yoksa bu burada temsili oluşturulan her hangi bir nöral alt yapıda gerçekte tek tip hücre bulunduğu anlamına gelmez. Bunun ardından bu hücre modelleri ile çalışmada göreceğiniz gibi (D1 tip ortaboy dikensi sinir hücreleri, subtalamik çekirdek vb.) nöral alt yapılar oluşturulup, grup içi ve gruplar arası (nöral alt yapıların birbirleri ile olan) bağlantıları tanımlanarak Bazal Çekirdek devresi oluşturulmuştur.
Bu nöral alt yapıların içinden Striatum olarak bilinen bölge Dopamin nörotransmitterine duyarlıdır ve motor hareketlerin gerçekleştirilmesinde kilit rol oynar. Parkinson Hastalığı gibi motor kontrol rahatsızlıklarında da bu bölgedeki dopamin nörotransmitterinde anormallikler oluşur ve bunun sonucu olarak motor hareket bozuklukları meydana gelir. Bu sebeple hem hareket başlatmanın gösterilebilmesi hem de Parkinson Hastalığı modelinin oluşturulabilmesi için Striatumdaki hücreler dopamine duyarlı olarak modellenmişlerdir. 
Izhikevich sinir hücresi modeli diferansiyel denklemlerle ifade edilen doğrusal olmayan bir modeldir. Sonuç olarak kurulan modelde doğrusal olmayan bir modeldir. Izhikevich sinir hücresi modeli için durum portresi kullanarak, modelin nasıl davranış sergileyeceğini kısmi olarak kestirmek mümkündür. Ancak tüm model için böyle bir analiz yapmak mümkün değildir. Bunun yerine nöral alt yapıların davranışları, sinir hücrelerinin ateşleme yaptıkları zamanları gösteren grafikler (vuru-zaman grafikleri), nöral alt yapıların frekansları ve belirli zaman aralıklarındaki toplam ateşlemeleri gösteren ateşleme oranları üzerinden yorumlanır. Tez çalışmasında nöral alt yapıların davranışlarını yorumlamak içinde nöral alt yapıların vuru-zaman grafikleri, genel frekans yanıtları ve ateşleme oranları birleştirilerek benzetim sonuçları olarak sunulmuştur ve yorumlanmışlardır.
Sonuç olarak model üzerinde hareket başlatma ve bu işleyişin nöral alt yapılarda ve sinirsel yolaklar da nasıl meydana geldiği model üzerinde gösterilmiştir. Parkinson Hastalığı modeli mevcut model üzerindeki dopamin seviyesi değiştirilerek oluşturulmuş ve bu modele Derin Beyin Uyarımı gerçekleştirilerek istemli hareketin yeniden gerçeklenebilir hale geldiği gösterilmiştir.

Abstract:
Since the early ages, people have been interested in how consciousness occurs and how movements take place, and they have come up with many different approaches, not objective or related to it. With the development of the microscopes and the recognition of the electrical potentials in the nerve cells, this interest has been brought to a different dimension, giving rise to the notion of neuroscience. This field, which deals with the functioning of the nervous system, is divided into many different research topics, such as cognitive, behavioral, anatomical, physiological, and developmental neuroscience, along with the progress of the technology and the increase of existing knowledge. Within these, computational neuroscience aims to explain any process in the nervous system with computational methods. In doing so, mathematical and physical methods are used and the scope and content of the models are determined according to the problem to be addressed or the case to be examined. There are studies that model a whole brain based on a mathematical theory, as well as studies that model single cell morphology and ion channels, and specially developed simulation environments for their realization, and studies that try to explain the functioning of neural substructures
at lower levels with cellular activity.
Computational neuroscience aims to explain biological systems, which are difficult to understand, by using mathematical methods in a logical framework. In other words, it tries to make an understandable the system which has not yet been solved logically by using the language of mathematics as a specific fictional logic. Thus, since the processes expressed by mathematics can be operated on computers, it becomes possible to represent the models of biological systems in the simulation environments in this way. This feature helps to embody mathematical expressions from the model of the ion channel in the nerve cell to the mass model and gives computational neuroscience broad features. Computational neuroscience takes on a concrete level where we can understand the models that we cannot calculate analytically and cannot imagine their results. At the same time, the realization of the computational model created in the computer environment provides an artificial testing environment in studies because it has the ability to represent the target biological system with its specific characteristics. This artificial testing environment provides the user with a certain amount of preliminary information about the target biological structure within its capacity, and also unnaturel situations can be realized on this test environment. In addition, some situations that are unlikely to be achieved in experimental measurements can be tried and observed in this test environment. For example, it is not possible at present to stimulate each cell type in a neural substructure with the same stimuli. However, this can be tested on a model implemented in a simulation environment, and if such a technique is developed, it can provide information about how it will work or not work. In addition to preliminary information, models that mimic behavioral responses and diseases also carry information about the causes of malfunctioning. With the help of this information, the cause-and-effect
relationships of the diseases can be improved and new treatment methods can be developed.
Apart from these, the understanding of the biological system allows the ability of imitation, development and adaptation. This provides tips on how to develop new intelligent systems and technologies based on inspiration due to biological systems. In particular, they can provide guidance on topics such as learning and perception. One of the most common examples of these is artificial neural networks, although the relationship with biological systems disappeared. It is inspired by the biological visual system for image processing mechanisms of robotic and artificial intelligence systems. Computational neuroscience is also actively involved in such cases as more effective use of neuroprosthetics and imitation of touch feeling on prosthetics. 
In this thesis, it is aimed to examine the computational model of basal ganglia circuit which is active in the motor movements. The basal ganglia circuits are the area where the action selection and decision making are performed. The striatal region and dopamine neurotransmitter in this structure play a key role in the process of movement, the selection process is effected by the dopamine neurotransmitter in the striatum. The basal ganglia manage the movement-related processes through inhibitory connections, and basal ganglia is connected to the thalamus with inhibitory connection to determine the action. The passage of a movement is due to the disinhibitionon the thalamus. Disorders that occur in the basal ganglia circuit cause diseases that affect motor movements. The most known of these are the Parkinson's Disease (PH), which is caused due to prevalent dopamine deficiency in the striatum, and the Hungtington Disease, which is caused in the opposite situation. Different methods are used to improve the symptoms of Parkinson's disease, such as tremors and inability to perform voluntary movements. These include drug treatments that provide dopamine reinforcement such as levodopa, surgery in which a region of the brain is removed or damaged, and alternatively deep brain stimulation that has become more widespread in  recent years.
There are many different computational neuroscience studies about Basal ganglia circuit. Generally, most of these focused on action selection process. But there are also studies dealing with pyschiatric disorder, addiction, and specific behavioral tasks. Beside them there are some studies in which basal ganglia is used for robotic applications. Also some deep brain stimulation studies are conducted by using computational model of basal ganglia circuit. One of these studies is work of the Rubin and Terman in 2004. In these study, it is show that how DBS improve the tremor which is symptom of Parkinsons Disease.
In the thesis, it is aimed to show how action initiation is performed and create a model of Parkinson's Disease and show how DBS improve the voluntary movement in the Parkinson's Disease. In order to be able to show these, a holistic spiking neural network model has been constructed and the striatum region is set to be sensitive to dopamine. This will provide an environment in which we can observe the behavior of all neural substructures during the actiont initiation process and show the differences in the neural pathways depending on the amount of dopamine. Furthermore, thanks to the change in the amount of dopamine that can be performed in the striatum, the Parkinson's Disease model can be obtained as a result without any further modification. By applying deep brain stimulation to this obtained Parkinson's Disease model, the rehabilitation effect of deep brain stimulation on the action initiation can be shown.
In the realization of this holistic model, the spiking neural network model was used. This model contains neuron representations and allows you to make changes on a cellular basis. Effects that are caused by changes in the cellular basis can be observed on neural substructures and the entire structure. In addition, synaptic connections can be defined in the spiking neural network. Whereby the effect of synaptic connections can be also observed. In addition, different connection dynamics and learning rules can be applied on the model. When spiking neural network model is implemented, the Izhikevich neuron model was preferred for the representation of single neuron. The Izhikevich neuron model is competent to represent the membrane potential and it's the computational burden is low. Since the established model is large scale, the Izhikevick nerve cell model was preferred for cell representations. In this way, the entire basal ganglia thalamocortical circuit was modelled as spiking neural network with Izhikevich neuron.
The entire model comes from the representation of different neural substructures. Each of these neural infrastructures has its own special cell types. In this study, these neural infrastructures were modeled with one or more dominant cell types which situated in their structure. Each neural infrastructure has some of connections which based on biological system and they have also internal connections. These connections form a number of pathways called as direct, indirect and hyperdirect. In the thesis, it was represented and explained how these pathways are regulate the actions.
In this study open source coded BRAIN environment is used for simulations. This environment facilitates the establishment of neural models that are multi-part and affect each other, like spiking neural networks. It is also easy to make new additions, removements and modify models on the BRAIN environment. The result of the model can be given as membrane potential, raster plot (spiking-time grafh) and firing rates. In this study, single cell behaviors were evaluated with membrane potentials and behaviors of neural substructures were evaluated with raster plots and firing rates outputs. In addition, the general frequencies of neural substructures were calculated in order to comparison with the literature.
Unlike other studies, the thesis study showed the effects of neural pathways and movement on a holistic model. Parkinson's Disease model can be obtained on this model and unlike the study of Rubin and Terman, deep brain stimulation effect were shown with movement transition from the circuit.