Phd Degree / Doktora

Permanent URI for this collectionhttps://hdl.handle.net/11147/2869

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Author: search.filters.author.Karaçalı, Bilge

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  • Doctoral Thesis
    Beyin Fonksiyon Değişimlerini Elektroensefalografi ile Değerlendirmek için İşlemsel Bir Beyin Bağlantılılık Çerçevesi
    (2025) Onay, Fatih; Karaçalı, Bilge
    Parkinson hastalığı, beynin sinirsel aktiviteyi esnek bir şekilde koordine etme yeteneğini bozar. Bu durum, özellikle bazal gangliya ve ona bağlı kortiko-talamik döngüleri içeren devrelerde görülür. Sağlıklı bir beyin, motor ve bilişsel görevler sırasında, verimli kaynak tahsisini yansıtan dinamik senkronizasyon ve desenkronizasyon örüntüleri gösterir. Parkinson hastalığında ise bu sinirsel mekanizmalar patolojik sinirsel dinamiklere karşı daha savunmasız hale gelir, bu da beynin sorunsuz bilişsel ve motor kontrol için gereken aktivasyon örüntüleri arasında verimli bir şekilde geçiş yapma yeteneğini zayıflatır. Bu tez, bu değişikliklerin kontrollü pedal çevirme görevi sırasında nasıl ortaya çıktığını sinirsel değişkenliği inceleyerek araştırmaktadır. Bu amaçla, Parkinson hastalarından (donma gösteren hastalar dahil - PDFOG) ve sağlıklı kontrol gruplarından alt ekstremite pedal çevirme görevi sırasında toplanan EEG kayıtları kullanılmıştır. Pedal çevirme görevi sırasında senkronizasyon ve değişkenlik örüntülerini saptamak için sinirsel dinamikleri denemeler arası tutarlılık (inter-trial coherence - ITC) ve entropi ölçümleri aracılığıyla analiz ettik. ITC analizi, sağlıklı kontrol grubunun frontoparietal ağlarda güçlü delta bandı senkronizasyonunu koruduğunu ortaya koyarken, Parkinson hastalarının düşük frekanslı ITC'de giderek kötüleşme gösterdiğini ve PDFOG hastalarının en ciddi azalmalara sahip olduğunu gösterdi. Hasta gruplarını ayırt eden biyobelirteçler olarak, sensorimotor hazırlık sırasında delta baskınlığından beta bandı aktivitesine doğru sistematik frekans kaymaları keşfedildi. Entropi analizi ile Parkinson hastalığı ilerledikçe azalan karmaşıklık ve bilgi işleme kapasitesi tespit edildi. Vasicek ve permütasyon entropi ölçümlerini kullanarak, frontal, parietal ve oksipital bölgelerde azalan sinirsel değişkenlik ve motor aktivite başlatma sırasında azalmış karmaşıklık tespit ettik. Her iki yaklaşım da, sağ parietal ve frontoparietal ağların hastalığa bağlı işlev bozukluğuna karşı savunmasız olduğunu gösterdi. Bu bulgular, sinirsel senkronizasyon ve karmaşıklıktaki denemeler arası değişkenliğin, hastalık ilerlemesi için hassas belirteçler olarak hizmet ettiğini ortaya koymaktadır. Motor-bilişsel ağlardaki sinirsel kararlılık ve adaptasyonun kademeli olarak bozulması, yürüme donması mekanizmalarına dair yeni bilgiler sunmaktadır. Böylece, elde edilen bulgular entropi ve ITC yöntemlerinin nörodejeneratif bozuklukların tespitinde EEG tabanlı biyobelirteç olarak kullanılabileceğini göstermiştir.
  • Doctoral Thesis
    On the Characterization of Motor Imagery Functions Based on Systematic Timing Organization of the Human Brain
    (01. Izmir Institute of Technology, 2021) Olcay, Bilal Orkan; Karaçalı, Bilge
    The main objective of this thesis is to analyze the timing organization of the brain. The human brain is known to adjust its localized and also the reciprocal operations for each different cognitive task adaptively. This flexibility of the brain has attracted considerable interest in neuroscience. Elucidation of timing adaptation property of brain, however, remains as unresolved due to dynamically changing and nonlinear nature of the brain. In this thesis, we characterize the timing organization of the brain during motor imagery activity using electroencephalography signals. First, we propose a novel motor imagery activity recognition method that relies on the activity-specific time-lag between electroencephalography signals obtained from different brain regions. Next, we generalize this approach into three-parameter formulation to determine the timing profiles of activity-specific short-lived synchronization. The identification of activity-specific timing parameters was carried out using a heuristic approach that maximizes the average pairwise channel synchronizations during associated activity periods. Thereafter, we propose a novel BCI framework that find and use the timings of electroencephalography signals of localized brain regions that elicit localized activity-specific features. We identify the timings for each different brain regions by adopting a heuristic-probabilistic method. Finally, we propose a novel autoregressive modeling framework that finds a representative model for each different cognitive activity. We demonstrated the efficacy of the proposed methods on publicly available brain-computer interfacing datasets on motor imagery. The performance results indicate that considering the timing organization of the brain is crucial for accurate characterization of cognitive activity. In addition, it may also account for the inconsistency of brain computer interfacing performance obtained from different subjects.
  • Doctoral Thesis
    Development of a Unified Analysis Framework for Multicolor Flow Cytometry Data Based on Quasi-Supervised Learning
    (Izmir Institute of Technology, 2017) Köktürk Güzel, Başak Esin; Karaçalı, Bilge
    In this dissertation, automatic compensation and gating strategies are investigated for multi-color flow cytometry data analysis. We propose two clustering algorithms that combine the quasi-supervised learning algorithm with an expectation-maximization routine for automatic gating. The quasi-supervised learning algorithm estimates the posterior probabilities of the different cell populations at each sample in a dataset in a manner that does not involve fitting a parametric model to the data. We have developed two different binary divisive clustering algorithms based on expectation maximization with responsibility values calculated using the quasi-supervised learning algorithm instead of the probabilistic models used in conventional expectation maximization applications. Our clustering algorithms determine the number of clusters in run-time by measuring the overlap between the estimated clusters in each provisional division and comparing it with the previous one to determine whether the division is warranted or not. Since this type of clustering is indifferent to the underlying distribution of dataset, it is well suited to automatic flow cytometry gating. The second clustering algorithm improves upon the first one using a simulated annealing approach. Its iterative structure allows finding the global minimum of a cost functional that achieves the best separation point by gradually smoothing the decision regions in each iteration. Finally, we have developed a joint diagonalization and clustering method for automatic compensation of flow data based on the methods above. The proposed method identifies cell sub groups using the annealing-based model-free expectation-maximization algorithm and finds a data transformation matrix that achieves orthogonality of the covariance structure of each identified cell cluster using fast Frobenius diagonalization. We have tested all proposed algortihms on both synthetically created datasets and real multi-color flow cytometry datasets. The results show that our automated gating algorithms are very successful in identifying the distinct cell groups so long as there is enough statistical evidence for their presence. In addition, the automated compensation procedure was also successfully applied on the synthetically created dataset and real multi-color flow cytometry data of lymphocytes that are a low autofluorescence cell group. However, the automated compensation algorithm needs further study to be generalized to high autofluorescence cell types where proper compensation does not necessarily coincide with an orthogonal covariance structure.
  • Doctoral Thesis
    Automatic Identification of Evolutionary and Sequence Relationships in Large Scale Protein Data Using Computational and Graph-Theoretical Analyses
    (Izmir Institute of Technology, 2012) Doğan, Tunca; Karaçalı, Bilge
    In this study, computational methods are developed for the automatic identification of functional/evolutionary relationships between biomolecular sequences in large and diverse datasets. Different approaches were considered during the development and optimization of the methods. The first approach focused on the expression of gene and protein sequences in high dimensional vector spaces via non-linear embedding. This allowed statistical learning algorithms to be applied on the resulting embeddings in order to cluster and/or classify the sequences. The second approach revised the pairwise similarities between sequences following multiple sequence alignment in order to eliminate the unreliable connections due to remote homology and/or poor alignment. This is achieved by thresholding the pairwise connectivity map over 2 parameters: the inferred evolutionary distances and the number of gapless positions in each pairwise alignment. The resulting connectivity map was disjoint and consisted of clusters of similar proteins. The third and the final approach sought to associate the amino acid sequences with each other over highly conserved/shared sequence segments, as shared sequence segments imply conserved functional or structural attributes. An automated method was developed to identify these segments in large and diverse collections of amino acid sequences, using a combination of sequence alignment, residue conservation scoring and graph-theoretical approaches. The method produces a table of associations between the input sequences and the identified conserved regions that can reveal both new members to the known protein families and entirely new lines. The methods were applied to a dataset composed of 17793 human proteins sequences in order to obtain a global functional relation map. On this map, functional and evolutionary properties of human proteins could be found based on their relationships to the ones bearing functional annotations. The results revealed that conserved regions corresponded strongly to annotated structural domains. This suggests the method can also be useful in identifying novel domains on protein sequences.
  • Doctoral Thesis
    Automatic Identification of Abnormal Regiones in Digitized Histology Cross-Sections of Colonic Tissues and Adenocarcinomas Using Quasi-Supervised Learning
    (Izmir Institute of Technology, 2012) Önder, Devrim; Karaçalı, Bilge
    In this thesis, a framework for quasi-supervised histopathology image texture identi- cation is presented. The process begins with extraction of texture features followed by a quasi-supervised analysis. Throughout this study, light microscopic images of the hematoxylin and eosin stained colorectal histopathology sections containing adenocarcinoma were quantitatively analysed. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly and in bulk, and the other containing an unlabelled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues commonly used for conventional supervised learning and signicantly reduces the expert intervention. Several texture feature vector datasets corresponding to various feature calculation parameters were tested within the proposed framework. The resulting labelling and recognition performances were compared to that of a conventional powerful supervised classier using manually labelled ground-truth data that was withheld from the quasi-supervised learning algorithm. That supervised classier represented an idealized but undesired method due to extensive expert labelling. Several vector dimensionality reduction techniques were evaluated an improvement in the performance. Among the alternatives, the Independent Component Analysis procedure increased the performance of the proposed framework. Experimental results on colorectal histopathology slides showed that the regions containing cancer tissue can be identied accurately without using manually labelled ground-truth datasets in a quasi-supervised strategy.