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Yayın k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia(Taylor & Francis, 2023) Eroğlu, Günet; Arman, FehimLearning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.Yayın Attentional bias and training in social anxiety disorder(Turkish Neuropsychiatric Society, 2015-03) Fıstıkçı, Nurhan; Saatçioğlu, İbrahim Ömer; Keyvan, Ali; Topçuoǧlu, VolkanCognitive behavioral therapy (CBT) is one of the most effective treatment modalities for social anxiety disorder (SAD), showing a high level of clinical evidence supporting its effectiveness. On the other hand, lack of the desired benefit from this treatment in some patients causes continuation of the search for new techniques. Recent research studies have focused on attentional bias and attention training in SAD. Attention processes in SAD have been a major target of interest and investigation since the introduction of the first cognitive models explaining SAD. In the first model, it was highlighted that attention was self-focused. The relationship between threatening stimuli and attention was considered in the subsequent models. Attentional bias towards threat may take place in several ways, such as facilitated processing of threat, difficulty in disengaging attention from the threat and avoidance of attention from the threat. After these descriptions regarding the phenomenology of the disorder, treatments to modify attention, processes were developed. In spite of conflicting results, investigations on attentional training are promising. Attention processes, attentional bias and attentional training in SAD are discussed in this review.Yayın Convolutional attention network for MRI-based Alzheimer's disease classification and its interpretability analysis(IEEE, 2021-09-17) Türkan, Yasemin; Tek, Faik BorayNeuroimaging techniques, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), help to identify Alzheimer's disease (AD). These techniques generate large-scale, high-dimensional, multimodal neuroimaging data, which is time-consuming and difficult to interpret and classify. Therefore, interest in deep learning approaches for the classification of 3D structural MRI brain scans has grown rapidly. In this research study, we improved the 3D VGG model proposed by Korolev et al. [2]. We increased the filters in the 3D convolutional layers and then added an attention mechanism for better classification. We compared the performance of the proposed approaches for the classification of Alzheimer's disease versus mild cognitive impairments and normal cohorts on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We observed that both the accuracy and area under curve results improved with the proposed models. However, deep neural networks are black boxes that produce predictions that require further explanation for medical usage. We compared the 3D-data interpretation capabilities of the proposed models using four different interpretability methods: Occlusion, 3D Ultrametric Contour Map, 3D Gradient-Weighted Class Activation Mapping, and SHapley Additive explanations (SHAP). We observed that explanation results differed in different network models and data classes.Yayın Uyarlanır yerel bağlı katman kullanan dikkat tabanlı derin ağ ile sesli komut tanıma(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Turkan, Yasemin; Tek, Faik BoraySesli komut tanıma insan-makine ara yüzüyle ilişkili aktif bir araştırma konusudur. Dikkat tabanlı derin ağlar ile bu tür problemler başarılı bir şekilde çözülebilmektedir. Bu çalışmada, var olan bir dikkat tabanlı derin ağ yöntemi, uyarlanır yerel bağlı (odaklanan) katman kullanılarak daha da geliştirilmiştir. Orijinal yönteminde sınandığı Google ve Kaggle sesli komut veri setlerinde karşılaştırmalı olarak yapılan deneylerde önerdiğimiz uyarlanır yerel bağlı katman kullanan dikkat tabanlı ağın tanıma doğruluğunu %2.6 oranında iyileştirdiği gözlemledik.Yayın An adaptive locally connected neuron model: Focusing neuron(Elsevier B.V., 2021-01-02) Tek, Faik BorayThis paper presents a new artificial neuron model capable of learning its receptive field in the topological domain of inputs. The experiments include tests of focusing neuron networks of one or two hidden layers on synthetic and well-known image recognition data sets. The results demonstrated that the focusing neurons can move their receptive fields towards more informative inputs. In the simple two-hidden layer networks, the focusing layers outperformed the dense layers in the classification of the 2D spatial data sets. Moreover, the focusing networks performed better than the dense networks even when 70% of the weights were pruned. The tests on convolutional networks revealed that using focusing layers instead of dense layers for the classification of convolutional features may work better in some data sets.Yayın Alzheimer hastalığında olağan durum ağı bağlantısallığı(Türkiye Sinir Ve Ruh Sağlığı Derneği, 2019-12) Yıldırım, Elif; Soncu Büyükişcan, EzgiAmaç: Alzheimer hastalığı (AH) beyinde yapısal ve işlevsel değişimler meydana getiren nörodejeneratif bir hastalıktır. Gelişen beyin görüntüleme yöntemleri sayesinde AH patolojisine eşlik eden yapısal ve işlevsel bağlantılardaki bozulmalar gitgide daha görünür hale gelmiştir. AH’de dinlenim durumu bağlantısallığında, özellikle de olağan durum ağı (default mode network - DMN) olarak adlandırılan içsel bağlantısallık ağında farklılaşmalar görülmektedir. Bu çalışmada DMN bağlantısallık bulgularının incelenmesi ve tartışılması amaçlanmıştır. Yöntem: İşlevsel manyetik rezonans görüntüleme (fMRI) çalışmalarında en yaygın kullanılan 2 temel metodoloji (tohum temelli ve bağımsız bileşen analizi) temel alınarak alanda yapılan çalışmalar incelenmiştir. Bulgular: Çalışmalar genel olarak, DMN bağlantısallığının AH süreci boyunca ilerleyici bir şekilde bozulduğunu göstermektedir. DMN alt sistemlerinin AH’nin preklinik ve prodromal evrelerinde farklı bağlantısallık örüntüleri gösterdiği de belirtilmektedir. DMN’deki bozulmanın diğer bağlantısallık ağlarındaki farklılaşma ile ilişkili olabileceğini öne süren kanıtlar da mevcuttur. Buna ek olarak, bulgular DMN’nin AH ile ilişkili nöropatoloji ve genetik risk faktörleri ile olan ilişkisine de işaret etmektedir. Sonuç: AH’nin beyinde başta DMN olmak üzere diğer dinlenim durumu ağlarında işlevsel bozulmalara yol açan yaygın bir diskonneksiyon sendromu olduğu öne sürülebilir. Buna ek olarak, preklinik vakalarda ve risk taşıyan kişilerde de saptanabilen AH ile ilişkili işlevsel bağlantısallık değişimleri AH için muhtemel bir biyo-belirteç olabilir.Yayın Saliency detection based on hybrid artificial bee colony and firefly optimization(Springer Science and Business Media Deutschland GmbH, 2022-11) Yelmenoğlu, Elif Deniz; Çelebi, Numan; Taşçı, TuğrulSaliency detection is one of the challenging problems still tackled by image processing and computer vision research communities. Although not very numerous, recent studies reveal that optimization-based methods provide relatively accurate and fast solutions for such problems. This paper presents a novel unsupervised hybrid optimization method that aims to propose reasonable solution to saliency detection problem by combining the familiar artificial bee colony and firefly algorithms. The proposed method, HABCFA, is based on creating hybrid-personality individuals behaving like both bees and fireflies. A superpixel-based method is used to obtain better background intensity values in the saliency detection process, providing a better precision in extracting the salient regions. HABCFA algorithm is capable of achieving an optimum saliency map without requiring any extra mask or training step. HABCFA has produced superior performance against its basis algorithms, artificial bee colony, and firefly on four known benchmark problems regarding convergence rate and iteration count. On the other hand, the experimental results on four commonly used datasets, including MSRA-1000, ECSSD, ICOSEG, and DUTOMRON, demonstrate that HABCFA is adequately robust and effective in terms of accuracy, precision, and speed in comparison with the eleven state-of-the-art methods.
