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Yayın Optimal primary-secondary user pairing and power allocation in cognitive cooperative multiple access channels(IEEE, 2014) Bakşi, Saygın; Kaya, OnurWe develop jointly optimal power control and primary-secondary user partnering strategies for a cognitive cooperative multiple access channel with K primary and K secondary users. For each primary user, a cooperating secondary user is assigned. We consider both underlay and overlay modes for cognition/cooperation. In overlay mode, each secondary user decodes and relays part of its assigned primary user's message, and simultaneously transmits its own independent message, while ensuring the primary user achieves at least its single user capacity with power control. The encoding is based on channel adaptive block Markov superposition coding, where the powers assigned to primary and secondary user codewords are optimized so as to maximize either the system's sum rate, or the sum of secondary users' rates. In underlay mode, each secondary user employs independent signalling and allocates its power to maximize its own rate, without decreasing its assigned primary user's rate. The partnering problem for either mode is reduced to a maximum weighted matching (MWM) problem on a bipartite graph, and solved jointly optimally with the power allocation problem.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 Shrinkage of olfactory amygdala connotes cognitive impairment in patients with Parkinson’s disease(Springer, 2023-10) Ay, Ulaş; Yıldırım, Zerrin; Erdoğdu, Emel; Kıçik, Ani; Öztürk Işık, Esin; Demiralp, Tamer; Gürvit, HakanDuring the caudo-rostral progression of Lewy pathology, the amygdala is involved relatively early in Parkinson’s disease (PD). However, lesser is known about the volumetric differences at the amygdala subdivisions, although the evidence mainly implicates the olfactory amygdala. We aimed to investigate the volumetric differences between the amygdala’s nuclear and sectoral subdivisions in the PD cognitive impairment continuum compared to healthy controls (HC). The volumes of nine nuclei of the amygdala were estimated with FreeSurfer (nuclear parcellation-NP) from T1-weighted images of PD patients with normal cognition (PD-CN), PD with mild cognitive impairment (PD-MCI), PD with dementia (PD-D), and HC. The appropriate nuclei were then merged to obtain three sectors of the amygdala (sectoral parcellation-SP). The nuclear and sectoral volumes were compared among the four groups and between the hyposmic and normosmic PD patients. There was a significant difference in the total amygdala volume among the four groups. In terms of nuclei, the bilateral cortico-amygdaloid transition area (CAT) and sectors superficial cortex-like region (sCLR) volumes of PD-MCI and PD-D were less than those of the PD-CN and HC. A linear discriminant analysis revealed that left CAT and left sCLR volumes classified the PD-CN and cognitively impaired PD (PD-CI: PD-MCI plus PD-D) with 90.7% accuracy according to NP and 85.2% accuracy to SP. Similarly, left CAT and sCLR volumes correctly identified the hyposmic and normosmic PD with 64.8% and 61.1% accuracies. Notably, the left olfactory amygdala volume successfully discriminated cognitive impairment in PD and could be used as neuroimaging-based support for PD-CI diagnosis.Yayın Left/right and front/back in sign, speech, and co-speech gestures: what do data from Turkish sign language, croatian sign language, American sign language, Turkish, Croatian, and English reveal?(Versita, 2011-09) Arık, EnginResearch has shown that spoken languages differ from each other in their representation of space. Using hands, body, and physical space in front of signers to represent space, do sign languages differ from each other? To what extent are they similar to spoken languages in their expressions of spatial relations? The present study targeted these questions by exploring the descriptions of static situations in sign languages (Turkish Sign Language, Croatian Sign Language, American Sign Language) and spoken languages, including co-speech gestures (Turkish, Croatian, and English). It is found that signed and spoken languages differ from each other in their linguistic constructions for the left/right and front/back spatial relation. They also differ from one another in their mapping strategies. Crucially, being a signer does not require more direct iconic mappings than a speaker would use. It is also found that co-speech gestures can complement spoken language descriptions.Yayın Associations among adolescents' mindfulness, sympathy, cognitive empathy, and sibling relationships(Sage Publication, 2024-02) Barata, Özge; Acar, İbrahim Hakkı; Bostancı, SelenIn the current study, we examined the direct and indirect paths from mindfulness to adolescents’ sibling relationships through their cognitive empathy and sympathy. The sample consisted of 220 adolescents (50.9 % female) between age of 13 and 17 years (M = 15.86, SD = 0.91). Participants reported their mindfulness (acceptance and awareness), cognitive empathy and sympathy, and sibling relationships. The parallel mediation model revealed that mindful awareness and acceptance predicted kindness, involvement, and empathy within sibling relationships through sympathy. In addition, there was a significant indirect effect of mindful awareness to empathy in sibling relationships through cognitive empathy. Findings provided information regarding the importance of indirect contributions of mindfulness to sibling relationships through cognitive empathy and sympathy.Yayın Theta and Beta1 frequency band values predict dyslexia classification(John Wiley and Sons Ltd, 2025-12-29) Eroğlu, Günet; Harb, Mhd Raja AbouDyslexia, impacting children's reading skills, prompts families to seek cost-effective neurofeedback therapy solutions. Utilising machine learning, we identified predictive factors for dyslexia classification. Employing advanced techniques, we gathered 14-channel Quantitative Electroencephalography (QEEG) data from 200 participants, achieving 99.6% dyslexic classification accuracy through cross-validation. During validation, 48% of dyslexic children's sessions were consistently classified as normal, with a 95% confidence interval of 47.31 to 48.68. Focusing on individuals consistently diagnosed with dyslexia during therapy, we found that dyslexic individuals exhibited higher theta values and lower beta1 values compared to typically developing children. This study pioneers machine learning in predicting dyslexia classification factors, offering valuable insights for families considering neurofeedback therapy investment.
