Browsing by Author "Hunerli, Duygu"

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    Book Part
    Artificial Intelligence in Clinical Neuroscience
    (CRC Press, 2025) Ozturk, Seren Duzenli; Hunerli, Duygu; Aykan, Simge; Isbitiren, Yagmur Ozbek
    Artificial Intelligence (AI) is a branch of computer science that focuses on replicating human intelligence in machines (Malik & Solanki, 2021), allowing them to possess problem-solving (Zeigler, Muzy, & Yilmaz, 2009), and decision-making abilities akin to the human brain (Malik & Solanki, 2021). AI methods undergo training using extensive datasets, enabling them to perform specific tasks. Subsequently, they use this acquired knowledge to evaluate unfamiliar data and generate targeted outcomes. One of the remarkable aspects of AI is its capacity to swiftly process massive datasets without human intervention. Advancements in hardware technologies have facilitated a progression from conventional machine learning to deep learning within the field of AI, resulting in the emergence of widely used applications such as natural language processing, speech recognition, computer vision, and image classification parameters (Rana, Rawat, Bijalwan, & Bahuguna, 2018). Moreover, ongoing advancements in hardware aim to move towards neuromorphic hardware, which would lower the energy consumption of AI systems, emulating the energy efficiency of the human brain (Berggren et al., 2020). In essence, AI empowers machines to intelligently and intuitively tackle complex problems and make informed decisions. © 2025 Elsevier B.V., All rights reserved.
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    Article
    Can Volumetric Magnetic Resonance Imaging Evaluations Be Helpful in the Follow-Up of Cognitive Functions in Cognitively Normal Parkinson's Disease Patients?
    (Tubitak Scientific & Technological Research Council Turkey, 2024) Uysal, Hasan Armağan; Hunerli, Duygu; Çakmur, Raif; Dönmez Çolakoğlu, Beril; Ada, Emel; Yener, Görsev
    Background/aim: In this study, besides the evaluation of gray and white matter changes in cognitively normal Parkinson's disease (PDCN) patients with volumetric magnetic resonance imaging (MRI) parameters, it was tried to show that some neuropsychological tests may be impaired in PD-CN patients. Materials and methods: Twenty-six PD-CN patients and 26 healthy elderly (HC) participants were included in the current study. Global cognitive status was assessed using the mini-mental state examination (MMSE), and the Montreal cognitive assessment scale (MoCA). Attention and executive functions were evaluated using the Wechsler memory scale-revised (WMS-R) digit span test and trail making test (TMT) part A and part B, the Stroop test, semantic and phonemic fluency tests, and clock drawing test. Magnetic resonance imaging (MRI) was acquired according to the Alzheimer's disease neuroimaging initiative (ADNI) protocol. Results: There were no significant differences among groups regarding age, sex, handedness, and years of education. In the comparison of the PD-CN group and the HC group, there was a statistical decrease in the total animal scores, lexical fluency, TMT part A and TMT part B scores in the PD-CN group. Subcortical gray matter volumes (GMV) were significantly lower in PD-CN patients. The PD-CN group had a significantly reduced total volume of right putamen and left angular gyrus compared to that in the HC group. We observed that putamen and angular gyrus volumes were lower in PD-CN patients. On the other hand, TMT part B may be a useful pretest in detecting the conversion of mild cognitive impairment in PD. Conclusion: Significant MRI volumetric measurements and neuropsychological test batteries can be helpful in the clinical follow-up in PD-CN patients.