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Yayın Investigation of symptom-specific functional connectivity patterns in Parkinson’s disease(Springer-Verlag Italia S.R.L., 2025-06-14) Kıçik, Ani; Bayram, Ali; Erdoğdu, Emel; Kurt, Elif; Sarıdede, Dilek Betül; Cengiz, Sevim; Bilgiç, Başar; Hanağası, Haşmet; Öztürk Işık, Esin; Gürvit, Hakan; Tüzün, Erdem; Demiralp, TamerParkinson’s disease (PD) is a complex neurodegenerative disease, characterized by pronounced heterogeneity in symptoms. This study investigates the functional connectivity (FC) patterns associated with distinct symptom clusters, aiming to elucidate the heterogeneity in PD and uncover the neural mechanisms underlying its motor and cognitive symptoms. Resting-state functional MRI (rs-fMRI) data from 55 non-demented PD patients and 24 healthy controls (HC) were used to perform seed-to-seed FC analyses. A clustering algorithm was applied to the cognitive and motor scores of all PD patients to generate relatively homogeneous symptomatic subgroups. PD patients exhibited a general decrease in FC within a network comprising the sensorimotor network (SMN) and the visual network (VN) regions. Symptom-based clustering revealed three relatively homogeneous subgroups, exhibiting a gradient pattern: patients with greater motor deficits showed significant disconnection within the SMN, whereas patients with greater visuospatial deficits exhibited reduced FC in an extended subnetwork, with pronounced disconnections between the VN and SMN areas. Our study demonstrated a notable disconnection between the SMN and VN, indicating impaired visual-motor integration in PD. Stronger disconnection within the SMN was associated with greater motor dysfunction, and stronger visual-sensorimotor disconnections were associated with greater visuospatial deficits. These findings suggest that at least two separate routes of functional disconnection may be responsible for the inhomogeneous symptom distribution in PD.Yayın Automated diagnosis of Alzheimer’s Disease using OCT and OCTA: a systematic review(Institute of Electrical and Electronics Engineers Inc., 2024-08-06) Turkan, Yasemin; Tek, Faik Boray; Arpacı, Fatih; Arslan, Ozan; Toslak, Devrim; Bulut, Mehmet; Yaman, AylinRetinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have emerged as promising, non-invasive, and cost-effective modalities for the early diagnosis of Alzheimer's disease (AD). However, a comprehensive review of automated deep learning techniques for diagnosing AD or mild cognitive impairment (MCI) using OCT/OCTA data is lacking. We addressed this gap by conducting a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We systematically searched databases, including Scopus, PubMed, and Web of Science, and identified 16 important studies from an initial set of 4006 references. We then analyzed these studies through a structured framework, focusing on the key aspects of deep learning workflows for AD/MCI diagnosis using OCT-OCTA. This included dataset curation, model training, and validation methodologies. Our findings indicate a shift towards employing end-to-end deep learning models to directly analyze OCT/OCTA images in diagnosing AD/MCI, moving away from traditional machine learning approaches. However, we identified inconsistencies in the data collection methods across studies, leading to varied outcomes. We emphasize the need for longitudinal studies on early AD and MCI diagnosis, along with further research on interpretability tools to enhance model accuracy and reliability for clinical translation.
