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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 Corrigendum to “Detection of visual and frontoparietal network perfusion deficits in Parkinson’s disease dementia” [Eur. J. Radiol. 144 (2021) 109985](Elsevier Ireland Ltd, 2022-10-28) Azamat, Sena; Arslan, Dilek Betül; Erdoğdu, Emel; Kıçik, Ani; Cengiz, Sevim; Eryürek, Kardelen; Tüfekçioğlu, Zeynep; Bilgiç, Başar; Hanagasi, Haşmet; Demiralp, Tamer; Gürvit, Hakan; Öztürk Işık, EsinThe authors would like to add the following grant support that was accidentally not included in the original article. Acknowledgements: This study was supported by TUBITAK 1001 project #115S219, Istanbul University Scientific Research Projects Unit project #1567/42362 and Bogazici University Scientific Research Projects Unit project #15222. The authors would like to apologize for any inconvenience caused.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 Posterior atrophy is a neuroimaging marker of mild cognitive impairment in Parkinson's disease(Türk Nöropsikiyatri Derneği, 2026-02-02) Ay, Ulaş; Yıldırım, Zerrin; Kıcik, Ani; Erdoğdu, Emel; Bilgiç, Başar; Hanağası, Haşmet; Öztürk Işık, Esin; Demiralp, Tamer; Gürvit, HakanIntroduction: Although there are several studies on the neuroanatomical mechanisms underlying Parkinson's disease (PD)-associated cognitive impairment, the clinical usefulness of the findings from these investigations is limited. In this study, we aimed to identify magnetic resonance imaging (MRI) markers that can be practically utilized for diagnosing PD-associated cognitive impairment using a visual rating scale (VRS). Methods: Anatomical MRIs of cognitively normal (PD-CN), and PD with mild cognitive impairment (PD-MCI) patients were visually evaluated for six bilateral cortical regions. Then, hypothesis-driven cortical thickness analysis (CTA) was performed in the regions obtained from VRS. Results: As a consequence of VRS, a significant difference was found between the two groups with regards to right posterior atrophy (PA) scores (pFDR-corr = 0.042, Cohen's d= 1.06). Hypothesis-driven CTA confirmed the result of VRS by revealing cortical thinning at the precuneus and parieto-occipital sulcus junction (Max. T= 6.171, P= 0.0006, MNIx, y,z = 11.0,-62.2, 25.4). The area under the curve was 0.75, showing a good association between the PD-MCI and the right PA score. The cut-off for maximum accuracy was >= 2, based on the highest sum of sensitivity (0.68) and specificity (0.72). Conclusions: Our findings indicate that right PA atrophy may be helpful for clinicians in the diagnosis of PD-associated cognitive impairment.
