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20222
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Yazar: Arslan, Dilek Betül × Bölüm: Işık Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Psikoloji Bölümü ×

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  • Küçük Resim
    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, Esin
    The 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.
  • Küçük Resim
    Yayın
    Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning
    (Springer Science and Business Media Deutschland GmbH, 2022-12) Cengiz, Sevim; Arslan, Dilek Betül; Kıçik, Ani; Erdoğdu, Emel; Yıldırım, Muhammed; Hatay, Gökçe Hale; Tüfekçioğlu, Zeynep; Uluğ, Aziz Müfit; Bilgiç, Başar; Hanagasi, Haşmet; Demiralp, Tamer; Gürvit, Hakan; Öztürk Işıkk, Esin
    Objective: To investigate metabolic changes of mild cognitive impairment in Parkinson’s disease (PD-MCI) using proton magnetic resonance spectroscopic imaging (1H-MRSI). Methods: Sixteen healthy controls (HC), 26 cognitively normal Parkinson’s disease (PD-CN) patients, and 34 PD-MCI patients were scanned in this prospective study. Neuropsychological tests were performed, and three-dimensional 1H-MRSI was obtained at 3 T. Metabolic parameters and neuropsychological test scores were compared between PD-MCI, PD-CN, and HC. The correlations between neuropsychological test scores and metabolic intensities were also assessed. Supervised machine learning algorithms were applied to classify HC, PD-CN, and PD-MCI groups based on metabolite levels. Results: PD-MCI had a lower corrected total N-acetylaspartate over total creatine ratio (tNAA/tCr) in the right precentral gyrus, corresponding to the sensorimotor network (p = 0.01), and a lower tNAA over myoinositol ratio (tNAA/mI) at a part of the default mode network, corresponding to the retrosplenial cortex (p = 0.04) than PD-CN. The HC and PD-MCI patients were classified with an accuracy of 86.4% (sensitivity = 72.7% and specificity = 81.8%) using bagged trees. Conclusion: 1H-MRSI revealed metabolic changes in the default mode, ventral attention/salience, and sensorimotor networks of PD-MCI patients, which could be summarized mainly as ‘posterior cortical metabolic changes’ related with cognitive dysfunction.