Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning
| Autor: | Sevim Cengiz, Dilek Betul Arslan, Ani Kicik, Emel Erdogdu, Muhammed Yildirim, Gokce Hale Hatay, Zeynep Tufekcioglu, Aziz Müfit Uluğ, Basar Bilgic, Hasmet Hanagasi, Tamer Demiralp, Hakan Gurvit, Esin Ozturk-Isik |
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| Přispěvatelé: | Işık Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Psikoloji Bölümü, Işık Üniversitesi, Faculty Of Economics, Administrative And Social Sciences, Psychology Department, Erdoğdu, Emel |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Magnetic Resonance Spectroscopy
Diagnostic criteria Posterior cingulate cortex Movement Receptors Antigen T-Cell Biophysics Associations N Acetylaspartic acid Machine Learning Nondemented patients Cognitive dysfunction Robust Machine learning Magnetic resonance spectroscopy Humans Cognitive Dysfunction Radiology Nuclear Medicine and imaging Prospective Studies Connectivity Radiological and Ultrasound Technology Parkinson Disease Creatine Magnetic Resonance Imaging Hypoperfusion Parkinson’s disease Dementia Protons Networks Inositol |
| Popis: | This study was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) 1001 Grant #115S219 and Istanbul University Scientific Research Projects Unit project #1567/42362. 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. Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) Istanbul University Publisher's Version Q3 WOS:000828927500002 PubMed ID: 35867235 |
| Databáze: | OpenAIRE |
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