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Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning

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Abstract

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.

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Availability of data and material (data transparency)

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

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.

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Author notes

  1. Hakan Gurvit and Esin Ozturk-Isik share senior authorship.

Authors and Affiliations

  1. Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey

    Sevim Cengiz, Dilek Betul Arslan, Muhammed Yildirim, Gokce Hale Hatay, Aziz Müfit Uluğ & Esin Ozturk-Isik

  2. Neuroimaging Unit, Hulusi Behcet Life Sciences Research Center, Istanbul University, Istanbul, Turkey

    Ani Kicik, Emel Erdogdu & Tamer Demiralp

  3. Department of Physiology, Faculty of Medicine, Demiroglu Bilim University, Istanbul, Turkey

    Ani Kicik

  4. Department of Psychology, Faculty of Economics and Administrative Sciences, Isik University, Istanbul, Turkey

    Emel Erdogdu

  5. Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey

    Zeynep Tufekcioglu, Basar Bilgic, Hasmet Hanagasi & Hakan Gurvit

  6. Department of Neurology, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey

    Zeynep Tufekcioglu

  7. CorTechs Labs, San Diego, CA, USA

    Aziz Müfit Uluğ

  8. Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey

    Tamer Demiralp

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  1. Sevim Cengiz
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Contributions

All authors contributed to the conception and design of the study. The recruitment and neuropsychological and clinical assessments of the subjects were conducted by HG, HH, BB, and ZT. The genetic data analysis was performed by AK and ET. MR scanning protocol optimization and data collection were performed by SC, DBA, EE, AK, AMU, TD, and EO-I. New data analysis tools were developed by SC, MY, GHH, and EO-I. Data analysis was performed by SC and DBA. All authors contributed to the interpretation and discussion of the results. The first draft of the manuscript was written by SC and all authors revised it critically for important intellectual content. All authors read and approved the final manuscript. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Esin Ozturk-Isik.

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Cengiz, S., Arslan, D.B., Kicik, A. et al. Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning. Magn Reson Mater Phy 35, 997–1008 (2022). https://doi.org/10.1007/s10334-022-01030-6

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