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Leveraging MRI and machine-learning for early detection of cognitive impairment in Parkinson’s disease

L. Saadatpour, A. Vijayakumari, B. Walter, H. Fernandez (Cleveland, USA)

Meeting: 2023 International Congress

Abstract Number: 389

Keywords: Cognitive dysfunction, Magnetic resonance imaging(MRI), Parkinson’s

Category: Parkinson's Disease: Cognitive functions

Objective: To develop a machine learning model using MRI data that predicts the likelihood of cognitive impairment in newly diagnosed patients with Parkinson’s disease (PD).

Background: Mild cognitive impairment (MCI) often exists in newly diagnosed PD patients and more than half of patients can develop dementia within 10 years of diagnosis [1]. Therefore, early identification of MCI in PD is essential for proper management of the disease.

Method: We analyzed the baseline  T1-weighted MRI of  35 newly diagnosed PD patients and 72 age-matched healthy controls, with a MoCA score of 26 or higher at entry into the Parkinson’s Progression Marker Initiative (PPMI) [2]. Based on the follow-up MoCA scores at 4-years, PD patients were subsequently classified into two groups: cognitively-intact (MoCA=26 or greater) and cognitively-impaired (MoCA=25 or lower). To predict the likelihood of developing cognitive impairment after 4 years, for each patient’s baseline MRI, gray matter volumes from 48 regions of interest, including frontal, parietal, and subcortical regions, were calculated using Mahalanobis distance (MGMV) [3]. MGMV calculates the distance between brain regions in each patient, using a reference distribution from the healthy control brains. Random forest model was then used to create the prediction model for the development of cognitive impairment using the MGMV. We then calculated the accuracy, sensitivity and specificity of our machine learning model in predicting cognitive-impairment.

Results: Our PD cohort had a mean age of 59.8 years; 71% (n=25) males; mean baseline MoCA score of 28.4. The mean age of the age-matched controls was 60.2 years; 66% (n=48) males; average baseline MoCA score of 28.3. At 4 years, 22 PD patients remained cognitively-intact (mean MoCA = 27.5) and 13 were cognitively-impaired (mean MoCA =22.2). Using baseline MRI, our machine learning model predicted cognitively-impaired patients with an accuracy of 90.9% having 80% specificity and 100% sensitivity.

Conclusion: Our study proposes a machine learning model that utilizes baseline MRI data to distinguish between PD patients with cognitive impairment and those without, as defined by their follow-up MoCA scores. Further investigations using larger samples along with more detailed cognitive assessment tools will be necessary to confirm the accuracy and improve the generalizability of this model.

References: 1- Cammisuli DM., et al. Parkinson’s Disease-Mild Cognitive Impairment (PD-MCI): A Useful Summary of Update Knowledge. Front Aging Neurosci. 2019. 8(11):303.
2- Marek, K., et al., The Parkinson’s progression markers initiative (PPMI) – establishing a PD biomarker cohort. Ann Clin Transl Neurol, 2018. 5(12): p. 1460-1477.
3- Mahalanobis, P.C. On the generalized distance in statistics. . in Proceedings of the National Institute of Sciences. 1936. Calcutta.

To cite this abstract in AMA style:

L. Saadatpour, A. Vijayakumari, B. Walter, H. Fernandez. Leveraging MRI and machine-learning for early detection of cognitive impairment in Parkinson’s disease [abstract]. Mov Disord. 2023; 38 (suppl 1). https://www.mdsabstracts.org/abstract/leveraging-mri-and-machine-learning-for-early-detection-of-cognitive-impairment-in-parkinsons-disease/. Accessed May 9, 2025.
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