Journal of Movement Disorders

Review Article

Subjective Cognitive Complaints in Cognitively Normal Patients With Parkinson’s Disease: A Systematic Review: Jin Yong Hong, Phil Hyu Lee; J Mov Disord. 2023;16(1):1-12. Published online November 10, 2022; DOI: https://doi.org/10.14802/jmd.22059

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Subjective cognitive complaints (SCCs) refer to self-perceived cognitive decline and are related to objective cognitive decline. SCCs in cognitively normal individuals are considered a preclinical sign of subsequent cognitive impairment due to Alzheimer’s disease, and SCCs in cognitively normal patients with Parkinson’s disease (PD) are also gaining attention. The aim of this review was to provide an overview of the current research on SCCs in cognitively normal patients with PD. A systematic search found a lack of consistency in the methodologies used to define and measure SCCs. Although the association between SCCs and objective cognitive performance in cognitively normal patients with PD is controversial, SCCs appear to be predictive of subsequent cognitive decline. These findings support the clinical value of SCCs in cognitively normal status in PD; however, further convincing evidence from biomarker studies is needed to provide a pathophysiological basis for these findings. Additionally, a consensus on the definition and assessment of SCCs is needed for further investigations.

Citations

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Daily Emotional Experiences in Persons with Parkinson Disease: Relations to Subjective Cognitive Complaints and Quality of Life
Karen R. Hebert, Mackenzie Feldhacker
Physical & Occupational Therapy In Geriatrics.2024; 42(3): 228. CrossRef
Subjective Cognitive Complaints in Parkinson's Disease: A Systematic Review and Meta‐Analysis
Mattia Siciliano, Alessandro Tessitore, Francesca Morgante, Jennifer G. Goldman, Lucia Ricciardi
Movement Disorders.2024; 39(1): 17. CrossRef
Mild cognitive impairment in Parkinson's disease: current view
Kurt A. Jellinger
Frontiers in Cognition.2024;[Epub] CrossRef
Neurocognitive Impairment and Social Cognition in Parkinson’s Disease Patients
Triantafyllos Doskas, Konstantinos Vadikolias, Konstantinos Ntoskas, George D. Vavougios, Dimitrios Tsiptsios, Polyxeni Stamati, Ioannis Liampas, Vasileios Siokas, Lambros Messinis, Grigorios Nasios, Efthimios Dardiotis
Neurology International.2024; 16(2): 432. CrossRef
Cognitive disorders in Parkinson's disease
Victor Kholin, Iryna Karaban, Sergiy Kryzhanovskiy, Nina Karasevich, Natalia Melnik, Maryna Khodakovska, Hanna Shershanova, Natalia Movchun
Ageing & Longevity.2024; (2 2024): 51. CrossRef
Unveiling the role of subjective cognitive complaints in predicting cognitive impairment in Parkinson´s Disease– A longitudinal study with 4 year of follow up
Marta Magriço, Bruna Meira, Marco Fernandes, Manuel Salavisa, Marlene Saraiva, Cláudia Borbinha, João Pedro Marto, Raquel Barbosa, Paulo Bugalho
Neurological Sciences.2024; 45(11): 5271. CrossRef
Self‐ and study partner–reported cognitive decline in older adults without dementia: The role of α‐synuclein and amyloid biomarkers in the Alzheimer's Disease Neuroimaging Initiative
Kelsey R. Thomas, Katherine J. Bangen, Lindsay J. Rotblatt, Alexandra J. Weigand, Lauren Edwards, Duygu Tosun, Douglas Galasko
Alzheimer's & Dementia.2024; 20(11): 7777. CrossRef
Mitochondrial Dysfunction in Parkinson’s Disease: A Contribution to Cognitive Impairment?
Antonella Scorziello, Rossana Sirabella, Maria Josè Sisalli, Michele Tufano, Lucia Giaccio, Elena D’Apolito, Lorenzo Castellano, Lucio Annunziato
International Journal of Molecular Sciences.2024; 25(21): 11490. CrossRef
Total burden of cerebral small vessel disease predict subjective cognitive decline in patients with Parkinson’s disease
Wenchao Qiu, Weili Hu, Yingchao Ge, Peiting Liu, Minghui Zhao, Haifeng Lu, Jian Tao, Shouru Xue
Frontiers in Aging Neuroscience.2024;[Epub] CrossRef
Cognitive impairment in Parkinson’s disease and other parkinsonian syndromes
Alexandros Giannakis, Chrissa Sioka, Eugenia Kloufetou, Spiridon Konitsiotis
Journal of Neural Transmission.2024;[Epub] CrossRef
Association of Neuropsychiatric Symptom Profiles With Cognitive Decline in Patients With Parkinson Disease and Mild Cognitive Impairment
Young-gun Lee, Mincheol Park, Seong Ho Jeong, Kyoungwon Baik, Sungwoo Kang, So Hoon Yoon, Han Kyu Na, Young H. Sohn, Phil Hyu Lee
Neurology.2023;[Epub] CrossRef
Subjective cognitive complaints in patients with progressive supranuclear palsy
Jun Seok Lee, Jong Hyeon Ahn, Jong Mok Ha, Jinyoung Youn, Jin Whan Cho
Frontiers in Neurology.2023;[Epub] CrossRef
Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks
Kurt A. Jellinger
International Journal of Molecular Sciences.2023; 25(1): 498. CrossRef

Original Articles

Accuracy of Machine Learning Using the Montreal Cognitive Assessment for the Diagnosis of Cognitive Impairment in Parkinson’s Disease: Junbeom Jeon, Kiyong Kim, Kyeongmin Baek, Seok Jong Chung, Jeehee Yoon, Yun Joong Kim; J Mov Disord. 2022;15(2):132-139. Published online May 26, 2022; DOI: https://doi.org/10.14802/jmd.22012

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Abstract PDF Supplementary Material

Objective
The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson’s disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI.
Methods
In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson’s Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method.
Results
Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87–0.89).
Conclusion
Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.

Citations

Citations to this article as recorded by

Comparing Montreal Cognitive Assessment Performance in Parkinson’s Disease Patients: Age- and Education-Adjusted Cutoffs vs. Machine Learning
Kyeongmin Baek, Young Min Kim, Han Kyu Na, Junki Lee, Dong Ho Shin, Seok-Jae Heo, Seok Jong Chung, Kiyong Kim, Phil Hyu Lee, Young H. Sohn, Jeehee Yoon, Yun Joong Kim
Journal of Movement Disorders.2024; 17(2): 171. CrossRef
Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review
Callum Altham, Huaizhong Zhang, Ella Pereira, Farzin Hajebrahimi
PLOS ONE.2024; 19(5): e0303644. CrossRef

Constipation is Associated With Mild Cognitive Impairment in Patients With de novo Parkinson’s Disease: Sung Hoon Kang, Jungyeun Lee, Seong-Beom Koh; J Mov Disord. 2022;15(1):38-42. Published online November 17, 2021; DOI: https://doi.org/10.14802/jmd.21074

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Abstract PDF

Objective
The association between gastrointestinal (GI) symptoms and cognitive profile in patients with Parkinson’s disease (PD) at diagnosis remains unclear, although GI symptoms and cognitive impairment are highly prevalent in patients with PD. We investigated the relationship between constipation and cognitive status. We also aimed to identify the correlation between constipation and each neuropsychological dysfunction.
Methods
A total of 427 patients with de novo Parkinson’s disease with normal cognition (PD-NC, n = 170) and Parkinson’s disease with mild cognitive impairment (PD-MCI, n = 257) at Korea University Guro Hospital in Seoul, Korea were included. All patients underwent comprehensive neuropsychological tests and completed the Non-Motor Symptoms Scale (NMSS). The frequency and severity of constipation were assessed using the NMSS GI symptoms scale, we used logistic regression analysis and partial correlation analysis to determine the associations between constipation score, MCI, and each neuropsychological dysfunction.
Results
Frequent and severe constipation was associated with MCI in patients with PD at diagnosis regardless of disease severity. Specifically, constipation was related to poor performance in frontal-executive and visuospatial functions after controlling for age and sex.
Conclusion
Our findings may provide an understanding of constipation as a marker associated with cognitive impairment in individuals with PD. Therefore, the evaluation of cognitive function is warranted in PD patients with constipation, while further studies are necessary to investigate the detailed mechanism of our results.

Citations

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Defecation after magnesium supplementation enhances cognitive performance in triathletes
Chen-Chan Wei, M. Brennan Harris, Mengxin Ye, Andrew Nicholls, Ahmad Alkhatib, Luthfia Dewi, Chi-Yang Huang, Chia-Hua Kuo
Sports Medicine and Health Science.2024;[Epub] CrossRef
Constipation is associated with emotional and cognitive impairment in patients with Parkinson’s disease: A clinical and brain functional study
Meilan Su, Song Wang, Oumei Cheng, Kai Xie, Juan Peng, Xinyi Du, Lei Huang, Tian Feng
Neuroscience.2024; 559: 17. CrossRef
Constipation in Parkinson's Disease
Eamonn M. M. Quigley
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Wang Wang, Shujun Jiang, Chengcheng Xu, Lili Tang, Yan Liang, Yang Zhao, Guoxue Zhu
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The MMSE and MoCA for Screening Cognitive Impairment in Less Educated Patients with Parkinson’s Disease: Ji In Kim, Mun Kyung Sunwoo, Young H. Sohn, Phil Hyu Lee, Jin Y. Hong; J Mov Disord. 2016;9(3):152-159. Published online September 21, 2016; DOI: https://doi.org/10.14802/jmd.16020

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Abstract PDF

Objective
To explore whether the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) can be used to screen for dementia or mild cognitive impairment (MCI) in less educated patients with Parkinson’s disease (PD).
Methods
We reviewed the medical records of PD patients who had taken the Korean MMSE (K-MMSE), Korean MoCA (K-MoCA), and comprehensive neuropsychological tests. Predictive values of the K-MMSE and K-MoCA for dementia or MCI were analyzed in groups divided by educational level.
Results
The discriminative powers of the K-MMSE and K-MoCA were excellent [area under the curve (AUC) 0.86–0.97] for detecting dementia but not for detecting MCI (AUC 0.64–0.85). The optimal screening cutoff values of both tests increased with educational level for dementia (K-MMSE < 15 for illiterate, < 20 for 0.5–3 years of education, < 23 for 4–6 years, < 25 for 7–9 years, and < 26 for 10 years or more; K-MoCA < 7 for illiterate, < 13 for 0.5–3 years, < 16 for 4–6 years, < 19 for 7–9 years, < 20 for 10 years or more) and MCI (K-MMSE < 19 for illiterate, < 26 for 0.5–3 years, < 27 for 4–6 years, < 28 for 7–9 years, and < 29 for 10 years or more; K-MoCA < 13 for illiterate, < 21 for 0.5–3 years, < 23 for 4–6 years, < 25 for 7–9 years, < 26 for 10 years or more).
Conclusion
Both MMSE and MoCA can be used to screen for dementia in patients with PD, regardless of educational level; however, neither test is sufficient to discriminate MCI from normal cognition without additional information.

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Mild Cognitive Impairment in Parkinson’s Disease: Jae Woo Kim, Hee Young Jo, Min Jeong Park, Sang-Myung Cheon; J Mov Disord. 2008;1(1):19-25.; DOI: https://doi.org/10.14802/jmd.08004

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Abstract PDF

Background

To determine the frequency of mild cognitive impairment (MCI) of Parkinson’s disease (PD, PDMCI) and its subtypes among non-demented PD patients, and to identify the influence of the age and presenting symptom on the development of PDMCI.

Methods:

A total 141 non-demented PD patients underwent a comprehensive neuropsychological assessment including attention, language, visuospatial, memory and frontal functions. PDMCI was defined by neuropsychological testing and was classified into five subtypes. Patients were divided into two groups (tremor vs. akinetic-rigid type) for presenting symptom and three groups according to the age. Neuropsychological performance of patients was compared with normative data.

Results:

Almost half (49.6%) of non-demented PD patients had impairment in at least one domain and can be considered as having PDMCI. Executive type of PDMCI was the most frequent and amnestic, visuospatial, linguistic and attention types followed in the order of frequency. The population of PDMCI was increasing as the age of disease onset was higher. Whereas the frequency of executive and amnestic types of PDMCI was comparable in younger group, executive type was the most frequent in older group. The patients with tremor dominant type performed worse on tests, particularly on attention test.

Conclusions:

MCI was common even in the early stage of PD and the subtype was diverse. Unlike MCI developing Alzheimer’s disease later, executive type of PDMCI was the most common. Age was an important risk factor for development of MCI in PD. The concept of MCI should be introduced in PD.

Citations

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