Journal of Movement Disorders

Original Articles

The Amnesia Light and Brief Assessment (ALBA) and the door PICture Naming and Immediate Recall (PICNIR) brief tests for identifying mild cognitive impairment in Parkinson's disease: Kateřina Stolaríková, Aleš Bartoš, Kateřina Menšíková, Helena Kisvetrová, Jana Zapletalová, Sandra Kurčová, Raymond Rosales, Petr Kaňovský; Received October 11, 2025 Accepted February 13, 2026 Published online February 13, 2026; DOI: https://doi.org/10.14802/jmd.25271 [Accepted]

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Abstract PDF: Objective
To identify mild cognitive impairment (MCI) in Parkinson's disease (PD) using two brief tests the Amnesia Light and Brief Asssment (ALBA) and the door Picture Naming and Immediate Recall (dPICNIR) in 6–8 minutes.
Methods
The ALBA, the dPICNIR and the third version of the Addenbrooke’s Cognitive Examination III (ACE-III) were administered to 124 participants, equally divided into PD patients and socio-demographically matched normal controls (NC). The PD group was divided into those with normal cognitive functions (PD-CN) and with MCI (PD-MCI) using neuropsychological tests.
Results
Cognitive impairment in the PD group was mild, with significantly lower ACE-III scores than in NC (91 vs. 96 points). Despite these subtle deficits, gesture recall in the ALBA was significantly lower even in the PD-CN group compared to the NC. PD-MCI patients had other significant deficits in the ALBA and PICNIR tests. In the PD group, the gesture recall in the ALBA and correctly recalled pictures in the dPICNIR correlated with the results of verbal fluency and trail making tests, followed by memory tests and all ACE-III scores except visuospatial one. In contrast, correctly recalled sentence words in the ALBA correlated with the memory and language scores in the ACE-III test and memory test scores.
Conclusions
Subtle cognitive changes in PD can be detected through gesture recall test, even in those with normal cognition. The ALBA and PICNIR tests are effective in identifying MCI in PD and provide a brief and valid assessment of cognitive functions. They are freely available at www.abadeco.cz.

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; J Mov Disord. 2024;17(2):171-180. Published online February 13, 2024; DOI: https://doi.org/10.14802/jmd.23271

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Objective
The Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson’s disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels.
Methods
In this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning methods and different cutoff criteria were conducted on an additional 91 consecutive patients with PD.
Results
The cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60–80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10–12 years, and 21 or 20 years for 7–9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250).
Conclusion
Both the age- and education-adjusted cutoff methods and machine learning methods demonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.

Citations

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Does cognition affect supervised and unsupervised mobility differently in people with Parkinson’s disease? A cross-sectional study
Edoardo Bianchini, Francesco Garramone, Domiziana Rinaldi, Marika Alborghetti, Lanfranco De Carolis, Silvia Galli, Antonio Suppa, Marco Salvetti, Clint Hansen, Nicolas Vuillerme
Journal of NeuroEngineering and Rehabilitation.2026;[Epub] CrossRef
Deep learning-based MRI segmentation for substantia nigra in Parkinson's disease with cognitive impairment
Weimin Qi, Jing Wang, Zixuan Yang, Jiang Cheng, Xiaoyan Niu, Na Shao, Yazhou Ren, Jianhang He, Hui Li, Haining Li
Parkinsonism & Related Disorders.2026; 145: 108260. CrossRef
A spatio-temporal fusion-based approach for multi-dimensional classification of Parkinson’s disease progression using multi-modal dataset
Vinay Kukreja, Vandana Ahuja, Modafar Ati, Hariharan Shanmugasundaram, Murugaperumal Krishnamoorthy, Rishabh Sharma, Abhishek Bhattacherjee
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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.

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The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis
Yanyun Wu, Yangfan Cheng, Yi Xiao, Huifang Shang, Ruwei Ou
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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
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Validation of the Conversion between the Mini-Mental State Examination and Montreal Cognitive assessment in Korean Patients with Parkinson’s Disease: Ryul Kim, Han-Joon Kim, Aryun Kim, Mi-Hee Jang, Hyun Jeong Kim, Beomseok Jeon; J Mov Disord. 2018;11(1):30-34. Published online January 11, 2018; DOI: https://doi.org/10.14802/jmd.17038

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Abstract PDF

Objective
Two conversion tables between the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) have recently been established for Parkinson’s disease (PD). This study aimed to validate them in Korean patients with PD and to evaluate whether they could be influenced by educational level.
Methods
A total of 391 patients with PD who undertook both the Korean MMSE and the Korean MoCA during the same session were retrospectively assessed. The mean, median, and root mean squared error (RMSE) of the difference between the true and converted MMSE scores and the intraclass correlation coefficient (ICC) were calculated according to educational level (6 or fewer years, 7–12 years, or 13 or more years).
Results
Both conversions had a median value of 0, with a small mean and RMSE of differences, and a high correlation between the true and converted MMSE scores. In the classification according to educational level, all groups had roughly similar values of the median, mean, RMSE, and ICC both within and between the conversions.
Conclusion
Our findings suggest that both MMSE-MoCA conversion tables are useful instruments for transforming MoCA scores into converted MMSE scores in Korean patients with PD, regardless of educational level. These will greatly enhance the utility of the existing cognitive data from the Korean PD population in clinical and research settings.

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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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Review Articles

Gastrointestinal Autonomic Dysfunction in Patients with Parkinson’s Disease: Joong-Seok Kim, Hye-Young Sung; J Mov Disord. 2015;8(2):76-82. Published online May 31, 2015; DOI: https://doi.org/10.14802/jmd.15008

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Abstract PDF

Currently, gastrointestinal dysfunctions in Parkinson’s disease (PD) are well-recognized problems and are known to be an initial symptom in the pathological process that eventually results in PD. Gastrointestinal symptoms may result from the involvement of either the central or enteric nervous systems, or these symptoms may be side effects of antiparkinsonian medications. Weight loss, excessive salivation, dysphagia, nausea/gastroparesis, constipation, and defecation dysfunction all may occur. Increased identification and early detection of these symptoms can result in a significant improvement in the quality of life for PD patients.

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Electrophysiologic Assessments of Involuntary Movements: Tremor and Myoclonus: Hyun-Dong Park, Hee-Tae Kim; J Mov Disord. 2009;2(1):14-17.; DOI: https://doi.org/10.14802/jmd.09004

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Abstract PDF

Tremor is defined as a rhythmical, involuntary oscillatory movement of a body part. Although neurological examination reveals information regarding its frequency, regularity, amplitude, and activation conditions, the electrophysiological investigations help in confirming the tremor, in differentiating it from other hyperkinetic disorders like myoclonus, and may provide etiological clues. Accelerometer with surface electromyogram (EMG) can be used to document the dominant frequency of a tremor, which may be useful as certain frequencies are more characteristic of specific etiologies than others hyperkinetic disorders. It may show rhythmic bursts, duration and activation pattern (alternating or synchronous). Myoclonus is a quick, involuntary movement. Electrophysiological studies may helpful in the evaluation of myoclonus, not only for confirming the clinical diagnosis but also for understanding the underlying physiological mechanisms. Electroencephalogram (EEG)-EMG correlates can give us important information about myoclonus. Jerk-locked back-averaging and evoked potentials with recording of the long-latency, long-loop reflexes are currently available to study the pathophysiology of myoclonus.

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