Journal of Movement Disorders

Original Articles

Cognitive Impairment Alters Cortical Adaptation and Gait Regulation During Obstacle Walking in Parkinson’s Disease: Evidence from Temporal fNIRS and Gait Dynamics: Pei-Ling Wong, Yea-Ru Yang, Chen-Wei Hung, Nai-Chen Yeh, Shih-Jung Cheng, Ray-Yau Wang; Received December 25, 2025 Accepted March 3, 2026 Published online March 3, 2026; DOI: https://doi.org/10.14802/jmd.25346 [Accepted]

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Abstract PDF: Background
Obstacle crossing during walking poses a major fall risk in individuals with Parkinson’s disease (PD), particularly those with cognitive impairment. Although cognitive decline worsens gait, its specific effects on gait performance and cortical activation during obstacle walking remain unclear. The dynamic interaction between brain activity and gait across different walking phases is especially understudied in PD with mild cognitive impairment (PD-MCI) compared to those without cognitive impairment (PD-nonMCI).
Methods
Nineteen PD-nonMCI and fifteen PD-MCI participants performed obstacle walking while functional near-infrared spectroscopy (fNIRS) measured activation in the prefrontal cortex (PFC), supplementary motor area (SMA), and premotor cortex (PMC). Gait parameters (speed, cadence, stride length, stride time) and obstacle crossing metrics (crossing speed, stride length, stride time, step width) were analyzed across early (5–20 s) and late (20–40 s) phases. Generalized estimating equations examined group, phase, and interaction effects. Brain–gait associations were assessed using Spearman’s correlations.
Results
PD-MCI participants exhibited poorer obstacle walking performance but no significant phase-related behavioral change. Both groups showed higher PFC, SMA, and PMC activation during the early phase, reflecting greater neural engagement at task onset. However, SMA and PMC activation declined more steeply across phases in the PD-MCI group. In PD-MCI, obstacle walking performance correlated negatively with early-phase PMC and late-phase PFC activation.
Conclusions
PD-MCI participants showed poorer gait and higher cortical activation, indicating increased neural effort and reduced efficiency. These results highlight altered brain–gait coupling in PD-MCI and emphasize the need for interventions enhancing neural efficiency during complex walking.

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.

Brief communication

Feasibility and Preliminary Efficacy of Digital Cognitive Training in Parkinson’s Disease With Mild Cognitive Impairment: A Pilot Study: Dongje Lee, Hang-Rai Kim, Yu Jeong Park, Yisuh Ahn, Daeho Lee, Jungyeun Lee, Su Jin Chung, Seung Yeon Kim, Yeji Hwang, Ji Young Yun, Jin Whan Cho, Kyum-Yil Kwon, Seong-Beom Koh, Sung Hoon Kang; J Mov Disord. 2026;19(1):76-80. Published online August 26, 2025; DOI: https://doi.org/10.14802/jmd.25135

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Abstract PDF Supplementary Material

Objective
Cognitive impairment is common in patients with Parkinson’s disease (PD), and few pharmacological options are available for treating this condition. We evaluated the effects of a digital cognitive training program (SUPERBRAIN), which was previously shown to be effective in populations at risk of Alzheimer’s disease, on cognitive function in individuals with PD.
Methods
Twenty-three individuals with PD and mild cognitive impairment (PD-MCI) from four clinics were randomized to the intervention (n=16) or control (n=7) groups. The intervention group completed a 12-week, home-based, tablet-based cognitive training program (25–30 min/day, 7 days/week). Cognitive outcomes were assessed using the Seoul Neuropsychological Screening Battery pre- and post-intervention.
Results
The adherence rate was 79.36%. The intervention group showed significant improvements in the Seoul Verbal Learning Test (SVLT) delayed recall and the Controlled Oral Word Association Test, while no changes were observed in the control group. Analysis of covariance confirmed greater SVLT improvement in the intervention group (F statistic=7.15, p=0.015, partial η²=0.28).
Conclusion
SUPERBRAIN is feasible and can improve cognitive function in individuals with PD-MCI.

Citations

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Integrated bio-cooperative robotic platform for virtual cognitive training in Parkinson's disease: design and methodology of the OPERA project
Cristina Polito, Giulia Martinelli, Sara Della Bella, Eleonora Pavan, Ylenia Crocetto, Simona Abagnale, Cristiana Rondoni, Alfonso Voscarelli, Marco Pirini, Francesco Scotto di Luzio, Loredana Zollo, Anna Estraneo
Frontiers in Neurology.2026;[Epub] CrossRef

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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Abstract PDF

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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Han-Kyeol Kim, Min Seok Baek, Jeehyun Ham, Jin Yong Hong
Parkinsonism & Related Disorders.2026; 144: 108179. CrossRef
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Alexandros Giannakis, Chrissa Sioka, Eugenia Kloufetou, Spiridon Konitsiotis
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The Relationship Between Subjective Cognitive Complaints, Invalid Symptom Reporting, and Neurocognitive Test Performance Validity Among Adults Being Evaluated for ADHD
Matthew S. Phillips, Nataliya Turchmanovych-Hienkel, Mira I. Leese, Brian Ramanauskas, Hannah B. VanLandingham, Christopher Gonzalez, Gabriel P. Ovsiew, Anthony D. Robinson, Brian M. Cerny, Devin M. Ulrich, Jason R. Soble
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The Multifactorial Memory Questionnaire and Quality of Life: A Longitudinal Study in Parkinson’s Disease
Emily J. Corti, Natalie Gasson, Hayley Grant, Brayden Wisniewski, Andrea M. Loftus
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Heterogeneity of cognitive progression and clinical predictors in Parkinson’s disease–subjective cognitive decline
Jon Rodríguez-Antigüedad, Saül Martínez-Horta, Arnau Puig-Davi, Andrea Horta-Barba, Javier Pagonabarraga, Teresa de Deus Fonticoba, Silvia Jesús, Marina Cosgaya, Juan García Caldentey, María Asunción Ávila-Rivera, Nuria Caballol, Inés Legarda, Jorge Herná
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Matthew S. Phillips, George Whitman Kent, Hajar Ismail, Justyna Piszczor, Briana N. Galindo, Greg Shapiro, Lisette Gonzalez, Brian M. Cerny, Jason R. Soble
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The Role of Cognitive Reserve in Coping with Subjective Cognitive Complaints: An Exploratory Study of People with Parkinson’s Disease (PwPD)
Chiara Siri, Anna Carollo, Roberta Biundo, Maura Crepaldi, Luca Weis, Ioannis Ugo Isaias, Angelo Antonini, Maria Luisa Rusconi, Margherita Canesi
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Ryan Van Patten, Kyler Mulhauser, Tara A. Austin, John A. Bellone, Erica Cotton, Lawrence Chan, Elizabeth W. Twamley, Kelsey Sawyer, W. Curt LaFrance
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Karen R. Hebert, Mackenzie Feldhacker
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Subjective Cognitive Complaints in Parkinson's Disease: A Systematic Review and Meta‐Analysis
Mattia Siciliano, Alessandro Tessitore, Francesca Morgante, Jennifer G. Goldman, Lucia Ricciardi
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Kurt A. Jellinger
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Victor Kholin, Iryna Karaban, Sergiy Kryzhanovskiy, Nina Karasevich, Natalia Melnik, Maryna Khodakovska, Hanna Shershanova, Natalia Movchun
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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.

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Prediction of Amyloid Positivity in Lewy Body Disease Using Early-Phase 18F-FP-CIT PET Images
Seok Jong Chung, Su Hong Kim, Seong Ho Jeong, Hye Sun Lee, Yun Joong Kim, Young H. Sohn, Yong Jeong, Phil Hyu Lee
Clinical Nuclear Medicine.2026; 51(1): 13. CrossRef
Handcrafted MRI radiomics of enlarged perivascular spaces and machine learning predict cognitive impairment and sleep disturbance in young adults
Li Li, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu
Scientific Reports.2026;[Epub] CrossRef
Assessment of mental and behavioural non-motor symptoms of Parkinson’s Disease using Artificial Intelligence (AI): a systematic review
Shantao Chloe Chou, Cen Cong, Rosiered Brownson-Smith, Madison Milne-Ives, Edward Meinert
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Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model
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Vascular and glymphatic dysfunction as drivers of cognitive impairment in Alzheimer's disease: Insights from computational approaches
Gehan Fatima, Akm Ashiquzzaman, Sang Seong Kim, Young Ro Kim, Hyuk-Sang Kwon, Euiheon Chung
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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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Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis
Jing Wang, Yanfang Chen, Xiao Xie, Pengwei Wang, Hang Hu, Hongfang Han, Lihan Wang, Li Zhang
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Clinical Outcome Assessments in Parkinson's Disease: A Scoping Review of Current Rating Scales and Future Needs
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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.

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Purgation Therapy in Neurological Disorders: Bridging Siddha Medicine and Modern Neuroscience
Gangadharan Thamizhoviya, Arumugam Muthuvel
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Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease
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Constipation symptoms are associated with worse cognitive outcomes in older adults without dementia
Xiaochang Liu, Juan Zhou, Xinyan Xie, Wenzhe Zheng, Dan Liu, Guirong Cheng, Feifei Hu, Junyi Wang, Cheng Cai, Jing Liu, Qianqian Nie, Shiyue Li, Dan Song, Yuyang Cui, Jingjing Zhang, Hua Meng, Wei Tan, Yan Zeng
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Meilan Su, Song Wang, Oumei Cheng, Kai Xie, Juan Peng, Xinyi Du, Lei Huang, Tian Feng
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Constipation in Parkinson's Disease
Eamonn M. M. Quigley
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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

Citations to this article as recorded by

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