Identifying Mild Cognitive Impairment in Parkinson’s Disease With Electroencephalogram Functional Connectivity
Open accessAbstract
Objective: Cognitive impairment occurs frequently in Parkinson’s disease (PD) and negatively impacts the patient’s quality of life. However, its pathophysiological mechanism remains unclear, hindering the development of new therapies. Changes in brain connectivity are related to cognitive impairment in patients with PD, with the dorsolateral prefrontal cortex (DLPFC) being considered the essential region related to PD cognitive impairment. Nevertheless, few studies have focused on the global connectivity responsible for communication with the DLPFC node, the posterior division of the middle frontal gyrus (PMFG) in patients with PD; this was the focus of this study. Methods: We applied resting-state electroencephalography (EEG) and calculated a reliable functional connectivity measurement, the debiased weighted phase lag index (dWPLI), to examine inter-regional functional connectivity in 68 patients with PD who were classified into two groups according to their cognitive condition. Results: We observed that altered left and right PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed condition (r = −0.426, p < 0.001 and r = −0.437, p < 0.001, respectively). Exploratory results based on the MoCA subdomains indicated that poorer visuospatial function was associated with higher right PMFG-based functional connectivity (r = −0.335, p = 0.005), and poorer attention function was associated with higher left and right PMFG-based functional connectivity (r = −0.380, p = 0.001 and r = −0.256, p = 0.035, respectively). Further analysis using logistic regression and receiver operating characteristic (ROC) curves found that this abnormal functional connectivity was an independent risk factor for cognitive impairment [odds ratio (OR): 2.949, 95% confidence interval (CI): 1.294–6.725, p = 0.01 for left PMFG; OR: 11.278, 95% CI: 2.578–49.335, p = 0.001 for right PMFG, per 0.1 U], and provided moderate classification power to discriminate between cognitive abilities in patients with PD [area under the ROC curve (AUC) = 0.770 for left PMFG; AUC = 0.809 for right PMFG]. Conclusion: These preliminary findings indicate that abnormal PMFG-based functional connectivity patterns associated with cognitive impairment in the theta frequency bands under the eyes closed condition and altered functional connectivity patterns have the potential to act as reliable biomarkers for identifying cognitive impairment in patients with PD.
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Neurodegenerative diseases are usually characterized by a significant reduction in network connectivity, and cortical EEG-FC reflects the intensity of information flow among involved cortical regions.
“Neurodegenerative diseases are usually characterized by a significant reduction in network connectivity, and cortical EEG-FC reflects the intensity of information flow among involved cortical regions [57-59].”
From QEEG Signatures are Associated with Nonmotor Dysfunctions in Parkinson's Disease and Atypical Parkinsonism: An Integrative Analysis (2023) · cites this paper as [58]
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Cites
The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989) · Psychiatry Research · doi:10.1016/0165-1781(89)90047-4 · Cited by 35,234 · see citing works
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2005.53221.x · Cited by 25,553 · see citing works
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
Parkinsonism: onset, progression, and mortality
Hoehn, M. M., & Yahr, M. D. (1967) · Neurology · doi:10.1212/wnl.17.5.427 · Cited by 12,107 · see citing works
The assessment of anxiety states by rating
HAMILTON, M. (1959) · British Journal of Medical Psychology · doi:10.1111/j.2044-8341.1959.tb00467.x · Cited by 11,455 · see citing works
Hughes, A. J., Daniel, S. E., Kilford, L., & Lees, A. J. (1992) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.55.3.181 · Cited by 10,378 · see citing works
Development of a Rating Scale for Primary Depressive Illness
Hamilton, M. (1967) · British Journal of Social and Clinical Psychology · doi:10.1111/j.2044-8260.1967.tb00530.x · Cited by 8,410 · see citing works
Overdependence on degraded gist memory in Alzheimer's disease.
Gallo, D. A., Shahid, K. R., Olson, M. A., Solomon, T. M., Schacter, D. L., & Budson, A. E. (2006) · Neuropsychology · doi:10.1037/0894-4105.20.6.625 · Cited by 4,756 · see citing works
Systematic review of levodopa dose equivalency reporting in Parkinson's disease
Tomlinson, C. L., Stowe, R. L., Patel, S., Rick, C., Gray, R. G., & Clarke, C. E. (2010) · Movement Disorders · doi:10.1002/mds.23429 · Cited by 4,232 · see citing works
Brainstorm: A User-Friendly Application for MEG/EEG Analysis
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/879716 · Cited by 3,625 · see citing works
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284 · see citing works
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750 · see citing works
Litvan, I., Goldman, J. G., Tröster, A. I., Schmand, B. A., Weintraub, D., Petersen, R. C., Mollenhauer, B., Adler, C. H., Marder, K., Williams‐Gray, C. H., et al. (2012) · Movement Disorders · doi:10.1002/mds.24893 · Cited by 2,609 · see citing works
The Unified Parkinson's Disease Rating Scale (UPDRS): Status and recommendations
Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease (2003) · Movement Disorders · doi:10.1002/mds.10473 · Cited by 1,811 · see citing works
Vinck, M., Oostenveld, R., van Wingerden, M., Battaglia, F., & Pennartz, C. M. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.055 · Cited by 1,753 · see citing works
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734 · see citing works
The clinical symptoms of Parkinson's disease.
Sveinbjornsdottir, S. (2016) · Journal of Neurochemistry · doi:10.1111/jnc.13691 · Cited by 1,410 · see citing works
Basic mechanisms of cerebral rhythmic activities
Steriade, M., GLOOR, P., Llinás, R. R., Lopes, F. H. D. S., & Mesulam, M. M. (1990) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(90)90001-z · Cited by 1,131 · see citing works
Cognitive Impairment in Parkinson’s Disease: The Dual Syndrome Hypothesis
Kehagia, A. A., Barker, R. A., & Robbins, T. W. (2012) · Neurodegenerative Diseases · doi:10.1159/000341998 · Cited by 574 · see citing works
Causal interactions between fronto-parietal central executive and default-mode networks in humans
Chen, A. C., Oathes, D. J., Chang, C. E., Bradley, T., Zhou, Z.-W., Williams, L. M., Glover, G. H., Deisseroth, K. A., & Etkin, A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311772110 · Cited by 561 · see citing works
Parkinson’s disease dementia: a neural networks perspective
Gratwicke, J., Jahanshahi, M., & Foltynie, T. (2015) · Brain · doi:10.1093/brain/awv104 · Cited by 436 · see citing works
A framework for local cortical oscillation patterns
Donner, T. H., & Siegel, M. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.03.007 · Cited by 433 · see citing works
Distributed current estimates using cortical orientation constraints
Lin, F., Belliveau, J. W., Dale, A. M., & Hämäläinen, M. (2005) · Human Brain Mapping · doi:10.1002/hbm.20155 · Cited by 291 · see citing works
Test-retest reliability in EEG frequency analysis
Salinsky, M., Oken, B., & Morehead, L. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90203-g · Cited by 276 · see citing works
Diagnosis and the premotor phase of Parkinson disease
TOLOSA, E. S., Gaig, C., Santamaría, J., & Compta, Y. (2009) · Neurology · doi:10.1212/wnl.0b013e318198db11 · Cited by 257 · see citing works
Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108648 · Cited by 238 · see citing works
Clinical correlates of quantitative EEG in Parkinson disease
Geraedts, V. J., Boon, L. I., Marinus, J., Gouw, A. A., van Hilten, J. J., Stam, C. J., Tannemaat, M. R., & Contarino, M. F. (2018) · Neurology · doi:10.1212/wnl.0000000000006473 · Cited by 186 · see citing works
Dementia in Parkinson's disease
Hanagasi, H. A., Tüfekçıoğlu, Z., & Emre, M. (2017) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2017.01.012 · Cited by 156 · see citing works
Gorges, M., Müller, H., Lulé, D. E., Pinkhardt, E. H., Ludolph, A. C., & Kassubek, J. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.12.026 · Cited by 134 · see citing works
Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease
Hassan, M., Chaton, L., Benquet, P., Delval, A., Leroy, C., Plomhause, L., Moonen, A. J. H., Duits, A., Leentjens, A. F. G., van Kranen-Mastenbroek, V. H. J. M., et al. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.03.002 · Cited by 107 · see citing works
Betrouni, N., Delval, A., Chaton, L., Defebvre, L., Duits, A., Moonen, A. J. H., Leentjens, A. F. G., & Dujardin, K. (2018) · Movement Disorders · doi:10.1002/mds.27528 · Cited by 102 · see citing works
Lau, T., Gwin, J. T., McDowell, K., et al. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-47 · Cited by 99 · see citing works
Nissim, N. R., O’Shea, A., Indahlastari, A., Kraft, J. N., von Mering, O., Aksu, S., Porges, E., Cohen, R., & Woods, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00340 · Cited by 99 · see citing works
Polito, C., Berti, V., Ramat, S., Vanzi, E., Cristofaro, M. T. D., Pellicanò, G., Mungai, F., Marini, P., Formiconi, A. R., Sorbi, S., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.09.004 · Cited by 89 · see citing works
An Electroencephalography Connectomic Profile of Posttraumatic Stress Disorder
Toll, R. T., Wu, W., Naparstek, S., Zhang, Y., Narayan, M., Patenaude, B., Angeles, C. D. L., Sarhadi, K. J., Anicetti, N., Longwell, P., et al. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18080911 · Cited by 74 · see citing works
Screening Mild and Major Neurocognitive Disorders in Parkinson’s Disease
Lucza, T., Karádi, K., Kállai, J., Weintraut, R., Janszky, J., Makkos, A., Komoly, S., & Kovács, N. (2015) · Behavioural Neurology · doi:10.1155/2015/983606 · Cited by 56 · see citing works
Prediction of cognitive worsening in de novo Parkinson's disease: Clinical use of biomarkers
Arnaldi, D., Carli, F. D., Famà, F., Brugnolo, A., Girtler, N. G., Picco, A., Pardini, M., Accardo, J., Proietti, L., Massa, F., et al. (2017) · Movement Disorders · doi:10.1002/mds.27190 · Cited by 56 · see citing works
Chaturvedi, M., Bogaarts, J. G., Kozak, V. V., Hatz, F., Gschwandtner, U., Meyer, A., Fuhr, P., & Röth, V. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.07.017 · Cited by 49 · see citing works
Structural and Functional Brain Patterns of Non-Motor Syndromes in Parkinson’s Disease
Prell, T. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00138 · Cited by 48 · see citing works
Zhang, J., Gao, Y., He, X.-T., Feng, S., Hu, J., Zhang, Q., Zhao, J., Huang, Z., Wang, L., Ma, G., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07575-1 · Cited by 47 · see citing works
Wang, C., Costanzo, M. E., Rapp, P. E., Darmon, D. M., Nathan, D. E., Bashirelahi, K., Pham, D. L., Roy, M. J., & Keyser, D. O. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00571 · Cited by 38 · see citing works
Cortical phosphorylated α‐Synuclein levels correlate with brain wave spectra in Parkinson's disease
Caviness, J. N., Lue, L., Hentz, J. G., Schmitz, C. T., Adler, C. H., Shill, H. A., Sabbagh, M. N., Beach, T. G., & Walker, D. G. (2016) · Movement Disorders · doi:10.1002/mds.26621 · Cited by 37 · see citing works
Bertrand, J.-A., McIntosh, A. R., Postuma, R. B., Kovacevic, N., Latreille, V., Panisset, M., Chouinard, S., & Gagnon, J.-F. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0390 · Cited by 36 · see citing works
Mostile, G., Giuliano, L., Monastero, R., Luca, A., Cicero, C. E., Donzuso, G., Dibilio, V., Baschi, R., Terranova, R., Restivo, V., et al. (2019) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2019.03.027 · Cited by 29 · see citing works
Sánchez-Dinorín, G., Rodríguez‐Violante, M., Cervantes‐Arriaga, A., Navarro-Roa, C., Ricardo-Garcell, J., Rodríguez-Camacho, M. A., & Solís‐Vivanco, R. (2020) · Clinical Neurophysiology · doi:10.1016/j.clinph.2020.11.035 · Cited by 18 · see citing works
Modulating brain networks associated with cognitive deficits in Parkinson’s disease
Beheshti, I., & Ko, J. H. (2021) · Molecular Medicine · doi:10.1186/s10020-021-00284-5 · Cited by 18 · see citing works
Pahi, M. R., Cavalli, J., Nees, F., Flor, H., & Andoh, J. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz067 · Cited by 17 · see citing works
Graph Theory-Guided Transcranial Magnetic Stimulation in Neurodegenerative Disorders
Ko, J. H., Choi, Y. Y., & Eidelberg, D. (2014) · Bioelectronic Medicine · doi:10.15424/bioelectronmed.2014.00004 · Cited by 5 · see citing works
Pahi, M. R., Cavalli, J., Nees, F., Flor, H., & Andoh, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.05915 · Cited by 1 · see citing works
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Liu, H., Huang, Z., Deng, B., Chang, Z., Yang, X., Guo, X., Yuan, F., Yang, Q., Wang, L., Zou, H., et al. (2023) · Aging and disease · doi:10.14336/ad.2022.0514 · Cited by 20 · see citing works
- Authors
- Cai, M., Dang, G., Su, X., Zhu, L., Shi, X., Che, S., Lan, X., Luo, X., & Guo, Y.
- Affiliations
- Jinan University (CN)
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Guo, Yi
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2021-07-01
- Volume
- 13
- Pages
- 701499-701499
- ISSN
- 1663-4365 (ISSN-L)
- PMC
- PMC8281812
- OpenAlex
- W3183914724
- Cited by
- 22
- References
- 49
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 1 · 2023: 6 · 2022: 5
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Aging Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (JCYJ20170818111012390, 81901208)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Parkinson's disease, mild cognitive impairment, resting-state EEG, functional connectivity, dorsolateral prefrontal cortex, theta band, visuospatial ability, attention, cognitive impairment prediction, Montreal Cognitive Assessment, cognitive impairment classification, eyes-closed condition
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