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Disbrow EA, Glassy ND, Dressler EM, Russo K, Franz EA, Turner RS, Ventura MI, Hinkley L, Zweig R, Nagarajan SS, Ledbetter CR, Sigvardt KA. Cortical oscillatory dysfunction in Parkinson disease during movement activation and inhibition. PLoS One 2022; 17:e0257711. [PMID: 35245294 PMCID: PMC8896690 DOI: 10.1371/journal.pone.0257711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/08/2021] [Indexed: 12/27/2022] Open
Abstract
Response activation and inhibition are functions fundamental to executive control that are disrupted in Parkinson disease (PD). We used magnetoencephalography to examine event related changes in oscillatory power amplitude, peak latency and frequency in cortical networks subserving these functions and identified abnormalities associated with PD. Participants (N = 18 PD, 18 control) performed a cue/target task that required initiation of an un-cued movement (activation) or inhibition of a cued movement. Reaction times were variable but similar across groups. Task related responses in gamma, alpha, and beta power were found across cortical networks including motor cortex, supplementary and pre- supplementary motor cortex, posterior parietal cortex, prefrontal cortex and anterior cingulate. PD-related changes in power and latency were noted most frequently in the beta band, however, abnormal power and delayed peak latency in the alpha band in the pre-supplementary motor area was suggestive of a compensatory mechanism. PD peak power was delayed in pre-supplementary motor area, motor cortex, and medial frontal gyrus only for activation, which is consistent with deficits in un-cued (as opposed to cued) movement initiation characteristic of PD.
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Affiliation(s)
- Elizabeth A. Disbrow
- LSU Health Shreveport Center for Brain Health, Shreveport, Louisiana, United States of America
- Department of Neurology, LSU Health Shreveport, Shreveport, Louisiana, United States of America
- * E-mail:
| | - Nathaniel D. Glassy
- LSU Health Shreveport Center for Brain Health, Shreveport, Louisiana, United States of America
| | - Elizabeth M. Dressler
- LSU Health Shreveport Center for Brain Health, Shreveport, Louisiana, United States of America
| | - Kimberley Russo
- Department of Psychology, UC Berkeley, Berkeley, California, United States of America
| | - Elizabeth A. Franz
- Action Brain and Cognition Laboratory, Department of Psychology, and fMRIotago, University of Otago, Dunedin, New Zealand
| | - Robert S. Turner
- Department of Neurobiology and Center for the Neural Basis of Cognition University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Maria I. Ventura
- Department of Psychiatry, UC Davis, Sacramento, California, United States of America
| | - Leighton Hinkley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, United States of America
| | - Richard Zweig
- LSU Health Shreveport Center for Brain Health, Shreveport, Louisiana, United States of America
- Department of Neurology, LSU Health Shreveport, Shreveport, Louisiana, United States of America
| | - Srikantan S. Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, United States of America
| | - Christina R. Ledbetter
- LSU Health Shreveport Center for Brain Health, Shreveport, Louisiana, United States of America
- Department of Neurosurgery, LSU Health Shreveport, Shreveport, Louisiana, United States of America
| | - Karen A. Sigvardt
- Department of Neurology, UC Davis, Sacramento, California, United States of America
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Ophey A, Rehberg S, Giehl K, Eggers C, Reker P, van Eimeren T, Kalbe E. Predicting Working Memory Training Responsiveness in Parkinson's Disease: Both "System Hardware" and Room for Improvement Are Needed. Neurorehabil Neural Repair 2021; 35:117-130. [PMID: 33410387 DOI: 10.1177/1545968320981956] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background. Patients with Parkinson's disease (PD) are highly vulnerable to develop cognitive dysfunctions, and the mitigating potential of early cognitive training (CT) is increasingly recognized. Predictors of CT responsiveness, which could help to tailor interventions individually, have rarely been studied in PD. This study aimed to examine individual characteristics of patients with PD associated with responsiveness to targeted working memory training (WMT). Methods. Data of 75 patients with PD (age: 63.99 ± 9.74 years, 93% Hoehn & Yahr stage 2) without cognitive dysfunctions from a randomized controlled trial were analyzed using structural equation modeling. Latent change score models with and without covariates were estimated and compared between the WMT group (n = 37), who participated in a 5-week adaptive WMT, and a waiting list control group (n = 38). Results. Latent change score models yielded adequate model fit (χ2-test p > .05, SRMR ≤ .08, CFI ≥ .95). For the near-transfer working memory composite, lower baseline performance, younger age, higher education, and higher fluid intelligence were found to significantly predict higher latent change scores in the WMT group, but not in the control group. For the far-transfer executive function composite, higher self-efficacy expectancy tended to significantly predict larger latent change scores. Conclusions. The identified associations between individual characteristics and WMT responsiveness indicate that there has to be room for improvement (e.g., lower baseline performance) and also sufficient "hardware" (e.g., younger age, higher intelligence) to benefit in training-related cognitive plasticity. Our findings are discussed within the compensation versus magnification account. They need to be replicated by methodological high-quality research applying advanced statistical methods with larger samples.
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Affiliation(s)
| | | | | | - Carsten Eggers
- University Hospital of Marburg, Marburg, Germany.,Universities of Marburg and Gießen, Marburg, Germany
| | | | - Thilo van Eimeren
- University of Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
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