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Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits

Cognitive impairment is a common non-motor feature of Parkinson's disease (PD). Understanding the neural mechanisms of this deficit is crucial for the development of efficient methods for treatment monitoring and augmentation of cognitive functions in PD patients. The current study aimed to inv...

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Autores principales: Lebedev, Alexander V., Westman, Eric, Simmons, Andrew, Lebedeva, Aleksandra, Siepel, Françoise J., Pereira, Joana B., Aarsland, Dag
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982053/
https://www.ncbi.nlm.nih.gov/pubmed/24765065
http://dx.doi.org/10.3389/fnsys.2014.00045
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author Lebedev, Alexander V.
Westman, Eric
Simmons, Andrew
Lebedeva, Aleksandra
Siepel, Françoise J.
Pereira, Joana B.
Aarsland, Dag
author_facet Lebedev, Alexander V.
Westman, Eric
Simmons, Andrew
Lebedeva, Aleksandra
Siepel, Françoise J.
Pereira, Joana B.
Aarsland, Dag
author_sort Lebedev, Alexander V.
collection PubMed
description Cognitive impairment is a common non-motor feature of Parkinson's disease (PD). Understanding the neural mechanisms of this deficit is crucial for the development of efficient methods for treatment monitoring and augmentation of cognitive functions in PD patients. The current study aimed to investigate resting state fMRI correlates of cognitive impairment in PD from a large-scale network perspective, and to assess the impact of dopamine deficiency on these networks. Thirty PD patients with resting state fMRI were included from the Parkinson's Progression Marker Initiative (PPMI) database. Eighteen patients from this sample were also scanned with (123)I-FP-CIT SPECT. A standardized neuropsychological battery was administered, evaluating verbal memory, visuospatial, and executive cognitive domains. Image preprocessing was performed using an SPM8-based workflow, obtaining time-series from 90 regions-of-interest (ROIs) defined from the AAL brain atlas. The Brain Connectivity Toolbox (BCT) was used to extract nodal strength from all ROIs, and modularity of the cognitive circuitry determined using the meta-analytical software Neurosynth. Brain-behavior covariance patterns between cognitive functions and nodal strength were estimated using Partial Least Squares. Extracted latent variable (LV) scores were matched with the performances in the three cognitive domains (memory, visuospatial, and executive) and striatal dopamine transporter binding ratios (SBR) using linear modeling. Finally, influence of nigrostriatal dopaminergic deficiency on the modularity of the “cognitive network” was analyzed. For the range of deficits studied, better executive performance was associated with increased dorsal fronto-parietal cortical processing and inhibited subcortical and primary sensory involvement. This profile was also characterized by a relative preservation of nigrostriatal dopaminergic function. The profile associated with better memory performance correlated with increased prefronto-limbic processing, and was not associated with presynaptic striatal dopamine uptake. SBR ratios were negatively correlated with modularity of the “cognitive network,” suggesting integrative effects of the preserved nigrostriatal dopamine system on this circuitry.
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spelling pubmed-39820532014-04-24 Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits Lebedev, Alexander V. Westman, Eric Simmons, Andrew Lebedeva, Aleksandra Siepel, Françoise J. Pereira, Joana B. Aarsland, Dag Front Syst Neurosci Neuroscience Cognitive impairment is a common non-motor feature of Parkinson's disease (PD). Understanding the neural mechanisms of this deficit is crucial for the development of efficient methods for treatment monitoring and augmentation of cognitive functions in PD patients. The current study aimed to investigate resting state fMRI correlates of cognitive impairment in PD from a large-scale network perspective, and to assess the impact of dopamine deficiency on these networks. Thirty PD patients with resting state fMRI were included from the Parkinson's Progression Marker Initiative (PPMI) database. Eighteen patients from this sample were also scanned with (123)I-FP-CIT SPECT. A standardized neuropsychological battery was administered, evaluating verbal memory, visuospatial, and executive cognitive domains. Image preprocessing was performed using an SPM8-based workflow, obtaining time-series from 90 regions-of-interest (ROIs) defined from the AAL brain atlas. The Brain Connectivity Toolbox (BCT) was used to extract nodal strength from all ROIs, and modularity of the cognitive circuitry determined using the meta-analytical software Neurosynth. Brain-behavior covariance patterns between cognitive functions and nodal strength were estimated using Partial Least Squares. Extracted latent variable (LV) scores were matched with the performances in the three cognitive domains (memory, visuospatial, and executive) and striatal dopamine transporter binding ratios (SBR) using linear modeling. Finally, influence of nigrostriatal dopaminergic deficiency on the modularity of the “cognitive network” was analyzed. For the range of deficits studied, better executive performance was associated with increased dorsal fronto-parietal cortical processing and inhibited subcortical and primary sensory involvement. This profile was also characterized by a relative preservation of nigrostriatal dopaminergic function. The profile associated with better memory performance correlated with increased prefronto-limbic processing, and was not associated with presynaptic striatal dopamine uptake. SBR ratios were negatively correlated with modularity of the “cognitive network,” suggesting integrative effects of the preserved nigrostriatal dopamine system on this circuitry. Frontiers Media S.A. 2014-04-03 /pmc/articles/PMC3982053/ /pubmed/24765065 http://dx.doi.org/10.3389/fnsys.2014.00045 Text en Copyright © 2014 Lebedev, Westman, Simmons, Lebedeva, Siepel, Pereira and Aarsland. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Lebedev, Alexander V.
Westman, Eric
Simmons, Andrew
Lebedeva, Aleksandra
Siepel, Françoise J.
Pereira, Joana B.
Aarsland, Dag
Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits
title Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits
title_full Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits
title_fullStr Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits
title_full_unstemmed Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits
title_short Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits
title_sort large-scale resting state network correlates of cognitive impairment in parkinson's disease and related dopaminergic deficits
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982053/
https://www.ncbi.nlm.nih.gov/pubmed/24765065
http://dx.doi.org/10.3389/fnsys.2014.00045
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