Cognitive phenotypes in parkinson's disease differ in terms of brain‐network organization and connectivity

Lopes, Renaud; Delmaire, Christine; Defebvre, Luc; Moonen, Anja J.H.; Duits, Annelien; Hofman, Paul; Leentjens, Albert F.G.; Dujardin, Kathy

doi:10.1002/hbm.23474

Cited by 82 publications

(80 citation statements)

References 70 publications

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“…Indeed, it was found that the global connectivity measurement explained a significant proportion of the variability in the network connectivity metrics, such that the SMN remained the only network which had independently decreased connectivity. Global alterations in brain organization have been observed, with increased randomness being associated with worsening cognitive function . These results suggest that in addition to network targeted degeneration, there may also be a less constrained degenerative process occurring.…”

Section: Discussionmentioning

confidence: 96%

Network basis of the dysexecutive and posterior cortical cognitive profiles in Parkinson's disease

et al. 2019

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A BS TRACT: Background: The dual syndrome hypothesis of cognitive impairment in PD suggests that two cognitive profiles exist with distinct pathological mechanisms and a differential risk for further cognitive decline. How these profiles relate to network dysfunction has never been explicitly characterized. Objective: First, to assess intranetwork functional connectivity while considering global connectivity, and second, to relate network connectivity with measures of the dysexecutive and posterior cortical profiles. Methods: Eighty-two subjects with idiopathic PD and 37 age-matched controls underwent resting-state functional MRI and comprehensive neuropsychological assessment. Intranetwork and global connectivity was compared between groups. Measures of the dysexecutive and posterior cortical profiles were related to network connectivity while considering demographic and disease-related covariates. Results: PD subjects show decreased connectivity within several cortical networks. However, only the sensorimotor network displayed a loss of connectivity independent of the observed decreased global connectivity. The dysexecutive factor was independently related to increased motor severity, less education, and decreased connectivity in the sensorimotor network. The posterior cortical factor was related to increased age, less education, decreased connectivity in the central executive network, as well as increased connectivity in the temporal network. Conclusions: Our results provide evidence supporting a network-specific process of degeneration in the sensorimotor network which contributes to the dysexecutive cognitive profile. In contrast, connectivity of the temporal and central executive network is related to the posterior cortical profile, representing a distinct network signature of this syndrome.

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Section: Discussionmentioning

confidence: 96%

Network basis of the dysexecutive and posterior cortical cognitive profiles in Parkinson's disease

et al. 2019

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“…With this approach, anatomic brain regions are considered to be nodes, linked by edges, which represent the connectivity measured by the temporal correlation of BOLD fluctuations between the nodes. To date, only a few RS-fMRI studies using graph theoretical approach have been conducted in patients with PD [24][25][26][27][28][29][30][31] with a degree of inconsistency between studies mostly related to the small number of subjects or the inclusion of subjects across different PD stages.…”

Section: Introductionmentioning

confidence: 99%

Functional Connectivity Signatures of Parkinson’s Disease

Tessitore

Cirillo

Micco

2019

JPD

100

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Resting-state functional magnetic resonance imaging (RS-fMRI) studies have been extensively applied to analyze the pathophysiology of neurodegenerative disorders such as Parkinson's disease (PD). In the present narrative review, we attempt to summarize the most recent RS-fMRI findings highlighting the role of brain networks reorganization and adaptation in the course of PD. We also discuss limitations and potential definition of early functional connectivity signatures to track and predict future PD progression. Understanding the neural correlates and potential predisposing factors of clinical progression and complication will be crucial to guide novel clinical trials and to foster preventive strategies.

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“…9 This was subsequently confirmed in a cross-sectional study. 9,10 Moreover, we showed that the identified clusters were associated with different patterns of functional connectivity using functional Magnetic Resonance Imaging (fMRI) 11 and highdensity-EEG. 12 However, the identification of these different levels of cognitive deficits with easily available markers is needed.…”

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confidence: 96%

Electroencephalography‐based machine learning for cognitive profiling in Parkinson's disease: Preliminary results

et al. 2018

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Background Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. Objective The aim of this study was to investigate the use of the combination of resting‐state EEG and data‐mining techniques to build characterization models. Methods Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine‐learning algorithms to build and train characterization models, namely, support vector machines and k‐nearest neighbors models. The models were then blindly tested on data from 18 patients. Results The overall classification accuracies were 84% and 88% for the support vector machines and k‐nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. Conclusion These results suggest that EEG features computed from a daily clinical practice exploration modality in—that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient—can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society

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Cognitive phenotypes in parkinson's disease differ in terms of brain‐network organization and connectivity

Cited by 82 publications

References 70 publications

Network basis of the dysexecutive and posterior cortical cognitive profiles in Parkinson's disease

Network basis of the dysexecutive and posterior cortical cognitive profiles in Parkinson's disease

Functional Connectivity Signatures of Parkinson’s Disease

Electroencephalography‐based machine learning for cognitive profiling in Parkinson's disease: Preliminary results

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