scholarly journals Cognitive impairment and resting-state network connectivity in Parkinson's disease

2014 ◽  
Vol 36 (1) ◽  
pp. 199-212 ◽  
Author(s):  
Hugo-Cesar Baggio ◽  
Bàrbara Segura ◽  
Roser Sala-Llonch ◽  
Maria-José Marti ◽  
Francesc Valldeoriola ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Resting State ◽  
Network Connectivity ◽  
Resting State Network

scholarly journals Effects of dopamine agonist treatment on resting-state network connectivity in Parkinson’s disease

2020 ◽  
Author(s):  
David M. Cole ◽  
Bahram Mohammadi ◽  
Maria Milenkova ◽  
Katja Kollewe ◽  
Christoph Schrader ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Dopamine Agonist ◽  
Resting State ◽  
Large Scale ◽  
Network Connectivity ◽  
Resting State Network ◽  
Large Scale Networks

ABSTRACTDopamine agonist (DA) medications commonly used to treat, or ‘normalise’, motor symptoms of Parkinson’s disease (PD) may lead to cognitive-neuropsychiatric side effects, such as increased impulsivity in decision-making. Subject-dependent variation in the neural response to dopamine modulation within cortico-basal ganglia circuitry is thought to play a key role in these latter, non-motor DA effects. This neuroimaging study combined resting-state functional magnetic resonance imaging (fMRI) with DA modification in patients with idiopathic PD, investigating whether brain ‘resting-state network’ (RSN) functional connectivity metrics identify disease-relevant effects of dopamine on systems-level neural processing. By comparing patients both ‘On’ and ‘Off’ their DA medications with age-matched, un-medicated healthy control subjects (HCs), we identified multiple non-normalising DA effects on frontal and basal ganglia RSN cortico-subcortical connectivity patterns in PD. Only a single isolated, potentially ‘normalising’, DA effect on RSN connectivity in sensori-motor systems was observed, within cerebro-cerebellar neurocircuitry. Impulsivity in reward-based decision-making was positively correlated with ventral striatal connectivity within basal ganglia circuitry in HCs, but not in PD patients. Overall, we provide brain systems-level evidence for anomalous DA effects in PD on large-scale networks supporting cognition and motivated behaviour. Moreover, findings suggest that dysfunctional striatal and basal ganglia signalling patterns in PD are compensated for by increased recruitment of other cortico-subcortical and cerebro-cerebellar systems.

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

2014 ◽  
Vol 8 ◽  
Author(s):  
Alexander V. Lebedev ◽  
Eric Westman ◽  
Andrew Simmons ◽  
Aleksandra Lebedeva ◽  
Françoise J. Siepel ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Resting State ◽  
Large Scale ◽  
Resting State Network

scholarly journals “Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis”: Answer to Wang and colleagues

2019 ◽  
Vol 66 ◽  
pp. 253-254
Author(s):  
Amée F. Wolters ◽  
Sjors C.F. van de Weijer ◽  
Albert F.G. Leentjens ◽  
Annelien A. Duits ◽  
Heidi I.L. Jacobs ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Resting State ◽  
Meta Analysis ◽  
Resting State Fmri

Distinct manifestation of cognitive deficits associate with different resting-state network disruptions in non-demented patients with Parkinson’s disease

2018 ◽  
Vol 265 (3) ◽  
pp. 688-700 ◽  
Author(s):  
Kazuya Kawabata ◽  
Hirohisa Watanabe ◽  
Kazuhiro Hara ◽  
Epifanio Bagarinao ◽  
Noritaka Yoneyama ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Deficits ◽  
Resting State ◽  
Resting State Network ◽  
Network Disruptions

scholarly journals Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis

2019 ◽  
Vol 62 ◽  
pp. 16-27 ◽  
Author(s):  
Amée F. Wolters ◽  
Sjors C.F. van de Weijer ◽  
Albert F.G. Leentjens ◽  
Annelien A. Duits ◽  
Heidi I.L. Jacobs ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Resting State ◽  
Meta Analysis ◽  
Resting State Fmri

scholarly journals Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity

2019 ◽  
Vol 92 (1101) ◽  
pp. 20180886 ◽  
Author(s):  
Christian Rubbert ◽  
Christian Mathys ◽  
Christiane Jockwitz ◽  
Christian J Hartmann ◽  
Simon B Eickhoff ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Resting State ◽  
Good Accuracy ◽  
Network Connectivity ◽  
High Sensitivity ◽  
Data Driven ◽  
Healthy Controls ◽  
Whole Brain ◽  
Model Based

Objective: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson’s disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI). Methods: Whole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance. Results: Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 – 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 – 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 – 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks. Conclusion: A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. Advances in knowledge: Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson’s disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

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