scholarly journals Postural instability via a loss of intermittent control in elderly and patients with Parkinson’s disease: A model-based and data-driven approach

2020 ◽  
Vol 30 (11) ◽  
pp. 113140
Author(s):  
Yasuyuki Suzuki ◽  
Akihiro Nakamura ◽  
Matija Milosevic ◽  
Kunihiko Nomura ◽  
Takao Tanahashi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Postural Instability ◽  
Data Driven ◽  
Intermittent Control ◽  
Model Based ◽  
Data Driven Approach

Motor patterns in Parkinson's disease: A data-driven approach

2009 ◽  
Vol 24 (7) ◽  
pp. 1042-1047 ◽  
Author(s):  
Stephanie M. van Rooden ◽  
Martine Visser ◽  
Dagmar Verbaan ◽  
Johan Marinus ◽  
Jacobus J. van Hilten
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Driven ◽  
Motor Patterns ◽  
Data Driven Approach

The spectrum of cognitive disorders in Parkinson's disease: A data-driven approach

2013 ◽  
Vol 28 (2) ◽  
pp. 183-189 ◽  
Author(s):  
Kathy Dujardin ◽  
Albert F.G. Leentjens ◽  
Carole Langlois ◽  
Anja J.H. Moonen ◽  
Annelien A. Duits ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Disorders ◽  
Data Driven ◽  
Data Driven Approach

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.

scholarly journals Cognitive Profiles in Parkinson’s Disease and Their Relation to Dementia: A Data-Driven Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Inga Liepelt-Scarfone ◽  
Susanne Gräber ◽  
Monika Fruhmann Berger ◽  
Anne Feseker ◽  
Gülsüm Baysal ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Hierarchical Cluster ◽  
Individual Performance ◽  
Data Driven ◽  
Data Sets ◽  
Cognitive Profiles ◽  
Cluster Analyses ◽  
Data Driven Approach

Parkinson’s disease is characterized by a substantial cognitive heterogeneity, which is apparent in different profiles and levels of severity. To date, a distinct clinical profile for patients with a potential risk of developing dementia still has to be identified. We introduce a data-driven approach to detect different cognitive profiles and stages. Comprehensive neuropsychological data sets from a cohort of 121 Parkinson’s disease patients with and without dementia were explored by a factor analysis to characterize different cognitive domains. Based on the factor scores that represent individual performance in each domain, hierarchical cluster analyses determined whether subgroups of Parkinson’s disease patients show varying cognitive profiles. A six-factor solution accounting for 65.2% of total variance fitted best to our data and revealed high internal consistencies (Cronbach’s alpha coefficients>0.6). The cluster analyses suggested two independent patient clusters with different cognitive profiles. They differed only in severity of cognitive impairment and self-reported limitation of activities of daily living function but not in motor performance, disease duration, or dopaminergic medication. Based on a data-driven approach, divers cognitive profiles were identified, which separated early and more advanced stages of cognitive impairment in Parkinson’s disease without dementia. Importantly, these profiles were independent of motor progression.

A Classification of Postural Sway Patterns During Upright Stance in Healthy Adults and Patients with Parkinson’s Disease

2011 ◽  
Vol 15 (8) ◽  
pp. 997-1010 ◽  
Author(s):  
Tomohisa Yamamoto ◽  
◽  
Yasuyuki Suzuki ◽  
Kunihiko Nomura ◽  
Taishin Nomura ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Postural Sway ◽  
Postural Instability ◽  
Elderly Subjects ◽  
Intermittent Control ◽  
Upright Stance ◽  
Muscle Torque ◽  
Ankle Muscles ◽  
Ankle Muscle

The intermittent control during human quiet upright stance is a new hypothesis which claims that the active neural feedback control generating the ankle muscle torque is switched off and on intermittently at appropriate timings. The intermittent strategy is capable of providing compliant posture while ensuring robust stability. Contrastingly, impairment of postural reflexes in patients with Parkinson’s disease (PD) causes postural instability. Here we hypothesize that the instability in PD patients might be due to a loss of appropriate intermittent activations in the feedback muscle torque during stance. In order to provide evidence for this hypothesis, we characterized stochastic postural sway patterns measured as changes in center of pressure (CoP) and activities of ankle muscles during quiet stance in healthy young and elderly subjects as well as PD patients. To this end, sway patterns and associated ankle muscle activities were quantified by several indices including the CoP sway area, scaling factors of double-power-law power spectra of the sway, as well as levels and patterns of the muscle activations. Hierarchical cluster analysis was performed to suggest that the sway patterns could be classified into two major types. The first type consisted mainly of sway and muscle activation patterns from healthy subjects and some PD patients with the mild level of severity, and they showed features indicating the intermittent control. The second type, consisting mainly of PD patients with relatively severe levels of motor symptoms, was accompanied with non-intermittent but tonic muscle activities and sway areas either smaller or larger than those in the first type. Moreover, the major two types were further classified into several subtypes with distinguishable characteristics. Results suggested that a loss of the intermittent activations in the ankle muscles could be a cause of the postural instability for a population of PD patients.

scholarly journals A data-driven approach for detecting gait events during turning in people with Parkinson's disease and freezing of gait

2020 ◽  
Vol 80 ◽  
pp. 130-136
Author(s):  
Benjamin Filtjens ◽  
Alice Nieuwboer ◽  
Nicholas D’cruz ◽  
Joke Spildooren ◽  
Peter Slaets ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
Data Driven ◽  
Data Driven Approach
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