scholarly journals Predictors of Mild Cognitive Impairment in Early-Stage Parkinson's Disease

2013 ◽  
Vol 3 (1) ◽  
pp. 168-178 ◽  
Author(s):  
Brenda Hanna-Pladdy ◽  
Katherine Jones ◽  
Romeo Cabanban ◽  
Rajesh Pahwa ◽  
Kelly E. Lyons
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Stage

Olfactory Function and Diffusion Tensor Imaging as Markers of Mild Cognitive Impairment in Early Stages of Parkinson's Disease

2021 ◽  
pp. 155005942110582
Author(s):  
Sophie A. Stewart ◽  
Laura Pimer ◽  
John D. Fisk ◽  
Benjamin Rusak ◽  
Ron A. Leslie ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Diffusion Tensor Imaging ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Neurodegenerative Disorder ◽  
Diffusion Tensor ◽  
Early Stage ◽  
White Matter Integrity

Parkinson's disease (PD) is a neurodegenerative disorder that is typified by motor signs and symptoms but can also lead to significant cognitive impairment and dementia Parkinson's Disease Dementia (PDD). While dementia is considered a nonmotor feature of PD that typically occurs later, individuals with PD may experience mild cognitive impairment (PD-MCI) earlier in the disease course. Olfactory deficit (OD) is considered another nonmotor symptom of PD and often presents even before the motor signs and diagnosis of PD. We examined potential links among cognitive impairment, olfactory functioning, and white matter integrity of olfactory brain regions in persons with early-stage PD. Cognitive tests were used to established groups with PD-MCI and with normal cognition (PD-NC). Olfactory functioning was examined using the University of Pennsylvania Smell Identification Test (UPSIT) while the white matter integrity of the anterior olfactory structures (AOS) was examined using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) analysis. Those with PD-MCI demonstrated poorer olfactory functioning and abnormalities based on all DTI parameters in the AOS, relative to PD-NC individuals. OD and microstructural changes in the AOS of individuals with PD may serve as additional biological markers of PD-MCI.

scholarly journals Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?

2020 ◽  
Vol 17 (7) ◽  
pp. 2594 ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Normal Cognition

Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson’s disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson’s Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson’s disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson’s Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.

scholarly journals Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson’s Disease Patients With Mild Cognitive Impairment

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiangbin Chen ◽  
Mengting Liu ◽  
Zhibing Wu ◽  
Hao Cheng
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Information Exchange ◽  
Early Stage ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Functional Brain Network

Recent studies have demonstrated structural and functional alterations in Parkinson’s disease (PD) with mild cognitive impairment (MCI). However, the topological patterns of functional brain networks in newly diagnosed PD patients with MCI are unclear so far. In this study, we used functional magnetic resonance imaging (fMRI) and graph theory approaches to explore the functional brain network in 45 PD patients with MCI (PD-MCI), 22 PD patients without MCI (PD-nMCI), and 18 healthy controls (HC). We found that the PD-MCI, PD-nMCI, and HC groups exhibited a small-world architecture in the functional brain network. However, early-stage PD-MCI patients had decreased clustering coefficient, increased characteristic path length, and changed nodal centrality in the default mode network (DMN), control network (CN), somatomotor network (SMN), and visual network (VN), which might contribute to factors for MCI symptoms in PD patients. Our results demonstrated that PD-MCI patients were associated with disrupted topological organization in the functional network, thus providing a topological network insight into the role of information exchange in the underlying development of MCI symptoms in PD patients.

Assessment of White Matter Lesions in Parkinson's Disease: Voxel-based Analysis and Tract-based Spatial Statistics Analysis of Parkinson's Disease with Mild Cognitive Impairment

2020 ◽  
Vol 17 (4) ◽  
pp. 480-486
Author(s):  
Wei Pu ◽  
Xudong Shen ◽  
Mingming Huang ◽  
Zhiqian Li ◽  
Xianchun Zeng ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Corpus Callosum ◽  
Fractional Anisotropy ◽  
Spatial Statistics ◽  
Diffusion Tensor ◽  
Tract Based Spatial Statistics

Objective: Application of diffusion tensor imaging (DTI) to explore the changes of FA value in patients with Parkinson's disease (PD) with mild cognitive impairment. Methods: 27 patients with PD were divided into PD with mild cognitive impairment (PD-MCI) group (n = 7) and PD group (n = 20). The original images were processed using voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). Results: The average age of pd-mci group was longer than that of PD group, and the course of disease was longer than that of PD group. Compared with PD group, the voxel based analysis-fractional anisotropy (VBA-FA) values of PD-MCI group decreased in the following areas: bilateral frontal lobe, bilateral temporal lobe, bilateral parietal lobe, bilateral subthalamic nucleus, corpus callosum, and gyrus cingula. Tract-based spatial statistics-fractional anisotropy (TBSS-FA) values in PD-MCI group decreased in bilateral corticospinal tract, anterior cingulum, posterior cingulum, fornix tract, bilateral superior thalamic radiation, corpus callosum(genu, body and splenium), bilateral uncinate fasciculus, bilateral inferior longitudinal fasciculus, bilateral superior longitudinal fasciculus, bilateral superior fronto-occipital fasciculus, bilateral inferior fronto-occipital fasciculus, and bilateral parietal-occipital tracts. The mean age of onset in the PD-MCI group was greater than that in the PD group, and the disease course was longer than that in the PD group. Conclusion: DTI-based VBA and TBSS post-processing methods can detect abnormalities in multiple brain areas and white matter fiber tracts in PD-MCI patients. Impairment of multiple cerebral cortex and white matter fiber pathways may be an important causes of cognitive dysfunction in PD-MCI.

scholarly journals Donepezil for mild cognitive impairment in Parkinson’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kyoungwon Baik ◽  
Seon Myeong Kim ◽  
Jin Ho Jung ◽  
Yang Hyun Lee ◽  
Seok Jong Chung ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Treatment Group ◽  
Outcome Measures ◽  
Rating Scale ◽  
Secondary Outcome ◽  
Control Group ◽  
Significant Difference

AbstractWe investigated the efficacy of donepezil for mild cognitive impairment in Parkinson’s disease (PD-MCI). This was a prospective, non-randomized, open-label, two-arm study. Eighty PD-MCI patients were assigned to either a treatment or control group. The treatment group received donepezil for 48 weeks. The primary outcome measures were the Korean version of Mini-Mental State Exam and Montreal Cognitive Assessment scores. Secondary outcome measures were the Clinical Dementia Rating, Unified Parkinson’s Disease Rating Scale part III, Clinical Global Impression scores. Progression of dementia was assessed at 48-week. Comprehensive neuropsychological tests and electroencephalography (EEG) were performed at baseline and after 48 weeks. The spectral power ratio of the theta to beta2 band (TB2R) in the electroencephalogram was analyzed. There was no significant difference in the primary and secondary outcome measures between the two groups. However, the treatment group showed a significant decrease in TB2R at bilateral frontotemporoparietal channels compared to the control group. Although we could not demonstrate improvements in the cognitive functions, donepezil treatment had a modulatory effect on the EEG in PD-MCI patients. EEG might be a sensitive biomarker for detecting changes in PD-MCI after donepezil treatment.

Analyses of Visuospatial and Visuoperceptual Errors as Predictors of Dementia in Parkinson’s Disease Patients with Subjective Cognitive Decline and Mild Cognitive Impairment

2020 ◽  
pp. 1-11
Author(s):  
Iván Galtier ◽  
Antonieta Nieto ◽  
María Mata ◽  
Jesús N. Lorenzo ◽  
José Barroso
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Poor Performance ◽  
Subjective Cognitive Decline ◽  
Independent Risk Factors

ABSTRACT Objective: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in Parkinson’s disease (PD) are considered as the risk factors for dementia (PDD). Posterior cortically based functions, such as visuospatial and visuoperceptual (VS-VP) processing, have been described as predictors of PDD. However, no investigations have focused on the qualitative analysis of the Judgment of Line Orientation Test (JLOT) and the Facial Recognition Test (FRT) in PD-SCD and PD-MCI. The aim of this work was to study the VS-VP errors in JLOT and FRT. Moreover, these variables are considered as predictors of PDD. Method: Forty-two PD patients and 19 controls were evaluated with a neuropsychological protocol. Patients were classified as PD-SCD and PD-MCI. Analyses of errors were conducted following the procedure described by Ska, Poissant, and Joanette (1990). Follow-up assessment was conducted to a mean of 7.5 years after the baseline. Results: PD-MCI patients showed a poor performance in JLOT and FRT total score and made a greater proportion of severe intraquadrant (QO2) and interquadrant errors (IQO). PD-SCD showed a poor performance in FRT and made mild errors in JLOT. PD-MCI and QO2/IQO errors were independent risk factors for PDD during the follow-up. Moreover, the combination of both PD-MCI diagnosis and QO2/IQO errors was associated with a greater risk. Conclusions: PD-MCI patients presented a greater alteration in VS-VP processing observable by the presence of severe misjudgments. PD-SCD patients also showed mild difficulties in VS-SP functions. Finally, QO2/IQO errors in PD-MCI are a useful predictor of PDD, more than PD-MCI diagnosis alone.

Sign in / Sign up

Export Citation Format

Share Document