Correlation between Executive Network Integrity and Sarcopenia in Patients with Parkinson’s Disease

Background: Sarcopenia is critically associated with morbidity and mortality in the progression of Parkinson’s disease (PD). However, analyses of clinical severity and brain changes, such as white matter (WM) alterations in PD patients with sarcopenia are limited. Further understanding of the factors associated with sarcopenia may provide a focused screen and potential for early intervention in PD patients. Methods: 52 PD patients and 19 healthy participants accepted dual-energy X-ray absorptiometry to measure the body composition. Using diffusion tensor imaging, the difference of WM integrity was measured between PD patients with sarcopenia (PDSa) and without sarcopenia (PDNSa). Multivariate analysis was performed to explore the relationships between clinical factors, WM integrity, and sarcopenia in PD patients. Results: 21 PD patients (40.4%) had sarcopenia. PDSa had a higher Unified Parkinson’s Disease Rating Scale (UPDRS III) score, lower body mass index (BMI) and lower fat weight compared with the PDNSa. Additionally, PDSa patients exhibited lower fractional anisotropy accompanied by higher radial diffusivity and/or higher mean diffusivity in the fronto-striato-thalamic circuits, including bilateral cingulum, left superior longitudinal fasciculus, left genu of corpus callosum, and right anterior thalamic radiation, which participate in the executive function. In addition, decreased muscle mass was associated with worse WM integrity in these regions. Multiple linear regression analysis revealed that WM integrity in the left cingulum, right anterior thalamic radiation, together with gender (male) significantly predicted muscle mass in PD patients. Conclusions: WM alterations in the executive network, such as the fronto-striato-thalamic circuits, may indicate a risk factor for ongoing sarcopenia in PD patients. The effectiveness of using executive function to serve as a prodromal marker of sarcopenia in PD patients should be evaluated in future studies.

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Structural brain signature of cognitive decline in Parkinson’s disease: DTI-based evidence from the LANDSCAPE study

Therapeutic Advances in Neurological Disorders ◽

10.1177/1756286419843447 ◽

2019 ◽

Vol 12 ◽

pp. 175628641984344 ◽

Cited By ~ 3

Author(s):

Martin Gorges ◽

Hans-Peter Müller ◽

Inga Liepelt-Scarfone ◽

Alexander Storch ◽

Richard Dodel ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

White Matter ◽

Corpus Callosum ◽

Cognitive Decline ◽

Diffusion Tensor ◽

Structural Connectivity ◽

Structural Data ◽

The Body ◽

Normal Cognitive Function

Background: The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia. Methods: Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures. Results: Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score. Conclusions: The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.

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Diffusion Kurtosis Imaging of Substantia Nigra Is a Sensitive Method for Early Diagnosis and Disease Evaluation in Parkinson’s Disease

Parkinson s Disease ◽

10.1155/2015/207624 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5 ◽

Cited By ~ 10

Author(s):

Guohua Zhang ◽

Yuhu Zhang ◽

Chengguo Zhang ◽

Yukai Wang ◽

Guixian Ma ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Substantia Nigra ◽

Rating Scale ◽

Diffusion Tensor ◽

Diffusion Kurtosis Imaging ◽

Healthy Controls ◽

Potential Biomarker ◽

Diffusion Kurtosis ◽

Sensitive Method

Background.To diagnose Parkinson disease (PD) in an early stage and accurately evaluate severity, it is important to develop a sensitive method for detecting structural changes in the substantia nigra (SN).Method.Seventy-two untreated patients with early PD and 72 healthy controls underwent diffusion tensor and diffusion kurtosis imaging. Regions of interest were drawn in the rostral, middle, and caudal SN by two blinded and independent raters. Mean kurtosis (MK) and fractional anisotropy in the SN were compared between the groups. Receiver operating characteristic (ROC) and Spearman correlation analyses were used to compare the diagnostic accuracy and correlate imaging findings with Hoehn-Yahr (H-Y) staging and part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III).Result.MK in the SN was increased significantly in PD patients compared with healthy controls. The area under the ROC curve was 0.976 for MK in the SN (sensitivity, 0.944; specificity, 0.917). MK in the SN had a positive correlation with H-Y staging and UPDRS-III scores.Conclusion.Diffusion kurtosis imaging is a sensitive method for PD diagnosis and severity evaluation. MK in the SN is a potential biomarker for imaging studies of early PD that can be widely used in clinic.

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Influence of Hypertension on Neurocognitive Domains in Nondemented Parkinson’s Disease Patients

Parkinson s Disease ◽

10.1155/2014/507529 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Jacob D. Jones ◽

Charles Jacobson ◽

Martina Murphy ◽

Catherine Price ◽

Michael S. Okun ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Executive Function ◽

Cognitive Decline ◽

Verbal Memory ◽

Rating Scale ◽

Cognitive Status ◽

Cross Sectional ◽

The Impact ◽

Health Comorbidities

Objective. Health comorbidities, particularly cardiovascular risk factors, are well known to pose risks for cognitive decline in older adults. To date, little attention has focused on the impact of these comorbidities on Parkinson’s disease (PD). This study examined the prevalence and contribution of comorbidities on cognitive status in PD patients, above and beyond the effects of disease severity.Methods. A cross sectional design was used, including neuropsychological data on 341 PD patients without severe cognitive decline. Comorbidity data were collected via medical chart review. Data were analyzed using a series of multiple hierarchical regressions, controlling for PD-related disease variables.Results. Overall sample characteristics are 69% male, disease duration 9.7 years, Unified Parkinson’s Disease Rating Scale 26.4, and age 64.7 years. Hypercholesterolemia (41.6%), hypertension (38.1%), and hypotension (30.2%) were the most reported comorbidities. The presence of hypertension significantly contributed to domains of executive function and verbal memory. The cooccurrence of orthostatic hypotension moderated the relationship between hypertension and executive function.Conclusions. This study on a large cohort of PD patients provides evidence for a detrimental influence of health comorbidities, particularly hypertension, on cognitive domains that have traditionally been conceptualized as being frontally and/or temporally mediated.

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The Influence of Playing a Non-Reward Game on Motor Ability and Executive Function in Parkinson’s Disease

Behavioural Neurology ◽

10.1155/2012/586269 ◽

2012 ◽

Vol 25 (2) ◽

pp. 119-125 ◽

Cited By ~ 2

Author(s):

Alisson Menezes Araújo Lima ◽

Fabiana de Campos Cordeiro Hirata ◽

Gabriela Sales de Bruin ◽

Rosa Maria Salani Mota ◽

Veralice Meireles Sales de Bruin

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Executive Function ◽

Depressive Symptoms ◽

Rating Scale ◽

Age At Onset ◽

Stroop Test ◽

Finger Tapping ◽

Walk Test ◽

Motor Ability

The aim of this study is to evaluate the acute effect of playing games on executive function and motor ability in Parkinson's disease (PD). Consecutive cases with PD were studied with the Unified Parkinson Disease Rating Scale (UPDRS), Mini-Mental State examination (MMSE), Beck Depression Inventory (BDI), Stroop test, finger tapping and 14-meter walk test. After randomization, patients performed a game of dominoes and were tested before and after experiment being further categorized as control, winners or non-winners. Forty patients, 27 male (67.5%), aged 48 to 84 years (63.2 ± 8.5), Hoehn & Yahr I to III were included. Twenty-eight (70%) presented depressive symptoms (BDI > 10). Groups (Control N = 13; Winners = 14 and Non-winners = 13) were not different regarding age, disease duration, age at onset, BMI, MMSE scores, depressive symptoms, levodopa dose, and previous practice of games. Winners presented significantly better results on executive function (Stroop test,p= 0.002) and on motor activity (Finger tapping,p= 0.01). Non-winners showed a trend of better performance in the 14-meter-walk test. This study shows that the practice of a non-reward game acutely improved memory and motor skills in PD. Our results suggest a role for the reward system in the modulation of the dopaminergic function of the basal ganglia in these patients.

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White Matter Microstructural Integrity and Executive Function in Parkinson's Disease

Journal of the International Neuropsychological Society ◽

10.1017/s1355617712001373 ◽

2013 ◽

Vol 19 (3) ◽

pp. 349-354 ◽

Cited By ~ 22

Author(s):

Catherine Gallagher ◽

Brian Bell ◽

Barbara Bendlin ◽

Matthew Palotti ◽

Ozioma Okonkwo ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Executive Function ◽

White Matter ◽

Diffusion Tensor ◽

A Priori ◽

Mean Diffusivity ◽

Pathological Process ◽

Intracranial Volume ◽

Mean Values

AbstractRecent studies suggest that white matter abnormalities contribute to both motor and non-motor symptoms of Parkinson's disease. The present study was designed to investigate the degree to which diffusion tensor magnetic resonance imaging (DTI) indices are related to executive function in Parkinson's patients. We used tract-based spatial statistics to compare DTI data from 15 patients to 15 healthy, age- and education-matched controls. We then extracted mean values of fractional anisotropy (FA) and mean diffusivity (MD) within an a priori frontal mask. Executive function composite Z scores were regressed against these DTI indices, age, and total intracranial volume. In Parkinson's patients, FA was related to executive composite scores, and both indices were related to Stroop interference scores. We conclude that white matter microstructural abnormalities contribute to cognitive deficits in Parkinson's disease. Further work is needed to determine whether these white matter changes reflect the pathological process or a clinically important comorbidity. (JINS, 2013, 19, 1–6)

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Locus Coeruleus Degeneration Correlated with Levodopa Resistance in Parkinson’s Disease: A Retrospective Analysis

Journal of Parkinson s Disease ◽

10.3233/jpd-212720 ◽

2021 ◽

pp. 1-10

Author(s):

Cheng Zhou ◽

Tao Guo ◽

JingJing Wu ◽

Linbo Wang ◽

Xueqin Bai ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Locus Coeruleus ◽

Rating Scale ◽

Linear Regression Analysis ◽

Animal Experiments ◽

Network Synchronization ◽

Clinical Issue ◽

The Relationship

Background: The widely divergent responsiveness of Parkinson’s disease (PD) patients to levodopa is an important clinical issue because of its relationship with quality of life and disease prognosis. Preliminary animal experiments have suggested that degeneration of the locus coeruleus (LC) attenuates the efficacy of levodopa treatment. Objective: To explore the relationship between LC degeneration and levodopa responsiveness in PD patients in vivo. Methods: Neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a good indicator of LC and substantia nigra (SN) degeneration, and levodopa challenge tests were conducted in 57 PD patients. Responsiveness to levodopa was evaluated by the rates of change of the Unified Parkinson’s Disease Rating Scale Part III score and somatomotor network synchronization calculated from resting-state functional MRI before and after levodopa administration. Next, we assessed the relationship between the contrast-to-noise ratio of LC (CNRLC) and levodopa responsiveness. Multiple linear regression analysis was conducted to rule out the potential influence of SN degeneration on levodopa responsiveness. Results: A significant positive correlation was found between CNRLC and the motor improvement after levodopa administration (R = 0.421, p = 0.004). CNRLC also correlated with improvement in somatomotor network synchronization (R = –0.323, p = 0.029). Furthermore, the relationship between CNRLC and levodopa responsiveness was independent of SN degeneration. Conclusion: LC degeneration might be an essential factor for levodopa resistance. LC evaluation using NM-MRI might be an alternative tool for predicting levodopa responsiveness and for helping to stratify patients into clinical trials aimed at improving the efficacy of levodopa.

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Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity

Parkinson s Disease ◽

10.1155/2016/8704910 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Mansu Kim ◽

Hyunjin Park

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rating Scale ◽

Diffusion Tensor ◽

Magnetic Resonance Images ◽

Future Research ◽

Support Vector ◽

Svm Classifier ◽

Research Database ◽

Significant Group

Background. It is critical to distinguish between Parkinson’s disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches.Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography.Methods. Diffusion magnetic resonance images of SWEDD (n=37) and PD (n=40) were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences.Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson’s Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections.Conclusions. The connections and regions identified represent candidates for future research investigations.

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Muscle strength and executive function as complementary parameters for the assessment of impairment in Parkinson's disease

Arquivos de Neuro-Psiquiatria ◽

10.1590/0004-282x20130175 ◽

2013 ◽

Vol 71 (12) ◽

pp. 948-954

Author(s):

Dannyel Barbirato ◽

Alessandro Carvalho ◽

Narahyana Bom de Araujo ◽

Jose Vicente Martins ◽

Andrea Deslandes

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Executive Function ◽

Disease Severity ◽

Rating Scale ◽

Motor Impairment ◽

Cognitive Status ◽

Cognitive Capacity ◽

Response To Treatment ◽

The Relationship

Objective To evaluate the relationship between the quantitative results of functional and cognitive performance of patients with Parkinson's disease (PD) and disease severity; and to study the relationship between patients' functional and cognitive capacity and motor impairment (Unified Parkinson's Disease Rating Scale - UPDRS III). Method Twenty-nine subjects clinically diagnosed with PD were classified into three groups according to disease severity using the modified Hoehn and Yahr Scale (H&Y). They were submitted to functional (Senior Fitness Test) and neuropsychological tests. Stepwise regression analysis showed a significant association between H&Y and upper limb strength (r 2 =0.30; p=0.005) and executive function (r 2 =0.37; p=0.004). In relation to UPDRS III, there was a significant association between lower limb strength (r 2 =0.27; p=0.010) and global cognitive status (r 2 =0.24; p=0.024). Conclusion The implementation of simple tests of functional capacity associated with neuropsychological testing can help to assess disease severity and motor impairment, and can be used to monitor the response to treatment in PD.

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Automated Assessment of the Substantia Nigra Pars Compacta in Parkinson’s Disease: A Diffusion Tensor Imaging Study

Journal of Personalized Medicine ◽

10.3390/jpm11111235 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1235

Author(s):

Niels Bergsland ◽

Laura Pelizzari ◽

Maria Marcella Laganá ◽

Sonia Di Tella ◽

Federica Rossetto ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Diffusion Tensor Imaging ◽

Substantia Nigra ◽

Rating Scale ◽

Diffusion Tensor ◽

Imaging Study ◽

Substantia Nigra Pars Compacta ◽

Disease Rating ◽

Magnetic Resonance Imaging Mri

The substantia nigra (SN) pars compacta (SNpc) and pars reticulata (SNpr) are differentially affected in Parkinson’s disease (PD). Separating the SNpc and SNpr is challenging with standard magnetic resonance imaging (MRI). Diffusion tensor imaging (DTI) allows for the characterization of SN microstructure in a non-invasive manner. In this study, 29 PD patients and 28 healthy controls (HCs) were imaged with 1.5T MRI for DTI. Images were nonlinearly registered to standard space and SNpc and SNpr DTI parameters were measured. ANCOVA and receiver operator characteristic (ROC) analyses were performed. Clinical associations were assessed with Spearman correlations. Multiple corrections were controlled for false discovery rate. PD patients presented with significantly increased SNpc axial diffusivity (AD) (1.207 ± 0.068 versus 1.156 ± 0.045, p = 0.024), with ROC analysis yielding an under the curve of 0.736. Trends with Unified Parkinson’s Disease Rating Scale (UPDRS) III scores were identified for SNpc MD (rs = 0.449), AD (rs = 0.388), and radial diffusivity (rs = 0.391) (all p < 0.1). A trend between baseline SNpr MD and H&Y change (rs = 0.563, p = 0.081) over 2.9 years of follow-up was identified (n = 14). In conclusion, SN microstructure shows robust, clinically meaningful associations in PD.

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Predicting Longitudinal Disease Severity for Individuals with Parkinson’s Disease using Functional MRI and Machine Learning Prognostic Models

10.1101/2020.04.20.050971 ◽

2020 ◽

Author(s):

Kevin P. Nguyen ◽

Vyom Raval ◽

Alex Treacher ◽

Cooper Mellema ◽

Frank Yu ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Disease Severity ◽

Clinical Decision Making ◽

Rating Scale ◽

Neurodegenerative Disorder ◽

Clinical Severity ◽

Predictive Values ◽

Updrs Score

AbstractParkinson’s disease is the second most common neurodegenerative disorder and is characterized by the loss of ability to control voluntary movements. Predictive biomarkers of progression in Parkinson’s Disease are urgently needed to expedite the development of neuroprotective treatments and facilitate discussions about disease prognosis between clinicians and patients. Resting-state functional magnetic resonance imaging (rs-fMRI) shows promise in predicting progression, with derived measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), having been previously been associated with current disease severity. In this work, ReHo and fALFF features from 82 Parkinson’s Disease subjects are used to train machine learning predictors of baseline clinical severity and progression at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Depression Rating Scale (MDS-UPDRS) score. This is the first time that rs-fMRI and machine learning have been combined to predict future disease progression. The machine learning models explain up to 30.4% (R2 = 0.304) of the variance in baseline MDS-UPDRS scores, 55.8% (R2 = 0.558) of the variance in year 1 scores, and 47.1% (R2 = 0.471) of the variance in year 2 scores with high statistical significance (p < 0.0001). For distinguishing high- and low-progression individuals (MDS-UPDRS score above or below the median), the models achieve positive predictive values of up to 71% and negative predictive values of up to 84%. The models learn patterns of ReHo and fALFF measures that predict better and worse prognoses. Higher ReHo and fALFF in regions of the default motor network predicted lower current severity and lower future progression. The rs-fMRI features in the temporal lobe, limbic system, and motor cortex were also identified as predictors. These results present a potential neuroimaging biomarker that accurately predicts progression, which may be useful as a clinical decision-making tool and in future trials of neuroprotective treatments.

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