Brain cholesterol metabolism and Parkinson's disease.

BACKGROUND: Circulating cholesterol levels have been linked to PD, but not directly to brain physiology. OBJECTIVE: To assess whether brain cholesterol metabolism is related to PD. METHODS: Sixty PD patients and 64 controls were recruited from an academic movement disorder clinic (2009-2012). Thirty-five PD patients and 33 controls returned approximately 36 months later. Fasting plasma (S)24-OH-cholesterol (brain-derived cholesterol metabolite) and 27-OH-cholesterol (peripheral cholesterol metabolite) were quantified. Odds ratios for PD were derived from logistic regression models, adjusting for potential confounders. Relationships between the oxysterols and clinical measurements were explored using Spearman correlation coefficients. RESULTS: Mean age of PD subjects was 63.8 ± 8.3 years and disease duration was 5.0 ± 5.4 years. Plasma (S)24-OH-cholesterol levels were inversely associated with the odds of having PD, with an odds ratio of 0.92 (95% confidence interval: 0.87-0.97) for each 1-ng/mL increase (P = 0.004). Compared to the lowest tertile, the odds ratio was 0.34 (0.12-0.98) for the second tertile (P = 0.045) and 0.08 (0.02-0.31) for the highest tertile (P < 0.001). Higher (S)24-OH-cholesterol levels also were correlated with better sense of smell (r = 0.35; P = 0.01). No significant associations were found between clinical measures and 27-OH-cholesterol, a peripheral cholesterol metabolite. Furthermore, (S)24-OH-cholesterol levels were stable over time, whereas 27-OH-cholesterol decreased with time in both cases and controls. CONCLUSIONS: Results indicate that plasma (S)24-OH-cholesterol (possibly reflecting brain cholesterol metabolism) is inversely linked to PD, is relatively stable over time, and may serve as a new biomarker for PD. Further investigation is necessary to determine the mechanistic and clinical implications. © 2019 International Parkinson and Movement Disorder Society.

Statins may facilitate Parkinson's disease: Insight gained from a large, national claims database.

OBJECTIVE: Using a large U.S. claims database (MarketScan), we investigated the controversy surrounding the role of statins in Parkinson's disease (PD). METHODS: We performed a retrospective case-control analysis. First, we identified 2322 incident PD cases having a minimum of 2.5 years of continuous enrollment prior to earliest diagnosis code or prescription of antiparkinson medication. A total of 2322 controls were then matched individually by age, gender, and a follow-up window to explore the relationship of statin use with incident PD. RESULTS: Statin usage was significantly associated with PD risk, with the strongest associations being for lipophilic (odds ratio = 1.58, P < .0001) versus hydrophilic (odds ratio = 1.19, P = .25) statins, statins plus nonstatins (odds ratio = 1.95, P < .0001), and for the initial period after starting statins (<1 year odds ratio = 1.82, 1-2.5 years odds ratio = 1.75, and ≥2.5 years odds ratio = 1.37; Ptrend < .0001). CONCLUSION: The use of statin (especially lipophilics) was associated with higher risk of PD, and the stronger association in initial use suggests a facilitating effect. © 2017 International Parkinson and Movement Disorder Society.

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

BACKGROUND: It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. METHODS: Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. RESULTS: First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. CONCLUSIONS: Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. TRIAL REGISTRATION: The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

History of smoking and olfaction in Parkinson's disease.

OBJECTIVE: Olfactory dysfunction is the most common pre-motor symptom in Parkinson's disease (PD), and smoking is known to be associated with lower risk of PD. This study tested the hypothesis that smoking is associated with better olfaction in PD. METHODS: Smoking history was obtained from 76 PD subjects (22 with a history of smoking [smokers], 54 who never smoked [nonsmokers]), and 70 controls (17 smokers, 53 nonsmokers). Olfaction was assessed using the 40-item University of Pennsylvania Smell Identification Test (UPSIT). The olfactory scores between groups and subgroups were compared using analysis of covariance with adjustment for age, gender, and monoamine oxidase B (MAO-B) inhibitor usage. RESULTS: Overall the olfactory score was lower in PD compared with controls (olfactory scores: 21.5 vs. 33.5, P < 0.0001). Among controls, there was no significant difference in olfaction between smokers and nonsmokers (olfactory scores, 33.2 vs. 34.2; P = 0.95). Among PD subjects, however, smokers scored significantly better regarding olfaction compared with nonsmokers (olfactory scores: 24.4 vs. 19.9, P = 0.02). CONCLUSIONS: These data suggest that a history of smoking is associated with better olfaction among PD patients. The finding may be related to why smoking may be protective against PD. Further studies are needed to confirm this finding and investigate the underlying mechanisms.

Lexical-semantic search related to side of onset and putamen volume in Parkinson's disease.

Parkinson's disease (PD) is characterized by dopaminergic cell loss and reduced striatal volume. Prior studies have demonstrated striatal involvement in access to lexical-semantic knowledge and damage to this structure may be evident in the lexical properties of responses. Semantic fluency task responses from early stage, non-demented PD participants with right (PD-R) or left (PD-L) lateralizing symptoms were compared to matched controls on lexical properties (word frequency, age of acquisition) and correlated with striatal volumes segmented from T1-weighted brain MR images. PD-R participants produced semantic fluency responses of a lower age of acquisition than PD-L and control participants (p < 0.05). PD-R age of acquisition responses correlated positively with putamen volume (p < 0.05), while age of acquisition of responses correlated negatively with caudate volume in controls (p < 0.05). Findings provide evidence for a role of the striatum in lexical-semantic access and qualitative changes in lexical access in select PD patients.

Prediction of emergency department resource requirements during triage: An application of current natural language processing techniques.

OBJECTIVE: Accurate triage in the emergency department (ED) is critical for medical safety and operational efficiency. We aimed to predict the number of future required ED resources, as defined by the Emergency Severity Index (ESI) triage protocol, using natural language processing of nursing triage notes. METHODS: We constructed a retrospective cohort of all 265,572 consecutive ED encounters from 2015 to 2016 from 3 separate clinically heterogeneous academically affiliated EDs. We excluded encounters missing relevant information, leaving 226,317 encounters. We calculated the number of resources used by patients in the ED retrospectively and based outcome categories on criteria defined in the ESI algorithm: 0 (30,604 encounters), 1 (49,315 encounters), and 2 or more (146,398 encounters). A neural network model was trained on a training subset to predict the number of resources using triage notes and clinical variables at triage. Model performance was evaluated using the test subset and was compared with human ratings. RESULTS: Overall model accuracy and macro F1 score for number of resources were 66.5% and 0.601, respectively. The model had similar macro F1 (0.589 vs 0.592) and overall accuracy (65.9% vs 69.0%) compared to human raters. Model predictions had slightly higher F1 scores and accuracy for 0 resources and were less accurate for 2 or more resources. CONCLUSIONS: Machine learning of nursing triage notes, combined with clinical data available at ED presentation, can be used to predict the number of required future ED resources. These findings suggest that machine learning may be a valuable adjunct tool in the initial triage of ED patients.

Prediction of emergency department patient disposition based on natural language processing of triage notes.

BACKGROUND: Nursing triage documentation is the first free-form text data created at the start of an emergency department (ED) visit. These 1-3 unstructured sentences reflect the clinical impression of an experienced nurse and are key in gauging a patient's illness. We aimed to predict final ED disposition using three commonly-employed natural language processing (NLP) techniques of nursing triage notes in isolation from other data. METHODS: We constructed a retrospective cohort of all 260,842 consecutive ED encounters in 2015-16, from three clinically heterogeneous academically-affiliated EDs. After exclusion of 3964 encounters based on completeness of triage, and disposition data, we included 256,878 encounters. We defined the outcome as: 1) admission, transfer, or in-ED death [68,092 encounters] vs. 2) discharge, "left without being seen," and "left against medical advice" [188,786 encounters]. The dataset was divided into training and testing subsets. Neural network regression models were trained using bag-of-words, paragraph vectors, and topic distributions to predict disposition and were evaluated using the testing dataset. RESULTS: Area under the curve for disposition using triage notes as bag-of-words, paragraph vectors, and topic distributions were 0.737 (95% CI: 0.734 - 0.740), 0.785 (95% CI: 0.782 - 0.788), and 0.687 (95% CI: 0.684 - 0.690), respectively. CONCLUSIONS: Nursing triage notes can be used to predict final ED patient disposition, even when used separately from other clinical information. These findings have substantial implications for future studies, suggesting that free text from medical records may be considered as a critical predictor in research of patient outcomes.

Cortical Thinning and Cognitive Impairment in Parkinson's Disease without Dementia.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized clinically by motor dysfunction (bradykinesia, rigidity, tremor, and postural instability), and pathologically by the loss of dopaminergic neurons in the substantia nigra of the basal ganglia. Growing literature supports that cognitive deficits may also be present in PD, even in non-demented patients. Gray matter (GM) atrophy has been reported in PD and may be related to cognitive decline. This study investigated cortical thickness in non-demented PD subjects and elucidated its relationship to cognitive impairment using high-resolution T1-weighted brain MRI and comprehensive cognitive function scores from 71 non-demented PD and 48 control subjects matched for age, gender, and education. Cortical thickness was compared between groups using a flexible hierarchical multivariate Bayesian model, which accounts for correlations between brain regions. Correlation analyses were performed among brain areas and cognitive domains as well, which showed significant group differences in the PD population. Compared to Controls, PD subjects demonstrated significant age-adjusted cortical thinning predominantly in inferior and superior parietal areas and extended to superior frontal, superior temporal, and precuneus areas (posterior probability >0.9). Cortical thinning was also found in the left precentral and lateral occipital, and right postcentral, middle frontal, and fusiform regions (posterior probability >0.9). PD patients showed significantly reduced cognitive performance in executive function, including set shifting (p = 0.005) and spontaneous flexibility (p = 0.02), which were associated with the above cortical thinning regions (p < 0.05).

Cortical gray and subcortical white matter associations in Parkinson's disease.

Cortical atrophy has been documented in both Parkinson's disease (PD) and healthy aging, but its relationship to changes in subcortical white matter is unknown. This was investigated by obtaining T1- and diffusion-weighted images from 76 PD and 70 controls at baseline and 18 and 36 months, from which cortical volumes and underlying subcortical white matter axial diffusivity (AD), radial diffusivity (RD), and fractional anisotropy (FA) were determined. Twelve of 69 cortical subregions had significant group differences, and for these, underlying subcortical white matter was explored. At baseline, higher cortical volumes were significantly correlated with lower underlying subcortical white matter AD, RD, and higher FA (ps ≤ 0.017) in PD. Longitudinally, higher rates of cortical atrophy in PD were associated with increased rates of change in AD RD, and FA values (ps ≤ 0.0013) in 2 subregions explored. The significant gray-white matter associations were not found in controls. Thus, unlike healthy aging, cortical atrophy and subcortical white matter changes may not be independent events in PD.

Lateralized Basal Ganglia Vulnerability to Pesticide Exposure in Asymptomatic Agricultural Workers.

Pesticide exposure is linked to Parkinson's disease, a neurodegenerative disorder marked by dopamine cell loss in the substantia nigra of the basal ganglia (BG) that often presents asymmetrically. We previously reported that pesticide-exposed agricultural workers (AW) have nigral diffusion tensor imaging (DTI) changes. The current study sought to confirm this finding, and explore its hemisphere and regional specificity within BG structures using an independent sample population. Pesticide exposure history, standard neurological exam, high-resolution magnetic resonance imaging (T1/T2-weighted and DTI), and [123I]ioflupane SPECT images (to quantify striatal dopamine transporters) were obtained from 20 AW with chronic pesticide exposure and 11 controls. Based on median cumulative days of pesticide exposure, AW were subdivided into high (AWHi, n = 10) and low (AWLo, n = 10) exposure groups. BG (nigra, putamen, caudate, and globus pallidus [GP]) fractional anisotropy (FA), mean diffusivity (MD), and striatal [123I]ioflupane binding in each hemisphere were quantified, and compared across exposure groups using analysis of variance. Left, but not right, nigral and GP FA were significantly lower in AW compared with controls (p's < .029). None of the striatal (putamen and caudate) DTI or [123I]ioflupane binding measurements differed between AW and controls. Subgroup analyses indicated that significant left nigral and GP DTI changes were present only in the AWHi (p ≤ .037) but not the AWLo subgroup. AW, especially those with higher pesticide exposure history, demonstrate lateralized microstructural changes in the nigra and GP, whereas striatal areas appear relatively unaffected. Future studies should elucidate how environmental toxicants cause differential lateralized- and regionally specific brain vulnerability.

Higher Plasma LDL-Cholesterol is Associated with Preserved Executive and Fine Motor Functions in Parkinson's Disease.

Plasma low density lipoprotein (LDL) cholesterol has been associated both with risk of Parkinson's disease (PD) and with age-related changes in cognitive function. This prospective study examined the relationship between baseline plasma LDL-cholesterol and cognitive changes in PD and matched Controls. Fasting plasma LDL-cholesterol levels were obtained at baseline from 64 non-demented PD subjects (62.7 ± 7.9 y) and 64 Controls (61.3 ± 6.8 y). Subjects underwent comprehensive neuropsychological testing at baseline, 18-, and 36-months. Linear mixed-effects modeling was used to assess the relationships between baseline LDL-cholesterol levels and longitudinal cognitive changes. At baseline, PD patients had lower scores of fine motor (p<0.0001), executive set shifting (p=0.018), and mental processing speed (p=0.049) compared to Controls. Longitudinally, Controls demonstrated improved fine motor and memory test scores (p=0.044, and p=0.003), whereas PD patients demonstrated significantly accelerated loss in fine motor skill (p=0.002) compared to Controls. Within the PD group, however, higher LDL-cholesterol levels were associated with improved executive set shifting (ß=0.003, p<0.001) and fine motor scores (ß=0.002, p=0.030) over time. These associations were absent in Controls (p>0.7). The cholesterol - executive set shifting association differed significantly between PDs and Controls (interaction p=0.005), whereas the cholesterol - fine motor association difference did not reach significance (interaction, p=0.104). In summary, higher plasma LDL-cholesterol levels were associated with better executive function and fine motor performance over time in PD, both of which may reflect an effect on nigrostriatal mediation. Confirmation of these results and elucidation of involved mechanisms are warranted, and might lead to feasible therapeutic strategies.

The Key Determinants to Quality of Life in Parkinson's Disease Patients: Results from the Parkinson's Disease Biomarker Program (PDBP).

BACKGROUND: The impact of motor- and non-motor symptoms on health-related quality of life (HRQOL) in Parkinson's disease (PD) has received increasing attention. OBJECTIVES: To address this, the study explored a large cohort of patients enrolled in the PD Biomarker Program. METHODS: The PD Questionnaire-39 (PDQ-39) measured HRQOL, whereas the Unified PD Rating Scale (UPDRS) assessed motor and non-motor symptoms. Determinants of HRQOL in PD patients were identified by stepwise linear regression analysis. The relationship between the PDQ-39 and UPDRS subscale scores then was explored through structural equation modeling. RESULTS: The mean disease duration was 6.8 years and the mean PDQ-39 summary index (PDQ-39SI) was 18.4. UPDRS-I (non-motor function) and UPDRS-II (motor questionnaire) scores demonstrated the strongest correlations with PDQ-39SI (r Ϡ 0.4, P < 0.05), whereas UPDRS-III (motor exam) and UPDRS-IV (motor complications) scores were correlated moderately with PDQ-39SI (0.3 < r < 0.4, P < 0.05). Multiple linear stepwise regression analyses showed that age (ß= -0.13, P < 0.001), education (ß= -0.07, P = 0.008), UPDRS-I (ß= 0.32, P = 0.000), and UPDRS-II (ß= 0.44, P < 0.001) significantly contributed to HRQOL, and cumulatively accounted for 69.1% of the PDQ-39SI variance. UPDRS-II score was the primary predictor of PDQ-39SI, accounting for 57.3% of the variance, whereas UPDRS-I score accounted for 7.5%. UPDRS-III and -IV and other factors measured did not survive stepwise regression. Structural equation modeling confirmed the association of UPDRS-II (ß= 0.67, P < 0.001) and UPDRS-I (ß= 0.35, P < 0.001) with the PDQ-39SI. CONCLUSION: Both motor and non-motor function scores impacted significantly HRQOL in PD. UPDRS-III, however, has limited contributions to HRQOL although it is used as a main outcome in many clinical trials.

Stage-dependent loss of cortical gyrification as Parkinson disease "unfolds".

OBJECTIVE: Nigrostriatal terminal losses are known to progress most rapidly in early-stage Parkinson disease (PD) and then plateau, whereas cortical pathology continues and may provide a better marker of PD progression in later stages. We investigated cortical gyrification indices in patients with different durations of PD, since cortical folding may capture complex processes involving transverse forces of neuronal sheets or underlying axonal connectivity. METHODS: Longitudinal cohort structural MRI were obtained at baseline, 18 months, and 36 months from 70 patients with PD without dementia and 70 control participants. Cortical local gyrification index (LGI) was compared between controls and PD subgroups based upon duration of illness (DOI, <1 year [PDE, n = 17], 1-5 years [PDM, n = 19], >5 years [PDL, n = 24]) and adjusted using false discovery rate. Associations between LGI and clinical measurements were assessed using multiple linear regression. Areas having significantly reduced LGI also were analyzed using baseline data from a newly established cohort (PD n = 87, control n = 66) to validate our findings. RESULTS: In the longitudinal cohort, PDL had significantly reduced overall gyrification, and bilaterally in the inferior parietal, postcentral, precentral, superior frontal, and supramarginal areas, compared to controls (p < 0.05). Longitudinally, loss of gyrification was accelerated in PDM participants, compared to controls. LGI showed robust correlations with DOI and also was correlated with PD-related clinical measurements. Similar results were obtained in the validation sample. CONCLUSIONS: Loss of cortical gyrification may be accelerated within the first few years after PD diagnosis, and become particularly prominent in later stages. Thus, it may provide a metric for monitoring progression in vivo.

The pattern of gray matter atrophy in Parkinson's disease differs in cortical and subcortical regions.

Cortical and subcortical gray matter (GM) atrophy may progress differently during the course of Parkinson's disease (PD). We delineated and compared the longitudinal pattern of these PD-related changes. Structural MRIs and clinical measures were obtained from 76 PD with different disease durations and 70 Controls at baseline, 18-, and 36 months. Both cortical and subcortical (putamen, caudate, and globus pallidus) GM volumes were obtained, compared, and associated with PD clinical measures at baseline. Their volumes and rates of change also were compared among Controls, PDs, and PD subgroups based on duration of illness [≤1 year (PD(E)), 1-5 years (PD(M)), and >5 years (PD(L))]. Compared to Controls, PD subjects displayed smaller cortical GM and striatal(putamen, caudate, ps ≤0.001), volumes at baseline. Cortical GM volumes were negatively associated with disease duration at baseline, whereas striatal volumes were not. PD subjects demonstrated accelerated volume loss in cortical GM (p = 0.006), putamen (p = 0.034), and caudate (p = 0.008) compared to Controls. Subgroup analyses demonstrated that accelerated cortical atrophy reached statistical significance in PD subjects with duration of illness 1-5 years (PD(M), ps<0.001) and the trend of accelerated atrophy seemed to persist until later stages, whereas striatal atrophy occurred in PD subjects with PD(E) (p = 0.021 for putamen, p = 0.005 for caudate) and PD(M) (p = 0.002 for putamen, p = 0.001 for caudate) that significantly slowed down in PD(L) (ps for PD(L) vs PD(E) or PD(M): <0.01). The pattern of GM loss in PD differs in cortical and subcortical regions, with striatal atrophy occurring earlier and extra-striatal cortical atrophy later.

Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors.

Medication non-adherence is a major concern in the healthcare industry and has led to increases in health risks and medical costs. For many neurological diseases, adherence to medication regimens can be assessed by observing movement patterns. However, physician observations are typically assessed based on visual inspection of movement and are limited to clinical testing procedures. Consequently, medication adherence is difficult to measure when patients are away from the clinical setting. The authors propose a data mining driven methodology that uses low cost, non-wearable multimodal sensors to model and predict patients' adherence to medication protocols, based on variations in their gait. The authors conduct a study involving Parkinson's disease patients that are "on" and "off" their medication in order to determine the statistical validity of the methodology. The data acquired can then be used to quantify patients' adherence while away from the clinic. Accordingly, this data-driven system may allow for early warnings regarding patient safety. Using whole-body movement data readings from the patients, the authors were able to discriminate between PD patients on and off medication, with accuracies greater than 97% for some patients using an individually customized model and accuracies of 78% for a generalized model containing multiple patient gait data. The proposed methodology and study demonstrate the potential and effectiveness of using low cost, non-wearable hardware and data mining models to monitor medication adherence outside of the traditional healthcare facility. These innovations may allow for cost effective, remote monitoring of treatment of neurological diseases.

A data mining methodology for predicting early stage Parkinson's disease using non-invasive, high-dimensional gait sensor data.

Parkinson's disease (PD) is the second most common neurological disorder after Alzheimer's disease. Key clinical features of PD are motor-related and are typically assessed by healthcare providers based on qualitative visual inspection of a patient's movement/gait/posture. More advanced diagnostic techniques such as computed tomography scans that measure brain function, can be cost prohibitive and may expose patients to radiation and other harmful effects. To mitigate these challenges, and open a pathway to remote patient-physician assessment, the authors of this work propose a data mining driven methodology that uses low cost, non-invasive sensors to model and predict the presence (or lack therefore) of PD movement abnormalities and model clinical subtypes. The study presented here evaluates the discriminative ability of non-invasive hardware and data mining algorithms to classify PD cases and controls. A 10-fold cross validation approach is used to compare several data mining algorithms in order to determine that which provides the most consistent results when varying the subject gait data. Next, the predictive accuracy of the data mining model is quantified by testing it against unseen data captured from a test pool of subjects. The proposed methodology demonstrates the feasibility of using non-invasive, low cost, hardware and data mining models to monitor the progression of gait features outside of the traditional healthcare facility, which may ultimately lead to earlier diagnosis of emerging neurological diseases.

Dopaminergic modulation of arm swing during gait among Parkinson's disease patients.

BACKGROUND: Reduced arm swing amplitude, symmetry, and coordination during gait have been reported in Parkinson's disease (PD), but the relationship between dopaminergic depletion and these upper limb gait changes remains unclear. OBJECTIVE: We aimed to investigate the effects of dopaminergic drugs on arm swing velocity, symmetry, and coordination in PD. METHODS: Forearm angular velocity was recorded in 16 PD and 17 control subjects (Controls) during free walking trials. Angular velocity amplitude of each arm, arm swing asymmetry, and maximum cross-correlation were compared between control and PD groups, and between OFF- and ON-medication states among PD subjects. RESULTS: Compared to Controls, PD subjects in the OFF-medication state exhibited lower angular velocity amplitude of the slower- (p = 0.0018), but not faster- (p = 0.2801) swinging arm. In addition, PD subjects demonstrated increased arm swing asymmetry (p = 0.0046) and lower maximum cross-correlation (p = 0.0026). Following dopaminergic treatment, angular velocity amplitude increased in the slower- (p = 0.0182), but not faster- (p = 0.2312) swinging arm among PD subjects. Furthermore, arm swing asymmetry decreased (p = 0.0386), whereas maximum cross-correlation showed no change (p = 0.7436). Pre-drug angular velocity amplitude of the slower-swinging arm was correlated inversely with the change in arm swing asymmetry (R = -0.73824, p = 0.0011). CONCLUSIONS: This study provides quantitative evidence that reduced arm swing and symmetry in PD can be modulated by dopaminergic replacement. The lack of modulations of bilateral arm coordination suggests that additional neurotransmitters may also be involved in arm swing changes in PD. Further studies are warranted to investigate the longitudinal trajectory of arm swing dynamics throughout PD progression.

Microstructural changes in the substantia nigra of asymptomatic agricultural workers.

Parkinson's disease (PD) is marked by the loss of dopamine neurons in the substantia nigra (SN). Although the exact etiology is unknown, sporadic PD is hypothesized to be a result of genetic susceptibility interacting with environmental insult. Epidemiological studies suggest that pesticide exposure is linked to higher PD risk, but there are no studies demonstrating SN changes with chronic pesticide exposure in human subjects. Thus, high resolution T2-weighted magnetic resonance imaging (MRI) and diffusion tensor (DTI) images were obtained from 12 agricultural workers with chronic pesticide exposure, 12 controls, and 12 PD subjects. Neither controls nor pesticide-exposed subjects, had any parkinsonian symptoms. Exposure history to pesticides was assessed by a structured questionnaire. DTI measures in the SN, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), were obtained for all subjects and compared among groups. Compared to controls, PD patients showed the expected significant changes in all DTI measurements in the SN. The pesticide-exposed subjects, compared to controls, had significantly lower FA values (p=0.022, after multiple comparisons correction), but no significant differences in RD, MD, or AD measures. The study is the first to demonstrate microstructural changes in the SN of human subjects with chronic pesticide exposure. The changes detected by MRI may mark "one of the hits" leading to PD, and underlie the increased risk of PD in pesticide users found in epidemiological studies. Further human studies assisted by these imaging markers may be useful in understanding the etiology of PD.

Striatal shape in Parkinson's disease.

Parkinson's disease (PD) is marked pathologically by nigrostriatal dopaminergic terminal loss. Histopathological and in vivo labeling studies demonstrate that this loss occurs most extensively in the caudal putamen and caudate head. Previous structural studies have suggested reduced striatal volume and atrophy of the caudate head in PD subjects. The spatial distribution of atrophy in the putamen, however, has not been characterized. We aimed to delineate the specific locations of atrophy in both of these striatal structures. T1- and T2-weighted brain MR (3T) images were obtained from 40 PD and 40 control subjects having no dementia and similar age and gender distributions. Shape analysis was performed using doubly segmented regions of interest. Compared to controls, PD subjects had lower putamen (p = 0.0003) and caudate (p = 0.0003) volumes. Surface contraction magnitudes were greatest on the caudal putamen (p ≤ 0.005) and head and dorsal body of the caudate (p ≤ 0.005). This spatial distribution of striatal atrophy is consistent with the known pattern of dopamine depletion in PD and may reflect global consequences of known cellular remodeling phenomena.