Brainstem neuromelanin and iron MRI reveals a precise signature for idiopathic and LRRK2 Parkinson's disease.

Neuromelanin (NM) loss in substantia nigra pars compacta (SNc) and locus coeruleus (LC) reflects neuronal death in Parkinson's disease (PD). Since genetically-determined PD shows varied clinical expressivity, we wanted to accurately quantify and locate brainstem NM and iron, to discover whether specific MRI patterns are linked to Leucine-rich repeat kinase 2 G2019S PD (LRRK2-PD) or idiopathic Parkinson's disease (iPD). A 3D automated MRI atlas-based segmentation pipeline (3D-ABSP) for NM/iron-sensitive MRI images topographically characterized the SNc, LC, and red nucleus (RN) neuronal loss and calculated NM/iron contrast ratio (CR) and normalized volume (nVol). Left-side NM nVol was larger in all groups. PD had lower NM CR and nVol in ventral-caudal SNc, whereas iron increased in lateral, medial-rostral, and caudal SNc. The SNc NM CR reduction was associated with psychiatric symptoms. LC CR and nVol discriminated better among subgroups: LRRK2-PD had similar LC NM CR and nVol as that of controls, and larger LC NM nVol and RN iron CR than iPD. PD showed higher iron SNc nVol than controls, especially among LRRK2-PD. ROC analyses showed an AUC > 0.92 for most pairwise subgroup comparisons, with SNc NM being the best discriminator between HC and PD. NM measures maintained their discriminator power considering the subgroup of PD patients with less than 5 years of disease duration. The SNc iron CR and nVol increase was associated with longer disease duration in PD patients. The 3D-ABSP sensitively identified NM and iron MRI patterns strongly correlated with phenotypic PD features.

Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease.

BACKGROUND: Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments. OBJECTIVES: To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use. METHODS: We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not. RESULTS: Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG. CONCLUSIONS: It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.

Comparison of the Results of a Parkinson's Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial.

Background: For specialists in charge of Parkinson's disease (PD), one of the most time-consuming tasks of the consultations is the assessment of symptoms and motor fluctuations. This task is complex and is usually based on the information provided by the patients themselves, which in most cases is complex and biased. In recent times, different tools have appeared on the market that allow automatic ambulatory monitoring. The MoMoPa-EC clinical trial (NCT04176302) investigates the effect of one of these tools-Sense4Care's STAT-ON-can have on routine clinical practice. In this sub-analysis the agreement between the Hauser diaries and the STAT-ON sensor is analyzed. Methods: Eighty four patients from MoMoPa-EC cohort were included in this sub-analysis. The intraclass correlation coefficient was calculated between the patient diary entries and the sensor data. Results: The intraclass correlation coefficient of both methods was 0.57 (95% CI: 0.3-0.73) for the OFF time (%), 0.48 (95% CI: 0.17-0.68) for the time in ON (%), and 0.65 (95% CI%: 0.44-0.78) for the time with dyskinesias (%). Furthermore, the Spearman correlations with the UPDRS scale have been analyzed for different parameters of the two methods. The maximum correlation found was -0.63 (p < 0.001) between Mean Fluidity (one of the variables offered by the STAT-dON) and factor 1 of the UPDRS. Conclusion: This sub-analysis shows a moderate concordance between the two tools, it is clearly appreciated that the correlation between the different UPDRS indices is better with the STAT-ON than with the Hauser diary. Trial Registration: https://clinicaltrials.gov/show/NCT04176302 (NCT04176302).

Estimating dyskinesia severity in Parkinson's disease by using a waist-worn sensor: concurrent validity study.

Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson's disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson's patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient's activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician's assessment and the sensor output was analyzed with the Spearman's correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33-0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76-0.97: p < 0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use.

BACKGROUND: A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods). OBJECTIVE: The goal of this study was to analyze the accuracy of this algorithm under real conditions of use. METHODS: This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard. RESULTS: The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%. CONCLUSIONS: The kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting.

The Effects of Intensive Speech Treatment on Conversational Intelligibility in Spanish Speakers With Parkinson's Disease.

Purpose: The purpose of this study was to examine the effects of intensive speech treatment on the conversational intelligibility of Castilian Spanish speakers with Parkinson's disease (PD), as well as on the speakers' self-perceptions of disability. Method: Fifteen speakers with a medical diagnosis of PD participated in this study. Speech recordings were completed twice before treatment, immediately posttreatment, and at a 1-month follow-up session. Conversational intelligibility was assessed in 2 ways-transcription accuracy scores and intelligibility ratings on a 9-point Likert scale. The Voice Handicap Index (Núñez-Batalla et al., 2007) was administered as a measure of self-perceived disability. Results: Group data revealed that transcription accuracy and median ease-of-understanding ratings increased significantly immediately posttreatment, with gains maintained at the 1-month follow-up. The functional subscale of the Voice Handicap Index decreased significantly posttreatment, suggesting a decrease in perceived communication disability after speech treatment. Conclusion: These findings support the implementation of intensive voice treatment to improve conversational intelligibility in Spanish speakers with PD with dysarthria as well as to improve the speakers' perception of their daily communicative capabilities. Clinical and theoretical considerations are discussed.

A "HOLTER" for Parkinson's disease: Validation of the ability to detect on-off states using the REMPARK system.

The treatment of Parkinson's disease (PD) with levodopa is very effective. However, over time, motor complications (MCs) appear, restricting the patient from leading a normal life. One of the most disabling MCs is ON-OFF fluctuations. Gathering accurate information about the clinical status of the patient is essential for planning treatment and assessing its effect. Systems such as the REMPARK system, capable of accurately and reliably monitoring ON-OFF fluctuations, are of great interest. OBJECTIVE: To analyze the ability of the REMPARK System to detect ON-OFF fluctuations. METHODS: Forty-one patients with moderate to severe idiopathic PD were recruited according to the UK Parkinson's Disease Society Brain Bank criteria. Patients with motor fluctuations, freezing of gait and/or dyskinesia and who were able to walk unassisted in the OFF phase, were included in the study. Patients wore the REMPARK System for 3days and completed a diary of their motor state once every hour. RESULTS: The record obtained by the REMPARK System, compared with patient-completed diaries, demonstrated 97% sensitivity in detecting OFF states and 88% specificity (i.e., accuracy in detecting ON states). CONCLUSION: The REMPARK System detects an accurate evaluation of ON-OFF fluctuations in PD; this technology paves the way for an optimisation of the symptomatic control of PD motor symptoms as well as an accurate assessment of medication efficacy.

Corrigendum: Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

[This corrects the article on p. 431 in vol. 8, PMID: 28919877.].

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

BACKGROUND: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson's (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III). METHOD: Seventy-five patients suffering from Parkinson's disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient's home. Convergence between the algorithm and the scale was evaluated by using the Spearman's correlation coefficient. RESULTS: Correlation with the UPDRS-III was moderate (rho -0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho -0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was -0.67 (p < 0.01). CONCLUSION: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson's disease and motor fluctuations.

A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson's Disease Patients.

Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson's disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 × 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients' symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 × 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykinetic gait. Results obtained in 12 patients exhibit a sensitivity and specificity over 80%. Additionally, the system enables working 23 days (at waking hours) with a 1200 mAh battery and a sampling rate of 50 Hz, opening up the possibility to be used for other applications like wellbeing and sports.

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer.

Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Nigral and striatal connectivity alterations in asymptomatic LRRK2 mutation carriers: A magnetic resonance imaging study.

BACKGROUND: The study of functional connectivity by means of magnetic resonance imaging (MRI) in asymptomatic LRRK2 mutation carriers could contribute to the characterization of the prediagnostic phase of LRRK2-associated Parkinson's disease (PD). The objective of this study was to characterize MRI functional patterns during the resting state in asymptomatic LRRK2 mutation carriers. METHODS: We acquired structural and functional MRI data of 18 asymptomatic LRRK2 mutation carriers and 18 asymptomatic LRRK2 mutation noncarriers, all first-degree relatives of LRRK2-PD patients. Starting from resting-state data, we analyzed the functional connectivity of the striatocortical and the nigrocortical circuitry. Structural brain data were analyzed by voxel-based morphometry, cortical thickness, and volumetric measures. RESULTS: Asymptomatic LRRK2 mutation carriers had functional connectivity reductions between the caudal motor part of the left striatum and the ipsilateral precuneus and superior parietal lobe. Connectivity in these regions correlated with subcortical gray-matter volumes in mutation carriers. Asymptomatic carriers also showed increased connectivity between the right substantia nigra and bilateral occipital cortical regions (occipital pole and cuneus bilaterally and right lateral occipital cortex). No intergroup differences in structural MRI measures were found. In LRRK2 mutation carriers, age and functional connectivity correlated negatively with striatal volumes. Additional analyses including only subjects with the G2019S mutation revealed similar findings. CONCLUSIONS: Asymptomatic LRRK2 mutation carriers showed functional connectivity changes in striatocortical and nigrocortical circuits compared with noncarriers. These findings support the concept that altered brain connectivity precedes the onset of classical motor features in a genetic form of PD. © 2016 International Parkinson and Movement Disorder Society.

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer.

BACKGROUND: After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. OBJECTIVE: To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS: Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS: Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION: The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.

Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients.

Freezing of gait (FOG) is a common motor symptom of Parkinson's disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier's outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e., second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach provides 98.7% accuracy and a geometric mean of 96.1%. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90% and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor.

Sleep Disorders in Parkinsonian and Nonparkinsonian LRRK2 Mutation Carriers.

OBJECTIVE: In idiopathic Parkinson disease (IPD) sleep disorders are common and may antedate the onset of parkinsonism. Based on the clinical similarities between IPD and Parkinson disease associated with LRRK2 gene mutations (LRRK2-PD), we aimed to characterize sleep in parkinsonian and nonmanifesting LRRK2 mutation carriers (NMC). METHODS: A comprehensive interview conducted by sleep specialists, validated sleep scales and questionnaires, and video-polysomnography followed by multiple sleep latency test (MSLT) assessed sleep in 18 LRRK2-PD (17 carrying G2019S and one R1441G mutations), 17 NMC (11 G2019S, three R1441G, three R1441C), 14 non-manifesting non-carriers (NMNC) and 19 unrelated IPD. RESULTS: Sleep complaints were frequent in LRRK2-PD patients; 78% reported poor sleep quality, 33% sleep onset insomnia, 56% sleep fragmentation and 39% early awakening. Sleep onset insomnia correlated with depressive symptoms and poor sleep quality. In LRRK2-PD, excessive daytime sleepiness (EDS) was a complaint in 33% patients and short sleep latencies on the MSLT, which are indicative of objective EDS, were found in 71%. Sleep attacks occurred in three LRRK2-PD patients and a narcoleptic phenotype was not observed. REM sleep behavior disorder (RBD) was diagnosed in three LRRK2-PD. EDS and RBD were always reported to start after the onset of parkinsonism in LRRK2-PD. In NMC, EDS was rarely reported and RBD was absent. When compared to IPD, sleep onset insomnia was more significantly frequent, EDS was similar, and RBD was less significantly frequent and less severe in LRRK2-PD. In NMC, RBD was not detected and sleep complaints were much less frequent than in LRRK2-PD. No differences were observed in sleep between NMC and NMNC. CONCLUSIONS: Sleep complaints are frequent in LRRK2-PDand show a pattern that when compared to IPD is characterized by more frequent sleep onset insomnia, similar EDS and less prominent RBD. Unlike in IPD, RBD and EDS seem to be not markers of the prodromal stage of LRRK2-PD.

Validation of a portable device for mapping motor and gait disturbances in Parkinson's disease.

BACKGROUND: Patients with severe idiopathic Parkinson's disease experience motor fluctuations, which are often difficult to control. Accurate mapping of such motor fluctuations could help improve patients' treatment. OBJECTIVE: The objective of the study was to focus on developing and validating an automatic detector of motor fluctuations. The device is small, wearable, and detects the motor phase while the patients walk in their daily activities. METHODS: Algorithms for detection of motor fluctuations were developed on the basis of experimental data from 20 patients who were asked to wear the detector while performing different daily life activities, both in controlled (laboratory) and noncontrolled environments. Patients with motor fluctuations completed the experimental protocol twice: (1) once in the ON, and (2) once in the OFF phase. The validity of the algorithms was tested on 15 different patients who were asked to wear the detector for several hours while performing daily activities in their habitual environments. In order to assess the validity of detector measurements, the results of the algorithms were compared with data collected by trained observers who were accompanying the patients all the time. RESULTS: The motor fluctuation detector showed a mean sensitivity of 0.96 (median 1; interquartile range, IQR, 0.93-1) and specificity of 0.94 (median 0.96; IQR, 0.90-1). CONCLUSIONS: ON/OFF motor fluctuations in Parkinson's patients can be detected with a single sensor, which can be worn in everyday life.

The onset of nonmotor symptoms in Parkinson's disease (the ONSET PD study).

Nonmotor symptoms (NMS) in Parkinson's disease (PD) can precede onset of motor symptoms. Relationship between premotor symptoms onset and motor features is limited. Our aim is to describe the presence and perceived onset of NMS in PD as well as their possible association with motor phenotype. Presence and onset of NMS were assessed by a custom-made questionnaire in 109 newly diagnosed untreated PD patients and 107 controls from 11 Spanish and Austrian centers. Seventeen of thirty-one NMS were more common in patients than controls (P < 0.05). They were usually mild and frequently reported to occur at different time-spans before motor symptoms. Anhedonia, apathy, memory complaints, and inattention occurred more frequently during the 2-year premotor period. Those reported more frequently in the 2- to 10-year premotor period were smell loss, mood disturbances, taste loss, excessive sweating, fatigue, and pain. Constipation, dream-enacting behavior, excessive daytime sleepiness, and postprandial fullness were frequently perceived more than 10 years before motor symptoms. No correlation between NMS burden and motor severity, age, or gender was observed. NMS associated in four clusters: rapid eye movement sleep behavior disorder symptoms-constipation, cognition-related, mood-related, and sensory clusters. No cluster was associated with a specific motor phenotype or severity. NMS are common in early unmedicated PD and frequently reported to occur in the premotor period. They are generally mild, but a patient subgroup showed high NMS burden mainly resulting from cognition-related symptoms. Certain NMS when present at the time of assessment or in the premotor stage, either alone or in combination, allowed discriminating PD from controls.

A double closed loop to enhance the quality of life of Parkinson's Disease patients: REMPARK system.

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.

Blind randomized controlled study of the efficacy of cognitive training in Parkinson's disease.

The aim of this study was to analyze the efficacy of a cognitive training program on cognitive performance and quality of life in nondemented Parkinson's disease patients. Participants who met UK Brain Bank diagnosis criteria for Parkinson's disease, with I-III Hoehn & Yahr, aged 50-80, and nondemented (Mini-Mental State Examination ≥ 23) were recruited. Patient's cognitive performance and functional and quality-of-life measures were assessed with standardized neuropsychological tests and scales at baseline and after 4 weeks. Subjects were randomly and blindly allocated by age and premorbid intelligence (Vocabulary, Wechsler Adult Intelligence Scale-III) into 2 groups: an experimental group and a control group. The experimental group received 4 weeks of 3 weekly 45-minute sessions using multimedia software and paper-and-pencil cognitive exercises, and the control group received speech therapy. A total of 28 patients were analyzed. Compared with the control group participants (n = 12), the experimental group participants (n = 16) demonstrated improved performance in tests of attention, information processing speed, memory, visuospatial and visuoconstructive abilities, semantic verbal fluency, and executive functions. There were no observable benefits in self-reported quality of life or cognitive difficulties in activities of daily living. We concluded that intensive cognitive training may be a useful tool in the management of cognitive functions in Parkinson's disease. © 2011 Movement Disorder Society.

What contributes to driving ability in Parkinson's disease.

PURPOSE: To determine the most significant clinical predictors that influence driving ability in Parkinson disease (PD). METHODS: National-multi-centre, cross-sectional study covering PD outpatients. Clinical assessment was based on the following questionnaires: cognition (SCOPA-Cog); motor impairment and disabilities (SCOPA motor); depression/anxiety; sleep (SCOPA-Sleep); psychosis and severity/global impairment (HY and CISI-PD). Driving status data was obtained using a standardized questionnaire. Comparisons between drivers and ex-drivers were calculated using chi(2) and Student t-tests as appropriate. Multi-variate logistic regression analysis was performed to identify independent driving ability clinical predictors. RESULTS: Compared with the drivers, the ex-drivers were older (p = 0.00005), had longer disease duration (p = 0.03), had more overall cognitive dysfunction (p = 0.004) and had greater motor impairment, as measured by the CISI (p = 0.02), HY stage (p = 0.034) and by the SCOPA-motor scale (p = 0.002) and difficulty in activities of daily life (p = 0.002). In the regression model analysis, aging and ADL impairment were the principal clinical predictors that differentiated drivers from ex-drivers. CONCLUSIONS: Although overall driving impairment in PD is associated with advancing disease severity, driving ability seems to be more strongly influenced by age and ADL impairment. Multi-disciplinary teams are required to assess driving ability in patients with PD and develop rehabilitation measures for safer driving.