Alternating Finger Tapping Research Articles

The digital signature of emergent tremor in Parkinson’s disease

Emergent tremor in Parkinson’s disease (PD) can occur during sustained postures or movements that are different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. In this study, we investigated whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device could be reliably identified and distinguished from voluntary tapping. Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society – Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labeled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, p < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity, and arrhythmicity after tremor strikes were excluded (p < 0.01). The results demonstrate that emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand.

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

A Placebo-Controlled Study to Assess the Sensitivity of Finger Tapping to Medication Effects in Parkinson's Disease.

Movement Disorder Society-Unified Parkinson's Rating Scale Part III (MDS-UPDRS III) is the gold standard for assessing medication effects in patients with Parkinson's disease (PD). However, short and rater-independent measurements would be ideal for future trials. To assess the ability of 3 different finger tapping tasks to detect levodopa/carbidopa-induced changes over time and to determine their correlation and compare their discriminatory power with MDS-UPDRS III. This was a randomized, double-blind, crossover study in 20 patients with PD receiving levodopa/carbidopa and placebo capsules after overnight medication withdrawal. Pre- and up to 3.5 hours postdose, MDS-UPDRS III and tapping tasks were performed. Tasks included 2 touchscreen-based alternate finger tapping tasks (index finger versus index-middle finger tapping) and a thumb-index finger task using a goniometer. In the alternate index finger tapping task, levodopa/carbidopa compared with placebo resulted in significantly faster (total taps: 12.5 [95% confidence interval, CI, 6.7-18.2]) and less accurate tapping (total spatial error: 240 mm [95% CI, 123-357 mm]) with improved rhythm (intertap interval standard deviation [SD], -16.3% [95% CI, -29.9% to 0.0%]). In the thumb-index finger task, tapping was significantly faster (mean opening velocity, 151 degree/s [64-237 degree/s]), with a higher mean amplitude (8.4 degrees [3.7-13.0 degrees]) and improved rhythm (intertap interval SD, -46.4% [95% CI, -63.7% to -20.9%]). The speed-related endpoints showed a moderate-to-strong correlation with the MDS-UPDRS III (r=0.45-0.70). The effect sizes of total taps and spatial error in the alternate index finger tapping task and opening velocity in the thumb-index finger task were comparable with the MDS-UPDRS III. In contrast, the MDS-UPDRS III performed better than the alternate index-middle finger task. The alternate index finger and the thumb-index finger tapping tasks provide short, rater-independent measurements that are sensitive to levodopa/carbidopa effects with a similar effect size as the MDS-UPDRS III.

Quantitative Digitography Measures Motor Symptoms and Disease Progression in Parkinson's Disease.

Background:Assessment of motor signs in Parkinson’s disease (PD) requires an in-person examination. However, 50% of people with PD do not have access to a neurologist. Wearable sensors can provide remote measures of some motor signs but require continuous monitoring for several days. A major unmet need is reliable metrics of all cardinal motor signs, including rigidity, from a simple short active task that can be performed remotely or in the clinic.Objective:Investigate whether thirty seconds of repetitive alternating finger tapping (RAFT) on a portable quantitative digitography (QDG) device, which measures amplitude and timing, produces reliable metrics of all cardinal motor signs in PD.Methods:Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and clinical motor assessment. Eighteen individuals were followed longitudinally with repeated assessments for an average of three years and up to six years.Results:QDG-RAFT metrics showed differences between PD and controls and provided correlated metrics for total motor disability (MDS-UPDRS III) and for rigidity, bradykinesia, tremor, gait impairment, and freezing of gait (FOG). Additionally, QDG-RAFT tracked disease progression over several years off therapy and showed differences between akinetic-rigid and tremor-dominant phenotypes, as well as people with and without FOG.Conclusions:QDG is a reliable technology, which could be used in the clinic or remotely. This could improve access to care, allow complex remote disease management based on data received in real time, and accurate monitoring of disease progression over time in PD. QDG-RAFT also provides the comprehensive motor metrics needed for therapeutic trials.

Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance.

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.

Differential Effects of Pathological Beta Burst Dynamics Between Parkinson's Disease Phenotypes Across Different Movements.

Background: Resting state beta band (13–30 Hz) oscillations represent pathological neural activity in Parkinson’s disease (PD). It is unknown how the peak frequency or dynamics of beta oscillations may change among fine, limb, and axial movements and different disease phenotypes. This will be critical for the development of personalized closed loop deep brain stimulation (DBS) algorithms during different activity states.Methods: Subthalamic (STN) and local field potentials (LFPs) were recorded from a sensing neurostimulator (Activa® PC + S, Medtronic PLC.) in fourteen PD participants (six tremor-dominant and eight akinetic-rigid) off medication/off STN DBS during 30 s of repetitive alternating finger tapping, wrist-flexion extension, stepping in place, and free walking. Beta power peaks and beta burst dynamics were identified by custom algorithms and were compared among movement tasks and between tremor-dominant and akinetic-rigid groups.Results: Beta power peaks were evident during fine, limb, and axial movements in 98% of movement trials; the peak frequencies were similar during each type of movement. Burst power and duration were significantly larger in the high beta band, but not in the low beta band, in the akinetic-rigid group compared to the tremor-dominant group.Conclusion: The conservation of beta peak frequency during different activity states supports the feasibility of patient-specific closed loop DBS algorithms driven by the dynamics of the same beta band during different activities. Akinetic-rigid participants had greater power and longer burst durations in the high beta band than tremor-dominant participants during movement, which may relate to the difference in underlying pathophysiology between phenotypes.

Quantitative Assessment of Motor Response to a Low Subacute Levodopa Dose in the Differential Diagnosis of Parkinsonisms at Disease Onset: Data from the BoProPark Cohort.

Background:Differential diagnosis between Parkinson’s disease (PD) and atypical parkinsonisms (APs) may be difficult at disease onset. The response to levodopa (LD) is a key supportive feature but its definition is largely empirical. Studies evaluating this issue by quantitative tests are scanty.Objective:We aimed to assess the utility of a subacute low LD dose kinetic-dynamic test in the differential diagnosis between PD and APs. It was applied at the baseline of a prospective follow-up in patients with parkinsonian signs within three years of disease motor onset (“BoProPark” cohort) and eventually diagnosed as PD or APs according to consensus criteria.Methods:Patients under at least 3-month LD therapy received a first morning fasting dose of LD/benserazide or carbidopa (100/25 mg) and underwent simultaneous serial assessments of plasma LD concentration and alternate finger tapping frequency up to 3 h. The main outcome was the extent of LD motor response, calculated by the area under the 3 h tapping effect–time curve (AUC_ETap). A receiver operating characteristic (ROC) curve analysis was performed to establish the optimal AUC_ETap cut-off to differentiate PD and APs.Results:The first 100 consecutive “BoProPark” patients were analyzed. Forty-seven patients were classified as possible, 37 as probable PD and 16 as APs. AUC_ETap medians were similar in the PD subgroups but reduced to a third in APs (p < 0.001). The optimal AUC_ETap cut-off value was >2186 [(tap/min) x min], with a sensitivity of 92% and a specificity of 75%. Accuracy of the test was 0.85 (95% CI 0.74–0.95), p < 0.0001.Conclusion:The estimation of 3 h AUC_ETap after a subacute low LD dose proved a reliable, objective tool to assess LD motor response in our cohort of patients. AUC_ETap value rounded to ≥2200 supports PD diagnosis, while lower values may alert to AP diagnoses.

A validated measure of rigidity in Parkinson's disease using alternating finger tapping on an engineered keyboard

IntroductionReliable and accurate measures of rigidity have remained elusive in remote assessments of Parkinson's disease (PD). This has severely limited the utility of telemedicine in the care and treatment of people with PD. It has also had a large negative impact on the scope of available outcomes, and on the costs, of multicenter clinical trials in PD. The goal of this study was to determine if quantitative measures from an engineered keyboard were sensitive and related to clinical measures of rigidity. MethodsSixteen participants with idiopathic PD, off antiparkinsonian medications, and eleven age-matched control participants performed a 30 second repetitive alternating finger tapping task on an engineered keyboard and were assessed with the Unified Parkinson's Disease Rating Scale – motor (UPDRS-III). ResultsThe speed of the key release was significantly slower in the PD compared to control cohorts (p < 0.0001). In the PD cohort key release speed correlated with the lateralized upper extremity UPDRS III rigidity score (r = - 0.58, p < 0.0001), but not with the lateralized upper extremity tremor score (r = - 0.14, p = 0.43). ConclusionsThis validated measure of rigidity complements our previous validation of temporal metrics of the repetitive alternating finger tapping task with the UPDRS III, bradykinesia and with the ability to quantify tremor, arrhythmicity and freezing episodes, and suggests that 30 seconds of alternating finger tapping on a portable engineered keyboard could transform the treatment of PD with telemedicine and the precision of multicenter clinical trials.

Detection of Mild Cognitive Impairment Through Natural Language and Touchscreen Typing Processing.

Mild cognitive impairment (MCI), an identified prodromal stage of Alzheimer's Disease (AD), often evades detection in the early stages of the condition, when existing diagnostic methods are employed in the clinical setting. From an alternative perspective, smartphone interaction behavioral data, unobtrusively acquired in a non-clinical setting, can assist the screening and monitoring of MCI and its symptoms' progression. In this vein, the diagnostic ability of digital biomarkers, drawn from Fine Motor Impairment (FMI)- and Spontaneous Written Speech (SWS)-related data analysis, are examined here. In particular, keystroke dynamics derived from touchscreen typing activities, using Convolutional Neural Networks, along with linguistic features of SWS through Natural Language Processing (NLP), were used to distinguish amongst MCI patients and healthy controls (HC). Analytically, three indices of FMI (rigidity, bradykinesia and alternate finger tapping) and nine NLP features, related with lexical richness, grammatical, syntactical complexity, and word deficits, formed the feature space. The proposed approach was tested on two demographically matched groups of 11 MCI patients and 12 HC, having undergone the same neuropsychological tests, producing 4,930 typing sessions and 78 short texts, within 6 months, for analysis. A cascaded-classifier scheme was realized under three different feature combinations and validated via a Leave-One-Subject-Out cross-validation scheme. The acquired results have shown: (a) keystroke features with a k-NN classifier achieved an Area Under Curve (AUC) of 0.78 [95% confidence interval (CI):0.68–0.88; specificity/sensitivity (SP/SE): 0.64/0.92], (b) NLP features with a Logistic regression classifier achieved an AUC of 0.76 (95% CI: 0.65–0.85; SP/SE: 0.80/0.71), and (c) an ensemble model with the fusion of keystroke and NLP features resulted in AUC of 0.75 (95% CI:0.63–0.86; SP/SE 0.90/0.60). The current findings indicate the potentiality of new digital biomarkers to capture early stages of cognitive decline, providing a highly specific remote screening tool in-the-wild.

Quantitative Digitography Measures Fine Motor Disturbances in Chronically Treated HIV Similar to Parkinson's Disease.

Introduction: Motor and cognitive deficits were compared in aging, chronically treated human immunodeficiency virus (HIV) people, people with mild-to-moderate stage Parkinson’s disease (PD), and healthy controls.Methods: Groups consisted of 36 people with PD, 28 with HIV infection, and 28 healthy controls. Motor function was assessed with the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS-III) and a rapid alternating finger tapping (RAFT) task on an engineered keyboard known as Quantitative Digitography (QDG). Executive function, verbal memory, and visuospatial processing were assessed using standard neuropsychological tests.Results: HIV demonstrated RAFT deficits similar to PD such as reduced amplitude (P = 0.023) and greater amplitude variability (P = 0.019) in the index finger when compared to controls. This fine motor disturbance correlated with HIV’s immune health, measured by their CD4+ T cell count (P < 0.01). The UPDRS did not yield motor differences between HIV and controls. Executive function and verbal memory were impaired in HIV (P = 0.006, P = 0.016, respectively), but not in PD; visuospatial processing was similarly impaired in HIV and PD (P < 0.05) although motor deficits predominated in PD.Conclusions: Fine motor bradykinesia measured quantitatively by QDG RAFT holds promise as a marker of motor decline related to current immune health in aging HIV patients and may be useful in longitudinal studies regarding mechanisms of immunosenescence vs. potential toxicity of combination antiretroviral therapy (cART) in this population. Additionally, motor and cognitive networks in HIV may be affected differently as the disease progresses as observed in the differential patterns of impairment between HIV and PD, providing insight into the mechanisms of brain deterioration in HIV.

Mathematical modeling and parameter estimation of levodopa motor response in patients with parkinson disease

Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures.

A Deep Learning Framework for the Remote Detection of Parkinson'S Disease Using Smart-Phone Sensor Data.

The assessment of Parkinson's disease (PD) using wearable sensors in non-clinical environments presents an opportunity for objective disease classification and severity prediction on a high-frequency and longitudinal basis. However, many challenges exist in analysing remotely collected data due to many sources of data corruption. Using a cohort of 1,815 participants (866 controls and 949 with PD) we implement a range of classification algorithms on Alternate Finger Tapping test data collected on smart-phones in remote environments. We compare the disease classification ability of two traditional machine learning methods against two state-of-the-art deep learning approaches, determining if the latter is successful without the definition of an explicit feature set. We find the deep learning approaches capable of disease classification, often outperforming traditional methods. We show similarities between the participants successfully classified through use of a manually extracted feature set, and the features learnt by a convolutional neural network. Finally, we discuss the broader challenges of analysing remotely collected datasets whilst highlighting the suitability of deep learning for assessing PD when large participant numbers are available.

Big data in Parkinson’s disease: using smartphones to remotely detect longitudinal disease phenotypes

Objective: To better understand the longitudinal characteristics of Parkinson’s disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones. Approach: Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls. The ability of these features to predict self-assigned severity measures is assessed whilst simultaneously inspecting the severity scoring system for floor-ceiling effects. Finally, we study the relationship between motor and non-motor longitudinal behaviour to determine if separate aspects of the disease are dependent on one another. Main results: We find that the test performances of the most severe subjects show significant correlations with self-assigned severity measures. Interestingly, less severe subjects do not show significant correlations, which is shown to be a consequence of floor-ceiling effects within the mPower self-reporting severity system. We find that motor performance after practise is a better predictor of severity than baseline performance suggesting that starting performance at a new motor task is less representative of disease severity than the performance after the test has been learnt. We find PD subjects show significant impairments in motor ability as assessed through the alternating finger tapping (AFT) test in both the short- and long-term analyses. In the AFT and memory tests we demonstrate that PD subjects show a larger degree of longitudinal performance variability in addition to requiring more instances of a test to reach a steady state performance than healthy subjects. Significance: Our findings pave the way forward for objective assessment and quantification of longitudinal learning rates in PD. This can be particularly useful for symptom monitoring and assessing medication response. This study tries to tackle some of the major challenges associated with self-assessed severity labels by designing and validating features extracted from big datasets in PD, which could help identify digital biomarkers capable of providing measures of disease severity outside of a clinical environment.

Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease

A mobile application named HLTapper was designed for Android mobile phones to collect and assess real-time alternating finger tapping movement data such as mean tapping speeds, temporal variations of tapping intervals, horizontal tapping variances, and vertical tapping variances, from built-in sensors while a subject performs the finger tapping movement task according to directions given through the application interface. The results of a controlled experiment (40 subjects including nine subjects with Parkinson disease, 11 healthy age-matched subjects, and 20 healthy young subjects) and a discriminant analysis revealed sensitivity of 85.71% and specificity of 91.42%, on an average, which suggests that HLTapper would be useful for early diagnosis and personalised treatment plan adjustments of patients with Parkinson disease.

Computer keyboard interaction as an indicator of early Parkinson's disease.

Parkinson’s disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).

A Mathematical Model of Levodopa Medication Effect on Basal Ganglia in Parkinson's Disease: An Application to the Alternate Finger Tapping Task.

Malfunctions in the neural circuitry of the basal ganglia (BG), induced by alterations in the dopaminergic system, are responsible for an array of motor disorders and milder cognitive issues in Parkinson's disease (PD). Recently Baston and Ursino (2015a) presented a new neuroscience mathematical model aimed at exploring the role of basal ganglia in action selection. The model is biologically inspired and reproduces the main BG structures and pathways, modeling explicitly both the dopaminergic and the cholinergic system. The present work aims at interfacing this neurocomputational model with a compartmental model of levodopa, to propose a general model of medicated Parkinson's disease. Levodopa effect on the striatum was simulated with a two-compartment model of pharmacokinetics in plasma joined with a motor effect compartment. The latter is characterized by the levodopa removal rate and by a sigmoidal relationship (Hill law) between concentration and effect. The main parameters of this relationship are saturation, steepness, and the half-maximum concentration. The effect of levodopa is then summed to a term representing the endogenous dopamine effect, and is used as an external input for the neurocomputation model; this allows both the temporal aspects of medication and the individual patient characteristics to be simulated. The frequency of alternate tapping is then used as the outcome of the whole model, to simulate effective clinical scores. Pharmacokinetic-pharmacodynamic modeling was preliminary performed on data of six patients with Parkinson's disease (both “stable” and “wearing-off” responders) after levodopa standardized oral dosing over 4 h. Results show that the model is able to reproduce the temporal profiles of levodopa in plasma and the finger tapping frequency in all patients, discriminating between different patterns of levodopa motor response. The more influential parameters are the Hill coefficient, related with the slope of the effect sigmoidal relationship, the drug concentration at half-maximum effect, and the drug removal rate from the effect compartment. The model can be of value to gain a deeper understanding on the pharmacokinetics and pharmacodynamics of the medication, and on the way dopamine is exploited in the neural circuitry of the basal ganglia in patients at different stages of the disease progression.

Pharmacodynamics of a low subacute levodopa dose helps distinguish between multiple system atrophy with predominant Parkinsonism and Parkinson's disease.

The differential diagnosis between multiple system atrophy with predominant parkinsonism (MSA-P) and Parkinson's disease (PD) may be challenging at disease onset. Levodopa responsiveness helps distinguish the two groups, but studies evaluating this issue using objective standardized tests are scanty. We retrospectively examined the extent of levodopa response by an objective kinetic-dynamic test in a series of patients prospectively followed up for a parkinsonian syndrome and eventually diagnosed as MSA-P or PD. Sixteen MSA-P and 31 PD patients under chronic levodopa therapy received a first morning fasting dose of levodopa/benserazide (100/25 mg) or levodopa/carbidopa (125/12.5 or 100/25 mg) and underwent simultaneous serial assessments of plasma levodopa concentration and alternate finger tapping frequency up to 3 h post dosing. The main levodopa pharmacodynamic variables were the maximum percentage increase in tapping frequency over baseline values (ΔTapmax %) and the area under the tapping effect-time curve (AUCTap). Levodopa pharmacokinetics did not show significant differences between MSA-P and PD, whereas both the magnitude and overall extent of levodopa tapping effect were markedly reduced in the MSA-P group (p < 0.001). The combined use of specific cut-off values for both the main pharmacodynamic variables, ΔTapmax % <20% and AUCTap <1900 [(tapping/min)·min], correctly discriminated 15 out of 16 MSA-P patients from PD patients. A combined estimation of these pharmacodynamic variables after a subacute low levodopa dose may be a simple and practical clinical tool to aid the differential diagnosis between MSA-P and PD.

Arrhythmokinesis is evident during unimanual not bimanual finger tapping in Parkinson's disease.

BackgroundArrhythmokinesis, the variability in repetitive movements, is a fundamental feature of Parkinson’s disease (PD). We hypothesized that unimanual repetitive alternating finger tapping (AFT) would reveal more arrhythmokinesis compared to bimanual single finger alternating hand tapping (SFT), in PD.MethodsThe variability of inter-strike interval (CVISI) and of amplitude (CVAMP) during AFT and SFT were measured on an engineered, MRI-compatible keyboard in sixteen PD subjects off medication and in twenty-four age-matched controls.ResultsThe CVISI and CVAMP of the more affected (MA) and less affected (LA) sides in PD subjects were greater during AFT than SFT (P < 0.05). However, there was no difference between AFT and SFT for controls. Both CVISI and CVAMP were greater in the MA and LA hands of PD subjects versus controls during AFT (P < 0.01). The CVISI and CVAMP of the MA, but not the LA hand, were greater in PDs versus controls during SFT (P < 0.05). Also, AFT, but not SFT, detected a difference between the MA and LA hands of PDs (P < 0.01).ConclusionsUnimanual, repetitive alternating finger tapping brings out more arrhythmokinesis compared to bimanual, single finger tapping in PDs but not in controls. Arrhythmokinesis during unimanual, alternating finger tapping captured a significant difference between both the MA and LA hands of PD subjects and controls, whereas that during a bimanual, single finger tapping task only distinguished between the MA hand and controls. Arrhythmokinesis underlies freezing of gait and may also underlie the freezing behavior documented in fine motor control if studied using a unimanual alternating finger tapping task.

Bimanual motor deficits in older adults predicted by diffusion tensor imaging metrics of corpus callosum subregions

Age-related changes in the microstructural organization of the corpus callosum (CC) may explain declines in bimanual motor performance associated with normal aging. We used diffusion tensor imaging in young (n = 33) and older (n = 33) adults to investigate the microstructural organization of seven specific CC subregions (prefrontal, premotor, primary motor, primary sensory, parietal, temporal and occipital). A set of bimanual tasks was used to assess various aspects of bimanual motor functioning: the Purdue Pegboard test, simultaneous and alternating finger tapping, a choice reaction time test and a complex visuomotor tracking task. The older adults showed age-related deficits on all measures of bimanual motor performance. Correlation analyses within the older group showed that white matter fractional anisotropy of the CC occipital region was associated with bimanual fine manipulation skills (Purdue Pegboard test), whereas better performance on the other bimanual tasks was related to higher fractional anisotropy in the more anterior premotor, primary motor and primary sensory CC subregions. Such associations were less prominent in the younger group. Our findings suggest that structural alterations of subregional callosal fibers may account for bimanual motor declines in normal aging.

Improved efficacy of temporally non-regular deep brain stimulation in Parkinson's disease

High frequency deep brain stimulation is an effective therapy for motor symptoms in Parkinson's disease. However, the relative clinical efficacy of regular versus non-regular temporal patterns of stimulation in Parkinson's disease remains unclear. To determine the temporal characteristics of non-regular temporal patterns of stimulation important for the treatment of Parkinson's disease, we compared the efficacy of temporally regular stimulation with four non-regular patterns of stimulation in subjects with Parkinson's disease using an alternating finger tapping task. The patterns of stimulation were also evaluated in a biophysical model of the parkinsonian basal ganglia that exhibited prominent oscillatory activity in the beta frequency range. The temporal patterns of stimulation differentially improved motor task performance. Three of the non-regular patterns of stimulation improved performance of the finger tapping task more than temporally regular stimulation. In the computational model all patterns of deep brain stimulation suppressed beta band oscillatory activity, and the degree of suppression was strongly correlated with the clinical efficacy across stimulation patterns. The three non-regular patterns of stimulation that improved motor performance over regular stimulation also suppressed beta band oscillatory activity in the computational model more effectively than regular stimulation. These data demonstrate that the temporal pattern of stimulation is an important consideration for the clinical efficacy of deep brain stimulation in Parkinson's disease. Furthermore, non-regular patterns of stimulation may ameliorate motor symptoms and suppress pathological rhythmic activity in the basal ganglia more effectively than regular stimulation. Therefore, non-regular patterns of deep brain stimulation may have useful clinical and experimental applications.