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).

Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease. Mobile technology can help clinical decision making by completing the information of motor status between hospital visits. This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing features based on a covariance, skewness, and kurtosis analysis of the timing information of the data to capture PD motor signs. We tested these features, both independently and in a multivariate framework, in a population of 21 PD and 23 control subjects, achieving a sensitivity/specificity of 0.81/0.81 for the best performing feature and 0.73/0.84 for the best multivariate method. The results of the alternating finger-tapping, an established motor test, measured in our cohort are 0.75/0.78. This paper contributes to the development of a home-based, high-compliance, and high-frequency PD motor test by analysis of routine typing on touchscreens.

BackgroundParkinson’s disease (PD) is the second most prevalent neurodegenerative disease and one of the most common forms of movement disorder. Although there is no known cure for PD, existing therapies can provide effective symptomatic relief. However, optimal titration is crucial to avoid adverse effects. Today, decision making for PD management is challenging because it relies on subjective clinical evaluations that require a visit to the clinic. This challenge has motivated recent research initiatives to develop tools that can be used by nonspecialists to assess psychomotor impairment. Among these emerging solutions, we recently reported the neuroQWERTY index, a new digital marker able to detect motor impairment in an early PD cohort through the analysis of the key press and release timing data collected during a controlled in-clinic typing task.ObjectiveThe aim of this study was to extend the in-clinic implementation to an at-home implementation by validating the applicability of the neuroQWERTY approach in an uncontrolled at-home setting, using the typing data from subjects’ natural interaction with their laptop to enable remote and unobtrusive assessment of PD signs.MethodsWe implemented the data-collection platform and software to enable access and storage of the typing data generated by users while using their computer at home. We recruited a total of 60 participants; of these participants 52 (25 people with Parkinson’s and 27 healthy controls) provided enough data to complete the analysis. Finally, to evaluate whether our in-clinic-built algorithm could be used in an uncontrolled at-home setting, we compared its performance on the data collected during the controlled typing task in the clinic and the results of our method using the data passively collected at home.ResultsDespite the randomness and sparsity introduced by the uncontrolled setting, our algorithm performed nearly as well in the at-home data (area under the receiver operating characteristic curve [AUC] of 0.76 and sensitivity/specificity of 0.73/0.69) as it did when used to evaluate the in-clinic data (AUC 0.83 and sensitivity/specificity of 0.77/0.72). Moreover, the keystroke metrics presented a strong correlation between the 2 typing settings, which suggests a minimal influence of the in-clinic typing task in users’ normal typing.ConclusionsThe finding that an algorithm trained on data from an in-clinic setting has comparable performance with that tested on data collected through naturalistic at-home computer use reinforces the hypothesis that subtle differences in motor function can be detected from typing behavior. This work represents another step toward an objective, user-convenient, and quasi-continuous monitoring tool for PD.

Introduction Evidence suggests that the cerebellum could play a role in the pathophysiology of orthostatic tremor. The link between orthostatic tremor and the cerebellum is of interest, especially in light of the role the cerebellum plays in cognition, and it raises the possibility that orthostatic tremor patients could have cognitive deficits consistent with cerebellar dysfunction. Our aim was to examine whether orthostatic tremor patients had cognitive deficits and distinct personality profiles when compared with matched controls. Methods Sixteen consecutive orthostatic tremor patients (65.7 ± 13.3 years) and 32 healthy matched controls underwent a neuropsychological battery and the Personality Assessment Inventory. In linear regression models, the dependent variable was each one of the neuropsychological test scores or the Personality Assessment Inventory subscales and the independent variable was orthostatic tremor vs. control. Results Adjusted for age in years, sex, years of education, comorbidity index, current smoker, and depressive symptoms, diagnosis (orthostatic tremor vs. healthy control) was associated with poor performance on tests of executive function, visuospatial ability, verbal memory, visual memory, and language tests, and on a number of the Personality Assessment Inventory subscales (somatic concerns, anxiety related disorders, depression, and antisocial features). Older-onset OT (>60 years) patients had poorer scores on cognitive and personality testing compared with their younger-onset OT counterparts. Conclusion Orthostatic tremor patients have deficits in specific aspects of neuropsychological functioning, particularly those thought to rely on the integrity of the prefrontal cortex, which suggests involvement of frontocerebellar circuits. Cognitive impairment and personality disturbances could be disease-associated nonmotor manifestations of orthostatic tremor.

Very little is known about the pathogenesis of orthostatic tremor (OT). We have observed that OT patients might have deficits in specific aspects of neuropsychological function, particularly those thought to rely on the integrity of the prefrontal cortex, which suggests a possible involvement of frontocerebellar circuits. We examined whether resting-state functional magnetic resonance imaging (fMRI) might provide further insights into the pathogenesis on OT. Resting-state fMRI data in 13 OT patients (11 women and 2 men) and 13 matched healthy controls were analyzed using independent component analysis, in combination with a “dual-regression” technique, to identify group differences in several resting-state networks (RSNs). All participants also underwent neuropsychological testing during the same session. Relative to healthy controls, OT patients showed increased connectivity in RSNs involved in cognitive processes (default mode network [DMN] and frontoparietal networks), and decreased connectivity in the cerebellum and sensorimotor networks. Changes in network integrity were associated not only with duration (DMN and medial visual network), but also with cognitive function. Moreover, in at least 2 networks (DMN and medial visual network), increased connectivity was associated with worse performance on different cognitive domains (attention, executive function, visuospatial ability, visual memory, and language). In this exploratory study, we observed selective impairments of RSNs in OT patients. This and other future resting-state fMRI studies might provide a novel method to understand the pathophysiological mechanisms of motor and nonmotor features of OT.

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Objective The recent advances in technology are opening a new opportunity to remotely evaluate motor features in people with Parkinson's disease (PD). We hypothesized that typing on an electronic device, a habitual behavior facilitated by the nigrostriatal dopaminergic pathway, could allow for objectively and nonobtrusively monitoring parkinsonian features and response to medication in an at‐home setting. Methods We enrolled 31 participants recently diagnosed with PD who were due to start dopaminergic treatment and 30 age‐matched controls. We remotely monitored their typing pattern during a 6‐month (24 weeks) follow‐up period before and while dopaminergic medications were being titrated. The typing data were used to develop a novel algorithm based on recursive neural networks and detect participants’ responses to medication. The latter were defined by the Unified Parkinson's Disease Rating Scale‐III (UPDRS‐III) minimal clinically important difference. Furthermore, we tested the accuracy of the algorithm to predict the final response to medication as early as 21 weeks prior to the final 6‐month clinical outcome. Results The score on the novel algorithm based on recursive neural networks had an overall moderate kappa agreement and fair area under the receiver operating characteristic (ROC) curve with the time‐coincident UPDRS‐III minimal clinically important difference. The participants classified as responders at the final visit (based on the UPDRS‐III minimal clinically important difference) had higher scores on the novel algorithm based on recursive neural networks when compared with the participants with stable UPDRS‐III, from the third week of the study onward. Conclusions This preliminary study suggests that remotely gathered unsupervised typing data allows for the accurate detection and prediction of drug response in PD. © 2019 International Parkinson and Movement Disorder Society

BackgroundUnilateral magnetic resonance-guided focused ultrasound (FUS) thalamotomy is efficacious for the treatment of medically refractory essential tremor (ET). Viability of bilateral FUS ablation is unexplored.MethodsPatients diagnosed with medically refractory ET and previously treated with unilateral FUS thalamotomy at least 5 months before underwent bilateral treatment. The timepoints were baseline (before first thalamotomy) and FUS1 and FUS2 (4 weeks before and 6 months after second thalamotomy, respectively). The primary endpoint was safety. Efficacy was assessed through the Clinical Rating Scale for Tremor (CRST), which includes subscales for tremor examination (part A), task performance (part B) and tremor-related disability (part C).ResultsNine patients were treated. No permanent adverse events were registered. Six patients presented mild gait instability and one dysarthria, all resolving within the first few weeks. Three patients reported perioral hypoesthesia, resolving in one case. Total CRST score improved by 71% from baseline to FUS2 (from 52.3±12 to 15.5±9.4, p<0.001), conveying a 67% reduction in bilateral upper limb A+B (from 32.3±7.8 to 10.8±7.3, p=0.001). Part C decreased by 81% (from 16.4±3.6 to 3.1±2.9, p<0.001). Reduction in head and voice tremor was 66% (from 1.2±0.44 to 0.4±0.54, p=0.01) and 45% (from 1.8±1.1 to 1±0.8, p=0.02), respectively.ConclusionBilateral staged FUS thalamotomy for ET is feasible and might be safe and effective. Voice and head tremor might also improve. A controlled study is warranted.