Clinical and kinematic characterization of parkinsonian soft signs in essential tremor.

BACKGROUND: Subtle parkinsonian signs, i.e., rest tremor and bradykinesia, are considered soft signs for defining essential tremor (ET) plus. OBJECTIVES: Our study aimed to further characterize subtle parkinsonian signs in a relatively large sample of ET patients from a clinical and neurophysiological perspective. METHODS: We employed clinical scales and kinematic techniques to assess a sample of 82 ET patients. Eighty healthy controls matched for gender and age were also included. The primary focus of our study was to conduct a comparative analysis of ET patients (without any soft signs) and ET-plus patients with rest tremor and/or bradykinesia. Additionally, we investigated the asymmetry and side concordance of these soft signs. RESULTS: In ET-plus patients with parkinsonian soft signs (56.10% of the sample), rest tremor was clinically observed in 41.30% of cases, bradykinesia in 30.43%, and rest tremor plus bradykinesia in 28.26%. Patients with rest tremor had more severe and widespread action tremor than other patients. Furthermore, we observed a positive correlation between the amplitude of action and rest tremor. Most ET-plus patients had an asymmetry of rest tremor and bradykinesia. There was no side concordance between these soft signs, as confirmed through both clinical examination and kinematic evaluation. CONCLUSIONS: Rest tremor and bradykinesia are frequently observed in ET and are often asymmetric but not concordant. Our findings provide a better insight into the phenomenology of ET and suggest that the parkinsonian soft signs (rest tremor and bradykinesia) in ET-plus may originate from distinct pathophysiological mechanisms.

The use of wearables for the diagnosis and treatment of Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient's medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.

Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson's Disease.

The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson's Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson's Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.

A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data.

Bradykinesia is a cardinal hallmark of Parkinson's disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson's Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson's disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia.

Modulation of beta bursts in subthalamic sensorimotor circuits predicts improvement in bradykinesia.

No biomarker of Parkinson's disease exists that allows clinicians to adjust chronic therapy, either medication or deep brain stimulation, with real-time feedback. Consequently, clinicians rely on time-intensive, empirical, and subjective clinical assessments of motor behaviour and adverse events to adjust therapies. Accumulating evidence suggests that hypokinetic aspects of Parkinson's disease and their improvement with therapy are related to pathological neural activity in the beta band (beta oscillopathy) in the subthalamic nucleus. Additionally, effectiveness of deep brain stimulation may depend on modulation of the dorsolateral sensorimotor region of the subthalamic nucleus, which is the primary site of this beta oscillopathy. Despite the feasibility of utilizing this information to provide integrated, biomarker-driven precise deep brain stimulation, these measures have not been brought together in awake freely moving individuals. We sought to directly test whether stimulation-related improvements in bradykinesia were contingent on reduction of beta power and burst durations, and/or the volume of the sensorimotor subthalamic nucleus that was modulated. We recorded synchronized local field potentials and kinematic data in 16 subthalamic nuclei of individuals with Parkinson's disease chronically implanted with neurostimulators during a repetitive wrist-flexion extension task, while administering randomized different intensities of high frequency stimulation. Increased intensities of deep brain stimulation improved movement velocity and were associated with an intensity-dependent reduction in beta power and mean burst duration, measured during movement. The degree of reduction in this beta oscillopathy was associated with the improvement in movement velocity. Moreover, the reduction in beta power and beta burst durations was dependent on the theoretical degree of tissue modulated in the sensorimotor region of the subthalamic nucleus. Finally, the degree of attenuation of both beta power and beta burst durations, together with the degree of overlap of stimulation with the sensorimotor subthalamic nucleus significantly explained the stimulation-related improvement in movement velocity. The above results provide direct evidence that subthalamic nucleus deep brain stimulation-related improvements in bradykinesia are related to the reduction in beta oscillopathy within the sensorimotor region. With the advent of sensing neurostimulators, this beta oscillopathy combined with lead location could be used as a marker for real-time feedback to adjust clinical settings or to drive closed-loop deep brain stimulation in freely moving individuals with Parkinson's disease.

Frequency of hand movements as a possible diagnostic tool for parkinsonian bradykinesia. Proposal of a simple bedside test.

BACKGROUND: Bradykinesia, dysrhythmia, and decrement in hand movements (HM) are core symptoms of Parkinson's disease (PD). The maximal rate of repetitive rhythm-preserving HM can be a diagnostic tool for PD bradykinesia. OBJECTIVES: To improve the clinical diagnosis of bradykinesia by identifying the frequencies at which rhythmic HM become irregular in PD patients compared to healthy age-matched controls. METHOD: Forty PD patients and 16 controls were asked to alternately perform left and right hand movements following the rate of a metronome with sound stimulation beginning at 85 beats per minute (BPM) and increasing in increments of 15 BPM up to 355 BPM. The rhythm of the HM for each rate was assessed visually, and the threshold frequency at which the subject could no longer rhythmically continue HM was measured by the metronome. The increasing rates of HM until reaching that threshold were compared between patients with PD and controls. RESULTS: The mean rates of a metronome in PD vs. healthy subjects were 173.3 ± 42.0 vs. 248.8 ± 48.5 BPM (p < 0.001) and 164.8 ± 34.2 vs. 241.2 ± 40.1 BPM (p < 0.001) for the dominant and non-dominant hands, respectively. The areas under the ROC curve were 0.929 [95%CI: (0.86-0.99)] for the dominant hand and 0.947 [95%CI: (0.88-1.0)] for the non-dominant hand. The BMP score cut-off value was 208 (sensitivity 72.7%, specificity 100%) for the dominant hand and 206 (sensitivity 87.5%, specificity 95%) for the non-dominant hand. CONCLUSIONS: The proposed test quantified the frequencies of rhythmic HMs in PD patients vs. controls and improved the diagnosis of bradykinesia in PD patients.

Development and validation of an instrument for measuring parkinsonian motor impairment: TRAPS-D.

BACKGROUND: Parkinson's disease is incurable, idiopathic, degenerative, and progressive, and affects about 1% of the elderly population. Multidisciplinary clinical treatment is the best and most adopted therapeutic option, while surgical treatment is used in less than 15% of those affected. In practice, there is a lack of reliable and validated scales for measuring motor impairment, and monitoring and screening for surgical indications. OBJECTIVE: To develop and validate an instrument for measuring parkinsonian motor impairment in candidates for neurosurgical treatment. METHOD: The development and validation methods followed published guidelines. The first part was the choice of domains that would make up the construct: cardinal signs of disease (tremor, rigidity (stiffness), posture/balance/gait, hypokinesia/akinesia, and speech), along with pain and dyskinesia. A multi-professional working group prepared an initial pilot instrument. Ten renowned specialists evaluated, judged, and suggested modifications to the instrument. The second phase was the evaluation of the content of each domain and the respective ability to classify commitment intensity. The third phase was the correction of the main flaws detected and new submission to the board. The instrument was applied to 41 candidates for neurosurgical treatment in two situations: with and without medication RESULTS: The final form received 100% agreement from the judges. Its average time for application was 8 min. It was very responsive (p = 0.001, Wilcoxon) in different situations (On-Off). CONCLUSION: TRASP-D is a valid instrument for measuring motor impairment in patients with Parkinson's disease who are candidates for neurosurgical treatment. It allows measurement in multiple domains with reliability and sensitivity.

Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters.

A wearable sensor system is available for monitoring of bradykinesia in patients with Parkinson's disease (PD), however, it remains unclear whether kinematic parameters would reflect clinical severity of PD, or would help clinical diagnosis of physicians. The present study investigated whether the classification model using kinematic parameters from the wearable sensor may show accordance with clinical rating and diagnosis in PD patients. Using the Inertial Measurement Units (IMU) sensor, we measured the movement of finger tapping (FT), hand movements (HM), and rapid alternating movements (RA) in 25 PD patients and 21 healthy controls. Through the analysis of the measured signal, 11 objective features were derived. In addition, a clinician who specializes in movement disorders viewed the test video and evaluated each of the Unified Parkinson's Disease Rating Scale (UPDRS) scores. In all items of FT, HM, RA, the correlation between the linear regression score obtained through objective features (angle, period, coefficient variances for angle and period, change rates of angle and period, angular velocity, total angle, frequency, magnitude, and frequency × magnitude) and the clinician's UPDRS score was analyzed, and there was a significant correlation (rho > 0.7, p < 0.001). PD patients and controls were classified by deep learning using objective features. As a result, it showed a high performance with an area under the curve (AUC) about as high as 0.9 (FT Total = 0.950, HM Total = 0.889, RA Total = 0.888, ALL Total = 0.926. This showed similar performance to the classification result of binary logistic regression and neurologist, and significantly higher than that of family medicine specialists. Our results suggest that the deep learning model using objective features from the IMU sensor can be usefully used to identify and evaluate bradykinesia, especially for general physicians not specializing in neurology.

A method for measuring time spent in bradykinesia and dyskinesia in people with Parkinson's disease using an ambulatory monitor.

BACKGROUND: Fluctuations in motor function in Parkinson's Disease (PD) are frequent and cause significant disability. Frequently device assisted therapies are required to treat them. Currently, fluctuations are self-reported through diaries and history yet frequently people with PD do not accurately identify and report fluctuations. As the management of fluctuations and the outcomes of many clinical trials depend on accurately measuring fluctuations a means of objectively measuring time spent with bradykinesia or dyskinesia would be important. The aim of this study was to present a system that uses wearable sensors to measure the percentage of time that bradykinesia or dyskinesia scores are above a target as a means for assessing levels of treatment and fluctuations in PD. METHODS: Data in a database of 228 people with Parkinson's Disease and 157 control subjects, who had worn the Parkinson's Kinetigraph ((PKG, Global Kinetics Corporation™, Australia) and scores from the Unified Parkinson's Disease Rating Scale (UPDRS) and other clinic scales were used. The PKG's provided score for bradykinesia and dyskinesia every two minutes and these were compared to a previously established target range representing a UPDRS III score of 35. The proportion of these scores above target over the 6 days that the PKG was worn were used to derive the percent time in bradykinesia (PTB) and percent time in dyskinesia (PTD). As well, a previously describe algorithm for estimating the amplitude of the levodopa response was used to determine whether a subject was a fluctuator or non-fluctuator. RESULTS: Using this approach, a normal range of PTB and PTD based on Control subject was developed. The level of PTB and PTD experienced by people with PD was compared with their levels of fluctuation. There was a correlation (Pearson's ρ = 0.4) between UPDRS II scores and PTB: the correlation between Parkinson Disease Questionnaire scores and UPDRS Total scores and PTB and slightly lower. PTB and PTD fell in response to treatment for bradykinesia or dyskinesia (respectively) with greater sensitivity than clinical scales. CONCLUSIONS: This approach provides an objective assessment of the severity of fluctuations in Parkinson's Disease that could be used in in clinical trials and routine care.

Apraxia of eyelid opening secondary to possible progressive supranuclear palsy: a case report.

Apraxia of eyelid opening (AEO) is a disabling syndrome characterized by inability to open the eyes at will, and patients occasionally attempt to open their eyes by contracting the frontalis muscles and touching their eye lids with their fingers. The exact pathophysiological mechanisms underlying this syndrome remain unknown. Previous reports suggest that AEO is often associated with blepharospasm and is occasionally seen in patients with Parkinson's disease or other movement disorders. These reports suggest that AEO may be caused by lesions at the basal ganglia. In this report, we show a video of typical AEO.

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer.

Motor fluctuations in Parkinson's disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson's patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson's patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson's Patients.

Parkinson's disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson's Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.

New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease.

Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.

Role of data measurement characteristics in the accurate detection of Parkinson's disease symptoms using wearable sensors.

BACKGROUND: Parkinson's disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. METHODS: Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. RESULTS: First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. CONCLUSIONS: Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.

Shedding Light on Nocturnal Movements in Parkinson's Disease: Evidence from Wearable Technologies.

In Parkinson's disease (PD), abnormal movements consisting of hypokinetic and hyperkinetic manifestations commonly lead to nocturnal distress and sleep impairment, which significantly impact quality of life. In PD patients, these nocturnal disturbances can reflect disease-related complications (e.g., nocturnal akinesia), primary sleep disorders (e.g., rapid eye movement behaviour disorder), or both, thus requiring different therapeutic approaches. Wearable technologies based on actigraphy and innovative sensors have been proposed as feasible solutions to identify and monitor the various types of abnormal nocturnal movements in PD. This narrative review addresses the topic of abnormal nocturnal movements in PD and discusses how wearable technologies could help identify and assess these disturbances. We first examine the pathophysiology of abnormal nocturnal movements and the main clinical and instrumental tools for the evaluation of these disturbances in PD. We then report and discuss findings from previous studies assessing nocturnal movements in PD using actigraphy and innovative wearable sensors. Finally, we discuss clinical and technical prospects supporting the use of wearable technologies for the evaluation of nocturnal movements.

Iroquois Homeobox Protein 2 Identified as a Potential Biomarker for Parkinson's Disease.

The diagnosis of Parkinson's disease (PD) is initiated after the occurrence of motor symptoms, such as resting tremors, rigidity, and bradykinesia. According to previous reports, non-motor symptoms, notably gastrointestinal dysfunction, could potentially be early biomarkers in PD patients as such symptoms occur earlier than motor symptoms. However, connecting PD to the intestine is methodologically challenging. Thus, we generated in vitro human intestinal organoids from PD patients and ex vivo mouse small intestinal organoids from aged transgenic mice. Both intestinal organoids (IOs) contained the human LRRK2 G2019S mutation, which is the most frequent genetic cause of familial and sporadic PD. By conducting comprehensive genomic comparisons with these two types of IOs, we determined that a particular gene, namely, Iroquois homeobox protein 2 (IRX2), showed PD-related expression patterns not only in human pluripotent stem cell (PSC)-derived neuroectodermal spheres but also in human PSC-derived neuronal cells containing dopaminergic neurons. We expected that our approach of using various cell types presented a novel technical method for studying the effects of multi-organs in PD pathophysiology as well as for the development of diagnostic markers for PD.

The utility of an RDoC motor domain to understand psychomotor symptoms in depression.

Despite the clinical impact of motor symptoms such as agitation or retardation on the course of depression, these symptoms are poorly understood. Novel developments in the field of instrumentation and mobile devices allow for dimensional and continuous recording of motor behavior in various settings, particularly outside the laboratory. Likewise, the use of novel assessments enables to combine multimodal neuroimaging with behavioral measures in order to investigate the neural correlates of motor dysfunction in depression. The research domain criteria (RDoC) framework will soon include a motor domain that will provide a framework for studying motor dysfunction in mood disorders. In addition, new studies within this framework will allow investigators to study motor symptoms across different stages of depression as well as other psychiatric diagnoses. Finally, the introduction of the RDoC motor domain will help test how motor symptoms integrate with the original five RDoC domains (negative valence, positive valence, cognitive, social processes, and arousal/regulation).

Using wearables to assess bradykinesia and rigidity in patients with Parkinson's disease: a focused, narrative review of the literature.

The potential of using wearable technologies for the objective assessment of motor symptoms in Parkinson's disease (PD) has gained prominence recently. Nonetheless, compared to tremor and gait impairment, less emphasis has been placed on the quantification of bradykinesia and rigidity. This review aimed to consolidate the existing research on objective measurement of bradykinesia and rigidity in PD through the use of wearables, focusing on the continuous monitoring of these two symptoms in free-living environments. A search of PubMed was conducted through a combination of keyword and MeSH searches. We also searched the IEEE, Google Scholar, Embase, and Scopus databases to ensure thorough results and to minimize the chances of missing relevant studies. Papers published after the year 2000 with sample sizes greater than five were included. Studies were assessed for quality and information was extracted regarding the devices used and their location on the body, the setting and duration of the study, the "gold standard" used as a reference for validation, the metrics used, and the results of each paper. Thirty-one and eight studies met the search criteria and evaluated bradykinesia and rigidity, respectively. Several studies reported strong associations between wearable-based measures and the gold-standard references for bradykinesia, and, to a lesser extent, rigidity. Only a few, pilot studies investigated the measurement of bradykinesia and rigidity in the home and free-living settings. While the current results are promising for the future of wearables, additional work is needed on their validation and adaptation in ecological, free-living settings. Doing so has the potential to improve the assessment and treatment of motor fluctuations and symptoms of PD more generally through real-time objective monitoring of bradykinesia and rigidity.

Fact-finding survey on diagnostic procedures and therapeutic interventions for parkinsonism accompanying dementia with Lewy bodies.

BACKGROUND: We performed a questionnaire survey of medical doctors engaged in the management of dementia to identify the actual status of treatment for dementia with Lewy bodies (DLB) in Japan. METHODS: Among participating medical doctors, we selected neurologists (Group N) and psychiatrists (Group P) because these physicians are usually involved in the management of DLB patients. The two groups were compared based on their diagnosis and treatment of DLB and in particular, parkinsonism. RESULTS: Neurological examinations and biomarker tests were less frequently performed by Group P than Group N. Antipsychotics and other psychotropics excluding anti-dementia drugs were significantly more frequently administered by Group P than Group N. The proportion of physicians who selected L-dopa as a first-line therapy for parkinsonism was significantly higher in Group N than in Group P. Despite these between-group differences, the following findings were common to the two groups: there was a discrepancy between the symptom that patients expressed the greatest desire to treat, and the awareness of physicians regarding the treatment of these symptoms; the initial agent was L-dopa; and physicians exercised caution against the occurrence of hallucinations, delusions, and other adverse drug reactions. CONCLUSIONS: The results of the present survey offer valuable insight for the formulation of future DLB therapeutic strategies.