Tremor is defined as rhythmic oscillatory activity of body parts. Four physiological basic mechanisms for such oscillatory activity have been described: mechanical oscillations; oscillations based on reflexes; oscillations due to central neuronal pacemakers; and oscillations because of disturbed feedforward or feedback loops. New methodological approaches with animal models, positron emission tomography, and mathematical analysis of electromyographic and electroencephalographic signals have provided new insights into the mechanisms underlying specific forms of tremor. Physiological tremor is due to mechanical and central components. Psychogenic tremor is considered to depend on a clonus mechanism and is thus believed to be mediated by reflex mechanisms. Symptomatic palatal tremor is most likely due to rhythmic activity of the inferior olive, and there is much evidence that essential tremor is also generated within the olivocerebellar circuits. Orthostatic tremor is likely to originate in hitherto unidentified brainstem nuclei. Rest tremor of Parkinson's disease is probably generated in the basal ganglia loop, and dystonic tremor may also originate within the basal ganglia. Cerebellar tremor is at least in part caused by a disturbance of the cerebellar feedforward control of voluntary movements, and Holmes' tremor is due to the combination of the mechanisms producing parkinsonian and cerebellar tremor. Neuropathic tremor is believed to be caused by abnormally functioning reflex pathways and a wide variety of causes underlies toxic and drug-induced tremors. The understanding of the pathophysiology of tremor has made significant progress but many hypotheses are not yet based on sufficient data. Modern neurology needs to develop and test such hypotheses, because this is the only way to develop rational medical and surgical therapies.

Background: Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. Objectives: The present study assessed the feasibility, reliability, and validity of smartphone‐based digital biomarkers of PD in a clinical trial setting. Methods: During a 6‐month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45‐day study in 35 age‐matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger‐tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit‐to‐stand transitions by gyroscopic and accelerometer data. Results: Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor‐based features showed moderate‐to‐excellent test‐retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P < 0.005. All active test features except sustained phonation were significantly related to corresponding International Parkinson and Movement Disorder Society–Sponsored UPRDS clinical severity ratings. On passive monitoring, time spent walking had a significant (P = 0.005) relationship with average postural instability and gait disturbance scores. Of note, for all smartphone active and passive features except postural tremor, the monitoring procedure detected abnormalities even in those Parkinson participants scored as having no signs in the corresponding International Parkinson and Movement Disorder Society–Sponsored UPRDS items at the site visit. Conclusions: These findings demonstrate the feasibility of smartphone‐based digital biomarkers and indicate that smartphone‐sensor technologies provide reliable, valid, clinically meaningful, and highly sensitive phenotypic data in Parkinson's disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Parkinsonian tremor is most likely due to oscillating neuronal activity within the CNS. Summarizing all the available evidence, peripheral factors only play a minor role in the generation, maintenance and modulation of PD tremor. Recent studies have shown that not a single but multiple oscillators are responsible. The most likely candidate producing these oscillations is the basal ganglia loop and its topographic organization might be responsible for the separation into different oscillators which, nevertheless, usually produce the same frequency. The neuronal mechanisms underlying these oscillations are not yet clear, but three hypotheses would be compatible with the presently available data from animal models and data recorded in patients. The first is a cortico-subthalamo-pallido-thalamic loop, the second is a pacemaker consisting of the external pallidum and the subthalamic nucleus, and the third is abnormal synchronization due to unknown mechanisms within the whole striato-pallido-thalamic pathway leading to a loss of segregation. Assuming the oscillator within the basal ganglia pathway, the mechanism of stereotactic surgery might be a desynchronization of the activity of the basal ganglia-thalamo-cortical or the cerebello-thalamo-cortical pathway.

Background Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits. Objective The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery. Methods People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed. Results People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis. Conclusions People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings. Trial Registration ClinicalTrials.gov: NCT02952911; https://clinicaltrials.gov/ct2/show/NCT02952911

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