Abstract:The temporal discrimination threshold (TDT) has been established as a biomarker of impaired temporal processing and endophenotype in various forms of focal dystonia patients, such as cervical dystonia, writer’s cramp or blepharospasm. The role of TDT in musician’s dystonia (MD) in contrast is less clear with preceding studies reporting inconclusive results. We therefore compared TDT between MD patients, healthy musicians and non-musician controls using a previously described visual, tactile, and visual-tactile… Show more
“…Although Musician’s dystonia is a disorder in which dexterity is impaired primarily during the performance of specific tasks, it is known that there are differences in hand perceptive or sensory-motor functions unrelated to specific motor tasks, such as longer temporal discrimination threshold (TDT), compared to healthy individuals ( 18 , 19 ). Conversely, it has been reported that TDT is not altered in musicians with dystonia ( 20 ). Furthermore, biomechanical factors seem to be related to musicians with dystonia, at least in a subgroup ( 11 , 21 ).…”
Section: Methodsmentioning
confidence: 92%
“…TOJT of the participants was defined as the time interval at which the correction rate exceeded 75% ( Supplementary Figure 1-3 ). We opted for mechanical stimulation instead of electrical stimulation for simplicity, while adhering to the target body parts of previous studies that measure TDT of patients with MD ( 20 ). Prior to measurements, we ensured that the magnitude of the mechanical stimuli was sufficiently above each participant’s tactile threshold.…”
BackgroundMusician’s dystonia is a task-specific movement disorder that deteriorates fine motor control of skilled movements in musical performance. Although this disorder threatens professional careers, its diagnosis is challenging for clinicians who have no specialized knowledge of musical performance.ObjectivesTo support diagnostic evaluation, the present study proposes a novel approach using a machine learning-based algorithm to identify the symptomatic movements of Musician’s dystonia.MethodsWe propose an algorithm that identifies the dystonic movements using the anomaly detection method with an autoencoder trained with the hand kinematics of healthy pianists. A unique feature of the algorithm is that it requires only the video image of the hand, which can be derived by a commercially available camera. We also measured the hand biomechanical functions to assess the contribution of peripheral factors and improve the identification of dystonic symptoms.ResultsThe proposed algorithm successfully identified Musician’s dystonia with an accuracy and specificity of 90% based only on video footages of the hands. In addition, we identified the degradation of biomechanical functions involved in controlling multiple fingers, which is not specific to musical performance. By contrast, there were no dystonia-specific malfunctions of hand biomechanics, including the strength and agility of individual digits.ConclusionThese findings demonstrate the effectiveness of the present technique in aiding in the accurate diagnosis of Musician’s dystonia.
“…Although Musician’s dystonia is a disorder in which dexterity is impaired primarily during the performance of specific tasks, it is known that there are differences in hand perceptive or sensory-motor functions unrelated to specific motor tasks, such as longer temporal discrimination threshold (TDT), compared to healthy individuals ( 18 , 19 ). Conversely, it has been reported that TDT is not altered in musicians with dystonia ( 20 ). Furthermore, biomechanical factors seem to be related to musicians with dystonia, at least in a subgroup ( 11 , 21 ).…”
Section: Methodsmentioning
confidence: 92%
“…TOJT of the participants was defined as the time interval at which the correction rate exceeded 75% ( Supplementary Figure 1-3 ). We opted for mechanical stimulation instead of electrical stimulation for simplicity, while adhering to the target body parts of previous studies that measure TDT of patients with MD ( 20 ). Prior to measurements, we ensured that the magnitude of the mechanical stimuli was sufficiently above each participant’s tactile threshold.…”
BackgroundMusician’s dystonia is a task-specific movement disorder that deteriorates fine motor control of skilled movements in musical performance. Although this disorder threatens professional careers, its diagnosis is challenging for clinicians who have no specialized knowledge of musical performance.ObjectivesTo support diagnostic evaluation, the present study proposes a novel approach using a machine learning-based algorithm to identify the symptomatic movements of Musician’s dystonia.MethodsWe propose an algorithm that identifies the dystonic movements using the anomaly detection method with an autoencoder trained with the hand kinematics of healthy pianists. A unique feature of the algorithm is that it requires only the video image of the hand, which can be derived by a commercially available camera. We also measured the hand biomechanical functions to assess the contribution of peripheral factors and improve the identification of dystonic symptoms.ResultsThe proposed algorithm successfully identified Musician’s dystonia with an accuracy and specificity of 90% based only on video footages of the hands. In addition, we identified the degradation of biomechanical functions involved in controlling multiple fingers, which is not specific to musical performance. By contrast, there were no dystonia-specific malfunctions of hand biomechanics, including the strength and agility of individual digits.ConclusionThese findings demonstrate the effectiveness of the present technique in aiding in the accurate diagnosis of Musician’s dystonia.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.