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Finger-tapping tasks are one of the most common paradigms used to study the human motor system in functional neuroimaging studies. These tasks can vary both in the presence or absence of a pacing stimulus as well as in the complexity of the tapping task. A voxel-wise, coordinate-based meta-analysis was performed on 685 sets of activation foci in Talairach space gathered from 38 published studies employing finger-tapping tasks. Clusters of concordance were identified within the primary sensorimotor cortices, supplementary motor area, premotor cortex, inferior parietal cortices, basal ganglia, and anterior cerebellum. Subsequent analyses performed on subsets of the primary set of foci demonstrated that the use of a pacing stimulus resulted in a larger, more diverse network of concordance clusters, in comparison to varying the complexity of the tapping task. The majority of the additional concordance clusters occurred in regions involved in the temporal aspects of the tapping task, rather than its execution. Tapping tasks employing a visual pacing stimulus recruited a set of nodes distinct from the results observed in those tasks employing either an auditory or no pacing stimulus, suggesting differing cognitive networks when integrating visual or auditory pacing stimuli into simple motor tasks. The relatively uniform network of concordance clusters observed across the more complex finger-tapping tasks suggests that further complexity, beyond the use of multi-finger sequences or bimanual tasks, may be required to fully reveal those brain regions necessary to execute truly complex movements.

Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.

We aimed to assess the intrasubject reproducibility of a technology-based levodopa (LD) therapeutic monitoring protocol administered in supervised versus unsupervised conditions in patients with Parkinson’s disease (PD). The study design was pilot, intrasubject, single center, open and prospective. Twenty patients were recruited. Patients performed a standardized monitoring protocol instrumented by an ad hoc embedded platform after their usual first morning LD dose in two different randomized ambulatory sessions: one under a physician’s supervision, the other self-administered. The protocol is made up of serial motor and non-motor tests, including alternate finger tapping, Timed Up and Go test, and measurement of blood pressure. Primary motor outcomes included comparisons of intrasubject LD subacute motor response patterns over the 3-h test in the two experimental conditions. Secondary outcomes were the number of intrasession serial test repetitions due to technical or handling errors and patients’ satisfaction with the unsupervised LD monitoring protocol. Intrasubject LD motor response patterns were concordant between the two study sessions in all patients but one. Platform handling problems averaged 4% of total planned serial tests for both sessions. Ninety-five percent of patients were satisfied with the self-administered LD monitoring protocol. To our knowledge, this study is the first to explore the potential of unsupervised technology-based objective motor and non-motor tasks to monitor subacute LD dosing effects in PD patients. The results are promising for future telemedicine applications.

Introduction Repetitive transcranial magnetic stimulation (rTMS) over the primary motor cortex (M1) can modulate brain activity both in the stimulated site and remote brain areas of the sensorimotor network. However, the modulatory effects of rTMS at different frequencies remain unclear. Here, we employed finger‐tapping task‐based fMRI to investigate alterations in activation of the sensorimotor network after the application of rTMS over the left M1 at different frequencies. Materials and Methods Forty‐five right‐handed healthy participants were randomly divided into three groups by rTMS frequency (HF, high‐frequency, 3 Hz; LF, low‐frequency, 1 Hz; and SHAM) and underwent two task‐fMRI sessions (RH, finger‐tapping with right index finger; LH, finger‐tapping with left index finger) before and after applying rTMS over the left M1. We defined regions of interest (ROIs) in the sensorimotor network based on group‐level activation maps (pre‐rTMS) from RH and LH tasks and calculated the percentage signal change (PSC) for each ROI. We then assessed the differences of PSC within HF or LF groups and between groups. Results Application of rTMS at different frequencies resulted in a change in activation of several areas of the sensorimotor network. We observed the increased PSC in M1 after high‐frequency stimulation, while we detected the reduced PSC in the primary sensory cortex (S1), ventral premotor cortex (PMv), supplementary motor cortex (SMA), and putamen after low‐frequency stimulation. Moreover, the PSC in the SMA, dorsal premotor cortex (PMd), and putamen in the HF group was higher than in the LF group after stimulation. Conclusion Our findings suggested that activation alterations within sensorimotor network are dependent on the frequency of rTMS. Therefore, our findings contribute to understanding the effects of rTMS on brain activation in healthy individuals and ultimately may further help to suggest mechanisms of how rTMS could be employed as a therapeutic tool. Repetitive transcranial magnetic stimulation (rTMS) over the primary motor cortex (M1) could modulate the activity both in the stimulated site and remote brain regions of the sensorimotor network. We employed finger‐tapping task‐based fMRI to investigate the altered activations of the sensorimotor network after the application of rTMS over the left M1 at different frequencies.

In this study, we collected various past study results on tools and analytical methods for measuring hand functions of patients with degenerative brain diseases, such as Parkinson’s disease and stroke, and selected and proposed appropriate hand function measurement tools, methods, and analysis software based on text network analysis. We searched the literatures using keywords related to degenerative brain disease and stroke patients for participant types, use of devices and sensors for the intervention types, and hand function assessment for measurement types. Among the 2484 literatures collected, 19 were eventually selected based on certain inclusion and exclusion criteria. As a result of text network analysis, the degree-centrality and the betweenness centrality were the highest in the keyword of Parkinson’s disease for the participant type, force sensor for the intervention type, and finger tapping for the measurement type. Based on these results, pinch gloves comprising an FSR sensor were manufactured, and software and contents were implemented to measure and analyze various quantitative parameter values during finger tapping. The software can evaluate endurance and agility by measuring the finger-tapping intensity and operation time using the index finger and thumb. The contents can evaluate the stability of hand functions by analyzing the coefficient of variation of the tapping interval, the average contact time, and the accuracy of hand functions by analyzing the reaction rate to the presented visual stimulus. As a result of comparing hand functions through 10 types of analysis parameters with a sample of 12 ordinary subjects (8 men and 4 women) using the manufactured pinch gloves, there was a difference between the two genders in the items evaluating muscle strength and agility, and a significant difference in the analysis parameters evaluating stability and accuracy. The results indicate that using the text network analysis-based hand function measurement tool and the method proposed in this study should help derive the objective research results as well as a quantitative comparison of research results of various researchers.

This paper proposes a method to quantitatively measure and evaluate finger tapping movements for the assessment of motor function using log-linearized Gaussian mixture networks (LLGMNs). First, finger tapping movements are measured using magnetic sensors, and eleven indices are computed for evaluation. After standardizing these indices based on those of normal subjects, they are input to LLGMNs to assess motor function. Then, motor ability is probabilistically discriminated to determine whether it is normal or not using a classifier combined with the output of multiple LLGMNs based on bagging and entropy. This paper reports on evaluation and discrimination experiments performed on finger tapping movements in 33 Parkinson’s disease (PD) patients and 32 normal elderly subjects. The results showed that the patients could be classified correctly in terms of their impairment status with a high degree of accuracy (average rate: 93:1 § 3:69%) using 12 LLGMNs, which was about 5% higher than the results obtained using a single LLGMN.

Neuroimaging studies suggest that the primary hand motor area and the cerebellum play a pivotal role in the control of finger tapping, but their differential contribution in this task is unknown. We used therefore repetitive transcranial magnetic stimulation (rTMS) in its virtual lesion mode (1 Hz, 10 min, 90% of motor threshold) to study the effects of transient disruption of the right lateral cerebellum (CB), the left primary hand motor area (M1), and the right brachial plexus (PL, control site) on various finger tapping tasks (paced finger tapping task: PFT; tapping with maximum speed: TAPMAX, and tapping with convenient speed: TAPCON) in healthy right-handed subjects. RTMS of the left M1 slowed finger tapping speed of the right hand in the TAPMAX task. This effect eliminated the right hand superiority in the TAPMAX task. In addition, rTMS of the left M1 resulted in slower tapping speeds for both hands during TAPCON. There were no other effects of rTMS on tapping speed or tapping variability. Findings indicate that M1 is essential for generating fastest finger movements.

Actions are often internally guided, reflecting our covert will and intentions. The dorsomedial prefrontal cortex, including the pre-Supplementary Motor Area (pre-SMA), has been implicated in the internally generated aspects of action planning, such as choice and intention. Yet, the mechanism by which this area interacts with other cognitive brain regions such as the dorsolateral prefrontal cortex, a central node in decision-making, is still unclear. To shed light on this mechanism, brain activity was measured via fMRI and intracranial EEG in two studies during the performance of visually cued repeated finger tapping in which the choice of finger was guided by either a presented number (external) or self-choice (internal). A functional-MRI (fMRI) study in 15 healthy participants demonstrated that the pre-SMA, compared to the SMA proper, was more active and also more functionally correlated with the dorsolateral prefrontal cortex during internally compared to externally guided action planning (p < 0.05, random effect). In a similar manner, an intracranial-EEG study in five epilepsy patients showed greater inter-regional gamma-related connectivity between electrodes situated in medial and lateral aspects of the prefrontal cortex for internally compared to externally guided actions. Although this finding was observed for groups of electrodes situated both in the pre-SMA and SMA-proper, increased intra-cluster gamma-related connectivity was only observed for the pre-SMA (sign-test, p < 0.0001). Overall our findings provide multi-scale indications for the involvement of the dorsomedial prefrontal cortex, and especially the pre-SMA, in generating internally guided motor planning. Our intracranial-EEG results further point to enhanced functional connectivity between decision-making- and motor planning aspects of the PFC, as a possible neural mechanism for internally generated action planning.

•Neural entrainment underpins sensorimotor synchronization to dynamic rhythmic stimuli.•Motor engagement enhances frequency adjustments to stimulus dynamics.•Intrinsic brain dynamics bias entrainment towards higher frequencies.•Only sustained tempo-changes elicit frequency adjustments in perceptual components.•ERFA is presented as methodological framework to investigate neural entrainment. Neural entrainment, defined as unidirectional synchronization of neural oscillations to an external rhythmic stimulus, is a topic of major interest in the field of neuroscience. Despite broad scientific consensus on its existence, on its pivotal role in sensory and motor processes, and on its fundamental definition, empirical research struggles in quantifying it with non-invasive electrophysiology. To this date, broadly adopted state-of-the-art methods still fail to capture the dynamic underlying the phenomenon. Here, we present event-related frequency adjustment (ERFA) as a methodological framework to induce and to measure neural entrainment in human participants, optimized for multivariate EEG datasets. By applying dynamic phase and tempo perturbations to isochronous auditory metronomes during a finger-tapping task, we analyzed adaptive changes in instantaneous frequency of entrained oscillatory components during error correction. Spatial filter design allowed us to untangle, from the multivariate EEG signal, perceptual and sensorimotor oscillatory components attuned to the stimulation frequency. Both components dynamically adjusted their frequency in response to perturbations, tracking the stimulus dynamics by slowing down and speeding up the oscillation over time. Source separation revealed that sensorimotor processing enhanced the entrained response, supporting the notion that the active engagement of the motor system plays a critical role in processing rhythmic stimuli. In the case of phase shift, motor engagement was a necessary condition to observe any response, whereas sustained tempo changes induced frequency adjustment even in the perceptual oscillatory component. Although the magnitude of the perturbations was controlled across positive and negative direction, we observed a general bias in the frequency adjustments towards positive changes, which points at the effect of intrinsic dynamics constraining neural entrainment. We conclude that our findings provide compelling evidence for neural entrainment as mechanism underlying overt sensorimotor synchronization, and highlight that our methodology offers a paradigm and a measure for quantifying its oscillatory dynamics by means of non-invasive electrophysiology, rigorously informed by the fundamental definition of entrainment.

BackgroundFine motor impairments are common in neurodegenerative disorders, yet standardized, quantitative measurements of motor abilities are uncommonly used in neurological practice. Thus, understanding and comparing fine motor abilities across disorders have been limited.ObjectivesThe current study compared differences in finger tapping, inter-tap interval, and variability in Alzheimer’s disease (AD), Parkinson’s disease (PD), mild cognitive impairment (MCI), and healthy older adults (HOA).MethodsFinger tapping was measured using a highly sensitive light-diode finger tapper. Total number of finger taps, inter-tap interval, and intra-individual variability (IIV) of finger tapping was measured and compared in AD (n = 131), PD (n = 63), MCI (n = 46), and HOA (n = 62), controlling for age and sex.ResultsAll patient groups had fine motor impairments relative to HOA. AD and MCI groups produced fewer taps with longer inter-tap interval and higher IIV compared to HOA. The PD group, however, produced more taps with shorter inter-tap interval and higher IIV compared to HOA.ConclusionsDisease-specific changes in fine motor function occur in the most common neurodegenerative diseases. The findings suggest that alterations in finger tapping patterns are common in AD, MCI, and PD. In addition, the present results underscore the importance of motor dysfunction even in neurodegenerative disorders without primary motor symptoms.