Statistical characteristics of finger-tapping data in Huntington’s disease
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Measuring the rate of finger tapping is a technique commonly used as an indicator of impairment in degenerative neurological conditions, such as Huntington’s disease. The information it provides can be greatly enhanced by analysing not simply the overall tapping rate, but also the statistical characteristics of the individual times between each successive response. Recent technological improvements in the recording equipment allow the responses to be analysed extremely quickly, and permit modification of the task in the interest of greater clinical specificity. Here we illustrate its use with some pilot data from a group of manifest HD patients and age-matched controls. Even in this small cohort, differences in the responses are apparent that appear to relate to the severity of the disease as measured by conventional behavioural tests.
KeywordsFinger tapping Huntington’s disease
Huntington’s disease (HD) is a devastating inherited neurodegenerative condition. It is characterised not only by the movement abnormalities, but also by cognitive impairment and abnormal behaviour, as well as weight problems and sleep disturbances . HD patients experience a complex variety of movement problems, which include not only chorea, but also akinesia and bradykinesia. A number of studies have assessed impairment of simple motion sequences in HD patients as a way of quantifying the progression of symptoms [2, 7, 12, 15].
Sequential hand movements in HD patients have been examined at various stages of the condition , with markedly slower execution of movements by the HD patients when compared with controls. As well as performing movements more slowly, HD patients are also slower in switching from one movement to the next . Garcia Ruiz and colleagues  studied the degree of bradykinesia and timing in genetically confirmed HD individuals compared with controls, using the four CAPIT timed tasks previously used for PD . There were no significant differences between patients with and without anti-dopaminergic drugs. These results have been reproduced in more recent studies that reported significantly reduced tapping rates in manifest HD patients as compared to controls, but not premanifest individuals; Unified Huntington’s Disease Rating Scale (UHDRS) motor scores and duration of the disease were highly correlated with the tapping results, something which did not correlate with the CAG repeat lengths [3, 13, 14, 15].
Longitudinal studies have shown a significant decline in tapping rate over a period of 3 years in manifest HD patients, and a strong correlation between UHDRS scores and the motor tests . Furthermore, the variability of finger tapping (using target intervals of 600 and 1,200 ms) correlated with an index of the probability of motor onset, estimated from CAG length and age .
Quick and easy-to-use hand tapping devices have enabled the number of taps in 30 s, and the variability in tapping rhythm and fatigue over the testing period, to be measured. Initial cross-sectional testing of HD patients using an early model of such a device showed that the tapping frequency correlated significantly with the motor UHDRS and independence scores . Longitudinal data from a small cohort followed over 10 years revealed that this correlation was maintained over time, suggesting the technique may provide an objective measure of disease progression. Recently, a large study, TrackHD, used a force transducer to measure paced finger tapping in premanifest and manifest HD patients; significant differences between premanifest and manifest patients were found, again suggesting that finger-tapping measures are an important way of monitoring the progression of the disorder .
Here we present a pilot study, using a new portable device in which the sensors are activated purely by contact and are independent of force; this facilitates the rapid collection of finger-tapping data, and allows the possibility of introducing modifications of the basic task that may have diagnostic utility. Because it provides information not merely about average tapping rates, but about the statistical distribution of the individual intervals between responses, it enables the behaviour to be characterised more specifically and quantitatively, sometimes revealing aberrant patterns of response that would not be detected by conventional average measures; this is likely to aid monitoring and diagnosis.
2 Materials and methods
Characterisation of the manifest (M) patient group and controls (C)
M (n = 8)
C (n = 3)
Age: mean ± SD
54.50 ± 10.08
54.33 ± 7.81
42.17 ± 1.80
UHDRS (motor score) (mean, range)
Total functional capacity
12.00 ± 0.71
VF (mean, range)
μ (reciprocal median latency)
mean ± SE (s−1)
4.08 ± 0.60
4.95 ± 0.85
3.18 ± 0.41
4.06 ± 0.53
2.14 ± 0.30
3.49 ± 0.74
σ (SD of main distribution)
mean ± SE (s−1)
0.75 ± 0.14
0.35 ± 0.12
0.49 ± 0.06
0.44 ± 0.21
0.41 ± 0.03
0.41 ± 0.07
2.2 Recording the finger tapping
3.1 Distributions of inter-tap intervals
Figure 1 (right) shows a typical distribution plot from one of the participants in the study. As usually found for saccadic and evoked manual reaction times, distributions of the intervals between taps in this task are skewed, with a long tail of longer latencies, and in general the reciprocal of reaction time, or promptness, follows a Gaussian distribution (and is therefore more amenable to statistical analysis). Consequently if inter-tap interval distributions are plotted cumulatively, on a probit scale, using a reciprocal abscissa (a reciprobit plot), they will be expected to generate a straight line, as seen on Fig. 1 (right). Such a distribution can be fully described by just two parameters: these are μ, its mean (which is also the reciprocal of the median latency), and σ, its standard deviation. A large value of μ corresponds to increased promptness or speed of response, and thus a shorter interval; because of the reciprocal relationship, the units for these two parameters are s−1 or Hz. The best-fit values of the parameters can be determined automatically by minimisation of the Kolmogorov–Smirnov one-sample statistic.
3.2 Values of the underlying parameters
The aim of this study was to evaluate the potential use of a new device for quantitative assessments in disorders, such as HD in which hand-tapping tapping is abnormal. It has two key features: first, the force required to be exerted by the subject to register a response is very low (essentially zero); this makes it feasible to use variations on the basic alternation task that can pose a greater challenge to a patient with relatively mild impairment. Figure 2d provides a particularly clear example of this, when comparing ordinary alternation with the pronation task: the difference in μ for the two types of task is equivalent to over 450 ms of latency difference. The second potential benefit is that by providing sequential information about individual responses in each direction, a great deal of data are generated in a short period of time, from which much more can be calculated than the average response time that has previously been conventionally used. For simple tapping, the parameter σ seems to provide particularly clear discrimination between subject groups (Fig. 3, right), and it is possible that a combination of μ and σ together may be helpful in this respect (Fig. 4). Because responses in the two directions are not conflated, lateral asymmetries (Fig. 2c), suggesting a relatively one-sided functional impairment, become obvious and can be quantified. Other kinds of idiosyncrasies, not previously noted (such as the bimodality of Fig. 2a, or the sporadic inattention of Fig. 2b) are also revealed, and are equally capable of quantification. Another advantage of the device is that it is small, lightweight and portable and the software is easy-to-use allowing its potential ease of introduction to a clinical environment.
This is, in other words, a simple tool that has the potential to address a general problem in studying not only in HD, but also other neurodegenerative disorders: a lack of objective and genuinely quantitative neurological tests, by which disease progress can be monitored and treatments evaluated with respect to the particular needs of individual patients. Obviously much more data are needed to provide true validation, and to discover what aspects of the interval distributions will be most useful for this process, and this work is currently in hand.
This research was supported by a grant from the Wellcome Trust (073735), by the National Institute of Health Research (NIHR), by the Oxford Biomedical Research Centre, and by the Dementias and Neurodegenerative Diseases Research Network (DENDRON).
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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