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Journal of Neurophysiology logoLink to Journal of Neurophysiology
. 2019 Nov 6;123(2):718–725. doi: 10.1152/jn.00165.2019

Temporal but not spatial dysmetria relates to disease severity in FA

Manuela Corti 1,3,*, Agostina Casamento-Moran 2,*, Stefan Delmas 2, Samantha Bracksieck 2, Jessica Bowman 1, Blake Meyer 1, Samantha Norman 1, Sub Subramony 3, Evangelos A Christou 2,✉
PMCID: PMC8091932  PMID: 31693434

Abstract

Friedreich’s ataxia (FA) is an inherited disease that causes degeneration of the nervous system. Features of FA include proprioceptive and cerebellar deficits leading to impaired muscle coordination and, consequently, dysmetria in force and time of movement. The aim of this study is to characterize dysmetria and its association to disease severity. Also, we examine the neural mechanisms of dysmetria by quantifying the EMG burst area, duration, and time-to-peak of the agonist muscle. Twenty-seven individuals with FA and 13 healthy controls (HCs) performed the modified Functional Ataxia Rating Scale and goal-directed movements with the ankle. Dysmetria was quantified as position and time error during dorsiflexion. FA individuals exhibited greater time but not position error than HCs. Moreover, time error correlated with disease severity and was related to increased agonist EMG burst. Temporal dysmetria is associated to disease severity, likely due to altered activation of the agonist muscle.

NEW & NOTEWORTHY For the first time, we quantified spatial and temporal dysmetria and its relation to disease severity in Friedreich’s ataxia (FA). We found that FA individuals exhibit temporal but not spatial dysmetria relative to healthy controls. Temporal dysmetria correlated to disease severity in FA and was predicted from an altered activation of the agonist muscle. Therefore, these results provide novel evidence that FA exhibit temporal but not spatial dysmetria, which is different from previous findings on SCA6.

Keywords: dysmetria, EMG, Friedreich’s ataxia

INTRODUCTION

The cardinal symptom of cerebellar deficiency is dysmetria. Dysmetria is defined as the inability to perform accurate movements. Dysmetria of an intended movement can occur in space (spatial dysmetria) and/or time (temporal dysmetria) and can be quantified as the position and time errors of an intended movement (Manto 2009). We have shown that individuals with spinocerebellar ataxia type 6 (SCA6) exhibit greater spatial but not temporal dysmetria relative to healthy controls (Casamento-Moran et al. 2015). Unlike other forms of ataxias, such as SCA6, Friedreich’s ataxia (FA) is associated with severe sensory impairments in addition to cerebellar deficits (Alper and Narayanan 2003; Cook and Giunti 2017; Cooper and Bradley 2002; Corben et al. 2014; Koeppen and Mazurkiewicz 2013). It is possible, therefore, that FA individuals could exhibit both spatial and temporal dysmetria. Nonetheless, no study has quantified spatial and temporal dysmetria and its relation to disease severity in FA. Here, we aim to characterize dysmetria in FA and determine its relation to disease severity.

FA affects ~1 in every 50,000 people in Caucasian populations of Europe, the Middle East, South Asia (Indian subcontinent), and North Africa (Pandolfo 2003). FA is an autosomal recessive condition caused by the inheritance of intronic GAA triplet repeat expansions in the frataxin (FXN) gene (Alper and Narayanan 2003; Campuzano et al. 996, 1997; Cooper and Bradley 2002). This mutation results in reduction of the level of frataxin in mitochondria, which leads to iron accumulation and decreased activity of iron-sulfur cluster enzymes required for energy production (Alper and Narayanan 2003; Beal 1998; Becker and Richardson 2001; Campuzano et al. 1996, 1997; Cooper and Bradley 2002; Li and Reichmann 2016; Martelli and Puccio 2014). Consequently, the spinal cord, sensory nerves, and cerebellum degenerate, causing neurological symptoms including limb ataxia, gait ataxia, poor balance control, dysmetria, dysdiadochokinesia, dysarthria, dysphagia, and visual/hearing loss.

Clinically, dysmetria is measured using the nose-to-finger-to nose test or the finger chase test (Gagnon et al. 2004). Upper limb dysmetria is evaluated with both the Friedreich Ataxia Rating Scale (FARS) (Bürk et al. 2013; Patel et al. 2016; Saute et al. 2012; Subramony et al. 2005) and Scale for the Assessment and Rating of Ataxia (SARA) (Schmitz-Hübsch et al. 2006), which score the accuracy of movement using a grading scale. Dysmetria of an intended movement can occur in space and/or time (Manto 2009). Individuals with SCA6, a form of ataxia with “pure” degeneration of the cerebellum (Sasaki et al. 1998), exhibit greater spatial but not temporal dysmetria relative to healthy controls (HCs) (Casamento-Moran et al. 2015). Furthermore, force dysmetria correlated with the impaired functional capacity in SCA6. Because FA includes additional impairments relative to SCA6, such as the loss of proprioception, vibration sense, light touch, pain, and temperature perception (Alper and Narayanan 2003; Cook and Giunti 2017; Cooper and Bradley 2002; Corben et al. 2014; Koeppen and Mazurkiewicz 2013), it is possible that would result in both spatial and temporal dysmetria. Nonetheless, to date, no study has quantified spatial and temporal dysmetria and its relation to disease severity in FA.

The aim of this study, therefore, was to characterize spatial and temporal dysmetria in individuals with FA and determine how it relates to disease severity [modified Functional Ataxia Rating Scale (mFARS) score]. Similar to our SCA6 study (Casamento-Moran et al. 2015), we quantified spatial and temporal dysmetria during a goal-directed ankle dorsiflexion task. Because FA is characterized by degeneration of both the cerebellum and most of the sensory pathways, we hypothesized that individuals with FA will exhibit greater spatial and temporal dysmetria compared with HC and that dysmetria will correlate with disease severity in FA.

METHODS

Participants

Twenty-seven individuals with a genetically confirmed diagnosis of FA (17 women, age 24.04 ± 15.34 yr) and 13 age-matched HC individuals (10 women, age 19.62 ± 6.37 yr) volunteered to participate in a cross-sectional controlled study at the University of Florida. Control individuals reported being healthy and had no evidence of neurological disease. Before participating in the study, all volunteering individuals signed written informed consents that were in accordance with the Declaration of Helsinki and approved by the University of Florida Institutional Review Board. A stipend was provided to all subjects.

Experimental Approach

All participants underwent a clinical evaluation and an experimental session. The clinical evaluation session included collection of medical history, genetic reports, and a neurological evaluation using the modified FARS scale (Table 1). The experimental session included the following: 1) instructions on how to perform the goal-directed movement task without the use of compensatory movements, 2) maximum voluntary contraction (MVC) task with ankle dorsiflexion, 3) practice of five trials of the goal-directed movement trials at a different target than the actual target, and 4) 50 goal-directed movement trials. In this study, we analyze and report the results from the movements performed in part 4.

Table 1.

Clinical characteristics and demographic

Age, yr BMI GAA1 GAA2 Modified
FARS		 Functional
Score		 Position Error Time Error EMG Area EMG Duration
Friedreich’s ataxia (FA)
    FA 01 19.00 24.22 713.00 960.00 40.60 4.00 64.27 23.57 32.50 460.68
    FA 03 17.00 19.34 633.00 966.00 52.30 4.00 46.96 78.60 80.43 716.69
    FA 04 50.00 29.34 62.50 36.11 23.87 45.71 307.94
    FA 05 12.00 19.47 850.00 1,050.00 61.00 3.50 101.19 30.10 40.14 336.62
    FA 06 65.00 33.79 42.00 762.00 40.33 4.00 48.37 29.37 53.78 372.95
    FA 07 17.00 17.92 899.00 899.00 40.00 4.00 38.72 34.54 88.81 701.87
    FA 09 12.00 18.33 866.00 966.00 65.00 4.00 81.18 52.73 31.69 393.81
    FA 11 16.00 14.79 416.00 663.00 31.00 2.00 97.53 21.54 22.82 285.43
    FA 12 16.00 19.70 599.00 799.00 40.33 2.40 45.52 27.58 29.25 265.12
    FA 13 11.00 16.93 866.00 1,133.00 53.00 4.00 23.98 16.72 33.46 391.98
    FA 15 16.00 23.30 700.00 1,000.00 60.00 4.00 43.33 28.87 37.16 368.39
    FA 17 15.00 16.70 61.00 2.00 42.31 43.09 38.40 367.26
    FA 18 18.00 19.02 800.00 1,200.00 61.00 4.00 39.52 58.41 46.45 462.79
    FA 20 15.00 16.84 900.00 1,300.00 66.00 5.00 63.00 23.20 61.96 523.00
    FA 21 38.00 20.57 450.00 450.00 50.00 5.00 47.61 21.10 66.24 467.10
    FA 22 19.00 16.69 833.00 833.00 57.00 5.00 75.33 25.99 40.95 510.50
    FA 23 11.00 28.28 933.00 933.00 55.80 5.00 64.72 41.41 41.59 341.24
    FA 25 65.00 29.07 174.00 811.00 50.00 4.00 55.10 53.97 74.68 550.51
    FA 26 20.00 16.11 600.00 1,200.00 44.60 3.00 75.50 36.65 15.64 392.33
    FA 27 23.00 27.31 766.00 966.00 72.00 5.00 66.34 82.40 126.13 334.65
    FA 28 15.00 20.47 733.00 866 77.00 5.00 102.25 38.36 25.25 314.55
    FA 30 38.00 27.66 500.00 933.00 44.41 21.74 12.41 352.10
    FA 31 36.00 18.93 502.00 856.00 40.30 3.50 84.87 29.01 21.76 397.29
    FA 32 16.00 16.70 566.00 766.00 27.00 2.00 23.19 28.00 16.94 317.59
    FA 33 17.00 19.52 1160.00 1,160.00 52.00 3.00 54.57 14.77 39.69 317.56
    FA 35 17.00 19.98 33.00 2.00 33.62 23.30 21.62 268.46
    FA 36 35.00 24.99 333.00 1,000.00 22.30 3.50 17.69 29.70 25.36 431.40
    Average ± SD 24.04 ± 15.34 21.33 ± 5.00 659.75 ± 257.65 936.33 ± 188.00 50.58 ± 13.93 3.72 ± 1.03 56.19 ± 23.43 34.76 ± 17.16 43.36 ± 25.83 405.55 ± 115.81
Healthy Controls (CTL)
    CTL 01 26.00 23.73 34.61 18.79 49.82 544.11
    CTL 03 27.00 26.12 81.37 11.76 13.95 258.93
    CTL 04 28.00 22.18 19.21 25.11 13.41 316.19
    CTL 05 21.00 23.90 45.26 17.25 15.17 247.58
    CTL 06 19.00 21.33 32.54 24.05 12.05 269.47
    CTL 07 21.00 26.00 50.66 13.43 12.01 230.49
    CTL 10 11.00 17.86 60.57 17.38 4.62 223.02
    CTL 11 12.00 22.26 29.30 26.33 8.07 253.88
    CTL 12 15.00 37.14 11.86 22.66 9.59 245.52
    CTL 14 25.00 24.07 63.59 16.66 1.71 207.91
    CTL 15 25.00 23.05 27.87 16.05 10.87 265.16
    CTL 16 14.00 18.83 45.31 20.50 15.03 234.74
    CTL 17 11.00 18.36 80.54 19.47 6.56 212.80
    Average ± SD 19.62 ± 6.37 23.45 ± 4.90 44.82 ± 21.93 19.19 ± 4.43 13.30 ± 11.71 269.98 ± 87.04
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BMI, body mass index; FARS, Friedreich Ataxia Rating Scale.

Clinical Evaluation Session

The modified Functional Ataxia Rating Scale (mFARS) is a subscale obtained from the FARS neurological domain that evaluates only the activities involving direct patient participation (bulbar, upper limb, lower limb, and upright stability) and excludes peripheral nerve testing. This scale scores up to 99 points, where the higher the score the greater the disease severity (Bürk et al. 2013; Patel et al. 2016; Saute et al. 2012; Subramony et al. 2005). We also rated overall mobility using the functional staging component of FARS (Bürk et al. 2013; Patel et al. 2016; Saute et al. 2012; Subramony et al. 2005).

GAA Repeat Expansion

All individuals with FA in the study were homozygous for the GAA expansion in intron 1 of frataxin gene. Repeat sizes were obtained by review of patient medical records.

Experimental Session

Experimental setup.

Each participant was seated comfortably in an upright position and faced a 32-in. monitor (Sync MasterTM 320 MP-2; Samsung Electronics America, Ridgefield Park, NJ) located 1.25 m away at eye level. The monitor was used to display the contraction produced by ankle dorsiflexion using a custom written program in Matlab (Math Works, Natick, MA). During the goal-directed tasks, the left hip joint was flexed at ~90° with 100 abduction and the knee was flexed at ~90°. The left foot rested on a customized foot pedal with an adjustable foot plate and straps over the metatarsals to secure and ensure an isolated dorsiflexion movement. The axis of rotation of the customized foot device was positioned in line with the axis of rotation of the left ankle to allow only dorsiflexion of the ankle.

Force.

The maximum voluntary force exerted during the MVC task was measured with a force transducer (model 41BN; Honeywell, Morristown, NJ). The ankle force signals were high-pass filtered at 0.03 Hz, amplified 50 times (Bridge-8; World Precision Instruments), sampled at 1000 Hz with a NI-DAQ card (model USB6210; National Instruments, Austin, TX), and stored on a personal computer.

Limb displacement.

The displacement of the left ankle (dorsiflexion) was measured using a low-friction potentiometer (SP22G-5K; Mouser Electronics, Mansfield, TX) located directly lateral to the fibular malleolus. The ankle position signals were sampled at 1,000 Hz with a NI-DAQ card (model USB6210; National Instruments) and stored on a personal computer.

EMG measurements.

Muscle activation was recorded with a Trigno wireless EMG system (Delsys, Boston, MA) and Bagnoli EMG system (Delsys) from the tibialis anterior (TA) and soleus (SOL) muscles. The recording electrodes were placed on the skin and in line with the muscle fibers. Specifically, the electrode for the TA was placed at one-third on the line between the proximal border of the fibula and the tip of the medial malleolus. For the SOL, the electrode was placed half-way between the end of the head of the gastrocnemius and the origin of the Achilles tendon. The EMG signals were band-pass filtered from 20 to 450 Hz, amplified 1,000 times, sampled at 1,000 Hz with a DAQ card (model USB6210; National Instruments), and stored on a personal computer.

MVC task.

The MVC was measured for ankle dorsiflexion. Each participant increased force to her/his maximum in 3 s and maintained the maximal force for 3 s. They exerted three to five MVCs or until two MVC trials were within 5% of each other. A 1-min rest was provided between consecutive trials to minimize fatigue.

Goal-directed movement task.

Participants performed ankle dorsiflexion movements to match a targeted position and time. The targeted position was 9° and targeted time-to-peak was 180 ms. The task was divided into three phases: 1) GET READY, 2) MOVE, and 3) FEEDBACK. The GET READY phase began with a red target on the monitor for 2 s indicating participants to prepare for the upcoming MOVE phase. The MOVE phase began when the red target changed to green indicating participants to perform the goal-directed movement. The green target remained on the monitor for 3 s, and participants were instructed to perform the contraction at their convenience (not a reaction time task). The recording of the task began when the participants initiated the contraction within the allotted time. The FEEDBACK phase began after each MOVE phase and lasted for 5 s. Details and graphical representation of the task are summarized in Chen et al. (2014) and (Fig. 1).

Fig. 1.

Fig. 1.

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Experimental setup. A: schematic drawing of the experimental setup and arrangement of the left foot. The left foot was placed and rested on a customized foot device with an adjustable foot plate and secured by a strap over the metatarsals. Participants performed fast goal-directed movements with ankle dorsiflexion. B: we quantified temporal dysmetria as the absolute horizontal displacement from the target center to the peak performance (time error) and spatial dysmetria as the absolute vertical displacement from the target center to the peak performance (position error).

Data Analysis

A custom Matlab program (Math Works) was generated for offline data analysis. We extrapolated position and time errors during concentric contractions. We eliminated the trials with errors greater than ±3 SD from the mean performance (<5% of the total trials).

Spatial and temporal dysmetria.

To calculate spatial and temporal dysmetria we quantified the position and time errors. Position error was quantified as the absolute vertical deviation of the peak displacement from the target, whereas time error was quantified as the absolute horizontal deviation of the peak displacement from the target. We normalized position error to the targeted peak displacement (9°; Eq. 1) and time error to the targeted time to peak displacement (180 ms; Eq. 2).

position error %=peak displacement error °targeted peak displacement °×100 (1)
time error %=time to peak displacement error mstargeted time to peak displacement ms×100 (2)

EMG.

The interference EMG for each trial was rectified and smoothed with a fourth-order Butterworth digital filter (filtfilt) with a 6-Hz cutoff frequency (Poston et al. 2008) (Fig. 2A). This filter was used to identify the onset, peak, and offset of the EMG burst of the TA and SOL muscles. We identified “EMG peak” as the point at which maximal movement amplitude occurred, “EMG onset” as the first time point at which the position reached 15% of the peak, and “EMG offset” as the last time point at which the position reached 15% of the peak. We examined the activation of the TA and SOL muscles by quantifying the following variables (see Fig. 2B for a graphical representation of the variables): 1) EMG burst area: the sum of the EMG values from EMG burst onset to EMG burst offset (shaded area under the EMG burst curve) normalized to the EMG during MVC (sum of EMG recorded 360 ms around the peak EMG of the MVC task); 2) EMG burst duration: time between EMG onset and EMG offset; and 3) EMG burst time-to-peak: time between EMG onset and the peak EMG.

Fig. 2.

Fig. 2.

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EMG data processing and analysis. A: EMG signal was rectified and filtered. Bottom: rectified EMG signal with the associated movement that follows EMG. B: Matlab program was used to identify EMG onset and offset, EMG burst area, EMG burst duration, and EMG burst time to peak; EMG activation in Friedreich’s ataxia individuals (B, left) differ from control individuals (B, right). The associated movements are shown above the EMG signals. TTP, time-to-peak.

The EMG variability was calculated by the SD of the TA and SOL EMG burst area, duration, and time-to-peak across the 50 movement trials.

Statistical Analysis

We used an independent t test to compare dysmetria and the EMG between FA and HC. We performed simple linear regression analysis to predict disease severity and dysmetria. We performed stepwise linear regression analysis to determine the relative contribution of the EMG variables to dysmetria. The goodness-of-fit for each regression was given by the squared correlation (R2). The α-level for all statistical tests was 0.05. We used Hedges’ g to quantify the effect size of the independent t test results because the FA and HC groups exhibited different sample sizes. All statistical analyses were performed with the IBM statistics 24.0 statistical package (IBM, New York, NY). Data are reported as means ± SD within the text and as means ± SE in the figures.

RESULTS

A total of 40 individuals, 27 with diagnosis of FA and 13 controls, enrolled in the study. The two groups were similar in age [FA: 24.04 ± 15.34 yr; HCs: 19.62 ± 6.37 yr; t(38) = 0.99, P = 0.33], sex (FA: 17 women; HCs: 10 women; P = 0.24), and body mass index [BMI; FA: 21.33 ± 5.00 kg/m2; HCs: 23.45 ± 4.90 kg/m2; t(38) = −1.26, P = 0.21]. FA participants were moderately impaired with an average mFARS score of 50.57 ± 13.92 and functional score of 3.61 ± 1.14. The average length of GAA repeats was 659.75 ± 257.65 in the shorter allele (GAA1) and 936.46 ± 187.82 for the longer allele (GAA2). In summary, the physical characteristics (i.e., weight and BMI) were similar for FA and HCs; thus any differences in dysmetria are not related to physical characteristic differences between the two groups.

Dysmetria and Disease Severity

Dysmetria was quantified as position and time errors during goal-directed movements. FA individuals exhibited greater time error [FA: 34.76 ± 17.16%; HCs: 19.18 ± 4.42%; t(38) = 3.20, P < 0.003, Hedges’ g = 1.08] but not greater position error [FA: 56.19 ± 23.16%; HCs: 44.82 ± 21.93%; t(38) =1.47, P > 0.05, Hedges’ g = 0.50] than HCs (Fig. 3, A and B). Time error correlated with disease severity, as assessed with the mFARS score (R2 = 0.24, F1,24 = 7.76, P < 0.05; Fig. 4]. Therefore, it appears that FA results in temporal but not spatial dysmetria and that functional impairments are related to temporal dysmetria.

Fig. 3.

Fig. 3.

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Temporal but not spatial dysmetria in FA. A and B: Friedreich’s ataxia (FA) individuals (○) do not differ from healthy controls (HCs; ●) in position error (A) but present significant higher time error compared with HC (B). Results include the data of 27 FA and 13 HC participants, who performed 50 trials of the goal-directed movement task.

Fig. 4.

Fig. 4.

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Time error in Friedreich’s ataxia (FA) individuals predicted the modified Functional Ataxia Rating Scale (mFARS) score. Time error correlates with disease severity, as assessed with the mFARS score. Therefore, functional impairments are related to temporal dysmetria. Results include the data of 25 FA participants.

EMG and Dysmetria

FA and HC individuals had different EMG during the goal-directed task. When looking at the agonist muscle FA individuals exhibited greater EMG burst area [FA: 43.4 ± 25.8%; HCs: 13.3 ± 11.7%; t(38) = 3.98, P < 0.001, Hedges’s g = 1.33], longer EMG duration [FA: 405.5 ± 115.8 ms; HCs: 269.9 ± 87.0 ms; t(38) = 3.73, P < 0.001, Hedges’ g = 1.26], and longer EMG time-to-peak [FA: 227.3 ± 65.1 ms; HCs: 140.1 ± 31.9 ms; t(38) = 4.56, P < 0.001, Hedges’ g = 1.54] than HCs (Fig. 5, A–C). In addition, FA individuals exhibited greater variability in the agonist EMG burst area [FA: 15.9 ± 13.5%; HCs: 4.21 ± 5.58%; t(38) = 3.00, P < 0.01, Hedges’ g = 1.00], duration [FA: 135.7 ± 68.1 ms; HCs: 93.7 ± 52.7 ms; t(38) = 1.98, P < 0.05, Hedges’ g = 0.66], and time-to-peak [FA: 106.5 ±40.6 ms; HCs: 62.4 ± 17.3 ms; t(38) = 3.74, P < 0.001, Hedges’ g = 1.26] than HC (Fig. 5, D–F). When looking at the antagonist muscle FA individuals exhibited similar EMG burst area [FA: 32.39 ± 17.6%; HCs: 21.68 ± 14.19%; t(34) = 1.82, P > 0.1, Hedges’ g = 0.65], longer EMG burst duration [FA: 321.32 ± 83.91 ms; HCs: 215.94 ± 17.24 ms; t(34) = 4.31, P < 0.001, Hedges’ g = 1.52], and longer EMG burst time-to-peak [FA: 178.69 ± 73.36 ms; HCs: 112.36 ± 13.97 ms; t(34) = 3.08, P = 0.004, Hedges’ g = 1.10] than HCs. In contrast to the results of the agonist muscle, FA individuals exhibited similar variability in the antagonist EMG burst area [FA: 15.30 ± 9.68%; HCs: 10.22 ± 7.49%; t(34) = 1.59, P = 0.121, Hedges’ g = 0.56], duration [FA: 210.77 ± 221.81 ms; HCs: 98.81 ± 20.42 ms; t(34) = 1.73, P = 0.092, Hedges’ g = 0.62], and time-to-peak [FA: 180.5 ± 233.76 ms; HCs: 65.17 ± 18.24 ms; t(34) = 1.69, P = 0.099, Hedges’ g = 0.60].

Fig. 5.

Fig. 5.

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Friedreich’s ataxia (FA) and healthy control (HC) individuals have different EMG activation patterns. FA individuals (○) have significantly greater EMG burst area, longer EMG burst duration, and longer EMG burst time-to-peak in the tibialis anterior (TA) compared with HCs (●). In addition, FA also exhibit significantly greater EMG burst area, EMG duration, and EMG burst time-to-peak variability. Results include the data of 27 FA and 13 HC participants, who performed 50 trials of the goal-directed movement task.

To determine which variable from the EMG predicted the differences in temporal dysmetria between FA and HC, we performed a stepwise linear regression. We used all the EMG variables that were different between the groups as independent variables (see paragraph above). Time error was our dependent variable. We found that only the agonist area predicted time error (R2 = 0.47, F1,38 = 33.02, P < 0.001; Fig. 6). Therefore, temporal dysmetria in FA appears to be related to an increased agonist EMG burst.

Fig. 6.

Fig. 6.

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EMG burst area predicts temporal error. EMG burst area significantly predicated time error in Friedreich’s ataxia (FA; ○) and healthy control (HC; ●) individuals. Results include the data of 27 FA and 13 HC participants, who performed 50 trials of the goal-directed movement task.

DISCUSSION

No study has quantified spatial and temporal dysmetria and its relation to disease severity in FA. Based on our previous findings with SCA6 who exhibited only spatial dysmetria, we hypothesized that FA would exhibit both spatial and temporal dysmetria relative to HCs. We hypothesized this because FA results in both cerebellar and sensory degeneration, whereas SCA6 has primarily cerebellar degeneration. In contrast to our hypothesis, we found that FA individuals exhibit temporal but not spatial dysmetria relative to HCs. Temporal dysmetria correlated to disease severity in FA and was predicted from an altered activation of the agonist muscle. Therefore, these results provide novel evidence that FA exhibit temporal but not spatial dysmetria, which is different from previous findings on SCA6.

Dysmetria in FA

We found that FA individuals exhibit temporal but not spatial dysmetria relative to HCs. This was surprising given that SCA6, a form of ataxia with pure cerebellar degeneration, exhibits spatial but not temporal dysmetria. This finding generates the following question: why do FA exhibit temporal but not spatial dysmetria? One possibility is that the demyelination of peripheral nerves, which results in decreased conduction velocities in FA (Cruz Martínez and Anciones 1992; Cruz-Martínez et al. 1997; Peyronnard et al. 1976), underlies the observed temporal dysmetria and abnormal activation of the involved muscles. Another possibility is that diminished sensory information results in incorrect internal representation of the movement and, consequently, abnormal muscle activation. Goal-directed movements depend on multiple processing events including a comparison of perceived velocity and direction of the limb to an internal model of the efferent output and sensory expectation (Elliott et al. 2010). FA individuals present impaired or, in some cases, absence of sensory information, which may lead to formation of an incorrect internal representation of the motor and sensory consequences of their actions. The impaired internal representation might lead to incorrect specification of magnitude and timing of muscular forces resulting in an abnormal impulse generation affecting the overall temporal dimension. In line with this idea and similar to our observations, a study investigating jaw movement kinematics and EMG activity in FA found longer jaw movement durations and prolonged EMG bursts in FA relative to HCs (Devanne et al. 1995). Although we did not specifically address motor planning impairment in FA, the fast goal-directed movements used in this study are primarily controlled by preplanned descending cortical commands (Casamento-Moran et al. 2017), and therefore, our results support the idea that motor planning is impaired in FA (Corben et al. 2011). Lastly, it is possible that greater EMG activation represents a compensatory strategy for the reduced sensory information available. It has been suggested that greater muscle activation can increase the sensitivity of sensory receptors (Riemann and Lephart 2002). Thus a greater EMG burst might allow individuals with FA to obtain sensory information at the expense of reduced temporal control.

Our reasonings and observations, however, raise another question: why is spatial control not impaired in FA? Descriptive data show that FA individuals had greater position error compared with HCs but the difference was not statistically significant. We have previously shown that healthy children exhibit greater spatial errors than healthy young and older adults (Casamento-Moran et al. 2018; Fox et al. 2013); thus the cross-sectional nature of our study might prevent us from obtaining significant differences in spatial dysmetria between FA and HCs. Future studies could examine spatial dysmetria longitudinally in FA and HCs to see if the development of spatial control is impaired in FA. Lastly, task difficulty is seen as a function of movement amplitude and the size of the target, with smaller targets at a greater distance having the greatest accuracy demands (Corben et al. 2011; Fitts 1954; Fitts and Peterson 1964). It is possible that the targeted peak displacement (9°) used in our experimental setup was not enough to detect spatial dysmetria relative to HCs.

Temporal Dysmetria and Disease Severity in FA

The greater temporal dysmetria exhibit by individuals with FA was functionally relevant. Specifically, we found a positive correlation between temporal dysmetria and mFARS. This observation highlights the importance of temporal dimension in skilled actions (Kornysheva 2016). Producing muscle activations with inaccurate timing can have detrimental effects on performance (Kornysheva 2016). Impairment of temporal dimension may affect movement accuracy and smoothness (Germanotta et al. 2015). This is also evident in FA during speech (Anderson et al. 2008), which presents abnormal laryngeal timing and control (Blaney and Hewlett 2007a, 2007b).

Limitations and Future Considerations

Our experimental design studied a single goal-directed movement task during dorsiflexion with the targeted position of angle at 9° and the targeted time-to-peak at 180 ms. In future studies, it will be interesting to evaluate different task conditions (i.e., position and time targets) that provide stronger discrimination between FA and HCs. In this study, we characterize dysmetria in FA and its association to disease severity in a single joint of the lower extremity. Future studies should focus on understanding dysmetria in the upper extremity and the relationship with functional movements such as reaching and/or grasping. In addition, to further assess the roles of proprioceptive and cerebellar components to the abnormalities noted here, quantifying the proprioceptive deficits and assessing their impact on dysmetria may be useful. Finally, a direct comparison between SCA6 and FA individuals will help to provide a better understanding of the underlying mechanisms causing dysmetria in these conditions.

Conclusions

Here, we provide novel evidence that FA exhibit temporal but not spatial dysmetria. This is the result of the altered activation of the agonist muscle, likely due to reduced sensory information. This finding is interesting because it is different from our observations with individuals with SCA6, who exhibit spatial but not temporal dysmetria. In conclusion, different types of ataxia appear to exhibit unique end point control impairments and thus future studies should compare end point control in different forms of ataxia.

GRANTS

This study was supported by grants from Children Miracle Network and Friedreich’s Ataxia Research Alliance (to M. Corti).

DISCLOSURES

M. Corti is a co-founder of Aavanti, Inc. None of the other authors has any conflicts of interest, financial or otherwise, to disclose.

AUTHOR CONTRIBUTIONS

M.C., A.C.M., S.S., and E.A.C. conceived and designed research; M.C., A.C.M., S.D., S.B., J.B., and B.M. performed experiments; M.C., A.C.M., B.M., and S.N. analyzed data; M.C., A.C.M., S.S., and E.A.C. interpreted results of experiments; M.C., A.C.M., and E.A.C. prepared figures; M.C. and A.C.M. drafted manuscript; M.C., A.C.M., B.M., S.S., and E.A.C. edited and revised manuscript; M.C., S.S., and E.A.C. approved final version of manuscript.

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