Individuals with autism spectrum disorder show abnormalities during initial and subsequent phases of precision gripping

Abstract

Sensorimotor impairments are common in autism spectrum disorder (ASD), but they are not well understood. Here we examined force control during initial pulses and the subsequent rise, sustained, and relaxation phases of precision gripping in 34 individuals with ASD and 25 healthy control subjects. Participants pressed on opposing load cells with their thumb and index finger while receiving visual feedback regarding their performance. They completed 2- and 8-s trials during which they pressed at 15%, 45%, or 85% of their maximum force. Initial pulses guided by feedforward control mechanisms, sustained force output controlled by visual feedback processes, and force relaxation rates all were examined. Control subjects favored an initial pulse strategy characterized by a rapid increase in and then relaxation of force when the target force was low (Type 1). When the target force level or duration of trials was increased, control subjects transitioned to a strategy in which they more gradually increased their force, paused, and then increased their force again. Individuals with ASD showed a more persistent bias toward the Type 1 strategy at higher force levels and during longer trials, and their initial force output was less accurate than that of control subjects. Patients showed increased force variability compared with control subjects when attempting to sustain a constant force level. During the relaxation phase, they showed reduced rates of force decrease. These findings suggest that both feedforward and feedback motor control mechanisms are compromised in ASD and these deficits may contribute to the dyspraxia and sensorimotor abnormalities often seen in this disorder.

sensorimotor abnormalities are present in the majority of individuals with autism spectrum disorder (ASD) (Baranek 1999; Fournier et al. 2010a). They emerge early in infancy (Bryson et al. 2007; Provost et al. 2006), and they appear to be familial (Mosconi et al. 2010), but they have been studied far less frequently than the social-communication and cognitive impairments that define the disorder. Multiple types of sensorimotor impairments have been identified in ASD, including reduced postural stability (Fournier et al. 2010b; Minshew et al. 2007), atypical gait (Hallett et al. 1993; Vernazza-Martin et al. 2005), reduced coordination of upper limb movements (Cook et al. 2013; Glazebrook et al. 2007; Mari et al. 2003), macrographia (Fuentes et al. 2009), and atypical grasping behaviors (David et al. 2009, 2012), but the control processes and neurophysiological mechanisms underlying these dysfunctions are not well understood.

Sensorimotor behavior involves integration of multiple distinct motor control processes. Rapid movements are guided primarily by feedforward control mechanisms that plan motor output faster than sensory feedback can be used to make online adjustments (Desmurget et al. 1999; Mari et al. 2003; Prablanc and Martin 1992). Rapid eye movements and control of initial manual motor output have been shown to be impaired in ASD, suggesting that feedforward processes may be compromised (David et al. 2009, 2012; Glazebrook et al. 2007, 2009; Johnson et al. 2013; Mosconi et al. 2013; Schmitt et al. 2014). Sustained actions in which individuals attempt to maintain a constant level of motor output rely more on sensory feedback mechanisms that allow individuals to reactively adjust their motor behavior (Deutsch and Newell 2001, 2003). Sustained eye movements have been shown to be less accurate in individuals with ASD (Takarae et al. 2004), and sustained reaching movements show atypical kinematic profiles (Cook et al. 2013; Glazebrook et al. 2007; Mari et al. 2003). Studies of motor adaptation have indicated an increased reliance on proprioceptive relative to visual feedback mechanisms in ASD, suggesting that sensorimotor feedback processes are disrupted (Haswell et al. 2009; Izawa et al. 2012).

To assess feedforward and feedback motor control processes in ASD, we examined initial and sustained force output during a test of visually guided precision gripping. Precision gripping was studied because the ability to precisely regulate grip forces is necessary for many activities of daily living known to be impaired in ASD, such as writing and dressing (Fuentes et al. 2009). Precision gripping can be formulated as a triphasic action involving initial increases in force output to grip an object, maintenance of appropriate force levels to manipulate the object, and relaxation of grip forces to release the object (Potter et al. 2006; Spraker et al. 2012). The rise phase in which individuals increase their force to grip an object can be further decomposed into an initial pulse and corrective pulses made in response to sensory feedback. Importantly, the initial pulse is completed rapidly (∼200–300 ms) and thus is believed to be controlled primarily by feedforward processes. While prior studies have identified deficits in precision gripping in ASD (David et al. 2009, 2012), systematic analyses of the distinct phases of gripping behavior are needed to determine the motor control mechanisms that are affected.

Examining the distinct phases of precision grip in ASD could provide important insights into the disorder's neural underpinnings. Multiple cortico-cerebellar circuits are involved in generating internal action representations that consolidate feedforward control processes (Bastian 2006; Miall 1998), integrating cortical and spinal sensory afferents to modify outgoing motor commands (Stein and Glickstein 1992), and timing agonist/antagonist muscle synergies during the release of force (Serrien and Wiesendanger 1999; Vilis and Hore 1980). The cerebellum has been repeatedly implicated in postmortem and neuroimaging studies of ASD (Bailey et al. 1998; Bauman and Kemper 2005; Stanfield et al. 2008; Whitney et al. 2008), but the distinct circuits that are disrupted and the impact of cerebellar pathology on sensorimotor behaviors in ASD have not been established.

In the present study, we adapted a previously developed, objective approach to differentiate distinct types of initial pulse control strategies during precision gripping (Fishbach et al. 2005; Grafton and Tunik 2011; Novak et al. 2000; Wisleder and Dounskaia 2007). We predicted that, relative to control subjects, individuals with ASD would show elevated rates of initial pulses characterized by rapid increases and then relaxation of force rather than those characterized by more gradual increases in force. We also expected reduced accuracy of initial pulses in ASD. Consistent with the hypothesis that individuals with ASD show deficits in visual feedback control of motor output, we predicted increased force variability during the sustained phase. Finally, we hypothesized lower rates of force relaxation in individuals with ASD suggesting a reduced ability to rapidly terminate motor activity.

METHODS

Participants

Precision grip force was examined in 34 individuals with ASD and 25 healthy control subjects between 5 and 15 yr of age (Table 1). Participant groups were matched on age, sex, handedness, and nonverbal IQ.¹ Prior to testing, IQ was assessed with the Wechsler Abbreviated Scale of Intelligence for individuals 6 yr of age or older (ASD = 26; control = 19). The Wechsler Preschool and Primary Scale of Intelligence (ASD = 4; control = 4) or Differential Abilities Scales-II (ASD = 1) were used for children less than 6 yr of age.

Table 1.

Demographic characteristics of individuals with ASD and healthy control subjects

	ASD (n = 34)	Control (n = 25)	t	P
Age, yr	8.77 (2.64)	8.76 (3.11)	0.00	0.99
% Male*	82.8	72.0	1.01	0.31
% Right-handed*	70.6	90.9	3.40	0.18
Verbal IQ	92.60 (16.23)	111.32 (16.03)	19.60	0.00†
Performance IQ	99.94 (17.43)	106.60 (16.76)	2.20	0.14
Full-Scale IQ	95.66 (15.58)	110.40 (15.15)	13.36	0.00†

Values are means (SD) for n subjects. ASD, autism spectrum disorder.

^*χ² statistics. Significant values are in boldface:

^†statistically significant at α = 0.01.

Individuals with ASD were recruited through community advertisements and the clinical programs of the Center for Autism and Developmental Disabilities at the University of Texas Southwestern Medical Center. The diagnosis of ASD was established with the Autism Diagnostic Inventory-Revised (ADI; Lord et al. 1994), the Autism Diagnostic Observation Schedule-2 (ADOS; Lord et al. 2012), and expert clinical opinion based on DSM-V criteria. ASD participants were excluded if they had a known genetic or metabolic disorder. Control participants were recruited from the community and were required to have a score of 8 or lower on the Social Communication Questionnaire (Berument et al. 1999). Control participants were excluded for current or past psychiatric or neurological disorders, family history of ASD in first-, second-, or third-degree relatives, or a history in first-degree relatives of a developmental or learning disorder, psychosis, or obsessive-compulsive disorder.

No participants were taking medications known to affect motor function at the time of testing, including antipsychotics, stimulants, and anticonvulsants (Reilly et al. 2008). All participants had corrected or uncorrected far visual acuity of at least 20/40. No participant had a history of head injury, birth injury, or seizure disorder. After a complete description of the study, informed parental consent was obtained from parents or caregivers and children provided written assent. Study procedures were approved by the local Institutional Review Board.

Apparatus and Procedures

Participants were seated in a darkened room 53 cm from the center of a 27-in. computer screen (Fig. 1A). They were positioned on an adjustable chair so that visual stimuli were presented at their eye level. Participants rested their forearm and elbow in a relaxed position on a custom-made arm brace clamped to a table. Elbow position remained stationary at 90° flexion throughout testing. Participants used their thumb and index finger to press against two opposing ELFF-B4 precision load cells (Measurement Specialties, Hampton, VA; 1.27 cm in diameter) secured to a custom grip device attached to the arm brace. Analog signals from the load cells were amplified through a Coulbourn (V72-25) resistive bridge strain amplifier. A 16-bit A/D converter was used to sample the force output at 120 Hz.

Fig. 1.

A, top: individuals pressed against 2 opposing load cells while viewing visual feedback during each test of precision grip. A, bottom: while pressing on the load cells, participants viewed 2 horizontal bars presented against a black background. The target bar (red/green) was stationary during each trial. The target bar turned from red to green at the beginning of each trial to cue participants to begin pressing the load cells. The white force bar moved upward with increased force, and thus the discrepancy between the target and force bars provided online visual feedback to the participant about his/her performance. After 2 or 8 s, the target bar turned red again to cue the participant to stop pressing. B: representative trials of a 12-yr-old control subject's right hand grip force time series at 15% maximum voluntary contraction (MVC) showing different grip phases of the 2-s and 8-s tests (red, rise phase; blue, 5-s sustained phase; green, relax phase; 1st gray bar, start cue; 2nd gray bar, stop cue).

Prior to testing, each participant's maximum voluntary contraction (MVC) was calculated for each hand. To determine each participant's MVC, they were instructed to press on the load cells with as much force as possible during three separate trials. The mean of the maximum values for these trials was used as the estimate of each participant's MVC (Vaillancourt et al. 2003). During the precision grip test, participants viewed two horizontal bars: a red/green target bar and a white force bar. The white force bar moved upward with increased force, and participants were instructed to press on the load cells as quickly as possible when the target bar turned green so that the force bar reached the height of the target bar. They also were instructed to keep the force bar as close to the target bar as possible until it turned red again, and then to release the load cells as fast as possible. The target was set to 15%, 45%, or 85% of each participant's MVC, and its position was fixed at the center of the monitor. The location of the force bar was varied as a function of the target force level to maintain a constant visual gain of 2.97 pixels/N (visual angle 0.34°/N) across conditions. Thus the distance between the target and force bars was greater for trials with larger target force levels.

Participants completed 2- and 8-s trials of precision gripping. During the 2-s test, two blocks of five trials were presented for each hand at each force level. Each force trial was 2 s in duration and alternated with 2-s rest periods. A 15-s rest block was provided after each block of trials. During the 8-s test, participants completed two blocks of three trials for each hand at each force level. Eight-second trials were followed by 8-s rest periods, and each block was separated by 15 s of rest. For both tests, the same hand was never tested on consecutive blocks. The order of different force levels was randomized across blocks. The order of the two experiments (2 and 8 s) was randomly assigned to each participant. Prior to each experiment, all participants successfully completed practice trials at 30% of their MVC, using their dominant hand to demonstrate that they understood task instructions. All participants were able to complete these practice trials.

Force Data Analysis

Trials were excluded from analyses if the onset of force preceded the start cue. For each participant, only conditions with more than two valid trials were included in the final analyses. The number of participants included in group comparisons varied across conditions but was similar across groups for each analysis (ASD: n = 28–34; control: n = 20–25).

Each force trace was low-pass filtered via a double-pass second-order Butterworth filter with a cutoff of 15 Hz. To examine initial pulse characteristics (see below), the first, second, and third derivatives of the force data were calculated in MATLAB. Then, these derivative profiles were smoothed with the same filter using a 6-Hz cutoff because of the inflated noise induced from the differentiation procedure.

Force data were analyzed with a custom algorithm in MATLAB. The grip force onset was defined as the time point at which the rate of force increase first exceeded 5% of the peak rate of force increase and remained above this level for at least 100 ms (Fig. 1B) (Grafton and Tunik 2011). For the 2-s test, the offset of the rise phase was identified at 1 s after the peak rate of force increase, or at the stop cue if this occurred first. For the 8-s test, the end of the rise phase was marked when the rate of force increase fell below 5% of the peak rate of force increase, and the force level was within 90% to 110% of the mean force of the sustained phase. Different procedures were used for the two tests because participants were not able to consistently establish a period of sustained force during the 2-s trials.

The peak rate of force increase, duration, and accuracy of the rise phase each were examined. Force accuracy for the rise phase was calculated with the following formula:

$${\text{F}}_{\text{acc\_rise}}=\frac{{\text{F}}_{\text{rise}}-{\text{F}}_{\text{target}}}{{\text{F}}_{\text{rise}}{\text{+F}}_{\text{target}}}$$ (1)

where F_rise and F_target represent the force level at the rise phase offset and the target force level, respectively. This formula yields a unitless estimate of force accuracy ranging from −1 to 1. Responses in which the participant's force output accurately reached the target at the end of the rise phase yield a F_{acc_rise} = 0, whereas negative values reflect force undershooting and positive values indicate that force production exceeded the target force level.

We decomposed the force rise phase into initial and secondary corrective pulses, using a previously developed and automated scoring algorithm (Fishbach et al. 2005; Grafton and Tunik 2011; Novak et al. 2000; Wisleder and Dounskaia 2007). This algorithm objectively defines the end point of the initial pulse at the first zero-crossing in the force derivative traces. Pulses are categorized into different types depending on whether the earliest zero-crossing after the peak rate of force increase is identified in the first, second, or third derivative trace (Fig. 2). The following pulse types were examined.

Fig. 2.

Exemplar Type 1, 2, and 3 initial pulses of the 12-yr-old control participant in Fig. 1 at 15% MVC of the 2-s test. Top row: vertical gray line on left indicates the beginning of the trial for each trace, and gray vertical line on right represents the end of the initial pulse. The end of the initial pulse was defined at the first zero-crossing after the peak rate of force increase in the 1st (second row), 2nd (third row), or 3rd (bottom row) derivative trace. Asterisks indicate the zero-crossing that was used to determine each type of initial pulse. Dashed vertical lines show subsequent zero-crossings in other derivative traces, but these zero-crossings occurred later and thus were not used to calculate initial pulse type or characteristics. Three different types of initial pulses are shown from left to right. Type 1 (pulse-release): the corrective subpulse was in the opposite direction of the initial pulse. Therefore, the rate of force change (ROC) was used to identify the Type 1 initial pulse offset (asterisk), which is the first zero-crossing from (+) to (−). Type 2 (pulse-reacceleration): the corrective subpulses are in the same direction as the initial pulse, and they do not overlap temporally. The offset (asterisk) was defined as the first zero-crossing from (−) to (+) in the 2nd derivative of the force time series. Type 3 (overlapping pulse): the corrective subpulses are in the same direction as the initial pulse, and they overlap the initial pulse. The offset (asterisk) was determined at the first zero-crossing from (+) to (−) in the 3rd derivative of the time series.

Type 1 (pulse-release).

Type 1 initial pulses were characterized by an increase in and then rapid reduction in force. Given that the corrective pulse was the opposite direction of the initial pulse, the Type 1 initial pulse offset was identified at the first zero-crossing from (+) to (−) in the first derivative of the force time series following the peak rate of force increase.

Type 2 (pulse-reaccelerate).

Type 2 initial pulses were characterized by an increase in force followed by a pause and then secondary increases in force that did not temporally overlap with the initial pulse. The offset of Type 2 initial pulses were marked at the first zero-crossing from (−) to (+) in the second derivative of the force output following the peak rate of force increase.

Type 3 (overlapping pulses).

Type 3 pulses involved increases in force followed by one or more corrective increases in force that overlapped temporally with the initial pulse. The offset of the initial pulse was marked at the first zero-crossing from (+) to (−) in the third derivative of the force output following the peak rate of force increase of the initial pulse.

We compared the rates at which individuals with ASD and healthy control subjects produced each type of initial pulse across force levels and across the 2- and 8-s tests. Equation 1 was used to define the accuracy of initial pulses. The peak rate of force increase and duration of each initial pulse also were examined.

Sustained contractions were examined only for the 8-s test. The first and last seconds of the force time series were removed to minimize the influence of rise and relaxation phases of the force response on sustained force measurements as we have done previously (Fig. 1B) (Vaillancourt et al. 2003). Trials in which participants did not sustain contractions for >5 s or the force level returned to zero for ≥1 s were excluded. The mean and coefficient of variation (CoV) of the detrended sustained force time series were examined. The CoV was calculated by dividing the variability of the force time series by the mean force output and thus was used to examine sustained force variability while controlling for differences in mean force output between groups. To examine individuals' ability to rapidly terminate force, we examined participants' peak rate of force decrease during the relaxation phase by identifying the minimum value of the first derivative of the force trace following the stop cue.

Clinical Measures

The ADI is a semistructured parent/caregiver interview used to rate the level of abnormality for each of the core symptom domains of ASD, including social impairment, communication impairment, and restricted, repetitive behaviors (Lord et al. 1994; Rutter et al. 2012). The ADOS is a semistructured assessment of play, social abilities, communication skills, and imaginative use of materials performed with each individual with ASD by an examiner trained to research reliability. For both the ADI and the ADOS, higher scores reflect more severe abnormality in a given domain. These tests were used to establish a diagnosis of ASD in participants and to examine the relationship between grip force alterations and clinical features of ASD.

Statistical Analysis

No significant effects of hand or interactive effects of hand and group were found (all P > 0.05). Therefore, right and left hand performances were averaged for each force level of each test. A series of repeated-measures ANOVAs were conducted to compare groups on force performance across force levels during the rise, sustain, and relaxation phases. Separate analyses were conducted for the 2- and 8-s tests. Significant interactions were examined with Bonferroni post hoc tests at each force level. Because multiple participants did not have a sufficient number of trials (>2) for each initial pulse type for each force level to compare force characteristics (e.g., rate of force increase, duration, accuracy), comparisons of initial pulse characteristics were performed with separate ANOVAs. Pearson correlation coefficients were used to examine the relationships between force variables found to be different between groups and age, IQ, and clinical ratings of ASD based on the social affect total score of the ADOS and the social, communication, and repetitive behavior algorithm scores of the ADI.

RESULTS

Figure 3 shows raw traces of participants' force output at 15% MVC of the 2-s and 8-s tests for four healthy control subjects and four representative participants with ASD. Participants were selected on the basis of the representativeness of their performance relative to the group findings and to include a broad range of MVCs (20–213.3 N). As can be seen in Fig. 3, individuals with ASD showed a tendency to overshoot the target during the initial pulse and produce increased sustained force variability.

Fig. 3.

Grip force profiles during the 2-s (top) and 8-s (bottom) tests for representative participants with autism spectrum disorder (ASD; left) and healthy control subjects (right) performing trials at 15% of their MVC. Each line represents a different trial. Force traces were aligned at the start cue; dashed lines represent the target force level (numbers shown on right of each subplot), and vertical gray bars represent the timing of the stop cue. Initial pulse overshooting (*) can be seen from participants with ASD and healthy control subjects at the younger age (5 yr). Force variability (arrow) during the sustained phase of the 8-s trials is increased in ASD participants compared with control subjects.

Individuals with ASD had lower MVCs than control subjects for both their right (ASD = 53.6 N, SE = 3.7 N; control = 68.5 N, SE = 4.2 N) and left (ASD = 53.3 N, SE = 3.7 N; control = 63.5 N, SE = 4.2 N) hands (group main effect: F_1,128 = 9.89, P = 0.00). The difference in strength between groups did not differ across hands (group × hand interaction: F_1,128 = 0.35, P = 0.56).

Initial Pulse Characteristics

Figure 4 shows that for the 2-s test control subjects utilized a Type 1 strategy more frequently than other strategies at 15% MVC (F_2,168 = 10.64, P = 0.00; Type 1 = 0.49, Type 2 = 0.28, Type 3 = 0.23). They shifted to using the Type 2 strategy more frequently than other pulse types at 45% (F_2,168 = 13.38, P = 0.00; Type 1 = 0.35, Type 2 = 0.46, Type 3 = 0.19) and 85% (F_2,171 = 22.56, P = 0.00; Type 1 = 0.20, Type 2 = 0.58, Type 3 = 0.22) MVC.

Fig. 4.

Rates of different initial pulse types as a function of group and target force level during the 2-s (top) and 8-s (bottom) tests. *Between-group difference at P < 0.05; ^#within-group initial pulse type difference at P < 0.05, ^##within-group initial pulse type with significance at 0.01 level.

ASD participants favored a Type 1 strategy at 15% MVC (F_2,168 = 20.41, P = 0.00; Type 1 = 0.53, Type 2 = 0.25, Type 3 = 0.23), but, unlike control subjects, they also favored the Type 1 strategy at 45% MVC (F_2,168 = 11.03, P = 0.00; Type 1 = 0.46, Type 2 = 0.33, Type 3 = 0.23). They shifted to a Type 2 strategy at 85% MVC (F_2,171 = 11.47, P = 0.00; Type 1 = 0.33, Type 2 = 0.46, Type 3 = 0.20). Individuals with ASD showed higher rates of Type 1 pulses compared with control subjects at 45% and 85% MVC (45%: F_1,168 = 6.28, P = 0.01; 85%: F_1,171 = 4.80, P = 0.03) and lower rates of Type 2 pulses relative to healthy control subjects at these higher force levels (45%: F_1,168 = 6.37, P = 0.01; 85%: F_1,171 = 4.78, P = 0.03). For all force levels, both groups used the Type 3 strategy less frequently than the Type 1 or 2 strategy.

During the 8-s test, control subjects showed similar rates of Type 1 and Type 2 pulses at 15% and 45% MVC, and both strategies were used more frequently than Type 3 pulses (15% MVC: F_2,147 = 3.63, P = 0.03; Type 1 = 0.38, Type 2 = 0.34, Type 3 = 0.19; 45% MVC: F_2,156 = 6.10, P = 0.03; Type 1 = 0.39, Type 2 = 0.34, Type 3 = 0.13). Thus, relative to the 2-s test, healthy control subjects showed a more equal distribution of Type 1 and 2 pulses at 15% MVC, suggesting that they altered their control strategy in response to the increase in the duration of the task. At 85% MVC, they favored Type 2 relative to Type 1 and Type 3 pulses (F_2,138 = 13.84, P = 0.00; Type 1 = 0.22, Type 2 = 0.55, Type 3 = 0.19).

In contrast to healthy control subjects, ASD participants continued to show a bias toward Type 1 pulses at 15% MVC during the 8-s test (F_2,147 = 12.00, P = 0.00; Type 1 = 0.52, Type 2 = 0.22, Type 3 = 0.25). They showed a relatively even distribution of Type 1 and Type 2 pulses at 45% MVC (F_2,156 = 9.7, P = 0.00; Type 1 = 0.43, Type 2 = 0.33, Type 3 = 0.18) and then favored Type 2 pulses at 85% MVC (F_2,138 = 15.05, P = 0.00; Type 1 = 0.23, Type 2 = 0.51, Type 3 = 0.15). Individuals with ASD showed higher rates of Type 1 pulses compared with control subjects at 15% MVC (F_1,147 = 5.19, P = 0.02); the control strategies used at higher force levels did not differ between groups (45% MVC: F_1,156 = 2.04, P = 0.16; 85% MVC: F_1,138 = 0.35, P = 0.56).

Comparisons of the accuracy, rate of force increase, and duration of each initial pulse type are reported for individuals with ASD and healthy control subjects in Table 2. There were no group differences in initial pulse characteristics for the 2-s test. During the 8-s test, individuals with ASD showed increased initial pulse overshoot compared with control subjects at 15% MVC when using Type 1 or Type 3 pulses. They showed a reduced rate of force increase at 85% MVC compared with control subjects for all pulse types. At 45% MVC, the duration of their Type 2 pulses was shorter than for control subjects, whereas the duration of their Type 3 pulses was longer.

Table 2.

Primary pulse characteristics for individuals with ASD and healthy control subjects at different target force levels during 2- and 8-s tests

	2-s Test		8-s Test
	Control	ASD	Control	ASD
Force accuracy at primary pulse offset
15% MVC
Type 1	0.12 (0.04)	0.12 (0.04)	−0.09 (0.06)**	0.15 (0.05)
Type 2	−0.25 (0.05)††2-1;2-3	−0.22 (0.04)††2-1;2-3	−0.22 (0.06)	−0.20 (0.06)††2-1;2-3
Type 3	0.04 (0.05)	0.09 (0.04)	−0.05 (0.07)**	0.12 (0.06)
Avg.	−0.03 (0.03)	−0.00 (0.02)	−0.12 (0.04)	0.02 (0.03)
45% MVC
Type 1	−0.14 (0.04)	−0.15 (0.03)	−0.24 (0.06)	−0.17 (0.05)
Type 2	−0.31 (0.04)††2-1;2-3	−0.30 (0.03)††2-1;2-3	−0.31 (0.06)	−0.35 (0.05)††2-3;†2-1
Type 3	−0.15 (0.04)	−0.08 (0.03)	−0.16 (0.07)	−0.09 (0.06)
Avg.	−0.20 (0.02)	−0.18 (0.02)	−0.24 (0.04)	−0.20 (0.03)
85% MVC
Type 1	−0.19 (0.04)	−0.25 (0.03)	−0.39 (0.06)	−0.39 (0.06)
Type 2	−0.40 (0.04)††2-1;2-3	−0.37 (0.03)†2-1;2-3	−0.42 (0.05)	−0.49 (0.05)†2-3
Type 3	−0.25 (0.04)	−0.25 (0.03)	−0.28 (0.06)	−0.28 (0.06)
Avg.	−0.28 (0.02)	−0.29 (0.02)	−0.36 (0.03)	−0.38 (0.03)
Primary pulse duration, s
15% MVC
Type 1	0.34 (0.02)	0.30 (0.01)	0.28 (0.03)	0.35 (0.03)
Type 2	0.29 (0.02)††2-3	0.29 (0.02)	0.31 (0.03)	0.29 (0.03)
Type 3	0.36 (0.02)	0.33 (0.01)	0.34 (0.04)	0.33 (0.03)
Avg.	0.33 (0.01)	0.31 (0.01)	0.31 (0.02)	0.32 (0.02)
45% MVC
Type 1	0.32 (0.01)	0.30 (0.01)†1-3	0.28 (0.02)	0.29 (0.02)
Type 2	0.32 (0.01)	0.32 (0.01)	0.35 (0.02)*	0.28 (0.02)††2-3
Type 3	0.33 (0.01)	0.34 (0.01)	0.30 (0.03)*	0.37 (0.02)
Avg.	0.32 (0.01)	0.32 (0.01)	0.31 (0.01)	0.32 (0.01)
85% MVC
Type 1	0.36 (0.01)*	0.32 (0.01)	0.33 (0.02)	0.28 (0.02)
Type 2	0.31 (0.01)†1-2	0.31 (0.01)	0.34 (0.02)	0.29 (0.02)
Type 3	0.33 (0.01)	0.32 (0.01)	0.38 (0.03)	0.34 (0.02)
Avg.	0.33 (0.01)	0.32 (0.01)	0.35 (0.01)	0.31 (0.01)
Primary pulse peak ROC, N/s
15% MVC
Type 1	72.90 (6.55)	64.49 (5.79)	55.43 (8.42)	72.98 (7.20)
Type 2	41.16 (7.32)††2-1	30.29 (6.30)††2-1;2-3	31.74 (8.91)	29.44 (8.42)††2-1;2-3
Type 3	55.95 (7.32)	57.10 (6.08)	51.64 (9.48)	68.20 (8.12)
Avg.	56.67 (4.08)	50.63 (3.50)	46.27 (5.17)	56.88 (4.60)
45% MVC
Type 1	131.30 (11.14)	110.77 (9.97)	111.71 (10.41)	113.22 (10.18)
Type 2	82.11 (10.92)††2-1	73.01 (10.14)†2-1;2-3	76.39 (10.41)†2-1;2-3	69.06 (9.96)††2-1
Type 3	110.28 (11.38)	110.12 (9.97)	124.47 (13.04)	97.48 (11.84)
Avg.	107.89 (6.44)	97.97 (5.79)	104.19 (6.56)	93.25 (6.17)
85% MVC
Type 1	176.41 (13.62)	147.10 (11.31)	130.39 (14.83)*	108.61 (14.36)
Type 2	124.60 (12.18)†1-2	114.57 (11.12)	109.83 (12.53)*	87.53 (12.25)
Type 3	157.06 (13.62)	139.50 (11.72)	135.30 (17.32)*	108.29 (14.83)
Avg.	152.69 (7.60)	133.72 (6.58)	125.18 (8.67)	101.48 (8.00)

Values are means (SD). MVC, maximum voluntary contraction. Significant values are in boldface: statistical significance of primary pulse type at

^†0.05,

^††0.01 level; statistical significance of group at

^*0.05,

^**0.01 level.

Rise Phase

At the end of the rise phase, participants' accuracy decreased with increases in target force level for both tests (Fig. 5; 2 s: F_1.70,93.22 = 33.66, P = 0.00; 8 s: F_1.36,76.00 = 62.20, P = 0.00). For the 2-s test, individuals with ASD overshot the target at 15% MVC whereas healthy control subjects were closer to the target and tended to undershoot (F_1,55 = 9.00, P = 0.00). While individuals with ASD tended to be less accurate than healthy control subjects across other force levels and durations, neither the overall group differences or the group × force level interactions were significant (all P > 0.05).

Fig. 5.

Individuals with ASD show reduced force accuracy at the end of the rise phase compared with control subjects at 15% MVC during the 2-s test. Individuals with ASD showed reduced accuracy relative to control subjects at the end of the rise phase during the 2- and 8-s tests at other MVCs as well, but these differences were not significant. *Between-group difference at 0.05 level.

Sustained Phase

As expected, participants showed increases in mean force as the target force level was increased (F_1.08,60.73 = 252.58, P = 0.00) (Fig. 6, top). Compared with control subjects, individuals with ASD showed reduced mean force overall, and this reduction was more severe at higher force levels (target force × group interaction: F_1.08,60.73 = 9.63, P = 0.00; 15% MVC: F_1,56 = 2.62, P = 0.11, 45% MVC: F_1,56 = 6.64, P = 0.01; 85% MVC: F_1,56 = 9.27, P = 0.00). We examined the ratio of mean force to each participant's target force level to determine whether lower mean force in ASD was due to their lower MVCs. The group × force level interaction was significant because of reduced mean:target force levels for the ASD group compared with the control group at 85% MVC (F_1.16,64.83 = 4.19, P = 0.04; 15% MVC: F_1,56 = 2.48, P = 0.12, control = 1.06 N, SE = 0.06 N, ASD = 1.18 N, SE = 0.05 N; 45% MVC: F_1,56 = 0.58, P = 0.45, control = 0.96 N, SE = 0.02N, ASD = 0.94 N, SE = 0.02 N; 85% MVC: F_1,56 = 4.05, P = 0.048, control = 0.84 N, SE = 0.03 N, ASD = 0.77 N, SE = 0.02 N). Individuals with ASD showed increased CoV compared with control subjects across target force levels, suggesting that increases in sustained force variability in ASD were evident even after controlling for the modest decreases in mean force seen in ASD (Fig. 6, bottom; F_1,56 = 6.97, P = 0.01).

Fig. 6.

Individuals with ASD show reduced sustained mean force (top) and increased force coefficient of variation (CoV; bottom) compared with control subjects during the 8-s test. *Between-group differences at 0.05 level.

Relaxation Phase

Analyses of 2-s trials indicated that participants relaxed force more rapidly during the relaxation phase at larger force levels (target force main effect: F_1.19,65.45 = 190.44, P = 0.00) (Fig. 7). Individuals with ASD showed reduced rates of force decrease compared with control subjects across force levels (target force × group interaction: F_1.19,65.45 = 6.27, P = 0.01), particularly at 45% (F_1,55 = 11.48, P = 0.00) and 85% (F_1,55 = 7.24, P = 0.009) MVC. Across participants, the rate of force decrease was greater at larger force levels compared with lower force levels during the 8-s test as well (F_1.08,56.16 = 203.83, P = 0.00). Individuals with ASD showed reduced rates of force decrease (i.e., they were slower to relax force) compared with control subjects across all force levels (F_1.08,56.16 = 6.53, P = 0.01).

Fig. 7.

Peak rate of force decrease is reduced (slower relaxation) for individuals with ASD compared with control subjects. *Between-group differences at 0.05 level.

Clinical Correlations

Grip force performance was not associated with full-scale or nonverbal IQ for ASD participants (all P > 0.05). For healthy control subjects, increased rates of Type 1 pulses at 15% MVC during the 8-s test were associated with higher full-scale IQs (r = 0.49, P = 0.02).

Increased age was associated with a reduction in the rate at which Type 1 initial pulses were used for the 2-s test at both 45% and 85% MVCs for individuals with ASD only (r = −0.38, P = 0.03). Age was not associated with the rate of different pulse types for healthy control subjects. Both groups demonstrated age-related increases in mean sustained force at both 45% and 85% MVC (ASD: r = 0.70, P = 0.00; control: r = 0.80, P = 0.00) and reductions in sustained force CoV across all target force levels (ASD: r = −0.37, P = 0.00; control: r = −0.72, P = 0.00). Age-related reductions in CoV were stronger for control subjects compared with individuals with ASD (Fisher's Z = 1.86, P = 0.03). Increased age also was associated with increased rates of force decrease during the relaxation phase for all force levels on the 2-s (ASD: r = −0.70, P = 0.00; control: r = −0.70, P = 0.00) and 8-s (ASD: r = −0.70, P = 0.00; control: r = −0.70, P = 0.00) tests. The strength of age-associated decreases in rate of force relaxation was not different between control subjects and individuals with ASD (P > 0.05).

Increased rates of Type 1 pulses at 45% and 85% MVC of the 2-s test were associated with more severe clinically rated ADI social-communication abnormalities in ASD (r = 0.42, P = 0.02). No other relationships between force performance and clinical ratings of ASD symptoms were significant.

DISCUSSION

In the present study of precision gripping, we found that individuals with ASD utilize an initial pulse strategy characterized by rapid increases in and then release of force more frequently than control subjects. While control subjects also use this strategy when target force levels and grip durations are relatively low, they adapt to increased demands on their force output by transitioning to a strategy in which they increase force more gradually, pause, and then increase their force output again. Our results also indicate that, when sustaining a constant force level, individuals with ASD show increased output variability suggesting that they have a reduced ability to translate visual feedback information into precise motor commands. Finally, patients showed a consistent reduction in the rate at which they released their grip, indicating a reduced ability to rapidly terminate force output.

Precision Grip Abnormalities in ASD

While prior studies have suggested that individuals with ASD show a reduced ability to integrate load and lifting forces during gripping (David et al. 2009, 2012), ours is the first known study to identify differences in the underlying strategy used by individuals with ASD to control initial force output. The duration of initial pulses ranged between 200 and 300 ms, suggesting that they are completed before visual feedback is likely to have a large impact on force output and thus are largely controlled by feedforward mechanisms (Kawato 1999; Miall 1998; Wisleder and Dounskaia 2007). Our findings that individuals with ASD utilize an atypical initial pulse strategy and that the accuracy, rate of force increase, and duration of initial pulses are abnormal in ASD suggest that feedforward mechanisms involved in controlling initial motor output are disrupted.

From a sensorimotor efficiency perspective, healthy control subjects' transition from a Type 1 to a Type 2 initial pulse strategy at higher force levels and prior to longer sustained contractions is advantageous for reducing the operative cost on the neuromuscular system. While increasing force output involves temporal coordination of the agonist muscles of the thumb and index finger (Bastian 2006; Nowak et al. 2004; Potter et al. 2006), decreasing grip force requires coordination of both agonist and antagonist muscles of the fingers and hand (Bastian 2006; Day et al. 1998; Potter et al. 2006). The reduced mechanical requirements of a Type 2 approach likely explains why individuals tend to produce lower force levels than required when first manipulating an object of unknown weight, as this approach allows them to adjust their force output more efficiently (Nowak et al. 2004). In addition, producing excessive force during initial contractions and then relaxing force levels not only increases the difficulty of the action as more muscles are involved but also may lead to muscular fatigue and, therefore, disrupt control of subsequent force output.

Because our procedure for differentiating primary pulses was based on derivatives of force output that effectively amplify noise, it is possible that estimates of the timing and number of zero-crossings in third derivative traces may reflect Type 3 pulse strategies as well as low-amplitude oscillations from mechanical (e.g., electrical noise) and biological sources. A conservative filter cutoff (6 Hz) was used to minimize artifact from higher-frequency mechanical noise in derivative traces, but oscillations reflecting peripheral (e.g., joint, muscle fiber, motor unit, motor neurons) and central nervous system processes are more difficult to distinguish from pulse strategies. These oscillations could contribute to overestimates of Type 3 pulses and underestimates of Type 3 pulse durations. While we found that Type 3 pulses were less common than Type 1 and 2 pulses and of similar duration (see Table 2), Type 3 pulse results should be interpreted with caution because of the possible influence of both electrical and biological noise on our differentiation procedure.

Individuals with ASD also showed reduced mean force and increased force variability relative to control subjects when attempting to sustain a constant level of force. Reductions in mean force were largely reflective of decreased strength as indicated by patients' lower MVCs (Fellows et al. 2001; Hardan et al. 2003; Kern et al. 2011). However, after adjusting for overall decreases in mean force, individuals with ASD still showed increased force variability, suggesting that they have a reduced ability to accurately adjust their motor output online (Gepner and Mestre 2002). This impairment may represent a major component of the difficulties in performing skilled tasks of the hands and fingers that often are seen in individuals with ASD (Dziuk et al. 2007; Fuentes et al. 2009).

During the relaxation phase, patients showed reduced rates of force release. While individuals with ASD appear to show a protracted course of movement deceleration when making rapid saccadic eye movements (Glazebrook et al. 2009), to our knowledge their ability to terminate force output has not been previously examined. These results provide behavioral evidence suggesting that the termination of grip is impaired in ASD, which could be related to abnormal agonist and antagonist muscles (Vilis and Hore 1980). Direct electromyographic (EMG) measurements of muscle activation patterns during precision gripping and releasing in ASD are needed to determine the mechanisms underlying patients' reduced rates for force release.

Neural Mechanisms Underlying Visuomotor Abnormalities in ASD

The profile of visuomotor alterations seen here in ASD implicates dysfunctions in cortico-cerebellar networks involved in visuomotor control. The cerebellum appears to be a particularly important region both for generating internal action representations used to predictively control initial motor output (Bastian 2006; Kawato 1999) and for translating visual feedback information from posterior parietal cortex into reactive motor adjustments to control sustained motor behaviors (Coombes et al. 2010; Stein and Glickstein 1992; Vaillancourt et al. 2003). Prior studies of patients with cerebellar lesions have documented patterns of deficit during precision gripping that are similar to those we observed here in ASD, including excess initial force output, increased sustained force variability, and decreased rates of force relaxation (Fellows et al. 2001; Mai et al. 1988; Müller and Dichgans 1994; Nowak et al. 2002, 2004; Serrien and Wiesendanger 1999). The hypothesis that cerebellar alterations may contribute to precision gripping abnormalities in ASD also is supported by numerous postmortem studies of ASD patients documenting Purkinje cell and deep nuclear pathology (Bailey et al. 1998; Bauman and Kemper 2005; Whitney et al. 2008) and neuroimaging studies showing both structural and functional abnormalities of cerebellar circuits in ASD (Allen and Courchesne 2003; Catani et al. 2008; Courchesne et al. 1988; Groen et al. 2011; Mostofsky et al. 2009). Thus our neurobehavioral findings suggest that cerebellar pathology may contribute to the visuomotor deficits characteristic of this disorder.

While the profile of precision grip abnormalities seen here in ASD implicates the cerebellum, it also is possible that alterations of other cortical and subcortical regions contribute to visuomotor deficits in this disorder (Gordon and Duff 1999; Hermsdörfer et al. 2003, 2004; Quaney et al. 2005; Valvano and Newell 1998). During precision gripping, parietal and motor cortices are involved in processing sensory feedback and generating motor commands, respectively (Vaillancourt et al. 2006). Patients with focal lesions of premotor, primary motor, and parietal cortices have been shown to demonstrate excessive initial grip force and increased sustained force variability (Eidenmüller et al. 2014; Mostofsky et al. 2009). Patients with Parkinson's disease also have been found to demonstrate increased force variability during precision gripping, implicating the basal ganglia (Neely et al. 2013). While neuroimaging studies are necessary for establishing the neural mechanisms underlying visuomotor impairments in ASD, our findings collectively suggest that cerebellar pathology and dysfunctions in cortical and striatal circuits may contribute to the neurodevelopmental alterations seen in this disorder.

Clinical Associations

While both individuals with ASD and healthy control subjects showed age-related reductions in sustained force variability, this improvement was stronger in control subjects, suggesting that visuomotor deficits in ASD may persist during later childhood and into adolescence. Prior studies of healthy development have indicated that age-related decreases in force variability reflect an increased ability to utilize visual and haptic information to refine ongoing performance (Deutsch and Newell 2001, 2003; Potter et al. 2006). Our findings indicate that individuals with ASD show dysmaturation of these sensory feedback processes that are persistent and thus may be important targets for interventions throughout development.

The association between increased rates of Type 1 initial pulses and more severe social-communication abnormalities in ASD suggests that these deficits may reflect a common neurodevelopmental mechanism, such as cerebellar dysfunctions (Wang et al. 2014). In addition to being critical to sensorimotor control, the cerebellum also has been shown to be involved in social and cognitive development (Stoodley and Schmahmann 2009). Furthermore, decreased cerebellar volume has been found to be associated with increases in the volume of prefrontal cortices involved in cognitive and social processes in ASD (Carper and Courchesne 2005).

It also is possible that visuomotor deficits contribute to the development of social-communication impairments in affected individuals. Evidence for this hypothesis comes from findings that early sensorimotor abnormalities in ASD are associated with more severe social-communication features later in life (Sutera et al. 2007) and that sensorimotor developments in infancy and toddlerhood are important for increasing the quality and frequency of social interactions and language learning opportunities (Gallese et al. 2013; LeBarton and Iverson 2013). Visuomotor impairments were not associated with IQ or with the severity of repetitive behaviors, indicating that they may be selectively associated with social-communication dysmaturation in ASD.

Summary

Our results demonstrate that individuals with ASD show visuomotor impairments affecting initial motor output, sustained force output, and the ability to rapidly release force. Furthermore, we provide novel evidence that reduced control of initial pulses in ASD may reflect an atypical control strategy in patients and a failure to flexibly adapt feedforward control strategies to changing task demands. Our finding that alterations in feedforward control strategies are associated with more severe social-communication abnormalities suggests that the sensorimotor impairments present in the majority of individuals with ASD may play a more central role in this disorder than previously believed.

GRANTS

This study was supported by National Institute of Mental Health Award 092696 and National Institute of Neurological Disorders and Stroke Grant R01 NS-058487. This research also was supported by the Department of Defense Autism Research Program under Award W81XWH-11-1-0738. Views and opinions of, and endorsements by, the author(s) do not reflect those of the US Army or the Department of Defense.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the author(s).

AUTHOR CONTRIBUTIONS

Author contributions: Z.W., G.C.M., and R.K.G. analyzed data; Z.W. and M.W.M. interpreted results of experiments; Z.W. prepared figures; Z.W. drafted manuscript; Z.W., G.C.M., D.E.V., and M.W.M. approved final version of manuscript; S.P.W. and R.K.G. performed experiments; D.E.V. and M.W.M. conception and design of research; M.W.M. edited and revised manuscript.