Gait Pattern and Motor Performance During Discrete Gait Perturbation in Children With Autism Spectrum Disorders

Emilia Biffi, Cristina Costantini, Silvia Busti Ceccarelli, Ambra Cesareo, Gian Marco Marzocchi, Maria Nobile, Massimo Molteni, Alessandro Crippa, Emilia Biffi, Cristina Costantini, Silvia Busti Ceccarelli, Ambra Cesareo, Gian Marco Marzocchi, Maria Nobile, Massimo Molteni, Alessandro Crippa

Abstract

Quantitative evaluation of gait has been considered a useful tool with which to identify subtle signs of motor system peculiarities in autism spectrum disorder (ASD). However, there is a paucity of studies reporting gait data in ASD as well as investigating learning processes of locomotor activity. Novel advanced technologies that couple treadmills with virtual reality environments and motion capture systems allows the evaluation of gait patterns on multiple steps and the effects of induced gait perturbations, as well as the ability to manipulate visual and proprioceptive feedbacks. This study aims at describing the gait pattern and motor performance during discrete gait perturbation of drug-naïve, school-aged children with ASD compared to typically developing (TD) peers matched by gender and age. Gait analysis was carried out in an immersive virtual environment using a 3-D motion analysis system with a dual-belt, instrumented treadmill. After 6 min of walking, 20 steps were recorded as baseline. Then, each participant was exposed to 20 trials with a discrete gait perturbation applying a split-belt acceleration to the dominant side at toe-off. Single steps around perturbations were inspected. Finally, 20 steps were recorded during a post-perturbation session. At baseline, children with ASD had reduced ankle flexion moment, greater hip flexion at the initial contact, and greater pelvic anteversion. After the discrete gait perturbation, variations of peak of knee extension significantly differed between groups and correlated with the severity of autistic core symptoms. Throughout perturbation trials, more than 60% of parameters showed reliable adaptation with a decay rate comparable between groups. Overall, these findings depicted gait peculiarities in children with ASD, including both kinetic and kinematic features; a motor adaptation comparable to their TD peers, even though with an atypical pattern; and a motor adaptation rate comparable to TD children but involving different aspects of locomotion. The platform showed its usability with children with ASD and its reliability in the definition of paradigms for the study of motor learning while doing complex tasks, such as gait. The additional possibility to accurately manipulate visual and proprioceptive feedback will allow researchers to systematically investigate motor system features in people with ASD.

Keywords: autism spectrum disorder; dual-belt treadmill; gait analysis; motor adaptation; virtual reality.

Figures

FIGURE 1
FIGURE 1
(A) Child walking on the GRAIL system during the session. (B) 25 marker set. T10, On the 10th thoracic vertebrae. SACR, On the sacral bone; NAVE, On the navel; XYPH, Xiphoid process of the sternum; STRN, On the jugular notch of the sternum; LASIS, Left anterior superior iliac spine; RASIS, Right anterior superior iliac spine; LPSIS, Left posterior superior iliac spine; RPSIS, Right posterior superior iliac spine; LGTRO, On the center of the left greater trochanter; FLTHI, On 1/3 on the line between the LGTRO and LLEK; LLEK, On the lateral side of the joint axis; LATI, On 2/3 on the line between the LLEK and LLM; LLM, The center of left lateral malleolus.
FIGURE 2
FIGURE 2
The diagram shows the entire data analysis process from the step-by-step gait features extraction to the ANCOVA analysis, considering a single exemplifying gait feature (the range of motion of the ankle: “ROM ankle”). Light pink panel shows the single subject analysis (run subject per subject). (a) At first the gait features are extracted for each step, both for the right and left side. Then, (b) normality is checked considering the values of all the M steps and, if verified, (c) the mean value over all the steps is computed, for both the right and left side. Light blue panel depicts group analysis (run across all subject of TD and ASD group). Left and right (mean) values for each subject are collected and (d) normality is tested; (e)t-test or Wilcoxon test, depending on the data distribution, is performed to check for differences between left and right side. (f) When difference are not significant the mean value between left and right sides is computed for each subject. (g) Shapiro–Wilk test is run to check for normality within groups and, since verified, (h) ANCOVA analysis with QI as covariate is performed.
FIGURE 3
FIGURE 3
Step-by-step kinematics for the pelvic, hip, knee and ankle joint for TD (left) and ASD (right) subjects. Left and right sides are distinguished. Each waveform represents a normalized (from 0 to 100) step of one subject. All the steps and the subjects are overlapped.
FIGURE 4
FIGURE 4
Step-by-step moments for the ankle, knee and hip joint for TD (left) and ASD (right) subjects. Left and right sides are divided. Each waveform represents a normalized (from 0 to 100) step of one subject. All the steps and the subjects are overlapped.
FIGURE 5
FIGURE 5
3 dimensional plots of the step-by-step spatiotemporal gait features (step length, stance period and walking speed) for TD (left) and ASD (right) subjects. Each point is referred to a step of one subject.
FIGURE 6
FIGURE 6
Correlation between (A) hip flexion at IC, (B) pelvic tilt at IC, (C) mean pelvic tilt at IC and SRS. Red dots are children with ASD, blue dots are TD children. The black line represents the regression curve.
FIGURE 7
FIGURE 7
Correlation between the delta peak of knee extension and ADOS. The black line represents the regression curve.
FIGURE 8
FIGURE 8
Example of adaptation to discrete perturbation for hip flexion at initial contact. Red dots are children with ASD, and the red line represents the regression curve; blue dots are TD children and the blue line represents the related regression curve.

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