Quantifying Patient-Specific Changes in Neuromuscular Control in Cerebral Palsy
Conditions: Cerebral Palsy
Sponsor: University of Washington

 
 
 
 
 
 
Quantifying Patient-Specific Changes in 
Neuromuscular Control in Cerebral Palsy 
 
NCT #: NCT02699554 
Date: 3/15/2022 
 

STUDY PROTOCOL 
Background 
Current treatments for individuals with cerebral palsy (CP), such as orthopaedic surgery, fail to 
make significant improvements in gait for 50% of treated patients. CP is caused by an injury to 
the brain near the time of birth; however, quantifying how this injury contributes to impaired 
movement remains challenging. We theorize that patient-specific differences in neuromuscular 
control contribute to the variable and unsatisfactory  outcomes after treatment in CP; quantifying 
these differences is key to understanding and improving pathologic movement. Recent evidence 
from human and animal studies suggests that control of movement can be described by muscle 
synergies; low-dimensional, weighted groups of muscles that are consistently activated together 
in fixed ratios. Synergies are calculated from electromyography (EMG) using matrix factorization 
algorithms, such as nonnegative matrix factorization (NNMF). During gait, synergies can 
describe muscle activity in unimpaired individuals. After stroke, even fewer synergies are 
required, suggesting simplified control that may contribute to impaired movement. In a study of 
over 500 individuals with CP, we demonstrated that synergy complexity was also reduced 
compared to unimpaired individuals. Further, our preliminary results suggest that individuals 
with complex synergies, more similar to unimpaired individuals, are more likely to have good 
outcomes after a variety of treatments compared to individuals with reduced synergy 
complexity. 
These results motivate our proposed research to use synergies as a framework to quantify 
altered neuromuscular control in CP and inform treatment planning. There are several important 
knowledge gaps that need to be addressed to determine the clinical utility of synergy analyses. 
First, we need to determine if an individual’s synergies predict treatment outcomes and provide 
additional insight beyond traditional tools of clinical motion analysis. Second, we need to 
establish the adaptability of synergies. EMG is typically only collected before treatment in 
clinical motion analysis and we do not know if or how neuromuscular control changes after 
treatment. If synergies can change, new treatment strategies such as biofeedback training may 
be able to target altered synergies and improve movement. If synergies do not change, we can 
use an individual’s synergies to predict responses to different treatments and identify their “best” 
possible gait pattern given their neurologic capacity. Finally, from a theoretical perspective, we 
need to further develop our understanding of the contribution of altered synergies to impaired 
movement in CP. We developed a new algorithm in OpenSim, an open-source platform for 
musculoskeletal modeling, which allows users to specify and test different control strategies. We 
will use this platform to create patient-specific simulations and evaluate how changes in 
synergies influence muscle recruitment, movement, and energy costs of walking. 
The long-term goals of the proposed research are to improve movement and quality of life for 
individuals with CP. This proposal will determine if quantifying altered neuromuscular control 
with synergy analysis and musculoskeletal simulation can provide clinicians with new tools to 
optimize treatment and improve walking ability. Our specific aims are to: 
Aim 1: Determine if pre-operative synergies predict outcomes at 12 months after orthopaedic 
surgery. We will recruit 55 individuals with diplegic cerebral palsy (CP) who are undergoing 
single-event multi-level orthopaedic surgery (SEMLS) at Gillette for standard of care. Each 
individual will receive gait analysis including EMG to evaluate synergies before and after 
surgery, which is part of standard of care. We hypothesize that individuals with more complex 
synergies (i.e., synergies similar to unimpaired individuals) will have greater improvements in 
walking kinematics, energy costs, and quality of life 12 months after orthopaedic surgery. 

Aim 2: Evaluate stability of synergies with respect to time and treatment. With the same 
participants as Aim 1, we will compare synergies during gait between two visits before surgery 
and 6 and 12 months after surgery. This investigation will determine whether synergies are 
repeatable between days in CP and whether synergies adapt or change in response to surgical 
changes to the musculoskeletal system. 
 
Human Subjects Involvement, Characteristics, and Design 
 
Aims 1 and 2 will use the same set of subjects to evaluate if synergies predict outcomes after 
SEMLS (Aim 1) and if synergies change after surgery (Aim 2). We will recruit 55 individuals with 
CP who have been recommended for surgical treatment at Gillette Children’s Specialty 
Healthcare over three years. Our inclusion criteria for Aims 1 and 2 are: age > 6, diagnosis of 
spastic diplegic cerebral palsy, GMFCS Levels I-III, absence of dystonia/ataxia/athetosis, no 
prior surgical procedures, no pharamacological spasticity treatments (e.g. botulinum toxin, 
baclofen, etc.) within 3 months prior to first gait analysis, capacity to follow instructions and 
provide informed assent, ability to walk across 30 ft. walkway at least 10 times, absence of 
acute or chronic pain in lower-extremities, and no current pharmacological agents that impact 
neuromuscular control. Subjects will be recruited who are being evaluated for SEMLS surgery 
and includes one or more of the following procedures: femoral and tibial derotation osteotomies, 
surgical lengthening of the gastrocnemius, psoas, hamstrings and adductors, bone and soft 
tissue alignment of the foot, botulinum toxin injection, distal femoral extension osteotomy, and 
patellar tendon advancement. Subjects will not be excluded on the basis of ethnic background, 
race, sex, or socioeconomic factors. 
 
Each subject will receive instrumented gait analysis before and after treatment. Post-operative 
gait analyses will be at 6 and 12 months to ensure that the patients are ambulatory and to 
evaluate neuromuscular control over a full year of recovery. Each gait analysis will include 
kinematics, kinetics, electromyography (EMG), and a physical exam. EMG will be collected for 5 
muscles on each leg. Subjects will perform a minimum of 5 trials walking across the laboratory 
at each visit. The physical exam will be completed by an experienced pediatric physical 
therapist and include evaluations of range of motion, spasticity (Modified Ashworth Score, 
Tardieu Scale, and instrumented spasticity measurement system), selective motor control 
(SCALE), manual muscle strength, and bone alignment. 
 
a. Potential risks:  
 
There are minor risks associated with completing motion analysis during gait; however, these 
risks are not greater than minimal risk and are similar to activities of daily living. Tripping and 
falling are possibilities and we will take numerous precautions to prevent such risk, as described 
below. Fatigue and muscle soreness are also potential risks and we will provide adequate time 
to rest and recover for all tasks.   
 
b. Recruitment and informed consent:  
Children with cerebral palsy will be recruited from Gillette Children’s Specialty Healthcare using 
brochures designed to describe the research and involvement. An experienced research 
coordinator will assist with recruiting and contacting potential participants. Before gait analysis, 
each subject will receive a thorough explanation of the protocols from a member of the research 
team. We will obtain informed permission from the parent or guardian and assent from the child. 

Children that do not voluntarily communicate their assent and understand the procedures will 
not be included in the study. Information will be provided verbally and in writing. Subjects will be 
reassured of their right to withdraw at any time from the study.  
 
c. Protection against risks:  
 
We take numerous precautions to protect against risks associated with using motion capture to 
analyze human movement. To prevent tripping and falling we ensure that the floor of the lab is 
free from wires or other obstructions. Additionally, we can provide additional support through 
harnesses, bracing, or walking beside the subject, if necessary. Electromyography is also safe 
and has been tested and used extensively during gait and similar tasks. We will inspect all wires 
and connections before every experiment. Subjects will be given frequent breaks during all 
experiments to avoid fatigue. For all experiments, subjects can terminate the experiment at any 
time. To protect against any breach in confidentiality, data collected for this study will be stored 
securely on password-protected computers. Codes instead of names of subjects will be used on 
all data files and reports. Any data used for publication or presentations will be de-identified. 
 
d. Potential benefits of the proposed research to human subjects and others:  
 
There are no immediate potential benefits to the participants in this study. 
 
e. Importance of the knowledge to be obtained: 
 
The potential risks associated with this study are reasonable in the context of the knowledge 
and advancement in our understanding of cerebral palsy that will be gained. We anticipate that 
the proposed experiments will provide a new framework for quantifying altered neuromuscular 
control in cerebral palsy and improving treatment planning. This research will provide insight 
into the impact of orthopaedic surgery and evaluate the potential of using measures of altered 
neuromuscular control to predict outcomes. The results from this work will also be extendable to 
other clinical populations such as stroke, traumatic brain injury, spinal cord injury, and multiple 
sclerosis, which also exhibit impaired neuromuscular control. 
 
 
 

STATISTICAL ANALYSIS 
 
Aims 1 and 2 prospectively evaluate neuromuscular control among individuals with cerebral 
palsy who are receiving single-event multi-level orthopaedic surgery (SEMLS) as part of their 
standard care at Gillette Children’s Specialty Healthcare. Our sample size analysis, based upon 
our prior analyses of synergy complexity repeatability and associations with outcomes, suggest 
a sample size of 55 subjects for 90% power to detect ½ a standard deviation change in synergy 
complexity assuming a 20% drop-out rate, alpha = 0.05. This sample size is based upon Aim 2 
to detect if synergies change after treatment. In our preliminary analysis, a ½ standard deviation 
change in synergy complexity (Dynamic Motor Control, DMC) was associated with a 5-point 
difference in the Gait Deviation Index (GDI), which is generally the threshold for defining a 
clinically significant improvement in walking ability in clinical motion analysis. We are evaluating 
synergy complexity using DMC, which we previously evaluated and tested on over 500 children 
with cerebral palsy and 84 typically-developing children. In this prior research, DMC could 
differentiate between functional ability levels among individuals with cerebral palsy.  
 
We use causal modeling and Bayesian Additive Regression Trees (Table 1) to evaluate whether 
pre-operative synergy complexity and structure are associated with changes in GDI after 
SEMLS at 12 months post-op. We constructed our causal model with a Directed Acyclic Graph 
(DAG) . The logic behind our DAG is as follows: 
1. Our objective was to determine the impact of SEMLS on change in GDI (ΔGDI). Thus, 
SEMLS is our exposure and ΔGDI is our outcome. SEMLS induces a change in 
impairments (ΔImp) that causes the observed ΔGDI.  
2. The covariates we identified as common causes of both SEMLS and ΔGDI included: 
Age, baseline GDI (GDIpre), and baseline impairment (Imppre). Baseline impairments 
represent a set of variables collected during clinical gait analysis to evaluate neurologic 
and orthopedic impairments (Table 1).  
3. Surgical treatment history (Hx) is a common cause of baseline impairment (Imppre) and 
whether or not SEMLS is recommended. 
4. We included a general severity (Sev) measure as an unmeasured factor that impacts 
baseline impairment (Imppre) and surgical treatment history (Hx). 
 
From the DAG we determined the adjustment set needed (e.g., regression, BART) to evaluate 
the total causal effect of SEMLS on ΔGDI. For this DAG, the minimal sufficient adjustment set to 
estimate the total causal effect of SEMLS on ΔGDI was: Age, GDIpre, and Imppre. We also 
determined the adjustment set to evaluate the total causal effect of baseline impairment (Imppre) 
on ΔGDI and GDIpre. The minimal sufficient adjustments sets were Age and Hx for ΔGDI and 
Age for GDIpre. The plausibility of a DAG can be evaluated by identifying conditional 
independencies, variables that should be independent given the causal relationships defined in 
the DAG. We identified the adjustment sets and independencies with dagitty4 and all analyses 
were conducted in R (version 4.1.0)5. 
 
To assess the total causal effects of SEMLS and baseline impairments (Imppre) on change in 
GDI (ΔGDI) we used Bayesian Additive Regression Trees (BART), a machine learning method 
that uses a boosted ensemble of regression trees for nonparametric function estimation relying 
on a Bayesian probability model6. Like other tree-based regression methods, an advantage of 
BART is that it can handle nonlinear effects and interactions7. For causal modeling, recent work 
has demonstrated that BART-based models achieve accurate and precise causal predictions8,9. 
For this analysis, we used BART models to estimate ΔGDI using the adjustment sets identified 
by the DAG. Thus, to identify the impact of SEMLS on ΔGDI, we included the covariates Age, 

GDIpre, and Imppre. Baseline impairments were not available for all participants. Missing data in 
Imppre were imputed using multivariate imputation by chained equations (MICE)10. We used the 
bartMachine package to implement the analysis11. We optimized the hyperparameters for each 
BART model using 10-fold cross-validation. We report the pseudo-R2 (1 – SSE/SST) for each 
BART model and used k-fold cross-validation (k = 10) to determine the out-of-sample root mean 
square error (RMSE). 
 
To assess the relative effects of individual variables from BART, we used accumulated local 
effect (ALE) analysis12. The ALE analysis is similar to a partial dependence plot, but the 
averaging is done locally to avoid including observations that are unlikely to ever be realized 
(e.g., someone walking three standard deviations slower than average but with a normal 
cadence). The ALE plots illustrate the impact of each variable over the range of values for that 
variable, conditioned on the other covariates in the model. Thus, ALE plots can be useful for 
examining nonlinear effects identified by BART. For example, the ALE plot can highlight 
nonlinear effects such as when a variable impacts GDI with a deviation from average (i.e., a U-
shaped plot) or when a variable only impacts GDI above or below a certain cut-off (i.e., a step 
function or discontinuity). 
 
Table 1: Variable definitions 
Variable 
Description 
GDI 
Overall measure of the deviation in an individual’s kinematics compared to nondisabled 
peers scaled such that mean(sd) over the nondisabled population is 100(10)13. Kinematics 
were evaluated using marker-based motion analysis and a modified plug-in-gait marker set. 
SEMLS 
Binary variable indicating whether or not child had single-event multi-level orthopedic 
surgery, defined as a surgery with two or more orthopedic surgeries on at least one leg. 
Hx 
Binary list of prior surgical treatments. 
Age 
Years from birth defined as days/365.25 
Impairments 
Spasticity: Mean modified Ashworth score across plantarflexors, hamstrings, hip adductors, 
and rectus femoris. 
Strength: Mean manual muscle strength score across hip flexors/extensors, knee 
flexors/extensors, and ankle dorsiflexors/plantarflexors where 1 is defined as a ‘visible or 
palpable contraction’ and 5 is defined as ‘full range of motion against gravity’. 
Static Motor Control (SMC): Mean static motor control score across hip abduction, hip 
flexion, hip extension, knee extension, and ankle plantarflexion where 0 is very little or no 
control of single joint movement, 1 is impaired voluntary movement at a single joint, and 2 
is good voluntary movement at a joint. 
Dynamic Motor Control (DMC): Measure of the complexity of muscle activity during gait 
evaluated from synergy analysis of EMG data. Complexity is evaluated as the total variance 
accounted for by one synergy of EMG data during CGA and compared to nondisabled peers 
scaled such that mean(sd) over the nondisabled population is 100(10)14,15. 
Torsional Deformity: Femoral anteversion and tibial torsion (bimalleolar axis angle) 
measured during physical exam. 
Contracture: Measures of joint range of motion from physical exam including maximum 
ankle dorsiflexion with the knee extended, maximum knee extension, unilateral popliteal 
angle, and maximum hip extension measured during the Thomas Test. 
 
 
 

REFERENCES 
 
1.  
Verma T, Pearl J. Equivalence and synthesis of causal models. UCLA, Computer Science 
Department Los Angeles, CA; 1991.  
2.  
Brewer LE, Wright JM, Rice G, Neas L, Teuschler L. Causal inference in cumulative risk 
assessment: The roles of directed acyclic graphs. Environ Int. 2017 May 1;102:30–41.  
3.  
Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol. 
2008 Oct 30;8(1):70.  
4.  
Textor J, van der Zander B, Gilthorpe MS, Liśkiewicz M, Ellison GT. Robust causal inference 
using directed acyclic graphs: the R package ‘dagitty.’ Int J Epidemiol. 2016 Dec 
1;45(6):1887–1894.  
5.  
R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, 
Austria: R Foundation for Statistical Computing; 2021. Available from: https://www.R-
project.org/ 
6.  
Chipman HA, George EI, McCulloch RE. BART: Bayesian additive regression trees. Ann Appl 
Stat. Institute of Mathematical Statistics; 2010 Mar;4(1):266–298.  
7.  
Tan YV, Roy J. Bayesian additive regression trees and the General BART model. 
ArXiv190107504 Stat [Internet]. 2019 Jan 22 [cited 2021 Jul 27]; Available from: 
http://arxiv.org/abs/1901.07504 
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Hill JL. Bayesian Nonparametric Modeling for Causal Inference. J Comput Graph Stat. 
Taylor & Francis; 2011 Jan 1;20(1):217–240.  
9.  
Dorie V, Hill J, Shalit U, Scott M, Cervone D. Automated versus Do-It-Yourself Methods for 
Causal Inference: Lessons Learned from a Data Analysis Competition. Stat Sci. Institute of 
Mathematical Statistics; 2019 Feb;34(1):43–68.  
10.  Buuren S van, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained 
Equations in R. J Stat Softw. 2011;45(3):1–67.  
11.  Kapelner A, Bleich J. bartMachine: Machine Learning with Bayesian Additive Regression 
Trees. J Stat Softw. 2016;70(4):1–40.  
12.  Molnar C, Bischl B, Casalicchio G. iml: An R package for Interpretable Machine Learning. 
JOSS. Journal of Open Source Software; 2018;3(26):786.  
13.  Schwartz MH, Rozumalski A. The Gait Deviation Index: a new comprehensive index of gait 
pathology. Gait Posture. 2008 Oct;28(3):351–357. PMID: 18565753 
14.  Shuman BR, Goudriaan M, Desloovere K, Schwartz MH, Steele KM. Associations Between 
Muscle Synergies and Treatment Outcomes in Cerebral Palsy Are Robust Across Clinical 
Centers. Arch Phys Med Rehabil. 2018;99(11):2175–2182. PMCID: PMC6179956 
15.  Shuman BR, Schwartz MH, Steele KM. Electromyography Data Processing Impacts Muscle 
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Comput Neurosci. 2017;11:50. PMCID: PMC5460588 
 

 
 
 
 
 
Version Date: September 2015 
 
Page 1 of 3 
 
Dynamic motor control in patients with CP  
Parent Consent Form 
 
Your child is being asked to participate in a research study that will investigate how muscles 
work together to control movement while walking. Your child is being contacted as a potential 
participant because your child has cerebral palsy (CP), had a recent gait analysis, and is a 
candidate for surgery at Gillette Children’s Specialty Healthcare. 
We ask that you read this form. Please take your time to ask any questions and feel comfortable 
making a decision whether or not you agree for your child to be in the study. 
This study is being conducted by Michael Schwartz, Ph.D. from the Center for Gait and Motion 
Analysis at Gillette and Dr. Katherine Steele, PhD from the University of Washington. 
Before you learn about the study, it is important you know the following: 
• You and your child’s participation is completely voluntary. 
• You and your child may not directly benefit by participating in the study but knowledge 
gained from your participation may help others. 
• You or your child’s decision to participate or not to participate will not affect your 
clinical care in any way at Gillette.  
Study Purpose 
The goal of this study is to better understand how muscles function together as children with CP 
walk. In particular, we are interested in whether the ability to control muscle activity influences 
outcomes, and whether this ability is affected by treatment. We think that if a child has good 
neuromuscular control, they will have a better outcome following surgery but sometimes this 
control is hard to measure. We want to develop a system that measures motor control reliably 
and easily so that clinicians can make better treatment decisions and improve the health of our 
patients. This study will use three dimensional motion capture and muscle activity data collected 
during gait analyses.  
Study Procedures 
If you agree to allow your child to participate in this study, your child will complete additional 
gait analyses before and/or after their standard-of-care surgery. At approximately 12 months 
after surgery, they will undergo a full gait analysis, similarly to their recent gait analysis. At 6 
months after surgery, they will undergo a shorter gait analysis. Some participants will also 
undergo this shorter gait analysis prior to their surgery. 
 
 

 
 
 
 
 
Version Date: September 2015 
 
Page 2 of 3 
 
Risks of Study Participation 
Because we will collecting identifiable information (e.g. child’s name, medical record number), 
there may be a risk of losing confidentiality. The data we collect will be stored as part of your 
child’s medical record. 
Benefits of Study Participation 
The gait analysis 12 months after surgery is typically requested as part of your child’s standard 
of care. Study funds will cover this cost. In the future, this research may lead to a better 
understanding of neuromuscular control in patients with CP and how this affects their outcomes 
following surgery. 
Alternatives to Study Participation 
This study is completely voluntary. The alternative is not to participate. If you and your child do 
not participate it will not impact your relationship with Gillette Children’s Specialty Healthcare 
in any way. 
Study Costs/Compensation 
Your child will receive a gift card after each research visit. All costs associated with each 
additional gait analysis are covered by study funds.  
Confidentiality 
The records of this study will be kept private. In any publications or presentations, we will not 
include any information that will make it possible to identify you or your child as a participant. 
However, your child’s record for the study may be reviewed by departments at Gillette or 
researchers at the University of Washington with appropriate regulatory oversight for human 
subject studies. All information collected for the study will be stored in a secured Gillette 
Children’s Specialty Healthcare database. To these extents, confidentiality is not absolute. 
Voluntary Nature of the Study 
Participation in this study is voluntary. Your decision whether or not to allow your child to 
participate will not affect your current or future relationship with Gillette. If you decide to 
participate, you or your child are free to withdraw at any time without affecting these 
relationships. 
 
Care of Subject in Case of Accident 
In the event that this research activity results in injury, treatment will be available, including first 
aid, emergency treatment, and follow-up care as needed. Care for such injuries will be billed in 
the ordinary manner, to you or your insurance company. If you think that your child has suffered 
a research related injury, let study staff know right away.  
 
Contacts and Questions 

 
 
 
 
 
Version Date: September 2015 
 
Page 3 of 3 
 
The Gillette researcher conducting this study is Michael Schwartz, PhD and the research is 
coordinated by Meghan Munger, MPH, CCRC. You may ask any questions you have now, or if 
you have questions later, you are encouraged to contact Michael at 612-229-3929 
(mschwartz@gillettechildrens.com) or Meghan at 612-229-1757 
(meghanemunger@gillettechildrens.com). 
If you have any questions or concerns regarding the study and would like to talk to someone 
other than the researcher(s), you are encouraged to contact the Patient Representative of the 
Quality Improvement Resources Department at Gillette Children’s Specialty Healthcare, 200 
East University Ave, St. Paul MN 55101, Telephone 651-229-1706 or 1-800-719-4040 (toll free) 
or email qualityrep@gillettechildrens.com. You may also send feedback by going to 
https://www.gillettechildrens.org/contact-us/ and completing the feedback form. 
You will be offered a copy of this form for your records. 
Statement of Consent 
I have read the above information. The research project and procedures have been explained to 
me. I have asked questions and have received answers. I consent to have my child participate in 
the study. I will receive a signed copy of this consent form for my records. I am not giving up 
any legal rights by signing this form. 
 
____________________________________ 
Date: ___________________ 
Signature of Parent or Legal Representative 
____________________________________ 
Printed Name of Person Consenting 
 
____________________________________ 
Date: ___________________ 
Signature of Person Obtaining Consent 
Statement of Consent for Participants Who Reach the Age of Majority 
The participant turned 18 years old during their involvement in this study. I have reviewed study 
information and consent to continue my participation. 
 
____________________________________ 
Date: ___________________ 
Signature of Participant