Does reduced reactivity explain altered postural control in Parkinson’s disease? A predictive simulation study

Does reduced reactivity explain altered postural control in Parkinson’s disease? 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A predictive simulation study View ORCID Profile Julian Shanbhag , View ORCID Profile Sophie Fleischmann , View ORCID Profile Iris Wechsler , View ORCID Profile Heiko Gassner , View ORCID Profile Jürgen Winkler , View ORCID Profile Bjoern M. Eskofier , View ORCID Profile Anne D. Koelewijn , View ORCID Profile Sandro Wartzack , View ORCID Profile Jörg Miehling doi: https://doi.org/10.1101/2025.04.30.651447 Julian Shanbhag 1 Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julian Shanbhag For correspondence: shanbhag{at}mfk.fau.de Sophie Fleischmann 2 Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sophie Fleischmann Iris Wechsler 1 Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Iris Wechsler Heiko Gassner 3 Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany 4 Digital Health and Analytics, Fraunhofer Institute for Integrated Circuits IIS , Erlangen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Heiko Gassner Jürgen Winkler 5 Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jürgen Winkler Bjoern M. Eskofier 2 Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bjoern M. Eskofier Anne D. Koelewijn 6 Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anne D. Koelewijn Sandro Wartzack 1 Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandro Wartzack Jörg Miehling 1 Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jörg Miehling Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Postural instability represents one of the cardinal symptoms of Parkinson’s disease (PD). Still, internal processes leading to this instability are not fully understood. Simulations using neuromusculoskeletal human models could help understand these internal processes leading to PD-associated postural deficits. In this paper, we investigated whether reduced reactivity amplitudes resulting from impairments due to PD can explain postural instability as well as increased muscle tone as often observed in individuals with PD. To simulate reduced reactivity, we gradually decreased previously optimized gain factors within the postural control circuitry of our model performing a quiet upright standing task. After each reduction step, the model was again optimized. Simulation results were compared to experimental data collected from 31 individuals with PD and 31 age- and sex-matched healthy control participants. Analyzing our simulation results, we showed that muscle activations increased with a model’s reduced reactivity, as well as joint angles’ ranges of motion (ROMs). However, sway parameters such as center of pressure (COP) path lengths and COP ranges did not increase as observed in our experimental data. These results suggest that a reduced reactivity does not directly lead to increased sway parameters, but could cause increased muscle tone leading to subsequent postural control alterations. To further investigate postural stability using neuromusculoskeletal models, analyzing additional internal model parameters and tasks such as perturbed upright standing requiring comparable reaction patterns could provide promising results. By enhancing such models and deepening the understanding of internal processes of postural control, these models may be used to assess and evaluate rehabilitation interventions in the future. Impact Statement Altered postural control in Parkinson’s disease can be partly associated to reduced reactivity. Additional changes in the neural circuitry need to be further investigated to fully explain the observed differences. I. INTRODUCTION Upright standing is an inherently unstable body pose requiring control by the central nervous system (CNS) through specific muscle reactions, a neuromuscular process called postural control. Due to neurological disorders, such as Parkinson’s disease (PD), postural control impairment is common during the disease course. The internal processes underlying both healthy and impaired postural control in PD still remain not fully understood, with postural instability resulting from such impairments. While experimental measurements analyze PD-associated changes in postural control, they have to focus on accessible postural control outcomes and cannot provide insights into internal neural processes. Forward dynamic simulations using musculoskeletal models can help to investigate and understand these internal postural control processes by replicating these neural circuits. Postural instability is a cardinal symptoms of PD that worsens with disease progression and increases fall risk [ 1 ]. This instability is evident in reduced stability margins and increased postural sway compared to healthy individuals [ 2 ]– [ 7 ]. Differences are observed in parameters such as increased sway range or area. Furthermore, postural reactions to external perturbations show to be smaller and slower (bradykinetic) compared to healthy persons [ 1 ]. Even individuals with PD without diagnosed balance dysfunctions can already exhibit altered sway parameters [ 5 ]. To analyze upright standing balance, forward dynamic postural control simulations can be used. Current research shows considerable variation in simulation detail, depending on the scope. Shanbhag et al . [ 8 ] reviewed modelling approaches that used different biomechanical models, sensor information, and control strategies. Many studies use torque-actuated single, double or triple inverted pendulum models to investigate postural control [ 9 ]–[ 14 ]. These models simplify the body’s degrees of freedom (DOFs), focusing on lower limb motion, as most postural control reactions emerge from the ankle and hip joints, eventually complemented by knee joint movements [ 15 ]. However, in order to address physiological and pathophysiological postural control aspects in more detail, musculoskeletal models with more DOFs could provide additional insights. Koelewijn et al . [ 16 ] and Suzuki et al . [ 17 ] both used musculoskeletal models incorporating proprioceptive muscle information to control upright standing. Jiang et al . [ 18 ] added vestibular and visual sensor information to their musculoskeletal model. Shanbhag et al . [ 19 ] published a detailed neuromusculoskeletal postural control model considering somatosensory, vestibular, and visual inputs, along with physiologically plausible neural delays based on sensor type and muscle position. Up to now, only a few models have investigated aspects of postural control in the context of PD specifically, such as Boonstra et al . [ 20 ], Kim et al . [ 21 ], Dash et al . [ 22 ], Feller et al . [ 23 ], Omura et al . [ 24 ], and Rahmati et al . [ 25 ]. Of the aforementioned studies, only Omura et al . [ 24 ] used a musculoskeletal model to investigate postural control in individuals with PD – most models are limited to one or two DOF inverted pendulum models. However, it is essential to consider muscles in postural control simulations, as symptoms in PD, such as increased muscle tone, could independently affect other postural control aspects, such as sway behaviour. Using predictive neuromusculoskeletal simulations allows us to determine the influence of isolated parameter adaptations on postural control. Furthermore, gaining deeper insights into postural control processes in PD will enable the application of these models for assessing and evaluating rehabilitation interventions in the future. In this paper, we investigate how reduced reactivity amplitudes affect postural control. We will use the term reactivity to describe the model’s reactive behaviour based on control gain factors leading to reflex amplitudes. We will answer the research question whether reducing previously optimized gain factors replicates postural control aspects associated with PD. We hypothesize that this reduction will increase sway parameters and result in higher muscle activations as a compensatory mechanism. This approach could explain reduced postural stability and higher muscle tone often observed in individuals with PD. Additionally, our investigation will provide insights into possible internal postural control processes and identify potential elements responsible for postural instability in PD. Examining the impact of reducing previously optimized control gains will reveal whether a non-ideal optimization could induce PD-associated postural control. II. MATERIALS AND METHODS A previously published postural control model [ 19 ] served as a starting point to simulate upright standing postural control in the sagittal plane (section II-A). In section II-B, we describe the model adaptations to investigate whether reduced control gains replicate PD-associated motion behaviour. Forward dynamic simulations were performed using SCONE 2.3.1 [ 26 ] with Hyfydy [ 27 ] (section II-C). Experimental data from individuals with PD and healthy control (HC) participants, as described in section II-D, were used to evaluate simulations. Therefore, the analysis of the experimental data also focused on anterior-posterior (AP) movements (sagittal plane). A. Postural control model of upright standing The postural control circuitry based on Shanbhag et al . [ 19 ] consisted of a musculoskeletal model and a neural controller comparing sensor information to a reference state resulting in specific muscle excitations, all under the influence of neural delays. Details about the corresponding elements of this postural control model are presented in the Supplementary Materials. B. Simulation of pathologies We aimed to investigate whether reducing control gains could cause PD associated motion behaviour, since reduced reactivity is often observed in individuals with PD. Reduced gain factors represent a decreased reactive response to sensory-detected deviations from a reference state. An optimization according to Shanbhag et al . [ 19 ] — assumed to be physiological postural control — served as a starting point. From this previously optimized simulation, we gradually reduced gain factors. We modified the model in two different ways to analyze resulting postural control. First, we reduced all gain factors equally, in the following called whole-body approach (WA). Second, we reduced only distal gain factors for lower leg muscles (gastrocnemius medialis, soleus, tibialis anterior) investigating the influence of distal elements to be more affected, in the following called distal approach (DA). Gain factors were reduced gradually (2.5 % intervals) from 100 % to 0 % for both approaches. The control model, including all remaining free parameters such as feedforward muscle excitations as well as not previously reduced gain factors (only applicable for DA), was re-optimized at each reduction step. An overview of the postural control model with its modifications to simulate a decreasing reactivity is represented in Fig. 1 . Download figure Open in new tab Fig. 1. The postural control model consists of a biomechanical human model and a neural controller considering neural delays leading to muscle excitations based on the differences between current and reference states, as described in [ 19 ]. Gradually reduced gain factors, used to simulate decreasing reactivity, are incorporated through the additional factor w g . C. Simulation approach We investigated changes in postural control by applying gradually reduced gain factors, as described in section II-B, to the following scenario: Full simulations lasted 75 seconds, evaluating the last 60 seconds due to the initial non-representative pose from default SCONE settings for standing scenarios [ 26 ]. Sampling frequency was set to 200 Hz. We tested the postural control model and its adaptations for quiet upright standing to ensure comparability to our experimental study data (section II-D). D. Experimental data We conducted a study to investigate the postural control of individuals with PD and compare these study outcomes with our simulation results. 31 individuals with PD were recruited to participate in this study. Exclusion criteria were orthopedic or neurological comorbidities potentially affecting gait and balance. An age- and sex-matched HC group of 31 participants was used for comparison during the standing task. Study population characteristics are provided in Table I . All individuals with PD performed assessments in ON medication without the presence of clinically relevant motor fluctuations or dyskinesia (one participant with active deep brain stimulation). Disease duration was defined as the time from clinical diagnosis of PD. The Hoehn & Yahr (H&Y) and UPDRS-III scores were conducted by certified clinical experts. Details regarding the statistical analyses verifying the groups’ sex- and age-matching, as well as the determining group differences in the Montreal Cognitive Assessment (MoCA) and Falls Efficacy Scale International (FES-I), are presented in the Supplementary Materials. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the FAU Erlangen-Nürnberg (protocol code 20-473 1-B, date of approval 09 January 2023). All participants have signed a written consent form for this study. View this table: View inline View popup Download powerpoint TABLE I. Characteristics of study cohort. All participants performed an 60-second upright standing task with eyes open. Feet were placed shoulder-width apart, hands placed on the side of the hips during the whole task. The view was directed on a sign at eye level two meters in front of them. We collected the data using an optical motion capture system (VICON Vero, ten cameras, 100 Hz) and two force plates (AMTI, 1000 Hz). We used a full-body marker set consisting of 45 reflective markers according to the Plug-in Gait model [ 28 ], with additional markers on the medial knees and ankles as well as on both trochanters. Information about the data evaluation procedure is outlined in the Supplementary Materials. III. RESULTS All determined biomechanical and sway parameters for the HC and PD groups are summarized in Table II . We used the Mann-Whitney U test to compare each parameter between groups, ensuring robustness regardless of data distribution normality. By determining which parameters differed significantly between these two participant groups, comparisons of simulated results with specific experimental outcomes were feasible. Significant increases were observed in center of pressure (COP) path length, COP range, and all ranges of motion (ROMs) for pelvis, hip, knee, and ankle angles in individuals with PD compared to HC participants. No significant differences were detected for the COP position and the mean COP frequency, though a non-significant forward shift in COP position was noted in individuals with PD. View this table: View inline View popup Download powerpoint TABLE II. Resulting parameters from experimental data of the HC group as well as individuals with PD. All parameters focus on AP movements. We further divided the PD group into patients with and without postural instability based on their H&Y score to get an impression about the parameters’ progression direction depending on disease severity. The parameters are visualized in Fig. 2 . Due to the small number of highly affected individuals with PD (PD-PI: four with H&Y 3, one with H&Y 4), we did not further analyze these data statistically according to these subgroups. However, parameters tended to increased in the more severely affected patient subgroup. Download figure Open in new tab Fig. 2. COP parameters and joint angles’ ROMs during quiet upright standing of a control group and individuals with PD, separated into patients without (PD-noPI) and with postural instability (PD-PI). Feet were placed shoulder-width apart during this standing task. All parameters focus on AP movements. We conducted simulations with gradually reduced gain factors. The WA model was able to fulfill the upright standing task until 67.5 %, and the DA model until 57.5 % of optimal gain factors. For smaller gain factors, the model was not able to keep standing for the 75-second simulation. In the following, only successful tasks were further analyzed. Fig. 3 represents resulting parameters for the different simulations, with specific values in Tables I and II in the Supplementary Materials. Correlations between gain factor reductions and each parameter were determined using Spearman’s correlation coefficient. Both WA and DA simulations showed heterogeneous results when reducing gain factors. The COP path lengths decreased in both WA and DA. The COP range remained unchanged for both approaches. Relative COP positions showed a forward shift with smaller gains in both, but significantly for WA only. The mean COP frequency did not change significantly for the WA, but decreased for the DA with smaller gain factors. For the WA, pelvis, hip, and ankle ROMs were increased with decreased gain factors; knee ROMs showed little effects. For the DA, only the ankle’s ROM changed remarkably depending on gain factors; however, none of the joint angle ROMs changes correlated significantly with the adapting gain factors. Download figure Open in new tab Fig. 3. COP parameters, joint angles’ ROMs and mean muscle activations of quiet upright standing simulations depending on different gain factor reductions visualized with additional regression curves. All parameters focus on AP movements. Both approaches (WA and DA) resulted in consistently increasing mean muscle activations with reduced gain factors. IV. DISCUSSION A. Comparison of experimental data and simulation results We aimed to answer the research question of whether reducing gain factors in postural control simulations replicates motion aspects that can be associated with PD. We hypothesized that reducing gain factors would lead to increased sway parameters and higher muscle activations. For both simulation approaches (WA and DA), the model exhibited significantly higher muscle activations with decreased gain factors. This finding aligns with reports about increased muscle tone in individuals with PD [ 7 ], [ 29 ], [ 30 ]. We could not directly compare this aspect to our experimental data since we did not conduct maximum voluntary contraction measurements. However, this increased muscle activity can be understood as a compensatory mechanism for the reduced reactivity to maintain balance during upright standing. These higher muscle activations also represent increased co-contractions and higher joint stiffness, which could explain the decreased sway parameters. Simulation results showed diverse parameter trends compared to our experimental data on postural stability. Generally, parameters based on joint angles showed comparable trends, parameters based on the COP tended to vary more between experimental and simulation data. For individuals with PD compared to the HC group, and for gradually reduced gain factors in our WA simulations, we found increasing ROMs for the pelvis, hip, and ankle joints. The DA did not show significant trends for the joint angles’ ROMs. For the WA, the COP position showed a significant forward shift with decreasing gain factors, aligning with the trend observed in our experimental data, even if not statistically significant. This effect could already be seen in other experimental studies [ 5 ], [ 31 ]. This tendency was also recognized for the DA but was not significant. Aligning with our experimental data, the COP frequency showed no significant changes in WA simulations. The DA represented significantly decreasing frequencies with reducing gain factors. However, we observed higher-frequency COP movements in our overall simulation results. This observation suggests that our model may still include aspects that lead to higher frequencies in the COP. Although we observed significantly increased COP path lengths in our experimental data of individuals with PD, simulation results of both, WA and DA, showed significantly decreased COP path lengths with reduced gain factors. Our experimental data showed significantly higher COP ranges of individuals with PD; however, simulation results did not correlate with varied gain factors. Therefore, reduced reactivity cannot explain the changes in COP path length and range seen in our experimental data. However, it has to be mentioned that there are also findings from different studies describing smaller COP ranges for individuals with PD, e.g. from Horak et al . [ 32 ]. Reasons for these differences compared to our study outcomes could be a smaller but higher affected patient group (7 patients, average H&Y 3.4 ± 0.2) as well as a different movement task that could significantly influence outcome parameters. Therefore, study cohorts and movement tasks should be carefully identified before comparing different study results. Overall, the WA resulted in parameter changes more similar to experimental data of individuals with PD, suggesting that all gain factors could be affected similarly in PD. As postural control reactions are centrally processed in the body’s CNS, and degenerative processes due to PD also occur within the CNS, it is reasonable to assume that the gain factors are not influenced by the position of the respective muscle, but are rather uniformly affected in the same incorrect manner. Differences not explained by reduced gain factors, representing the model’s decreased reactivity, could result from different elements within the neural circuit for postural control. One aspect also associated with PD is increased noise during motor tasks. Feller et al . [ 23 ] reported a decreased signal-to-noise ratio in processing sensory signals in individuals with PD. This additional noise might explain the higher COP path lengths and ranges observed in the PD group, which our current simulations have not confirmed yet due to uniform noise levels for all investigated scenarios. Additionally, it could be questioned, whether individuals with PD actually use the same optimization objectives, with factors like fear of falling potentially playing an important role. B. Findings gained from experimental data Analyzing our experimental data of individuals with PD and an age- and sex-matched control group, we found significant differences in COP path lengths, COP ranges, as well as all joint angles’ ROMs. This aligns with many findings from literature [ 3 ], [ 4 ], [ 33 ], [ 34 ]. We did not see significant group differences in sway frequencies, based on COP data. However, other studies could observe differences in sway frequencies. Kamieniarz et al . [ 35 ] and Rocchi et al . [ 36 ] found higher mean frequencies of individuals with PD compared to a control group, whereas Kwon et al . [ 34 ] detected lower mean frequencies of the PD group compared to healthy agematched participants. This discrepancy suggests that this aspect is not unambiguous and might depend on the patient group, especially the number of participants with different disease stages or symptom severity. In this respect, our patient population was quite homogeneous and did not represent many patients suffering from severe postural instability represented by an H&Y score of 3 or higher. This could explain why no significant differences in sway frequencies could be identified in our study. The same applies to the COP position. Błaszczyk et al . [ 5 ] and Bloem et al . [ 31 ] could identify a forward-shifted COP for individuals with PD. We did not see significant differences in our experimental data. However, especially in Fig. 2 , a tendency towards a forward shift of the COP is prevalent. C. Limitations We reduced the control gains compared to previously optimized values to simulate the effects of inadequate gain scaling. Since PD involves complex alterations within the CNS and motor control, it is plausible that changes in motion behaviour are also influenced by additional parameters within the model. In our simulations, we reduced control gains equally for every control element. Currently, sensory information type is not considered, which could be influenced differently in PD. By considering this aspect in a next step, differences that can be observed in short and long latency reflexes between individuals with PD and healthy persons could be taken into account [ 37 ], [ 38 ]. The current model only represented movements in the sagittal plane and was controlled symmetrically. However, important aspects of postural instability in PD are also observed in the body’s frontal and coronal plane [ 39 ] and show to be partly asymmetrical as well [ 20 ], [ 30 ]. We used a generic, not a person-specific scaled musculoskeletal human model to conduct our simulations. We would expect simulation results to be even more similar to experimental data using person-specific scaled models representing the study’s participants. Additionally, it is important to consider that we assumed the initial model to represent physiologically plausible postural control. Nevertheless, a model representing human-like movements does not necessarily prove internal neural control processes to exactly align with the model. Our study consisted of 31 individuals with PD with an average H&Y of 2.13 ± 0.56, but only five had a H&Y of 3 or 4, limiting our group with diagnosable postural instability. To observe even more distinct differences between individuals with PD and HC participants, we would recommend including more participants suffering from postural instability. We could not compare amplitudes of simulated muscle activations with experimental data, as participants did not perform maximum voluntary contraction measurements before conducting EMG data. Understanding absolute muscle activations and their changes based on disease stages would be beneficial for comparison with our simulations. Lastly, we could only measure quiet upright standing; exploring postural control in additional scenarios would be extremely insightful and could further deepen our understanding of the adapted model’s gain factors. V. CONCLUSION In this paper, we investigated how reduced reactivity affects postural control using forward dynamic simulations. We found that some parameter changes associated with PD were observed in our simulation results. Especially muscle tone showed to be increased, compensating for reduced reactivity. However, other aspects, like increased COP path lengths and COP ranges, could not be observed in our simulations. This suggests that reduced reactivity alone is not the only factor causing postural instability in PD. Other elements of the postural control circuitry need to be investigated next to explain remaining differences between individuals with PD and healthy persons reflecting postural instability. By deepening our understanding of internal postural control processes, these models and predictive simulations may be used in the future for assessing and evaluating rehabilitation interventions. Once capable of reproducing common differences in motion behaviour between healthy persons and individuals with PD, models can provide tailored intervention recommendations based on a person’s previous measurements and further person-specific simulations. S upplementary M aterials The Supplementary Materials file includes additional information about the postural control model, the experimental data, data evaluation, and detailed tables presenting the biomechanical parameters of the simulation results. Funder Information Declared Deutsche ForschungsgemeinschaftDeutsche Forschungsgemeinschaft, , 442419336 Fraunhofer Internal ProgramsFraunhofer Internal Programs, , 044–602140 , 044–602150 Footnotes This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1483 – Project-ID 442419336, EmpkinS. This work was further supported by the Fraunhofer Internal Programs under grant Attract 044–602140 and 044–602150. R eferences [1]. ↵ J.-H. Park , Y.-J. Kang , and F. B. Horak , “ What Is Wrong with Balance in Parkinson’s Disease? ” Journal of Movement Disorders , vol. 8 , no. 3 , pp. 109 – 114 , 2015 , ISSN: 2005-940X, 2093-4939 . DOI: 10.14802/jmd.15018 . OpenUrl CrossRef PubMed [2]. ↵ F. Doná , C. Aquino , J. Gazzola , et al. , “ Changes in postural control in patients with Parkinson’s disease: A posturographic study ,” Physiotherapy , vol. 102 , no. 3 , pp. 272 – 279 , 2016 , ISSN: 00319406 . DOI: 10.1016/j.physio.2015.08.009 . 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