14:30
NeuroMuscular
Chair: Ciska Heida
14:30
15 mins
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Decoding force by bridging neural and muscular properties in vivo
Antonio Gogeascoechea, Rafael Ornelas-Kobayashi, Utku S. Yavuz, Massimo Sartori
Abstract: BACKGROUND: Human motor control is a bundle of complex neuromusculoskeletal processes. Although current electromyography (EMG)-driven modeling frameworks (e.g. [1]) aim at representing such complexity, they often fail to capture the interaction between neural and mechanical mechanisms of movement. The ability to decode motor units (MUs) from high-density EMGs enables extending current neuromusculoskeletal models into MU-specific formulations where the neural information is preserved. Herein, we propose a high-resolution framework to generate MU-specific neuromusculoskeletal models based on the identification of MU-twitch properties.
METHODS: We recorded torque and high-density EMGs from the lower leg during isometric dorsi-plantarflexion contractions across multiple activation levels and ankle positions [2]. We decomposed the EMGs into MU spike trains and calculated their recruitment thresholds and discharge rates. We computed a linear combination of these neural features and mapped them into contractile properties (contraction time and peak amplitude) found in humans [3]. We employed the resulting properties to design twitch models as impulse responses of a second-order system [4]. The MU-specific activation dynamics were defined as the convolution between the MU-twitch responses and their corresponding spike trains. The resulting activation profiles were used to drive a subject-specific musculoskeletal model which allowed computing joint moments. Moreover, we compared our methodology with the conventional EMG-driven framework.
RESULTS: For the MU-driven models, the normalized RMSE values between the reference and predicted torques were below 0.5 across all conditions. The EMG-driven models, contrastingly, were unable to adapt to all conditions, providing high errors (nRMSE > 0.5) in the plantar-flexed and low activation conditions.
CONCLUSION: Our proposed methodology showed robustness in predicting torque across multiple conditions and provides a deeper insight into force-generation processes of human movement.
REFERENCES:
[1] C. Pizzolato et al., (2015) J. Biomech., vol. 48, no. 14, pp. 3929–3936.
[2] M. Sartori, U. Yavuz, and D. Farina, (2017). Sci. Rep., vol. 7, no. 1, p. 13465.
[3] R. A. Garnett, M. J. O’Donovan, J. A. Stephens, and A. Taylor, (1979) J. Physiol., vol. 287, no. 1, pp. 33–43.
[4] A. J. Fuglevand, D. A. Winter, and A. E. Patla, (1993) J. Neurophysiol., vol. 70, no. 6, pp. 2470–2488.
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14:45
15 mins
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Detection of treatment and quantification of Parkinson’s disease motor severity using finger-tapping tasks and machine learning
Ahnjii ZhuParris, Eva Thijssen, Willem Elzinga, Soma Makai-Boloni, Wessel Kraaij, Geert Jan Groeneveld, Robert-Jan Doll
Abstract: Background
Validation of objective biomarkers to monitor treatment effects among Parkinson’s Disease (PD) patients would benefit anti-parkinsonian drug development. We investigated the performance of machine learning-derived composite biomarkers using finger tapping tasks features to classify treatment effects and to estimate motor symptom severity among PD patients.
Methods
In a placebo-controlled, cross-over design study, data were collected from 20 PD patients. The Alternative Index Finger Tapping (IFT), Alternate Index and Middle Finger Tapping (IMFT), and Thumb-Index Finger Tapping (TIFT) tasks, and the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) III assessments were performed during the placebo and levodopa/carbidopa treatments. We compared the performance of various composite biomarkers to classify the treatment classes and to estimate the MDS-UPDRS III total scores. The composite biomarkers represent each of the tapping tasks individually, the tapping tasks collectively, and the MDS-UPDRS III item scores. For each composite biomarker, we considered the baseline-uncorrected and corrected values. We compared the performance of three classification models to discriminate between the placebo and active treatments. We used Linear Regression (with Elastic net regularization) to estimate the MDS-UPDRS scores. We used repeated nested cross-validation to build and validate each of the models. For both the classification and regression models, we trained the models on data collected during peak drug concentration, and used the trained models to predict the treatment class probability and the MDS-UPDRS III scores from baseline to the last timepoint.
Results
The IFT composite biomarker (baseline-corrected) achieved the highest classification performance with 83.50% accuracy and 93.95% precision. The IFT composite biomarker (baseline-corrected) also achieved the optimal regression performance with a Mean Absolute Error of 7.87 points, and a Pearson’s correlation of 0.69. Together, these models demonstrate that the baseline-uncorrected features outperform that of the baseline-corrected features.
Conclusion
We demonstrated that the IFT composite biomarker outperformed both the 3 tapping task and the MDS-UPDRS III composite biomarkers for the classification of treatment effects. However, the tapping composite biomarkers were not suitable for estimating the MDS-UPDRS III scores. To conclude, the tapping composite biomarkers are suited to complement, but not replace, the clinical assessments of anti-Parkinsonian drug effects.
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15:00
15 mins
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Neuromuscular control of the wrist in amyotrophic lateral sclerosis
Diederik Stikvoort, Just Plouvier, Boudewijn Sleutjes, Stephan Goedee, Winfred Mugge, Alfred Schouten, Frans van der Helm, Leonard van den Berg
Abstract: Amyotrophic lateral sclerosis (ALS) is a relentless neurodegenerative disorder with ultimately fatal consequences. The expression of symptoms is highly heterogeneous, with large variation in degeneration rates of both upper and lower motor neurons (UMN, LMN)1. As a result, symptomatic motor behavior in ALS originates from a complex interplay of central and peripheral symptoms, including hyperreflexia, spasticity, rigidity and weakness2. UMN symptoms can be particularly hard to detect due to degeneration of all classes of motor neurons in the anterior horn of the spinal cord2. Secondary changes in muscles and connective tissue further alter the limb’s dynamics and the ensuing reflexive behavior3. Here, we present a protocol to quantify wrist neuromuscular control of ALS patients in order to explore their associations with the clinical manifestation of the disease.
We plan to recruit 20 ALS patients, excluding participants with a muscle strength of the wrist muscles below an MRC of 3 (Medical Research Council scale, 0-5), presence of active psychiatric diseases such as frontotemporal dementia, concomitant neuropathy, history or presence of brain injury or other cerebral diseases. Reference data will be derived from age-gender matched controls. Wrist perturbations and closed-loop system-identification techniques will be employed to estimate the neuromuscular control of the wrist joint. Participants will perform multiple tasks while supplemented with unpredictable multisine torque perturbations, using a robotic manipulator, to elicit a wide range of neuromuscular control as previously described4¬. Parameterization of the wrist-dynamics using a neuromuscular model provides metrics describing the contribution of muscle- and reflex-dynamics to the observed motor behavior. Clinical metrics will be obtained, including muscle tone of the arm, reflex score of the examined arm, revised ALS Functional Rating Scale score (ALSFRS-R) and the fine motor function subscore (ALSFRS-R items 4-6). Comparable patterns of change in neuromuscular control will be identified through hierarchical clustering of the neuromuscular parameters. The clinical characteristics of these clusters will subsequently be compared.
This study is the first to explore neuromuscular control in ALS with neuromuscular modeling and system-identification techniques. Quantifying changes related to UMN degeneration may be particularly useful for following disease progression in a clinical or clinical trial setting.
1. van Es MA, et al.. Amyotrophic lateral sclerosis. Lancet 2017;390(10107):2084-2098.
2. Swash M. Why are upper motor neuron signs difficult to elicit in amyotrophic lateral sclerosis? Journal of neurology, neurosurgery, and psychiatry 2012;83(6):659-662.
3. Kamper DG, Schmit BD, Rymer WZ. Effect of muscle biomechanics on the quantification of spasticity. Ann Biomed Eng 2001;29(12):1122-1134.
4. Mugge W, et al.. A rigorous model of reflex function indicates that position and force feedback are flexibly tuned to position and force tasks. Experimental Brain Research 2010;200(3-4):325-340.
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15:15
15 mins
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Assessment of the function of the anal sphincter, new techniques with 4D ultrasound
Irina De Alba Alvarez, Chris de Korte, Anique Bellos-Grob, Shreya Das, Frieda van den Noort
Abstract: Introduction: The external anal sphincter (EAS) is a pelvic floor muscle (PFM) which, in addition of giving support to the pelvic floor, constricts the anal canal and it is voluntarily relaxed for defecation. Damage in the EAS can lead to clinical conditions like i.e., fecal incontinence, pelvic organ prolapse. A way to evaluate the damaged EAS is measuring its strain. The aim of this research is to evaluate quantitively the displacement and strain of the EAS during a pelvic floor maxima contraction. Method: Five volunteers participated in the study and the data was acquired using the 3D transperineal ultrasound. Data processing was done using the strain software generated by the MUSIC center of the Radboudumc. Result: The software properly gives a quantitative result of the strain and displacement according to the ultrasound images. It was possible to determine the different strain direction and percentage of strain and showed to be different in each volunteer. Discussion: There is a considerable variation between percentage of strain during contractions of all volunteers which can be due to anatomical variations, transducer probe movement and different level of EAS control between volunteers. Bigger sample in the following studies can lead to better understanding of differences in strain between rest and maximal contraction values. These data may be useful to better understand and help clinically assess the structure and function of EAS.
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15:30
15 mins
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Cortical activity related to sensorimotor synchronization guided by different types of external cues
Janne Heijs, Silvana Huertas-Penen, Bettina Schwab, Richard van Wezel, Tjitske Heida
Abstract: Sensorimotor synchronization (SMS) involves the adaptation of voluntary movements to external, rhythmic, sensory stimuli, called cues. SMS can, for instance, be used to investigate the timing mechanisms of voluntary movements. Voluntary movements are characterized by event-related desynchronization (ERD) of the sensorimotor cortex during movement execution (cortical activity), followed by event-related synchronization (ERS) after movement termination (cortical inactivity). Little is known about the effect of cue characteristics on the oscillatory activity (ERD/ERS) during SMS. Therefore, the aim of this study is to evaluate the cortical activity related to SMS guided by different types of external sensory cues, to assess the effect of type, frequency and rhythmicity of cues.
Twenty healthy subjects performed a finger tapping experiment, following different sensory cues in a 2x2x2-design, to evaluate the effect of: 1) cueing type: visual (flickering circle) vs. auditory cues (repetitive tones); 2) cueing frequency: 1Hz (discrete) vs. 3.2Hz cues (continuous); and 3) cueing rhythmicity: isorhythmic (one rhythm) vs. polyrhythmic cues (two rhythms in a [2:3]-relationship). A 32-channel EEG system recorded the electrocortical activity. ERD/ERS of the α-band (8-12Hz) and β-band (12-30Hz) was evaluated around finger tap (FT).
Summary of the results: 1) The cueing frequency affected oscillatory activity in central and parietal electrodes. With 1Hz cues, β-ERD during FT was followed by β-ERS after FT, while 3.2Hz cues showed a sustained β-ERD. 2) Small differences in oscillatory activity were observed between cueing types. With 1Hz-isorhythmic cues, β-ERS after FT was stronger in central electrodes for auditory cues, but more widespread towards parietal electrodes for visual cues. Moreover, α-ERS during FT was stronger for visual cues. 3) A strong effect of cueing rhythmicity was observed, especially for visual cues. Polyrhythmic cues caused a widespread increase in α- and β-ERD, while isorhythmic cues showed α-ERS in central and parietal electrodes.
These results showed that the characteristics of the cue and the related movement affect the cortical oscillations during SMS in a finger tapping task, suggesting differences in movement execution, complexity or processing of the cues. Similar studies should be performed in patients with Parkinson’s disease, for whom cueing is a common therapy to improve gait.
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15:45
15 mins
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Neural-data driven optimization of biophysical models to assess subject-specific motoneuron pool properties in vivo
Rafael Ornelas Kobayashi, Antonio Gogeascoechea, Massimo Sartori
Abstract: As the final common pathway of the central nervous system, alpha-motoneurons (MNs) are key for unravelling the neural mechanisms and adaptations underlying motor control, both in healthy and impaired individuals. Depending on the neurophysiological condition of an individual, including age, level of training, severity of motor disorder or neuronal lesion, pools of MNs may exhibit distinct neuro-anatomical properties and firing behaviours. For this reason, the ability to assess the subject-specific characteristics of MN pools is essential for understanding and controlling motor impairment and neurorehabilitation technologies. However, measuring the properties of complete human MN pools in vivo remains an open challenge. Therefore, this work proposes combining high-density electromyography decomposition, biophysical neuronal modelling and metaheuristic optimization into a novel neural-data driven framework. Briefly, this approach consists of decoding neural data from human MNs in vivo to drive a parameter optimization algorithm that fits biophysically realistic MN models to reproduce experimental spike trains. First, we demonstrate that this framework provides subject-specific estimates of MN pool properties from the tibialis anterior muscle on five healthy individuals. Second, we present a methodology for interpolating the MN properties of the entire pool, thereby enabling the creation complete in silico MNs for each subject. Third, we show that neural-data driven in silico MN pools reproduce the firing characteristics (i.e., recruitment time error < 0.01 s and discharge rate error = 0.208 Hz) of in vivo decoded MNs. Additionally, we show that this approach enables estimating muscle activation profiles (R² = 0.86 ± 0.04 with p-values < 0.005) during force-tracking tasks involving isometric ankle dorsi-flexion, at different levels of amplitude.
This neural-data driven framework for estimating MN pool properties can open new avenues for understanding human neuromechanics and, particularly, MN pool dynamics, in a person-specific way. Moreover, it enables the creation of personalized computational models to develop neurorehabilitation therapies and motor restoring technologies according to each individual.
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