13:00
Motor Control II
Chair: Dirkjan Veeger
13:00
15 mins
|
DETERMINATION OF MUSCULAR ACTIVITY IN THE LOWER LIMB DURING WALKING USING FDG-PET
Sjoerd Kolk, Olga Schenk, Eric Visser, Vivian Weerdesteyn, Nico Verdonschot
Abstract: Background
The goal of the TLEMsafe project is to predict functional outcome of extensive orthopedic interventions on the lower limb. Functional outcome is predicted using subject-specific musculo-skeletal (M-S) models. The most important model outcome is muscular activity during walking, which thus needs to be extensively validated, initially using healthy subjects. The purpose of this study was to determine the contribution of each muscle of the lower limb to walking. In contrast to other available methods (e.g. electromyography), positron emission tomography (PET) in combination with [F-18]fluorodeoxyglucose (FDG) allows detailed study of muscular activity in walking regardless of whether a muscle is situated superficially or inferiorly [1]. The FDG is taken up by muscles that are active during walking.
Methods
Ten healthy subjects walked on a treadmill at their own comfortable walking speed for a total of 90 minutes, 60 minutes before and 30 minutes after intravenous injection of 50 MBq FDG. A PET scan of the lower limb was made subsequently. Regions-of-interest (ROI) were drawn on 54 muscles using detailed anatomical MRI scans that were already available. The ROI were then superimposed on the PET scan, yielding muscle FDG uptake.
Results
The muscles with the highest uptake were the soleus, gastrocnemius medialis, tibialis anterior, the gluteal muscles, and the vastus lateralis and intermedius. There were no significant differences within subjects between dominant and non-dominant side (p=0.59), nor between subjects based on age (p=0.44) or gender (p=0.09).
Conclusions
Our data indicate that FDG-PET has a high discriminative ability and is useful for the investigation of muscular activity during walking. These data are extremely valuable in the validation of M-S models. A limitation is that we only analyzed single slices, which might not represent whole muscle activity. This will be subject to future study.
Acknowledgement: The TLEMsafe Project (http://www.tlemsafe.eu/) is financially supported under the Seventh Framework Programme (FP7) of the European Commission.
REFERENCES
[1] N. Oi, et al., “FDG-PET imaging of lower extremity muscular activity during level walking”. J Orthop Sci, Vol. 8, pp. 55-61, (2003).
|
13:15
15 mins
|
COMPARISON OF RECRUITMENT CRITERIA TO SOLVE MUSCLE INDETERMINANCY DURING ACTIVITIES OF DAILY LIFE
Roberto Garcia van der Westen, Pieter Oomen, Lodewijk van Rhijn, Kenneth Meijer
Abstract: Knee osteoarthritis (KNOA) is a condition characterized by the degeneration of the cartilage within the tibio-femoral joint. Increasing longevity of the population give arise to an increasing number of cases over time. The degeneration of articular cartilage is a complex process which involves interrelated biological, mechanical and structural pathways. Clinical and laboratory reports lead to conflicting conclusions on the influence of mechanical effects in the progression of KNOA. Therefore, the early diagnosis and treatment of KNOA could improve significantly from a better understanding of mechanical factors such as peak loads and moments.
Unfortunately, it is impossible to measure the above described parameters in-vivo, at least not on a healthy population. Musculoskeletal modeling has therefore the potential to become a very powerful tool in the assessment of KNOA. Musculoskeletal modeling is a non-invasive method that allows the estimation of loads and moments of the knee joint. Before the implementation of such a tool in a clinical setting much research is needed in terms of understanding such models and how to validate them.
The current study explores the influence of different recruitment criteria on the estimated knee loads. Motion capture data during activities of daily life were taken from 5 older healthy female participants and used in the AnyBody modeling software to create scaled patient specific models. Muscle activation, forces and moments on the knee were calculated based on an inverse dynamics approach, which uses optimization to solve the statical indeterminacy.
Polynomial (p=2,3,4,and 5), Min/Max and composite criteria were compared. Obtained average peak loads are comparable to those shown in other studies (peak PD force = 3.1±0.7 BW), but increased compared to in-vivo studies with telemetrised prosthesis data (peak PD force = 2.5 BW).
In terms of peak loads, all criteria except for the 2th order polynomial showed good agreement. Polynomial p=2 overestimated the proximo-distal forces by 13% for gait compared to other criteria. No significant differences were observed for stair ascent or descent. Muscle activation patterns showed good agreement with measured EMG data, only the Min/Max recruitment showed very fast activations, which might not be physiological feasible.
Polynomial of the 3rd order showed smoother activations and similar peak forces. The assumption that the central nervous system tries to reduce the load on the muscles, might be good enough to solve the optimization problem for most activities. Moreover, from the available algorithms, the polynomial of 3rd order shows the most attractive features, both physiologically and computationally.
|
13:30
15 mins
|
GAIT FEATURE DETECTION BASED ON MARKERLESS MOTION TRACKING
Bart Klaassen, Leendert Schaake, Jaap Buurke, Bart Koopman, Martijn van Eenennaam, Hans Rietman
Abstract: Markerless Motion Tracking (MMT) is a hot topic in surveillance technology, but can also be applied in human kinematics. For example in gait event detection to determine spatio-temporal parameters (e.g. step intervals, heel strike/toe-off postures) and normalization of kinematic or EMG data by detecting individual stride lengths. In daily clinical practice, video images are combined with EMG and/or ground reaction forces for clinical gait assessment of patients. Gait event detection, like identification of Initial Contact Times (ICT), is usually done manually from these video recordings. This can be labor intensive and prone to errors or bias. Therefore the question rises if this detection could be automated for certain events. In this research, main focus is set on ICT extractions from standard video images without any markers on the patient. One of the prerequisites for reproducible gait feature extractions is that the viewing angle from a video camera towards the patient must be perpendicular and approximately constant in order to extract information properly. Therefore, an automatic camera system has been developed, which enables following patients from the side during a walking measurement. OpenTLD [1] has been chosen as the primary platform for the markerless ICT extractions. It can be adjusted and incorporated in additional Matlab scripts that have been developed for extracting ICT. It is based on Tracking-Learning-Detection and outputs x and y coordinates of the tracked item [2]. These scripts can be run in a post-analysis setup. For validating OpenTLD and the combined Matlab scripts, measurements were done with three patients, where the patients had to walk over a 7 meter track followed by the camera system. As a reference, a foot pressure sensor was placed on the calcaneus. Contact times from this sensor were compared with the extracted post ICT results. Differences between the two methods were calculated for slow walking (< 1 m*s^-1), normal walking (about 1.4 m*s^-1) and fast walking (> 2 m*s^-1) speeds. Comparison between post ICT extractions minus foot pressure sensors results in a -0,02 ± 0,10 seconds delay combined over all subjects and all walking speeds. Therefore it shows that as a first attempt, the automatic process for detecting ICT from standard video images using MMT is possible and that the delay is within a certain range which should not affect the outcome for clinical evaluations. Further research is required to identify the cause and significance of this delay, and explore extraction of other gait events.
|
13:45
15 mins
|
ESTIMATING THE HIP FRACTURE RISK IN SIDEWAYS FALLS
Astrid van der Zijden, Esther Tanck, Dennis Janssen, Nico Verdonschot
Abstract: Sideways falls onto the hip are a major cause of hip fractures in the elderly [1]. The fracture risk depends on the ratio of the load applied to the bone during impact and the fracture (failure) load [2]. Previous studies indicated that Martial Arts (MA) fall techniques decrease hip impact forces in sideways falls [3].
The aim of this study is to combine various modeling techniques to assess the different aspects involving the femoral fracture risk and to examine whether fall biomechanics are different in falls involving the MA technique versus a natural fall arrest strategy (Block).
In vivo falling experiments with judokas were conducted and 3D marker and force plate data were recorded (Vicon, Oxford, UK and Motion Analysis Inc., CA, USA). The impact forces and energies of the body segments before, during, and after impact were derived from the kinematic data by use of Matlab (R2011B, Mathworks, USA) and 3D musculoskeletal modeling (AnyBody™). Additionally, loading configurations derived from the in vivo experiments were applied to finite element models of the femoral bone to predict the fracture loads (Marc Mentat 2007 r1). Finally, the relation between the impact and fracture loads was determined to examine the protective effects of the MA technique on the hip fracture risk.
The results from this study have provided insight on the effects of MA fall techniques on hip impact load and energies in sideways falls, and contribute to guidelines for fall training programs for the elderly and osteoporotic patients. The innovative combining of modeling techniques has shown the importance of applying realistic fall loading configurations to fracture models: the accuracy of predicting fracture load and location has improved compared to previous studies in literature. Furthermore, the models developed in our study can be used to evaluate other fracture prevention interventions and designs, for example compliant flooring and hip protectors.
REFERENCES
[1] P. Kannus, P. Leiponen, J. Parkkari, M. Palvanen and M. Jarvinen, “A sideways fall and hip fracture”, Bone, Vol. 39(2), pp. 383–384, (2006).
[2] W.C. Hayes, E.R. Myers, S.N. Robinovitch, A. van den Kroonenberg, A.C. Courtney and T.A. McMahon, “Etiology and prevention of age-related hip fractures”, Bone, Vol. 18(1), pp. S77–S86, (1996).
[3] B.E. Groen, V. Weerdesteyn and J. Duysens, “Martial arts fall techniques decrease the impact forces at the hip during sideways falling”, J of Biomech., Vol. 40(2), pp. 458–462, (2007).
|
14:00
15 mins
|
STANDARDIZED HANDWRITING TO ASSESS BRADYKINESIA, TREMOR AND MICROGRAPHIA IN PARKINSON'S DISEASE
Esther Smits, Antti Tolonen, Luc Cluitmans, Mark van Gils, Bernard Conway, Rutger Zietsma, Natasha Maurits
Abstract: Objective: To assess whether standardized handwriting can provide quantitative measures to distinguish patients diagnosed with Parkinson’s disease (PD) from age- and gender-matched healthy control participants (HC).
|
14:15
15 mins
|
AUTOMATED CMAP SCAN PATTERN ANALYSIS DETECTS MOTOR UNIT LOSS
Boudewijn Sleutjes, Inger Montfoort, Joleen Blok
Abstract: The compound muscle action potential (CMAP) scan is a rapid, non-invasive electrophysiological method from which information on pathologically enlarged motor units (MUs) and/or MU loss can be obtained. The CMAP scan is constructed by plotting recorded CMAP amplitude versus gradually increasing stimulus intensity. The presence of large consecutive differences between successive CMAP amplitudes, also called steps, is indicative of neurogenic disease [1]. Recently, we developed a novel automated method to quantify the CMAP scan pattern in a single parameter, N50. N50 expresses how many of the largest consecutive differences are needed to constitute 50% of the maximum CMAP [2].
The aim of this study is to link N50 to the underlying (patho) physiology by assessing its relation to the number of MUs present. To evaluate this relation, we compared the performance of N50 with that of motor unit number estimates (MUNE) and with a CMAP scan simulation model. Use of a simulation model has as an advantage that it has a known input against with the automated CMAP scan analysis can be compared. Thenar CMAP scans and MUNE were recorded on the same day in 51 Guillain-Barré (GBS) patients and 12 amyotrophic lateral sclerosis (ALS) patients at multiple time points during follow-up. N50 was calculated for the resulting 173 CMAP scans and compared with the MUNE data. In addition, the CMAP scan simulation model was used to generate CMAP scans from which N50 was calculated for each simulated CMAP scan. The number of stimuli was set to 500 and number of MUs was altered from 5 up to 400 MUs.
The results show that in the affected muscles N50 and MUNE were highly correlated in conditions of marked MU loss (≤ 80 MUs, r = 0.73, n = 68, p < 0.001). The curve of N50 with simulated number of MUs affirms the trend found in N50 with the MUNE data. We conclude that the automated CMAP scan pattern analysis via novel parameter N50 is a quick and reasonably accurate method to detect MU loss in a range relevant to motor neuron disease. Hence, it may be used to monitor neurogenic disease progression.
REFERENCES
[1] J.H. Blok, A. Ruitenberg, E.M. Maathuis and G.H. Visser, “The electrophysiological muscle scan”, Muscle & Nerve, Vol. 36, pp. 436 – 446, (2007).
[2] B.T.H.M. Sleutjes, I. Montfoort, E.M. Maathuis, J. Drenthen, P. van der Meer, G.H. Visser and J.H. Blok, “Automatic extract of clinical information from the CMAP scan”, 3rd Dutch BME Conference 2011, Jan. 20 - 21, 2011, Egmond aan Zee, The Netherlands
|
|
