Loes C Derikx

Radboud University Medical Centre (Radboudumc), Nymegen, Gelderland, Netherlands

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Publications (5)16.85 Total impact

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    ABSTRACT: In musculoskeletal modelling, several optimization techniques are used to calculate muscle forces, which strongly influence resultant hip contact forces (HCF). The goal of this study was to calculate muscle forces using four different optimization techniques, i.e., two different static optimization techniques, computed muscle control (CMC) and the physiological inverse approach (PIA). We investigated their subsequent effects on HCFs during gait and sit to stand and found that at the first peak in gait at 15-20% of the gait cycle, CMC calculated the highest HCFs (median 3.9 times peak GRF (pGRF)). When comparing calculated HCFs to experimental HCFs reported in literature, the former were up to 238% larger. Both static optimization techniques produced lower HCFs (median 3.0 and 3.1 pGRF), while PIA included muscle dynamics without an excessive increase in HCF (median 3.2 pGRF). The increased HCFs in CMC were potentially caused by higher muscle forces resulting from co-contraction of agonists and antagonists around the hip. Alternatively, these higher HCFs may be caused by the slightly poorer tracking of the net joint moment by the muscle moments calculated by CMC. We conclude that the use of different optimization techniques affects calculated HCFs, and static optimization approached experimental values best. © 2014 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res. © 2014 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.
    Journal of orthopaedic research : official publication of the Orthopaedic Research Society. 12/2014;
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    ABSTRACT: There is an urgent need to improve the prediction of fracture risk for cancer patients with bone metastases. Pathological fractures that result from these tumors frequently occur in the femur. It is extremely difficult to determine the fracture risk even for experienced physicians. Although evolving, fracture risk assessment is still based on inaccurate predictors estimated from previous retrospective studies. As a result, many patients are surgically over-treated, whereas other patients may fracture their bones against expectations. We mechanically tested ten pairs of human cadaveric femurs to failure, where one of each pair had an artificial defect simulating typical metastatic lesions. Prior to testing, finite element (FE) models were generated and computed tomography rigidity analysis (CTRA) was performed to obtain axial and bending rigidity measurements. We compared the two techniques on their capacity to assess femoral failure load by using linear regression techniques, Student's t tests, the Bland-Altman methodology and Kendall rank correlation coefficients. The simulated FE failure loads and CTRA predictions showed good correlation with values obtained from the experimental mechanical testing. Kendall rank correlation coefficients between the FE rankings and the CTRA rankings showed moderate to good correlations. No significant differences in prediction accuracy were found between the two methods. Non-invasive fracture risk assessment techniques currently developed both correlated well with actual failure loads in mechanical testing suggesting that both methods could be further developed into a tool that can be used in clinical practice. The results in this study showed slight differences between the methods, yet validation in prospective patient studies should confirm these preliminary findings.
    Bone 10/2013; · 4.46 Impact Factor
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    ABSTRACT: Previously, we showed that case-specific non-linear finite element (FE) models are better at predicting the load to failure of metastatic femora than experienced clinicians. In this study we improved our FE modelling and increased the number of femora and characteristics of the lesions. We retested the robustness of the FE predictions and assessed why clinicians have difficulty in estimating the load to failure of metastatic femora. A total of 20 femora with and without artificial metastases were mechanically loaded until failure. These experiments were simulated using case-specific FE models. Six clinicians ranked the femora on load to failure and reported their ranking strategies. The experimental load to failure for intact and metastatic femora was well predicted by the FE models (R(2) = 0.90 and R(2) = 0.93, respectively). Ranking metastatic femora on load to failure was well performed by the FE models (τ = 0.87), but not by the clinicians (0.11 < τ < 0.42). Both the FE models and the clinicians allowed for the characteristics of the lesions, but only the FE models incorporated the initial bone strength, which is essential for accurately predicting the risk of fracture. Accurate prediction of the risk of fracture should be made possible for clinicians by further developing FE models.
    Journal of Bone and Joint Surgery - British Volume 08/2012; 94(8):1135-42. · 2.69 Impact Factor
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    ABSTRACT: Although asymmetric yielding in bone is widely shown in experimental studies, previous case-specific non-linear finite element (FE) studies have mainly adopted material behaviour using the Von Mises yield criterion (VMYC), assuming equal bone strength in tension and compression. In this study, it was verified that asymmetric yielding in FE models can be captured using the Drucker-Prager yield criterion (DPYC), and can provide better results than simulations using the VMYC. A sensitivity analysis on parameters defining the DPYC (i.e. the degree of yield asymmetry and the yield stress settings) was performed, focusing on the effect on bone failure. In this study, the implementation of a larger degree of yield asymmetry improved the prediction of the fracture location; variations in the yield stress mainly affected the predicted failure force. We conclude that the implementation of asymmetric yielding in case-specific FE models improves the prediction of femoral bone strength.
    Computer Methods in Biomechanics and Biomedical Engineering 02/2011; 14(2):183-93. · 1.39 Impact Factor
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    ABSTRACT: Parkinson's disease (PD) is characterized by striatal dopamine depletion, especially in the posterior putamen. The dense connectivity profile of the striatum suggests that these local impairments may propagate throughout the whole cortico-striatal network. Here we test the effect of striatal dopamine depletion on cortico-striatal network properties by comparing the functional connectivity profile of the posterior putamen, the anterior putamen, and the caudate nucleus between 41 PD patients and 36 matched controls. We used multiple regression analyses of resting-state functional magnetic resonance imaging data to quantify functional connectivity across different networks. Each region had a distinct connectivity profile that was similarly expressed in patients and controls: the posterior putamen was uniquely coupled to cortical motor areas, the anterior putamen to the pre-supplementary motor area and anterior cingulate cortex, and the caudate nucleus to the dorsal prefrontal cortex. Differences between groups were specific to the putamen: although PD patients showed decreased coupling between the posterior putamen and the inferior parietal cortex, this region showed increased functional connectivity with the anterior putamen. We conclude that dopamine depletion in PD leads to a remapping of cerebral connectivity that reduces the spatial segregation between different cortico-striatal loops. These alterations of network properties may underlie abnormal sensorimotor integration in PD.
    Cerebral Cortex 09/2009; 20(5):1175-86. · 8.31 Impact Factor