Cortical Thickness Adaptive Response to Mechanical Loading Depends on Periosteal Position and Varies Linearly With Loading Magnitude

Miller, Corey; Trichilo, Silvia; Pickering, Edmund; Martelli, Saulo; Delisser, Peter J; Meakin, Lee B; Pivonka, Peter

doi:10.3389/fbioe.2021.671606

Cited by 11 publications

(11 citation statements)

References 56 publications

(77 reference statements)

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“… 2012 ), who used an average of 100 cortical slices per section for their cortical bone adaptation analyses, we instead use a single cross sectional image; the latter approach has previously been shown to deliver equivalent results describing the adaptation response within a given section (Miller et al. 2021 ). The methods and results presented here focus on the midshaft, for analysis of the proximal-middle section please refer to Supplementary Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Net adaptation was calculated by adapting the minimum distance method presented in Miller et al. ( 2021 ); the baseline state (i.e. ) was interpolated such that the number of points was increased fourfold, the distance between each point ) and all interpolated points of was measured, and the shortest distance was taken as the representative measurement (shown on the periosteal surface in Fig.…”

Section: Methodsmentioning

confidence: 99%

“… 2015 ; Miller et al. 2021 ), iv) the periosteal and endosteal surface experience different amounts of formation and resorption with respect to applied loads (Roberts et al. 2020 ; Robinson et al.…”

Section: Introductionmentioning

confidence: 99%

“…Mechanical beam theory presents itself as an alternative method of performing stress/strain analysis in cortical bone analysis, with solutions obtained in seconds as opposed to hours (Miller et al. 2021 ; Trichilo 2018 ; Pickering et al. 2021 ; Lerebours et al.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous study (Miller et al. 2021 ), we re-analysed the CT data and showed that extracting local bone properties from the contralateral study was possible; however, the study did not track surfaces, instead only calculating relative adaptive quantities, i.e. .…”

Section: Introductionmentioning

confidence: 99%

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Cortical bone adaptation response is region specific, but not peak load dependent: insights from $$\mu$$CT image analysis and mechanostat simulations of the mouse tibia loading model

Miller,

Pickering,

Martelli

et al. 2023

Biomech Model Mechanobiol

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The two major aims of the present study were: (i) quantify localised cortical bone adaptation at the surface level using contralateral endpoint imaging data and image analysis techniques, and (ii) investigate whether cortical bone adaptation responses are universal or region specific and dependent on the respective peak load. For this purpose, we re-analyse previously published $$\mu$$ μ CT data of the mouse tibia loading model that investigated bone adaptation in response to sciatic neurectomy and various peak load magnitudes (F = 0, 2, 4, 6, 8, 10, 12 N). A beam theory-based approach was developed to simulate cortical bone adaptation in different sections of the tibia, using longitudinal strains as the adaptive stimuli. We developed four mechanostat models: universal, surface-based, strain directional-based, and combined surface and strain direction-based. Rates of bone adaptation in these mechanostat models were computed using an optimisation procedure (131,606 total simulations), performed on a single load case (F = 10 N). Subsequently, the models were validated against the remaining six peak loads. Our findings indicate that local bone adaptation responses are quasi-linear and bone region specific. The mechanostat model which accounted for differences in endosteal and periosteal regions and strain directions (i.e. tensile versus compressive) produced the lowest root mean squared error between simulated and experimental data for all loads, with a combined prediction accuracy of 76.6, 55.0 and 80.7% for periosteal, endosteal, and cortical thickness measurements (in the midshaft of the tibia). The largest root mean squared errors were observed in the transitional loads, i.e. F = 2 to 6 N, where inter-animal variability was highest. Finally, while endpoint imaging studies provide great insights into organ level bone adaptation responses, the between animal and loaded versus control limb variability make simulations of local surface-based adaptation responses challenging.

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Section: Methodsmentioning

confidence: 99%