The loading direction dramatically affects the mechanical properties of the mouse tibia.

Introduction: The in vivo tibial loading mouse model has been extensively used to evaluate bone adaptation in the tibia after mechanical loading treatment. However, there is a prevailing assumption that the load is applied axially to the tibia. The aim of this in silico study was to evaluate how much the apparent mechanical properties of the mouse tibia are affected by the loading direction, by using a validated micro-finite element (micro-FE) model of mice which have been ovariectomized and exposed to external mechanical loading over a two-week period. Methods: Longitudinal micro-computed tomography (micro-CT) images were taken of the tibiae of eleven ovariectomized mice at ages 18 and 20 weeks. Six of the mice underwent a mechanical loading treatment at age 19 weeks. Micro-FE models were generated, based on the segmented micro-CT images. Three models using unitary loads were linearly combined to simulate a range of loading directions, generated as a function of the angle from the inferior-superior axis (θ, 0°-30° range, 5° steps) and the angle from the anterior-posterior axis (ϕ, 0°: anterior axis, positive anticlockwise, 0°-355° range, 5° steps). The minimum principal strain was calculated and used to estimate the failure load, by linearly scaling the strain until 10% of the nodes reached the critical strain level of -14,420 µÎµ. The apparent bone stiffness was calculated as the ratio between the axial applied force and the average displacement along the longitudinal direction, for the loaded nodes. Results: The results demonstrated a high sensitivity of the mouse tibia to the loading direction across all groups and time points. Higher failure loads were found for several loading directions (θ = 10°, ϕ 205°-210°) than for the nominal axial case (θ = 0°, ϕ = 0°), highlighting adaptation of the bone for loading directions far from the nominal axial one. Conclusion: These results suggest that in studies which use mouse tibia, the loading direction can significantly impact the failure load. Thus, the magnitude and direction of the applied load should be well controlled during the experiments.

Guidance on the use of complex systems models for economic evaluations of public health interventions.

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

Metaparametric Neural Networks for Survival Analysis.

Survival analysis is a critical tool for the modeling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex machinery. However, current neural network models provide an imperfect solution for survival analysis as they either restrict the shape of the target probability distribution or restrict the estimation to predetermined times. As a consequence, current survival neural networks lack the ability to estimate a generic function without prior knowledge of its structure. In this article, we present the metaparametric neural network framework that encompasses the existing survival analysis methods and enables their extension to solve the aforementioned issues. This framework allows survival neural networks to satisfy the same independence of generic function estimation from the underlying data structure that characterizes their regression and classification counterparts. Furthermore, we demonstrate the application of the metaparametric framework using both simulated and large real-world datasets and show that it outperforms the current state-of-the-art methods in: 1) capturing nonlinearities and 2) identifying temporal patterns, leading to more accurate overall estimations while placing no restrictions on the underlying function structure.

Non-negative matrix factorisation of Raman spectra finds common patterns relating to neuromuscular disease across differing equipment configurations, preclinical models and human tissue.

Raman spectroscopy shows promise as a biomarker for complex nerve and muscle (neuromuscular) diseases. To maximise its potential, several challenges remain. These include the sensitivity to different instrument configurations, translation across preclinical/human tissues and the development of multivariate analytics that can derive interpretable spectral outputs for disease identification. Nonnegative matrix factorisation (NMF) can extract features from high-dimensional data sets and the nonnegative constraint results in physically realistic outputs. In this study, we have undertaken NMF on Raman spectra of muscle obtained from different clinical and preclinical settings. First, we obtained and combined Raman spectra from human patients with mitochondrial disease and healthy volunteers, using both a commercial microscope and in-house fibre optic probe. NMF was applied across all data, and spectral patterns common to both equipment configurations were identified. Linear discriminant models utilising these patterns were able to accurately classify disease states (accuracy 70.2-84.5%). Next, we applied NMF to spectra obtained from the mdx mouse model of a Duchenne muscular dystrophy and patients with dystrophic muscle conditions. Spectral fingerprints common to mouse/human were obtained and able to accurately identify disease (accuracy 79.5-98.8%). We conclude that NMF can be used to analyse Raman data across different equipment configurations and the preclinical/clinical divide. Thus, the application of NMF decomposition methods could enhance the potential of Raman spectroscopy for the study of fatal neuromuscular diseases.

Label-free fibre optic Raman spectroscopy with bounded simplex-structured matrix factorization for the serial study of serum in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease in urgent need of disease biomarkers for the assessment of promising therapeutic candidates in clinical trials. Raman spectroscopy is an attractive technique for identifying disease related molecular changes due to its simplicity. Here, we describe a fibre optic fluid cell for undertaking spontaneous Raman spectroscopy studies of human biofluids that is suitable for use away from a standard laboratory setting. Using this system, we examined serum obtained from patients with ALS at their first presentation to our centre (n = 66) and 4 months later (n = 27). We analysed Raman spectra using bounded simplex-structured matrix factorization (BSSMF), a generalisation of non-negative matrix factorisation which uses the distribution of the original data to limit the factorisation modes (spectral patterns). Biomarkers associated with ALS disease such as measures of symptom severity, respiratory function and inflammatory/immune pathways (C3/C-reactive protein) correlated with baseline Raman modes. Between visit spectral changes were highly significant (p = 0.0002) and were related to protein structure. Comparison of Raman data with established ALS biomarkers as a trial outcome measure demonstrated a reduction in required sample size with BSSMF Raman. Our portable, simple to use fibre optic system allied to BSSMF shows promise in the quantification of disease-related changes in ALS over short timescales.

Tensor electrical impedance myography identifies bulbar disease progression in amyotrophic lateral sclerosis

OBJECTIVE: Electrical impedance myography (EIM) is a promising biomarker for amyotrophic lateral sclerosis (ALS). A key issue is how best to utilise the complex high dimensional, multi-frequency data output by EIM to fully characterise the progression of disease. METHODS: Muscle volume conduction properties were obtained from EIM recordings of the tongue across three electrode configurations and 14 input frequencies (76 Hz-625 kHz). Analyses of individual frequencies, averaged EIM spectra and non-negative tensor factorisation were undertaken. Longitudinal data were collected from 28 patients and 17 healthy volunteers at 3-monthly intervals for a maximum of 9 months. EIM was evaluated against the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar sub-score, tongue strength and an overall bulbar disease burden score. RESULTS: Longitudinal changes to individual patient EIM spectra demonstrated complex shifts in the spectral shape. At a group level, a clear pattern emerged over time, characterised by an increase in centre frequency and general shift to the right of the spectral shape. Tensor factorisation reduced the spectral data from a total of 168 data points per participant per recording to a single value which captured the complexity of the longitudinal data and which we call tensor EIM (T-EIM). The absolute change in tensor EIM significantly increased within 3 months and continued to do so over the 9-month study duration. In a hypothetical clinical trial scenario tensor EIM required fewer participants (n = 64 at 50% treatment effect), than single frequency measures (n range 87-802) or ALSFRS-R bulbar subscore (n = 298). CONCLUSIONS: Changes to tongue EIM spectra over time in ALS are complex. Tensor EIM captured and quantified disease progression and was more sensitive to changes than single frequency EIM measures and other biomarkers of bulbar disease. SIGNIFICANCE: Objective biomarkers for the assessment of bulbar disease in ALS are lacking. Tensor EIM enhances the biomarker potential of EIM data and can improve bulbar symptom monitoring in clinical trials.

CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin.

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.

Positive interactions of mechanical loading and PTH treatments on spatio-temporal bone remodelling.

Osteoporosis is one of the most common skeletal diseases, but current therapies are limited to generalized antiresorptive or anabolic interventions, which do not target regions that would benefit from improvements to skeletal health. To improve the evaluation of treatment plans, we used a spatio-temporal multiscale approach that combines longitudinal in vivo micro-computed tomography (micro-CT) and in silico subject-specific finite element modeling to quantitatively map bone adaptation changes due to disease and treatment at high resolution. Our findings show time and region-dependent modifications in bone remodelling following one and two sets of mechanical loading and/or pharmacological interventions. The multiscale results highlighted that the distal section was unaffected by mechanical loading alone but the proximal tibia had the greatest gain from positive interactions of combined therapies. Mechanical loading abated the catabolic effect of PTH, but the main benefit of combined treatments occurred from the additive interactions of the two therapies in periosteal apposition. These results provide detailed insight into the efficacy of combined treatments, facilitating the optimisation of dosage and treatment duration in preclinical mouse studies, and the development of novel interventions for skeletal diseases. STATEMENT OF SIGNIFICANCE: Combined mechanical loading and pharmacotherapy have the potential to slow osteoporosis-induced bone loss but current therapies do not target the regions in need of strengthening. We show for the first time spatial region-dependant interactions between PTH and mechanical loading treatment in OVX mouse tibiae, highlighting local regions in the tibia that benefitted from separate and combined treatments. Combined experimental-computational analysis also detailed the lasting period of each treatment per location in the tibia, the extent of positive (or negative) interactions of the combined therapies, and the impact of each treatment on the regulation of bone adaptation spatio-temporally. This approach can be used to create hypothesis about the interactions of different treatments to optimise the design of biomaterials and medical interventions.

Tensor electrical impedance myography identifies clinically relevant features in amyotrophic lateral sclerosis.

Objective.Electrical impedance myography (EIM) shows promise as an effective biomarker in amyotrophic lateral sclerosis (ALS). EIM applies multiple input frequencies to characterise muscle properties, often via multiple electrode configurations. Herein, we assess if non-negative tensor factorisation (NTF) can provide a framework for identifying clinically relevant features within a high dimensional EIM dataset.Approach.EIM data were recorded from the tongue of healthy and ALS diseased individuals. Resistivity and reactivity measurements were made for 14 frequencies, in three electrode configurations. This gives 84 (2 × 14 × 3) distinct data points per participant. NTF was applied to the dataset for dimensionality reduction, termed tensor EIM. Significance tests, symptom correlation and classification approaches were explored to compare NTF to using all raw data and feature selection.Main Results.Tensor EIM provides highly significant differentiation between healthy and ALS patients (p< 0.001, AUROC = 0.78). Similarly tensor EIM differentiates between mild and severe disease states (p< 0.001, AUROC = 0.75) and significantly correlates with symptoms (ρ= 0.7,p< 0.001). A trend of centre frequency shifting to the right was identified in diseased spectra, which is in line with the electrical changes expected following muscle atrophy.Significance.Tensor EIM provides clinically relevant metrics for identifying ALS-related muscle disease. This procedure has the advantage of using the whole spectral dataset, with reduced risk of overfitting. The process identifies spectral shapes specific to disease allowing for a deeper clinical interpretation.

Pioneer neutrophils release chromatin within in vivo swarms.

Neutrophils are rapidly recruited to inflammatory sites where their coordinated migration forms clusters, a process termed neutrophil swarming. The factors that modulate early stages of neutrophil swarming are not fully understood, requiring the development of new in vivo models. Using transgenic zebrafish larvae to study endogenous neutrophil migration in a tissue damage model, we demonstrate that neutrophil swarming is a conserved process in zebrafish immunity, sharing essential features with mammalian systems. We show that neutrophil swarms initially develop around an individual pioneer neutrophil. We observed the violent release of extracellular cytoplasmic and nuclear fragments by the pioneer and early swarming neutrophils. By combining in vitro and in vivo approaches to study essential components of neutrophil extracellular traps (NETs), we provide in-depth characterisation and high-resolution imaging of the composition and morphology of these release events. Using a photoconversion approach to track neutrophils within developing swarms, we identify that the fate of swarm-initiating pioneer neutrophils involves extracellular chromatin release and that the key NET components gasdermin, neutrophil elastase, and myeloperoxidase are required for the swarming process. Together our findings demonstrate that release of cellular components by pioneer neutrophils is an initial step in neutrophil swarming at sites of tissue injury.

The Role of the Loading Condition in Predictions of Bone Adaptation in a Mouse Tibial Loading Model.

The in vivo mouse tibial loading model is used to evaluate the effectiveness of mechanical loading treatment against skeletal diseases. Although studies have correlated bone adaptation with the induced mechanical stimulus, predictions of bone remodeling remained poor, and the interaction between external and physiological loading in engendering bone changes have not been determined. The aim of this study was to determine the effect of passive mechanical loading on the strain distribution in the mouse tibia and its predictions of bone adaptation. Longitudinal micro-computed tomography (micro-CT) imaging was performed over 2 weeks of cyclic loading from weeks 18 to 22 of age, to quantify the shape change, remodeling, and changes in densitometric properties. Micro-CT based finite element analysis coupled with an optimization algorithm for bone remodeling was used to predict bone adaptation under physiological loads, nominal 12N axial load and combined nominal 12N axial load superimposed to the physiological load. The results showed that despite large differences in the strain energy density magnitudes and distributions across the tibial length, the overall accuracy of the model and the spatial match were similar for all evaluated loading conditions. Predictions of densitometric properties were most similar to the experimental data for combined loading, followed closely by physiological loading conditions, despite no significant difference between these two predicted groups. However, all predicted densitometric properties were significantly different for the 12N and the combined loading conditions. The results suggest that computational modeling of bone's adaptive response to passive mechanical loading should include the contribution of daily physiological load.

A systematic approach to the scale separation problem in the development of multiscale models.

Throughout engineering there are problems where it is required to predict a quantity based on the measurement of another, but where the two quantities possess characteristic variations over vastly different ranges of time and space. Among the many challenges posed by such 'multiscale' problems, that of defining a 'scale' remains poorly addressed. This fundamental problem has led to much confusion in the field of biomedical engineering in particular. The present study proposes a definition of scale based on measurement limitations of existing instruments, available computational power, and on the ranges of time and space over which quantities of interest vary characteristically. The definition is used to construct a multiscale modelling methodology from start to finish, beginning with a description of the system (portion of reality of interest) and ending with an algorithmic orchestration of mathematical models at different scales within the system. The methodology is illustrated for a specific but well-researched problem. The concept of scale and the multiscale modelling approach introduced are shown to be easily adaptable to other closely related problems. Although out of the scope of this paper, we believe that the proposed methodology can be applied widely throughout engineering.

Modelling and analysis of electrical impedance myography of the lateral tongue.

OBJECTIVE: Electrical impedance myography (EIM) performed on the centre of the tongue shows promise in detecting amyotrophic lateral sclerosis (ALS). Lateral recordings may improve diagnostic performance and provide pathophysiological insights through the assessment of asymmetry. However, it is not known if electrode proximity to the muscle edge, or electrode rotation, distort spectra. We evaluated this using finite element-based modelling. APPROACH: Nine thousand EIM from patients and healthy volunteers were used to develop a finite element model for phase and magnitude. Simulations varied electrode proximity to the muscle edge and electrode rotation. LT-Spice simulations assessed disease effects. Patient data were assessed for reliability, agreement and classification performance. MAIN RESULTS: No effect on phase spectra was seen if all electrodes remained in contact with the tissue. Small effects on magnitude were observed. Cole-Cole circuit simulations indicated capacitance reduced with disease severity. Lateral tongue muscle recordings in both patients and healthy volunteers were reproducible and symmetrical. Combined lateral/central tongue EIM improved disease classification compared to either placement alone. SIGNIFICANCE: Lateral EIM tongue measurements using phase angle are feasible. Such measurements are reliable, find no evidence of tongue muscle asymmetry in ALS and improve disease classification. Lateral measurements enhance tongue EIM in ALS.

Personalized estimation of one-year mortality risk after elective hip or knee arthroplasty for osteoarthritis.

AIMS: To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis. METHODS: A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression. RESULTS: The one-year mortality rates in the NJR were 10.8 and 8.9 per 1,000 patient-years after hip and knee arthroplasty, respectively. The Norwegian mortality rates were 9.1 and 6.0 per 1,000 patient-years, respectively. The strongest predictors of death in the final models were age, sex, body mass index, and American Society of Anesthesiologists grade. Exposure variables related to the intervention, with the exception of knee arthroplasty type, did not add discrimination over patient factors alone. Discrimination was good in both cohorts, with c-indices above 0.76 for the hip and above 0.70 for the knee. Time-dependent Brier scores indicated appropriate estimation of the mortality rate (≤ 0.01, all models). CONCLUSION: Simple demographic and clinical information may be used to calculate an individualized estimation for one-year mortality risk after hip or knee arthroplasty (https://jointcalc.shef.ac.uk). These models may be used to provide patients with an estimate of the risk of mortality after joint arthroplasty. Cite this article: Bone Joint Res 2020;9(11):808-820.

Bone remodelling in the mouse tibia is spatio-temporally modulated by oestrogen deficiency and external mechanical loading: A combined in vivo/in silico study.

Osteoporosis disrupts the healthy remodelling process in bone and affects its mechanical properties. Mechanical loading has been shown to be effective in stimulating bone formation to mitigate initial bone loss. However, no study has investigated the effects of repeated mechanical loading, with a pause of one week in between, in the mouse tibia with oestrogen deficiency. This study uses a combined experimental and computational approach, through longitudinal monitoring with micro-computed tomography, to evaluate the effects of loading on bone adaptation in the tibiae of ovariectomised (OVX) C57BL/6 mice from 14 to 22 weeks of age. Micro-FE models coupled with bone adaptation algorithms were used to estimate changes in local tissue strains due to OVX and mechanical loading, and to quantify the relationship between local strain and remodelling. The first in vivo mechanical loading increased apposition, by 50-150%, while resorption decreased by 50-60%. Both endosteal and periosteal resorption increased despite the second mechanical loading, and periosteal resorption was up to 70% higher than that after the first loading. This was found to correlate with an initial decrease in average strain energy density after the first loading, which was lower and more localised after the second loading. Predictions of bone adaptation showed that between 50 and 90% of the load-induced bone apposition is linearly strain driven at the organ-level, but resorption is more biologically driven at the local level. The results imply that a systematic increase in peak load or loading rate may be required to achieve a similar bone adaptation rate in specific regions of interests.

JointCalc: A web-based personalised patient decision support tool for joint replacement.

BACKGROUND AND PURPOSE: Health information systems (HIS) are expected to be effective and efficient in improving healthcare services, but empirical observation of HIS reveals that most perform poorly in terms of these metrics. Theoretical factors of HIS performance are widely studied, and solutions to mitigate poor performance have been proposed. In this paper we implement effective methods to eliminate some common drawbacks of HIS design and demonstrate the synergy between the methods. JointCalc, the first comprehensive patient-facing web-based decision support tool for joint replacement, is used as a case study for this purpose. METHODS AND RESULTS: User-centred design and thorough end-user involvement are employed throughout the design and development of JointCalc. This is supported by modern software production paradigms, including continuous integration/continuous development, agile and service-oriented architecture. The adopted methods result in a user-approved application delivered well within the scope of project. CONCLUSION: This work supports the claims of high potential efficiency of HIS. The methods identified are shown to be applicable in the production of an effective HIS whilst aiding development efficiency.

Multi-dimensional electrical impedance myography of the tongue as a potential biomarker for amyotrophic lateral sclerosis.

OBJECTIVE: In amyotrophic lateral sclerosis (ALS) bulbar disease biomarkers are lacking. We evaluated a novel tongue electrical impedance myography (EIM) system, utilising both 2D and 3D electrode configurations for detection of tongue pathology. METHODS: Longitudinal multi-frequency phase angle spectra were recorded from 41 patients with ALS (baseline, 3 and 6 months) and 30 healthy volunteers (baseline and 6 months). ALS functional rating scale-revised (ALSFRS-R) data and quantitative tongue strength measurements were collected. EIM data were analysed for reliability (intra-class correlation coefficient; ICC) and differences between patients and volunteers ascertained using both univariate (Mann-Whitney U test) and multivariate techniques (feature selection and L2 norm). RESULTS: The device produced highly reliable data (pooled ICC: 0.836). Significant EIM differences were apparent between ALS patients and healthy volunteers (P < 0.001). EIM data demonstrated a significant relationship to tongue strength and bulbar ALSFRS-R scores (P < 0.015). The EIM recordings revealed a group level longitudinal change over 6 months and consistently identified patients in whom symptoms or tongue strength changed. CONCLUSIONS: The novel EIM tongue system produces reliable data and can differentiate between healthy muscle and ALS-related disease. SIGNIFICANCE: Tongue EIM utilising multiple frequencies and electrode configurations has potential as a bulbar disease biomarker in ALS.

The SIPHER Consortium: Introducing the new UK hub for systems science in public health and health economic research.

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER ( Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium's vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing.

A new method to monitor bone geometry changes at different spatial scales in the longitudinal in vivo µCT studies of mice bones.

Longitudinal studies of bone adaptation in mice using in vivo micro-computed tomography (µCT) have been commonly used for pre-clinical evaluation of physical and pharmacological interventions. The main advantage of this approach is to use each mouse as its own control, reducing considerably the sample size required by statistical power analysis. To date, multi-scale estimation of bone adaptations become essential since the bone activity that takes place at different scales may be associated with different bone mechanisms. Measures of bone adaptations at different time scales have been attempted in a previous study. This paper extends quantification of bone activity at different spatial scales with a proposition of a novel framework. The method involves applying level-set method (LSM) to track the geometric changes from the longitudinal in vivo µCT scans of mice tibia. Bone low- and high-spatial frequency patterns are then estimated using multi-resolution analysis. The accuracy of the framework is quantified by applying it to two times separated scanned images with synthetically manipulated global and/or local activity. The Root Mean Square Deviation (RMSD) was approximately 1.5 voxels or 0.7 voxels for the global low-spatial frequency or local high-spatial frequency changes, respectively. The framework is further applied to the study of bone changes in longitudinal datasets of wild-type mice tibiae over time and space. The results demonstrate the ability for the spatio-temporal quantification and visualisation of bone activity at different spatial scales in longitudinal studies thus providing further insight into bone adaptation mechanisms.