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The purpose of this pilot study was to evaluate whether machine learning algorithms can be used to design shoe lasts in an accurate fashion (i.e. sub-millimeter error). ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.

Data reduction techniques are commonly applied to dynamic plantar pressure measurements, often prior to the measurement's analysis. In performing these data reductions, information is discarded from the measurement before it can be evaluated, leading to unkonwn consequences. In this study, we aim to provide the first assessment of what impact data reduction techniques have on plantar pressure measurements. Specifically, we quantify the extent to which information of any kind is discarded when performing common data reductions. Plantar pressure measurements were collected from 33 healthy controls, 8 Hallux Valgus patients, and 10 Metatarsalgia patients. Eleven common data reductions were then applied to the measurements, and the resulting datasets were compared to the original measurement in three ways. First, information theory was used to estimate the information content present in the original and reduced datasets. Second, principal component analysis was used to estimate the number of intrinsic dimensions present. Finally, a permutational multivariate ANOVA was performed to evaluate the significance of group differences between the healthy controls, Hallux Valgus, and Metatarsalgia groups. The evaluated data reductions showed a minimum of 99.1% loss in information content and losses of dimensionality between 20.8% and 83.3%. Significant group differences were also lost after each of the 11 data reductions (alpha = 0.05), but these results may differ for other patient groups (especially those with highly-deformed footprints) or other region of interest definitions. Nevertheless, the existence of these results suggest that the diagnostic content of dynamic plantar pressure measurements is yet to be fully exploited. ; This research has received funding from the European Union's Horizon 2020 reserch and innovation programme under the Marie Sklodowska-Curie grant agreement no. 746614.

While dynamic plantar pressure measurements are commonly used for clinical evaluation of gait-related problems, computational analysis techniques for these datasets are few and far between. To address this issue, we introduce an open-access database of plantar pressure videos for researchers to develop algorithms around. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.

This study characterizes plantar pressure differences between hallux valgus patients and healthy controls using statistical parametric mapping (SPM). The plantar pressure differences are minimal which, combined with previous studies, suggest high variability in the plantar pressures of hallux valgus patients. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.

Background Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. Research question We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. Methods To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. Results As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. Significance We therefore conclude that the subsampling of plantar pressure videos – a task which led to the discarding of gait information in our study – can be avoided using STAPP. ; This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 746614.

Quantitative analyses of plantar pressure images typically occur at the group level and under the assumption that individuals within each group display homogeneous pressure patterns. When this assumption does not hold, a personalized analysis technique is required. Yet, existing personalized plantar pressure analysis techniques work at the image level, leading to results that can be unintuitive and difficult to interpret. To address these limitations, we introduce PAPPI: the Personalized Analysis of Plantar Pressure Images. PAPPI is built around the statistical modelling of the relationship between plantar pressures in healthy controls and their demographic characteristics. This statistical model then serves as the healthy baseline to which an individual's real plantar pressures are compared using statistical parametric mapping. As a proof-of-concept, we evaluated PAPPI on a cohort of 50 hallux valgus patients. PAPPI showed that plantar pressures from hallux valgus patients did not have a single, homogeneous pattern, but instead, 5 abnormal pressure patterns were observed in sections of this population. When comparing these patterns to foot pain scores (i.e. Foot Function Index, Manchester-Oxford Foot Questionnaire) and radiographic hallux angle measurements, we observed that patients with increased pressure under metatarsal 1 reported less foot pain than other patients in the cohort, while patients with abnormal pressures in the heel showed more severe hallux valgus angles and more foot pain. Also, incidences of pes planus were higher in our hallux valgus cohort compared to the modelled healthy controls. PAPPI helped to clarify recent discrepancies in group-level plantar pressure studies and showed its unique ability to produce quantitative, interpretable, and personalized analyses for plantar pressure images. ; This research has received funding from imec ICON grant no. 150218 and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 746614.

Even though declines in sensorimotor performance during healthy aging have been documented extensively, its underlying neural mechanisms remain unclear. Here, we explored whether age-related subcortical atrophy plays a role in sensorimotor performance declines, and particularly during bimanual manipulative performance (Purdue Pegboard Test). The thalamus, putamen, caudate and pallidum of 91 participants across the adult lifespan (ages 20 – 79 years) were automatically segmented. In addition to studying age-related changes in the global volume of each subcortical structure, local deformations within these structures, indicative of subregional volume changes, were assessed by means of recently developed shape analyses. Results showed widespread age-related global and subregional atrophy, as well as some notable subregional expansion. Even though global atrophy failed to explain the observed performance declines with aging, shape analyses indicated that atrophy in left and right thalamic subregions, specifically subserving connectivity with the premotor, primary motor and somatosensory cortical areas, mediated the relation between aging and performance decline. It is concluded that subregional volume assessment by means of shape analyses offers a sensitive tool with high anatomical resolution in the search for specific age-related associations between brain structure and behavior. ; This work was supported by grants from the Research Fund of KU Leuven, Belgium (OT/11/071), the Flanders Fund for Scientific Research (G0721.12, G0708.14) and Grant P7/11 from the Inter-university Attraction Poles program of the Belgian Federal Government. I. Leunissen is funded by a postdoctoral fellowship of the Research Foundation - Flanders (FWO).

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings. ; Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [694665]; French government, through the 3IA Cote DAzur Investments in the Future project [ANR-19-P3IA-0002]; EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/N018702/1, MR/T020296/1, ISLRA-2009]; European Space AgencyEuropean Space AgencyEuropean Commission; Belgian Science Policy Office-ProdexBelgian Federal Science Policy Office; Research Foundation Flanders (FWO Vlaanderen)FWO [12M3119N, G0D7216N]; Wellcome Trust Investigator AwardWellcome Trust [096646/Z/11/Z]; Wellcome Trust Strategic AwardWellcome Trust [104943/Z/14/Z]; Polish National Agency for Academic ExchangePolish National Agency for Academic Exchange (NAWA) [PN/BEK/2019/1/00421]; Ministry of Science and Higher Education (Poland)Ministry of Science and Higher Education, Poland [692/STYP/13/2018]; AGH Science and Technology, Poland [16.16.120.773]; Linkoping University (LiU) Center for Industrial Information Technology (CENIIT); LiU Cancer [VINNOVA/ITEA3 17021 IMPACT]; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RMX18-0056]; "la Caixa" FoundationLa Caixa Foundation [100010434]; European UnionEuropean Commission [847648, LCF/BQ/PI20/11760029]; Ministerio de Ciencia e Innovacion" of SpainSpanish Government [RTI2018-094569-B-I00]; National Institute for Biomedical Imaging [5R01EB027585-02]