Human bone: the tissue characteristics determining its mechanical behaviour

The present thesis illustrates the research carried out during the PhD studies in Bioengineering. The research was aimed to characterise the human bone tissue, with particular regard to the differences between cortical and trabecular bone. The bone tissue characteristics that affect its mechanical properties were verified or identified, using an experimental approach, to corroborate or refute hypotheses based on the state of the art in bone tissue biomechanics. The studies presented in the present PhD thesis were designed to investigate aspects of bone tissue biomechanics, which were in need of a more in-depth examination since the data found in the literature was contradictory or scarce. In particular, the work was focalised on the characterisation of the basic structure of the bone tissue (groups of lamellae), its composition, its spatial organisation (trabecular bone microarchitecture) and their influence on the mechanical properties. In conclusion, the present thesis integrates eight different studies on the characterisation of bone tissue. A more in-depth examination of some of the aspects of bone tissue biomechanics where the data found in the literature was contradictory or scarce was performed. Bone tissue was investigated at several scales, from its composition up to its spatial organization, to determine which parameters influence the mechanical behaviour of the tissue. It was found that although the composition and real density of bone tissue are similar, the differences in structure at different levels cause differences between the two types of bone tissue (cortical and trabecular) in mechanical properties. However, the apparent density can still be considered a good predictor of the mechanical properties of both cortical and trabecular bone. Finally, it was found that the bone tissue characteristics might change when a pathology is present, as demonstrated for OA.

Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.