Experimental and numerical studies on the shared activation anchoring of NSMR CFRP applied to RC beams

A shared activation anchoring method used for carbon fiber reinforced polymer (CFRP) near surface mounted reinforcement (NSMR) strengthening is hypothesized to provide a mean to exploit the full material capacity and to tailor desired responses. To investigate strengthening efficiency, failure control as well as ductility levels, the developed strengthening system were mounted on reinforced concrete T-beams with a length of 6400 mm. Initial activation stresses of 50% (1100 MPa) and 70% (1540 MPa) were applied to an 8 mm CFRP rod by the anchor system. Then, in some beams finite element simulations were carried out for better understanding the obtained results with regard to the overall structural behaviour. Good correlations between the FE-simulation and tested responses were observed, where a high utilization of the CFRP material (up to 3300MPa) was reached. Installation of the activated system worked well, without premature failure. Additionally it was possible to control the failure development, where intermediate crack de-bonding was achieved when testing the beams with an activation level of approximately 50%, while fibre rupture occurred at the level of 70% activation, thus providing a CFRP strain of approximately 0,02.SFRH/BSAB/150266/2019; S&P Denmark and Reinholdt W. Jorck and Hustrus foundation. FCT, respectively, financed by European Social Fund and by national funds through the FCT/MCTE

Explainable AI for higher cognitive functions: How to provide explanations in the face of increasing complexity

Since the introduction of the term explainable artificial intelligence (XAI), many contrasting definitions and methods have been proposed. This lack of a common framework impedes not only further progress in the field but also the realization of existing regulations, such as the EU’s general data protection regulation on the ‘right to an explanation’ (Goodman & Flaxman, 2017). While some researchers use interpretation algorithms as post-hoc explanations (Samek et al., 2021; Ribeiro, 2016), others argue that we should use models which are interpretable in the first place (Rudin, 2019). Although the latter is important, developers are not always willing to sacrifice accuracy by choosing a less complex interpretable model. Here, we propose a working definition of what explaining an AI model means, focusing on robustness, representativeness, and comprehensibility as central properties, and on the importance of causal links (Miller, 2019). In addition, we suggest starting with simple models and gradually scaling up the level of complexity if necessary, whilst setting a case-specific threshold for its trade-off with accuracy and ensuring that we obtain explanations that meet the requirements of our working definition

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood

Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder : results from the ENIGMA MDD Working Group

It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging