Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.Comment: IPMI 201

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

Demonstrating a generic four-step approach for applying flexibility for congestion management in daily operation

New energy technologies like photovoltaics, electric vehicles, and heat pumps increasingly find their way to distribution networks. At the time the existing distribution networks were designed, only conventional loads were considered. The capacity of the (existing) distribution networks is therefore insufficient to handle the additional (bidirectional) peak load caused by these new technologies. The distribution system operator (DSO) is facing network congestion. Flexibility to shift and/or change power and energy in time and/or amount is considered as an option to mitigate network congestion with various implicit and explicit mechanisms. This leaves DSOs with the question on how to deploy such mechanisms seamlessly and effectively in daily business with uncertain congestion scenarios and complex integration processes. To tackle these operational challenges, this paper introduces a generic four-step approach to operationalize the flexibility need of a DSO, for any chosen implementation of an implicit or explicit flexibility mechanism (e.g. price-based schemes, flexibility markets, direct control). To this end, this paper addresses the steps: 1. Data acquisition, 2. Forecasting, 3. Decision-making, and 4. Flexibility mechanism interfacing. Furthermore, a particular implementation is described in relation to the Dutch’ H2020 InterFlex demonstrator, showing the field application of the proposed steps. In this demonstrator, a large amount of flexibility (26 electric vehicle charge points of 22kW, a 250kW/315kWh battery energy storage system, and a 260kWp photovoltaic installation) is connected to two 630kVA transformers in a residential area with approximately 350 apartments. The results of the implementation show that the proposed steps enable the DSO to predict congestion, put a monetary value on flexibility, and use this value to evaluate flexibility offered through – in this case study – a flexibility market

Exploring the Anatomical Basis of Effective Connectivity Models with DTI-Based Fiber Tractography

Diffusion tensor imaging (DTI) is considered to be a promising tool for revealing the anatomical basis of functional networks. In this study, we investigate the potential of DTI to provide the anatomical basis of paths that are used in studies of effective connectivity, using structural equation modeling. We have taken regions of interest from eight previously published studies, and examined the connectivity as defined by DTI-based fiber tractography between these regions. The resulting fiber tracts were then compared with the paths proposed in the original studies. For a substantial number of connections, we found fiber tracts that corresponded to the proposed paths. More importantly, we have also identified a number of cases in which tractography suggested direct connections which were not included in the original analyses. We therefore conclude that DTI-based fiber tractography can be a valuable tool to study the anatomical basis of functional networks

Food System Resilience

The COVID-19 crisis is just one in a series of shocks and stressors that exemplify the importance of building resilient food systems. To ensure that desired food system outcomes are less fluctuating, policy makers and other important stakeholders need a common narrative on food system resilience. The purpose of this paper is to work towards a joint understanding of food system resilience and its implications for policy making. The delivery of desired outcomes depends on the ability of food systems to anticipate, prevent, absorb, and adapt to the impacts of shocks and stressors. Based on our literature review we found four properties of food systems that enhance their resilience. We refer to these as the A B C D of resilience building: Agency, Buffering, Connectivity and Diversity. Over time, many food systems have lost levels of agency, buffering capacity, connectivity or diversity. One of the principal causes of this is attributed to the governance of food systems. Governance is inherently political: as a result of conflicting interests and power imbalances, food systems fail to deliver equitable and just access to food. Moreover, the impacts of shocks and stressors are not evenly distributed across actors in the food system. This paper has highlighted the importance of more inclusive governance to direct food system transformation towards such higher levels of resilience. We conclude that we cannot leave this to the market, but that democratic and before all independent, credible institutions are needed to create the necessary transparency between actors as to their interests, power and influence