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

Practices as a unit for design: an exploration of theoretical guidelines in a study on bathing

The sustainability challenges facing society today require approaches that look beyond single product- user interactions. Focusing on socially shared practices—e.g. cooking, laundering—has been identified as a promising direction. Building on a growing body of research in sustainable HCI that takes practices as unit of analysis, this article explores what it means to take practices as a unit of design. Drawing on theories of practice, it proposes that practice-oriented design approaches should: involve bodily performance, create crises of routine and generate a variety of performances. These guidelines were integrated into a Generative Improv Performances (GIP) approach, entailing a series of performances by improvisation actors with low- fidelity prototypes in a lab environment. The approach was implemented in an empirical study on bathing. Although the empirical example does not deal with common types of interactive technologies, the guidelines and GIP approach offer sustainable HCI a way to think beyond immediate interactions and to conceptualize change on a practice level

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

We predicted fluid intelligence 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