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We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets., Scientific Data, 9 (1), ISSN:2052-4463

© 2021, Springer Nature Switzerland AG.Digital cultural heritage resources are widely available on the web through the digital libraries of heritage institutions. To address the difficulties of discoverability in cultural heritage, the common practice is metadata aggregation, where centralized efforts like Europeana facilitate discoverability by collecting the resources’ metadata. We present the results of the linked data aggregation task conducted within the Europeana Common Culture project, which attempted an innovative approach to aggregation based on linked data made available by cultural heritage institutions. This task ran for one year with participation of twelve organizations, involving the three member roles of the Europeana network: data providers, intermediary aggregators, and the central aggregation hub, Europeana. We report on the challenges that were faced by data providers, the standards and specifications applied, and the resulting aggregated metadata.

In order to solve complex issues, professionals from different fields of expertise increasingly collaborate across domains. These collaborations require meaningful and efficient processes, particularly when design approaches and scientific knowledge is combined.Based on there view of existing literature on the role of incidental learning in ideation and boundary objects in multidisciplinary team creativity, it can be proposed that knowledge sharing can support collective creativity.This approachwas tested in a preliminary study of a collaborative creative task withpairs of design engineers and bio-scientists. Four pairs wereasked to jointly discuss a healthcare-centred innovation brief,and generate novel ideas based on their own expertise. The findings show that boundary objects supportknowledge sharing across disciplinesin creative collaborations-in thiscase, design engineering and synthetic biology.While strong role asymmetry was observed, it has been found that boundary objects are instrumental in ensuring both parties participate effectively in the creative session. In the study, designers were found to be steeringthe conversationsby generating boundary objectsmore frequently than scientists (66% of total number vs. 34%)and sharing knowledge on the design process withthem.This initial study willlater inform the development of a toolkit that aims to support science and design collaboration process.

Humans have the ability to reflect upon their perception, thoughts, and actions, known as metacognition (MC). The brain basis of MC is incompletely understood, and it is debated whether MC on different processes is subserved by common or divergent networks. We combined behavioral phenotyping with multi‐modal neuroimaging to investigate whether structural substrates of individual differences in MC on higher‐order cognition (MC‐C) are dissociable from those underlying MC on perceptual accuracy (MC‐P). Motivated by conceptual work suggesting a link between MC and cognitive perspective taking, we furthermore tested for overlaps between MC substrates and mentalizing networks. In a large sample of healthy adults, individual differences in MC‐C and MC‐P did not correlate. MRI‐based cortical thickness mapping revealed a structural basis of this independence, by showing that individual differences in MC‐P related to right prefrontal cortical thickness, while MC‐C scores correlated with measures in lateral prefrontal, temporo‐parietal, and posterior midline regions. Surface‐based superficial white matter diffusivity analysis revealed substrates resembling those seen for cortical thickness, confirming the divergence of both MC faculties using an independent imaging marker. Despite their specificity, substrates of MC‐C and MC‐P fell clearly within networks known to participate in mentalizing, confirmed by task‐based fMRI in the same subjects, previous meta‐analytical findings, and ad‐hoc Neurosynth‐based meta‐analyses. Our integrative multi‐method approach indicates domain‐specific substrates of MC; despite their divergence, these nevertheless likely rely on component processes mediated by circuits also involved in mentalizing. Hum Brain Mapp 37:3388–3399, 2016. © 2016 Wiley Periodicals, Inc.

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions primarily characterized by abnormalities in social cognition. Abundant previous functional MRI studies have shown atypical activity in networks encompassing medial prefrontal cortex (mPFC) and medial parietal regions corresponding to posterior cingulate cortex and precuneus (PCC/PCU). Conversely, studies assessing structural brain anomalies in ASD have been rather inconsistent. The current work evaluated whether structural changes in ASD can be reliability detected in a large multicenter dataset. Our comprehensive structural MRI framework encompassed cortical thickness mapping and structural covariance analysis based on three independent samples comprising individuals with ASD and controls (n = 220), selected from the Autism Brain Imaging Data Exchange open-access database. Surface-based analysis revealed increased cortical thickness in ASD relative to controls in mPFC and lateral prefrontal cortex. Clusters encompassing mPFC were embedded in altered inter-regional covariance networks, showing decreased covariance in ASD relative to controls primarily to PCC/PCU and inferior parietal regions. Cortical thickness increases and covariance reductions in ASD were consistent, yet of variable effect size, across the different sites evaluated and measurable both in children and adults. Our multi-site study shows regional and network-level structural alterations in mPFC in ASD that, possibly, relate to atypical socio-cognitive functions in this condition.

Item does not contain fulltext Simone Mooij-Valk Arrianus, Alexander de Grote Amsterdam:Ambo ,1999 90-263-1583-X

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