Publications by authors named "Stephan Heunis"

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study.

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Acting prosocially and feeling socially included are important factors for developing social relations. However, little is known about the development of neural trajectories of prosocial behavior and social inclusion in the transition from middle childhood to early adolescence. In this pre-registered study, we investigated the development of prosocial behavior, social inclusion, and their neural mechanisms in a three-wave longitudinal design (ages 7-13 years; N = 512; N = 456; N = 336).

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Sharing of data, processing tools, and workflows require open data hosting services and management tools. Despite FAIR guidelines and the increasing demand from funding agencies and publishers, only a few animal studies share all experimental data and processing tools. We present a step-by-step protocol to perform version control and remote collaboration for large multimodal datasets.

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As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.

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A variety of strategies are used to combine multi-echo functional magnetic resonance imaging (fMRI) data, yet recent literature lacks a systematic comparison of the available options. Here we compare six different approaches derived from multi-echo data and evaluate their influences on BOLD sensitivity for offline and in particular real-time use cases: a single-echo time series (based on Echo 2), the real-time T*-mapped time series (T*FIT) and four combined time series (T*-weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T*FIT-weighted). We compare the influences of these six multi-echo derived time series on BOLD sensitivity using a healthy participant dataset (N = 28) with four task-based fMRI runs and two resting state runs.

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Article Synopsis
  • Open science relies on sharing research data openly, especially in human studies, which requires obtaining consent from participants to ensure ethical standards are upheld.
  • Laws and regulations exist to protect participant privacy, but researchers often find it challenging to understand and comply with these requirements.
  • The Open Brain Consent initiative assists brain imaging researchers by providing resources, including compliant consent forms and user agreements, to facilitate ethical data sharing both in the EU and globally.
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Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data.

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Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal.

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Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

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Real-time functional magnetic resonance imaging (rtfMRI) allows visualisation of ongoing brain activity of the subject in the scanner. Denoising algorithms aim to rid acquired data of confounding effects, enhancing the blood oxygenation level-dependent (BOLD) signal. Further image processing and analysis methods, like general linear models (GLM) or multivariate analysis, then present application-specific information to the researcher.

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder in which the severity of symptoms varies over subjects. The iCAPs model (innovation-driven co-activation patterns) is a recently developed spatio-temporal model to describe fMRI data. In this study, the iCAPs model was employed to find functional imaging biomarkers for ASD in resting-state fMRI data.

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Optimizing chest compression (CC) performance during neonatal cardiopulmonary resuscitation (CPR) is critical to improving survival outcomes since current clinical protocols often achieve only a fraction of the native cardiovascular perfusion. This study presents the development of a diagnostic tool to unobtrusively measure the CC depth and force during neonatal CPR using sensors mounted on a glove platform. The performance of the glove was evaluated by infant manikin tests using the two-thumb (TT) and two-finger (TF) methods of CC during simulated, unventilated neonatal CPR.

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