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Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Departing from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for individual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.

The present preregistration describes a study for which data was collected between April and June 2021. The data has not been analyzed yet. The overarching goal of this study was to test the effectiveness of a VR-based biofeedback (BFB) training for police officers. The training aims to improve control of Heart-Rate variability (HRV) in an active and immersive VR context, in which police officers make shooting decisions under threat. The unique novel aspect of the study is that BFB training is integrated in an active decision-making context. Our first aim is to validate whether the VR training environment is indeed arousal-inducing and evokes the expected in-game behavior. To this end, we will compare the VR environment and a baseline assessment with respect to Heart-Rate (HR) changes and self-reported stress responses. Additionally, we will investigate the effect of behavioral priming on in-game shoot-don’t shoot decisions. The second aim is to investigate the effectiveness of the training. Therefore, we compare an experimental group (who received the training) with a control group (who did not receive the training but played the VR game once) with respect to in-game psychophysiological, behavioral and self-report data. We additionally investigated if biofeedback delivered in action was the causal mechanism explaining an improvement by changing the order in which participants in the experimental group receive the first biofeedback session in VR. Last for this aim, we investigate if the training has an effect on the feeling of self-efficacy and physiological awareness. Our third aim was to assess whether acquired HRV regulation skills would transfer to a police-relevant independent arousing decision-making context, outside of the game. To this end the experimental and control groups were compared with respect to HRV and shoot/don’t shoot decisions during a simulation exercise inspired by the dispatch priming task of Taylor (2020) that served as an independent transfer task, administered after the final game session. Below we only give an overview of the design and outcome measures of the current dataset, for details on the rationale and methods see previous reports (Brammer et al., 2021; Michela et al., 2022).

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

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.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

© 2021 American Neurological Association, Objective: Knowing explicitly where we are is an interpretation of our spatial representations. Reduplicative paramnesia is a disrupting syndrome in which patients present a firm belief of spatial mislocation. Here, we studied the largest sample of patients with delusional misidentifications of space (i.e. reduplicative paramnesia) after stroke to shed light on their neurobiology. Methods: In a prospective, cumulative, case-control study, we screened 400 patients with acute right-hemispheric stroke. We included 64 cases and 233 controls. First, lesions were delimited and normalized. Then, we computed structural and functional disconnection maps using methods of lesion-track and network-mapping. The maps were compared, controlling for confounders. Second, we built a multivariate logistic model including clinical, behavioural and neuroimaging data. Finally, we performed a nested cross-validation of the model with a support-vector machine analysis. Results: The most frequent misidentification subtype was confabulatory mislocation (56%), followed by place reduplication (19%) and chimeric assimilation (13%). Our results indicate that structural disconnection is the strongest predictor of the syndrome and included two distinct streams, connecting right fronto-thalamic and right occipito-temporal structures. In the multivariate model, the independent predictors of reduplicative paramnesia were the structural disconnection map, lesion sparing of right dorsal fronto-parietal regions, age and anosognosia. Good discrimination accuracy was demonstrated (area under the curve = 0.80[0.75-0.85]). Interpretation: Our results localize the anatomical circuits that may have a role in the abnormal spatial-emotional binding and in the defective updating of spatial representations underlying reduplicative paramnesia. This novel data may contribute to better understand the pathophysiology of delusional syndromes after stroke., The authors acknowledge our colleagues from the Department of Neurology and from the Language Research Laboratory for their contribution to patient screening. This work received funding from “PRÉMIO JOÃO LOBO ANTUNES” – SCML (grant to P.N.A.), from “Bolsa de Investigação em Doenças Vasculares Cerebrais 2017”–SPAVC (grant to P.N.A.) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 818521 to M.T.S.).“Data were provided in part bythe Human Connectome Project, WU-Minn Consortium(Principal Investigators: David Van Essen and KamilUgurbil; 1U54 MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

Contains fulltext : 202961.pdf (Publisher’s version ) (Open Access) We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG measures brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS was a solution to organise magnetic resonance imaging (MRI) data. The nature and acquisition parameters of MRI and MEG data are strongly dissimilar. Although there is no standard data format for MEG, we propose MEG-BIDS as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality. The standard also includes well-defined metadata, to facilitate future data harmonisation and sharing efforts. This responds to unmet needs from the multimodal neuroimaging community and paves the way to further integration of other techniques in electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to a multimodal data structure. We feature several data-analytics software that have adopted MEG-BIDS, and a diverse sample of open MEG-BIDS data resources available to everyone. 12 p.