Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

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

The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere

Improved strategies for the evaluation of cerebral blood flow and cerebrovascular reserve capacity using dynamic positron emission tomography

Measurement of cerebral blood flow (CBF) using positron emission tomography (PET) with 15O-labelled water is commonly achieved using the one-compartment model of Kety. Unlike this model, a recently developed two-compartment CBF model accounts for nonextracted intravascular radioactivity and therefore provides with perfusion (CBF) as well as vascular, i.e., blood volume (CBV) related, information. The two parameters obtained from this two-compartment analysis, namely cerebral water clearance, KH2O1 , a measure of perfusion, and the apparent tracer distribution volume, Vo, provide more complete haemodynamic information than CBF alone. Still, accurate K1 values can only be obtained if appropriate corrections for tracer delay (Deltat) and dispersion (tau) have been applied. Building on this two-compartment model, we have derived new nonlinear and linear solutions where all parameters involved appear explicitly. Simulations were performed to evaluate the accuracy and precision with which K1, k2, Vo, Deltat and tau can be determined from multiparameter regression of the model equations on dynamic PET data. It was found that K1 and k2 can be estimated with less than 2% error using 5-parameter fitting.We have applied our solutions to dynamic 15O-water bolus PET studies where a CBF change was produced either through neuronal activation triggered by visual stimulation (in normal subjects), or by external pharmacological control through vasodilatory insults induced by Diamox injection (in patients with cerebrovascular disease) and antioxidant LY231617 administration (in monkeys with unilateral middle cerebral artery occlusion).We found that in addition to accurate pixel-by-pixel estimation of K 1 and k2, accurate changes in K1, Vo and the ratio Vo/K1 between baseline and activation states can also be obtained using our solutions of the two-compartment CBF model with implicit corrections for delay, dispersion and residual vascular radioactivity. These parameter changes provide potential indices of cerebrovascular reserve capacity, and of variations in cerebral vascular volume and mean vascular transit time. Our method may prove to be an elegant tool in the selection of candidates for revascularisation surgery

Students' perspectives of learning anatomy

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Medical students’ Big Five Personality scores and the effects on the “selection process”

International audienceAssessment of the personalities of medical students not only aids the formulation of strategies for the best development of academic and clinical competencies but can also inform the process of selecting medical practitioners. The hypothesis tested was that medical students have distinct personality profiles that reflect the nature of the selection process. Two groups of French medical students were compared using the Big Five Inventory (BFI) to measure personality: an unselected group of Year 1 medical students (n = 1332; mean age 19.4 years ± 1.4; 68% females) and a group of academically successful Year 3 students (n = 403; mean age 21.3 ± 1.6; 65% female). The data collected further enabled comparisons in an international context where medical students were selected using different procedures. Year 3 French medical students, who represent only the top 15% of students initially admitted into the medical course, scored lower on two personality dimensions than the unselected Year 1 students: on Agreeableness and Openness to new experience (p < 0.001). In keeping with the findings in non-medical populations, both groups of female medical students scored higher on Agreeableness than did males. Nevertheless, the selection effect on Agreeableness and Openness held for both males and females. These findings contrast with medical student personality profiles in other countries that use less overtly competitive procedures to select medical students

Medical students’ attitudes toward the anatomy dissection room in relation to personality

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APPIAN: Automated Pipeline for PET Image Analysis

APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistration of PET images to T1-weighted magnetic resonance (MR) images, partial-volume correction (PVC), and quantification with tracer kinetic modeling. While there are alternate open-source PET pipelines, none offers all of the features necessary for making automated PET analysis as reliably, flexibly and easily extendible as possible. To this end, a novel method for automated quality control (QC) has been designed to facilitate reliable, reproducible research by helping users verify that each processing stage has been performed as expected. Additionally, a web browser-based GUI has been implemented to allow both the 3D visualization of the output images, as well as plots describing the quantitative results of the analyses performed by the pipeline. APPIAN also uses flexible region of interest (ROI) definition—with both volumetric and, optionally, surface-based ROI—to allow users to analyze data from a wide variety of experimental paradigms, e.g., longitudinal lesion studies, large cross-sectional population studies, multi-factorial experimental designs, etc. Finally, APPIAN is designed to be modular so that users can easily test new algorithms for PVC or quantification or add entirely new analyses to the basic pipeline. We validate the accuracy of APPIAN against the Monte-Carlo simulated SORTEO database and show that, after PVC, APPIAN recovers radiotracer concentrations within 93–100% accuracy

Selected 1 micron scans of BigBrain histological sections (v1.0)

International audienceThis dataset contains selected microscopic scans of tissue sections at 1 micron in-plane resolution from the BigBrain, a whole brain 3D reconstruction of a human postmortem brain. Each image is provided both in its original histological space, as well as aligned to the corresponding coronal section of the 3D- reconstructed BigBrain space. The images were acquired by rescanning the original histological sections of the BigBrain model at an in-plane resolution of 1μm. The sections were aligned to their corresponding coronal planes in the 20μm 3D-reconstructed BigBrain space using 2D non-linear transformations, and allow to identify microscopic features at the cellular level due to their increased resolution