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.

Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements.

Cognitive decline in normal ageing and Alzheimer's disease (AD) emerges from functional disruption in the coordination of large-scale brain systems sustaining cognition. Integrity of these systems can be examined by correlation methods based on analysis of resting state functional magnetic resonance imaging (fMRI). Here we investigate functional connectivity within the default mode network (DMN) in normal ageing and AD using resting state fMRI. Images from young and elderly controls, and patients with AD were processed using spatial independent component analysis to identify the DMN. Functional connectivity was quantified using integration and indices derived from graph theory. Four DMN sub-systems were identified: Frontal (medial and superior), parietal (precuneus-posterior cingulate, lateral parietal), temporal (medial temporal), and hippocampal (bilateral). There was a decrease in antero-posterior interactions (lower global efficiency), but increased interactions within the frontal and parietal sub-systems (higher local clustering) in elderly compared to young controls. This decreased antero-posterior integration was more pronounced in AD patients compared to elderly controls, particularly in the precuneus-posterior cingulate region. Conjoint knowledge of integration measures and graph indices in the same data helps in the interpretation of functional connectivity results, as comprehension of one measure improves with understanding of the other. The approach allows for complete characterisation of connectivity changes and could be applied to other resting state networks and different pathologies.

Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses.

Imaging cerebral glucose metabolism with positron emission tomography (PET) in Alzheimer's disease (AD) has allowed for improved characterisation of this pathology. Such patterns are typically analysed using either univariate or multivariate statistical techniques. In this work we combined voxel-based group analysis and independent component analysis to extract differential characteristic patterns from PET data of glucose metabolism in a large cohort of normal elderly controls and patients with AD. The patterns were used in conjunction with a support vector machine to discriminate between subjects with mild cognitive impairment (MCI) at risk or not of converting to AD. The method was applied to baseline fluoro-deoxyglucose (FDG)-PET images of subjects from the ADNI database. Our approach achieved improved early detection and differentiation of typical versus pathological metabolic patterns in the MCI population, reaching 80% accuracy (85% sensitivity and 75% specificity) when using selected regions. The method has the potential to assist in the advance diagnosis of Alzheimer's disease, and to identify early in the development of the disease those individuals at high risk of rapid cognitive decline who could be candidates for new therapeutic approaches.

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.

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.

15O Radioactivity clearance is faster after intracarotid bolus injection of 15O-labeled oxyhemoglobin than after 15O-water injection.

The authors tested the hypothesis that the oxygen content of brain tissue is negligible by injecting an intracarotid bolus of 15O-labeled tracer into rats. Under the hypothesis, the clearance rates of 15O radioactivity from the brain after injections of both 15O-labeled water (H(2)15O) and 15O-labeled oxyhemoglobin (HbO15O) should be identical. However, the logarithmic slope of the 15O radioactivity curve after HbO15O injection (0.494 +/- 0.071 min-1) was steeper than that after H(2)15O injection (0.406 +/- 0.038 min-1) (P<0.001, n = 13), where the time range used in the comparison was between 60 and 120 seconds after the injection. A possible interpretation of this result is that nonmetabolized O15O may dwell in the brain tissue for a finite period of time before it is eventually metabolized or returned to the blood stream unaltered. These findings contradict assumptions made by models currently used to measure cerebral oxygen metabolism.

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

Assessment of the personalities of medical students could enable medical educators to formulate strategies for the best development of academic and clinical competencies. In this article, we focus on the experience of students in the anatomy dissecting room. While there have been many attempts to evaluate the emotional responses of medical students to human cadaveric dissection, there has been no investigation into how different personality traits affect the responses. The main hypothesis tested was that there is a relationship between personality traits and attitudes toward the dissection room. For the present study, a group of French medical students (n = 403; mean age 21.3 ± 1.6; 65.3% female) completed a "Big Five" personality inventory and a questionnaire to assess their attitudes in regard to human dissection. The findings are consistent with our hypothesis, in that we found a relationship between reporting anxiety and four of the "Big Five" dimensions (all except openness). The rated level of anxiety was positively correlated with negative affectivity, more strongly at the beginning than at the end of the course. There were significant gender differences in attitudes toward dissection. The findings are discussed in relation to the possibility of preparing students for the dissecting room experience and also in relation to the students' understanding of mortality issues.