Open science: A journey from sharing research artefacts to collaborative research

International audienc

On the benefits of self-taught learning for brain decoding

We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. We show that such a self-taught learning process always improves the performance of the classifiers but the magnitude of the benefits strongly depends on the number of data available both for pre-training and finetuning the models and on the complexity of the targeted downstream task

Reviewing neuroimaging flexibility: Components and records of provenance

International audienc

On the validity of fMRI studies with subject-level data processed through different pipelines

In recent years, the lack of reproducibility of research findings has become an important source of concerns in many scientific fields, including functional Magnetic Resonance Imaging (fMRI). The low statistical power often observed in fMRI studies was identified as one of the leading causes of irreproducibility. The development of data sharing opens up new opportunities to achieve larger sample sizes by reusing existing data. FMRI studies proceed by first preparing subject-level data using a given analysis pipeline and then combining those into a group analysis. Historically the subject-level analysis pipeline was identical for all subjects. As practices evolve towards more data reuse, researchers might want to directly combine subject-level data thata were processed using different pipelines. Here, we investigate the impact of combining subject-level data processed with different pipelines in between-group fMRI studies. We used the HCP Young-Adult dataset (N=1,080 participants) processed with 24 different pipelines. We then performed between-group analyses comparing subject data processed with different pipelines. We worked under the null hypothesis of no differences between subjects and compared the estimated false-positive rates obtained with the nominal rates. We showed that the analytical variability induced by the parameters explored in this dataset increases the false positive rates of studies combining data from different pipelines. We conclude that different processed subject data cannot be combined without taking into account the processing applied on these data

Exploring fMRI Results Space : 31 Variants of an fMRI Analysis in AFNI, FSL, and SPM

Data sharing is becoming a priority in functional Magnetic Resonance Imaging (fMRI) research, but the lack of a standard format for shared data is an obstacle (Poline et al., 2012; Poldrack and Gorgolewski, 2014). This is especially true for information about data provenance, including auxiliary information such as participant characteristics and task descriptions. The three most commonly used analysis software packages [AFNI1 (Cox, 1996), FSL2 (Jenkinson et al., 2012), and SPM3 (Penny et al., 2011)] broadly conduct the same analysis, but differ in how fundamental concepts are described, and have a myriad of differences in the pre-processing and modeling steps. The practical consequence is that sharing analyzed data is further complicated by the idiosyncrasies of the particular software used

Anatomie du tractus cortico-spinal en tractographie : évaluation d'une méthode déterministe

Introduction : Si la substance grise a été largement étudiée en IRM fonctionnelle (IRMf), l'étude in vivo des tractus de substance blanche est plus récente. L'IRM en tenseur de diffusion permet désormais d'étudier son anatomie grâce à la tractographie. Notre objectif était l'étude du tractus cortico-spinal (TCS) en tenseur de diffusion et en tractographie chez des sujets sains. Matériel et méthodes : La population concernait 15 volontaires sains droitiers. Une IRM 3T anatomique T1 a permis la détermination des régions d'intérêts (ROI) au niveau du mésencéphale. L'IRMf a été analysée par le logiciel SPM5 afin d'obtenir une carte d'activation représentant l'activation motrice de la main au niveau du cortex moteur. L'IRM de diffusion a servi à reconstruire un tenseur (matrice 3x3) en chaque voxel de l'image. Après recalage des 3 séquences, nous avons effectué une tractographie du TCS par une méthode déterministe utilisant l'algorithme (Mori et al). Les tractographies ont été réalisées entre les deux ROI de chaque côté. Résultat : Cette méthode donne une représentation anatomique du TSC méconnaissent la partie ventro-latérale de la ROI fonctionnelle. Cette partie correspond aux croisements de fibres des autres faisceaux de fibres blanches traversant la région. Conclusion : La limite principale du tenseur se situe au niveau des croisements des fibres, car il ne représente correctement qu'une seule direction de diffusion. Cela ne permet pas actuellement de retrouver l'anatomie des faisceaux de fibres telle que nous la connaissons pas les dissections. Les méthodes déterministes mono-directionnelles ne sont pas suffisantes notamment dans le contexte de la chirurgie guidée par l'image. Elles doivent être enrichies de méthodes multidirectionnelles en utilisant des algorithmes plus complexes

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

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.Development of the BIDS Standard has been supported by the International Neuroinformatics Coordinating Facility, Laura and John Arnold Foundation, National Institutes of Health (R24MH114705, R24MH117179, R01MH126699, R24MH117295, P41EB019936, ZIAMH002977, R01MH109682, RF1MH126700, R01EB020740), National Science Foundation (OAC-1760950, BCS-1734853, CRCNS-1429999, CRCNS-1912266), Novo Nordisk Fonden (NNF20OC0063277), French National Research Agency (ANR-19-DATA-0023, ANR 19-DATA-0021), Digital Europe TEF-Health (101100700), EU H2020 Virtual Brain Cloud (826421), Human Brain Project (SGA2 785907, SGA3 945539), European Research Council (Consolidator 683049), German Research Foundation (SFB 1436/425899996), SFB 1315/327654276, SFB 936/178316478, SFB-TRR 295/424778381), SPP Computational Connectomics (RI 2073/6-1, RI 2073/10-2, RI 2073/9-1), European Innovation Council PHRASE Horizon (101058240), Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, ERAPerMed Pattern-Cog, and the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud.N