Stefano Moia

Hao-Ting Wang

Anibal S Heinsfeld

Dorota Jarecka

Yu-Fang Yang

Stephan Heunis

Michele Svanera

Benjamin De Leneer

Andrea Gondova

Sin Kim

Arshitha Basavaraj

Johanna M Bayer

Roza G Bayrak

Pierre-Louis Bazin

Isil Poyraz Bilgin

Steen Bollmann

Daniel Borek

Valentina Borghesani

Trang Cao

Gang Chen

Alejandro De

La Vega

Sebastian Dresbach

Philipp Ehses

Jan Ernsting

Inês Esteves

Oscar Ferrante

Kelly G Garner

Rémi Gau

Elodie Germani

Tara Ghafari

Satrajit S Ghosh

Sarah Elizabeth Goodale

Cassandra D Gould Van Praag

Samuel Guay

Omer Faruk Gulban

Yaroslav O Halchenko

Michael Hanke

Katja Heuer

Felix Hostaedter

Ruoqi Huang

Renzo Huber

Ole Jensen

Kan Keeratimahat

Julian Q Kosciessa

Sladjana Lukic

Neville Magielse

Christopher J Markiewicz

Caroline G Martin

Camille Maumet

Anna Menacher

Je Mentch

Christian Mönch

Shammi More

Leonardo Muller- Rodriguez

Samuel A Nastase

Eliana Nicolaisen- Sobesky

Dylan M Nielson

Christopher R Nolan

François Paugam

Pedro Pinheiro-Chagas

Ana Luísa Pinho

Alessandra Pizzuti

Benjamin Poldrack

Benedikt A Poser

Roberta Rocca

Jacob Sanz-Robinson

Kelvin Sarink

Kevin R Sitek

Nad- Ine Spychala

Rüdiger Stirnberg

Michaª Szczepanik

Mohammad Torabi

Roberto Roberto Toro

Sebastian Gw Urchs

Soe L Valk

Adina S Wagner

Laura K Waite

Alexander Q Waite

Lea Waller

Tyler James Wishard

Jianxiao Wu

Yuchen Zhou

Janine D Bijsterbosch

Ruth S Ammon

Proceedings of the OHBM Brainhack 2022

OHBM Brainhack 2022 took place in June 2022.

The rst hybrid OHBM hackathon, it had an inperson component taking place in Glasgow and three hubs around the globe to improve inclusivity and t as many timezones as possible. In the buzzing setting of the Queen Margaret Union and of the virtual platform, 23 projects were presented for development. Following are the reports of 14 of those, as well as a recapitulation of the organisation of the event.

Introduction

The Organisation of Human Brain Mapping Brain-Hack (shortened to OHBM Brainhack in the article) is a yearly satellite event of the main OHBM meet-ing, organised by the Open Science Special Interest Group following the model of Brainhack hackathons [START_REF] Gau | Brainhack: Developing a culture of open, inclusive, communitydriven neuroscience[END_REF]. Where other hackathons set up a competitive environment based on outperforming other participants' projects, Brainhacks fosters a collaborative environment in which participants can freely collaborate and exchange ideas within and between projects. This edition of the OHBM Brainhack, that ran across the world over four days, was particularly special for two reasons: it celebrated the tenth year anniversary of Brainhack, and, like the main OHBM conference, it was the rst edition to feature an in-person event after two years of virtual events. For this reasons, the whole organisation rotated around ve main principles:

1. Providing a hybrid event incorporating the positive aspects of in-person and virtual events alike, 2. Celebrating the 10 th anniversary of the Brain- hack by bringing back newcomer-friendly hands-on hacking and learning experience, enhancing the Hacktrack and formatting the Traintrack as a collection of materials to consult beforehand and as spontaneous meetings of the participants aimed to learn together, 3. Bridging the gap between the Brainhack community and the main neuroimaging software developer groups, e.g. AFNI, FSL, SPM, 4. Due to amount of work required to meet the previous three principles, incorporating from the beginning a team of core organisers with a democratic approach to organisation, with a member in charge of an aspect of the event, 5. Brainhack event organisation should always be experimental, trying dierent solutions and formats to nd a way to improve Brainhack events overall.

After a quick explanation of each main contribution of the core team, the next pages are dedicated to the summaries of the projects that were developed during the four days of hacking. [START_REF] Waller | ENIGMA HALFpipe: Interactive, reproducible, and ecient analysis for resting-state and task-based fMRI data[END_REF] Hacktrack Dorota Jarecka, Yu-Fang Yang, Hao-Ting Wang, Stefano Moia

The key component of each Brainhack is hacking.

The hacking part, known as hacktrack, is where attendees collaborate on projects and explore their own ideas. There are 4 elements of hacktrack that were organised: project submission, project pitch, hacking period and project summary. For the project submission, we used the GitHub issue submission process that was used during recent years. We updated and simplied a project template from previous years and asked project leaders to open an issue for each project. Each issue after quick check was approved by the moderators and automatic workows written by the team were responsible for sending project descriptions to the Brainhack page and setting Discord's channels. We received 38 projects that were submitted using this system. The project pitch was set for the morning of the rst day and everyone had 2 minutes to talk about the suggested project and possible collaborations. After the pitches people had a chance to talk to each other and join the projects they were interested in. This year, we tried to maximise the time for hacking by providing a sparse schedule for talks. The closing ceremony of the Brainhack featured 23 project reports, in which teams talked about their experiences and described the work they accomplished.

This edition we allowed remote attendance from other locations. We organised three hubs aiming to cover all time zones, including 1) Asia-Pacic, 2)

Glasgow, Europe, Middle East, and Africa, and 3) the Americas, to foster inclusiveness in the hybrid conference format. We also ensured that each hub had one live streamed session with the physical hub in Glasgow.

Traintrack

Yu-Fang Yang, Dorota Jarecka

Traintrack is the educational component of Brainhack events. The aim is to introduce tools and skills for attendees to start hacking. Unlike conventional scientic educational workshops centred around lectures and talks, data science skills are better learned through hands-on experience than lectures. With the Brainhack community growing mature, the community has developed their own curated educational material. Brainhack School has supplied high-quality content for independent study on a variety of themes.

This year, we combine the collaborative nature of brainhack projects and educational content to reimagine the format of traintack. Thus, we replaced tutorial lectures in the previous editions with curated online educational contents, released them prior to the main event, and attempted to integrate them with the hacktrack projects. This format also provides more time (i.e. schedule) and space (i.e.

minimising large space not used for hacking) for attendees to self-organise. Participants were encouraged to form study groups on ve suggested topics:

1) setting up your system for analysis 2) python for data analysis, 3) machine learning for neuroimaging, 4) version control systems, 5) cloud resource.

The curated content was advertised on the main hackathon website. One dedicated channel was created on the hackathon Discord server. Individuals could determine the nature of their experiences and the skills they liked to acquire. Participants could form their own study group and on any selected topic. We would like to continue the experimentation on this format in the coming year.

3 Platforms, website, and IT

Anibal Solon Heinsfeld

Trying to bring a positive experience for both virtual and in-person attendees, we implemented several integrated solutions to ease communication in the dierent phases of the Hackathon, focusing on a single platform for the main event.

The rst solution was the project's advertisement, in which the community promotes their projects, the goals for the Hackathon, and relevant information to get people interested and set to collaborate. To do so, we used the Github Issues feature in the Hackathon repository as the entrance for projects. Github Issues has been proven to be accepted by the community that relies on Github for code versioning, and was a successful approach in past hackathons.

In this edition, we were able to use Github Issue forms, a beta feature in Github. Past use of issues for project registration relies on Markdown code to specify which information the hacker needs to provide. However, the code can be easily broken and changed, which makes it harder to parse the information in automated setups. Towards this issue, the Issue Form can lower the barrier when submitting a project. By specifying form elds for the participants to ll, they faced a common web form instead of a Markdown editor, bringing more structure to their inputs and not requiring them to write code. After the organisers' quick validation, the project information was provided to the rest of the system. Per an automated pipeline, this information was compiled into the website.

The second solution was the central platform for These integrated solutions smoothed the organisation of the event, the virtual platform provided great support for the on-line participants. However, there was not a lot of interaction between in-person and online participants, and projects were mainly either virtual or in-person (with few exceptions). This is probably because hybrid hacking provides challenges for organisation and attendants alike, even just in the physical limitations of being able to have a video conference with a split team. It is important to consider, however, that this was also the rst in-person event for many participants, who preferred in-person interaction and collaboration rather than the same on-line interaction that characterised such events in the previous two years.

Project Reports

In total, 23 projects were presented at the Brainhack, of which 14 submitted a written report.

4. [START_REF] Cox | AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages[END_REF]). Tissue segmentation was restricted to S1 and was obtained by combining four approaches: (i) fsl-fast [START_REF] Smith | Advances in functional and structural MR image analysis and implementation as FSL[END_REF] for initial tissues probability map, (ii) semiautomatic histogram tting in ITK-SNAP, (iii) Segmentator [START_REF] Gulban | A scalable method to improve gray matter segmentation at ultra high eld MRI[END_REF], and (iv) manual editing. We used the LN2_LAYERS program from LAYNII open source software (Huber, Poser et al., 2021) to compute the equi-volume cortical depth measurements for the gray matter. Finally, we evaluated cortical depth proles for three quantitative MRI contrasts (R1, R2, proton density) and three microscopy contrasts (thionin, bieloschowsky, parvalbumin) by computing a voxel-wise 2D histogram of image intensity (Figure 1C). Some challenges are indicated by arrows 2 and 3 in the lower part of Figure 1C.

From this Brainhack project, we conclude that the richness of the data set must be exploited from multiple points of view, from enhancing the integration of MRI with microscopy data in visualization software to providing optimized multi-contrast and multi-modality data analysis pipeline for highresolution brain regions.

DataLad Catalog

Stephan Heunis, Adina S. Wagner, Alexander Q. Waite, Benjamin Poldrack, Christian Mönch, Julian Kosciessa, Laura Waite, Leonardo Muller-Rodriguez, Michael Hanke, Michaª Szczepanik, Remi Gau, Yaroslav O. Halchenko

The importance and benets of making research data Findable, Accessible, Interoperable, and Reusable are clear [START_REF] Wilkinson | The FAIR Guiding Principles for scientic data management and stewardship[END_REF]. But of equal importance is our ethical and legal obligations to protect the personal data privacy of research participants. So we are struck with this apparent contradiction: how can we share our data openly. . . yet keep it secure and protected?

To address this challenge: structured, linked, and machine-readable metadata presents a powerful opportunity. Metadata provides not only high-level information about our research data (such as study and data acquisition parameters) but also the descriptive aspects of each le in the dataset: such as le paths, sizes, and formats. With this metadata, we can create an abstract representation of the full dataset that is separate from the actual data content. This means that the content can be stored securely, while we openly share the metadata to make our work more FAIR.

In practice, the distributed data management system DataLad [START_REF] Halchenko | Datalad: Distributed system for joint management of code, data, and their relationship[END_REF] As a result of this hackathon project, a compilation of our progress (Figure 3) can be seen at https: //youtu.be/_5ZDctWv5X4 as a video. Our future eorts will involve sophisticating the particle simulations, the initial simulation parameters to generate further variations of the visual eects, and potentially synchronizing the simulation eects with musical beats.

Method

The hippocampus is a structure commonly associated with memory function and dementia, and the left hippocampus is proposed to have higher discriminative power for identifying the progression of Alzheimer's disease than the right hippocampus in multiple studies [START_REF] Schu | Mri of hippocampal volume loss in early alzheimer's disease in relation to apoe genotype and biomarkers[END_REF]. We obtained left hippocampal volumes using three widely-used

Results

We found that for The FSL-FreeSurfer and FSL- Thanks to eorts of the neuroimaging community, not least the brainhack community [START_REF] Gau | Brainhack: Developing a culture of open, inclusive, communitydriven neuroscience[END_REF], datasets are increasingly shared on open data repositories like OpenNeuro (Markiewicz et al., 2021) using standards like BIDS [START_REF] Gorgolewski | The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments[END_REF] for interoperability. As the amount of datasets and data repositories increases, we need to nd better ways to search across them for samples that t our research questions. In the same way that the wide adoption of BIDS makes data sharing and tool development easier, the wide adoption of consistent vocabulary for demographic, clinical and other sample metadata would make data search and integration easier. We imagine a future platform that allows cross dataset search and the pooling of data across studies. Eorts to establish such metadata standards have had some success in other communities [START_REF] Field | The minimum information about a genome sequence (MIGS) specication[END_REF][START_REF] Stang | Advancing the science for active surveillance: Rationale and design for the observational medical outcomes partnership[END_REF], but adoption in the neuroscience community so far has been slow. We have used the space of the brainhack to discuss which challenges are hindering wide adoption of metadata standards in the neuroimaging community and what could be done to accelerate it.

We believe that an important social challenge for the wider adoption of metadata standards is that it is hard to demonstrate their value without a practical use case. We therefore think that rather than focusing on building better standards, in the short term we need to prioritize small, but functional demonstrations that help convey the value of these standards and focus on usability and ease of adoption. Having consistent names and format for even a few metadata variables like age, sex, and diagnosis already allows for interoperability and search across datasets. Selecting a single vocabulary that must be used for annotating e.g. diagnosis necessarily lacks some precision but avoids the need to align slightly dierent versions of the same terms. Accessible tools can be built to facilitate the annotation process of such a basic metadata standard. The best standard will be poorly adopted if there are no easy to use tools that implement it. Eorts like the neurobagel project (neurobagel.org/) are trying to implement this approach to demonstrate a simple working use case for cross dataset integration and search. Our goal is to use such simpler demonstrations to build awareness and create a community around the goal of consistent metadata adoption.

Our long term goal is to use the awareness of the value of shared metadata standards to build a community to curate the vocabularies used for annotation. The initially small number of metadata variables will have to be iteratively extended through a community driven process to determine what elds should be standardized to serve concrete use cases.

Rather than creating new vocabularies the goal should be to curate a list of existing ones that can be contributed to where terms are inaccurate or missing. The overall goal of such a community should be to build consensus on and maintain shared standards for the annotation of neuroimaging metadata that support search and integration of data for an ever more reproducible and generalizable neuroscience.

The NARPS Open Pipelines Project

Elodie Germani, Arshitha Basavaraj, Trang Cao, Rémi Gau, Anna Menacher, Camille Maumet

The goal of the NARPS Open Pipelines Project All resulting statistic maps were shared on Neur-oVault [START_REF] Gorgolewski | NeuroVault.org: A web-based repository for collecting and sharing unthresholded statistical maps of the human brain[END_REF] and can be used With these improvements, we hope that it will be easier for new people to contribute to reproduction of new pipelines. We hope to continue growing the codebase in the future. [START_REF] Wilkinson | The FAIR Guiding Principles for scientic data management and stewardship[END_REF], standards [START_REF] Gorgolewski | The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments[END_REF], and ontologies [START_REF] Poldrack | The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience[END_REF], as well as practices of metaanalysis [START_REF] Dockès | NeuroQuery, comprehensive meta-analysis of human brain mapping[END_REF][START_REF] Yarkoni | Large-scale automated synthesis of human functional neuroimaging data[END_REF] and data sharing [START_REF] Gorgolewski | NeuroVault.org: A web-based repository for collecting and sharing unthresholded statistical maps of the human brain[END_REF]. However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specic brain regions and given be- Building on what we have already achieved, we will continue to promote and document best practices.

haviors
Further development is ongoing and anyone that is interested in physiological signal collection for f/MRI data, independently of their level and type of expertise, is highly encouraged to check Physiopy out, to join the community, or to contribute in any way. A more ecient approach to handling multiplicity focuses on the calibration of eect estimation under a Bayesian multilevel modeling framework with a prior assumption of, for example, normality across space [START_REF] Chen | Handling Multiplicity in Neuroimaging through Bayesian Lenses with Multilevel Modeling[END_REF]. The methodology has previously been implemented at the region level into the AFNI program RBA [START_REF] Chen | Sources of Information Waste in Neuroimaging: Mishandling Structures, Thinking Dichotomously, and Over-Reducing Data[END_REF] using Stan through the R package brms [START_REF] Bürkner | Brms: An R Package for Bayesian Multilevel Models Using Stan[END_REF].

We intend to achieve two goals in this project:

(i) To re-implement the methodology using PyMC improve the performance and exibility of the modeling approach.

(ii) To explore the possibility of analyzing voxellevel data using the multilevel modeling approach 4.13.2 Implementation using PyMC

We used the dataset from [START_REF] Chen | Handling Multiplicity in Neuroimaging through Bayesian Lenses with Multilevel Modeling[END_REF] to validate our PyMC implementation. The data contain the subject-level response variable y and a predictor of the behavioral measure x from S = 124 subjects at R = 21 regions. The modeling framework is formulated for the data y rs of the sth subject at the rth region as below, y rs ∼ N (µ rs , σ 2 )

µ rs = α 0 + α 1 x s + θ 0r + θ 1r x s + η s θ 0r θ 1r ∼ N (0 2×1 , S 2×2 ) η s ∼ N (0, τ 2 )
where r = 1, 2, . . . , R and s = 1, 2, . . . , S

In the model, µ rs and σ are the mean eect and standard deviation of the sth subject at the rth region, α 0 and α 1 are the overall mean and slope eect across all regions and subjects, θ 0r and θ 1r are the mean and slope eect at the rth region, η s is the mean eect of the sth subject, S 2×2 is the variance-covariance of the mean and slope eect at the rth region, and τ is the standard deviation of the sth subject's eect η s . We implemented this model using the PyMC probabilistic programming framework [START_REF] Salvatier | Probabilistic programming in python using PyMC3[END_REF], and the BAyesian Model-Building Interface (BAMBI) [START_REF] Capretto | Bambi: A simple interface for tting bayesian linear models in python[END_REF]. The latter is a high-level interface that allows for specication of multilevel models using the formula notation that is also adopted by brms. A notebook describing the implementation is available here. Our PyMC implementation was successfully validated: as shown in Figure 7a and Figure 7b, the posterior distributions from the PyMC implementation matched very well with their counterparts from the brms output.

Extension of Bayesian multilevel modeling to voxel-level analysis

After exploring the model on the region level, we wanted to see if recent computational and algorithmic advances allow us to employ the multilevel modeling framework on the voxel level as well. We obtained the OpenNeuro dataset ds000117 [START_REF] Wakeman | A multi-subject, multi-modal human neuroimaging dataset[END_REF] from an experiment based on a face processing paradigm.

Using HALFpipe [START_REF] Waller | ENIGMA HALFpipe: Interactive, reproducible, and ecient analysis for resting-state and task-based fMRI data[END_REF], which is based on fMRIPrep [START_REF] Esteban | fMRIPrep: A robust preprocessing pipeline for functional MRI[END_REF], the functional images were preprocessed with default settings and z -statistic images were calculated for the contrast famous faces + unfamiliar faces versus 2

• scrambled faces.

We applied the same modeling framework and PyMC code as for region-based analysis, but without the explanatory varaiable x in the model (Equation ( 1)). To reduce computational and memory complexity, the z -statistic images were downsampled to an isotropic resolution of 5mm Approaching the organisation of an event as an experiment allows incredible freedom and dynamicity, albeit knowing that there will be risks and venues of improvement for the future.

The organisation managed to provide a positive onsite environment, aiming to allow participants to self-organise in the spirit of the Brainhack [START_REF] Gau | Brainhack: Developing a culture of open, inclusive, communitydriven neuroscience[END_REF], with plenty of moral -and physicalsupport.

The technical setup, based on heavy automatisation ow to allow project submission to be streamlined, was a fundamental help to the organisation team, that would benet even more from the improvement of such automatisation ows.

This year, representatives of AFNI, FSL, and SPM (among the major neuroscience software developers) took part in the event, and their presence was appreciated both by other participants and themselves. In the future, connecting to more developers, not only from the MRI community, might improve the quality of the Brainhack even more.

The most challenging element of the organisation was setting up an hybrid event. While this element did not go as smoothly as it could have, this ex-perimental setup seemed to have worked, allowing the participation of about 70 participants online.

However, there is still a lot to improve for a truly hybrid event. For instance, it is important to allow spaces (both in time and space) for participants onsite to interact with online participants, and more attention, time, volunteers, and equipment has to be put to achieve a smooth online participation.

For this reason, the Open Science Special Interest Overall this HBM Brainhack was a successful outcome for the organisation team experiment, and we hope that our ndings will be helpful to future Brainhack events organisations.

  real-time communication, namely Discord. For the rst time using the platform for an OHBM Hackathon, Discord showed potential in bringing an allin-one solution. Its track record with dierent communities and their formats was an essential prospect for the success of a hybrid hackathon, together with the dierent ways of communicating provided by the platform. Specically, Discord oered chat and audio/video channels, with ne-tuned controls on permissions to see a channel, speak and use the camera, and send messages. With these features, we were able to create experiences for the attendants, such as text channels for consolidating information about the hackathon, main stages controlled by the hub hosts, a channel to join projects and hubs, and integrated text & voice channels for each project. The main stage was connected to a laptop in the venue, providing synchronous streaming for announcements, project pitches and progress reports for those participating virtually. The project channels were automatically created together with the Github Issues. However, given the thriving number of projects, the Discord server was replete with project channels. Such a scenario was overwhelming for the attendants, especially for those approaching Discord for the rst time. To ameliorate this issue, a main projects channel was created, so attendants could automatically join projects via related emoji reactions. The project channels were of public access; however, only displayed upon joining the project. Besides initial technical hiccups, the platform proved a good alternative for such an event format.

Figure 1 :

 1 Figure 1: A) Neuroglancer visualization, B) section query notebook, C) Cortical ROI and corresponding depth histograms extracted from the dierent contrasts available.

4. 2

 2 Figure 2: A team of brainhack volunteers, applied for Oracle Cloud Credits to support open source projects in and around brainhack with powerful cloud resources on the Oracle Cloud: https://brainhack.org/brainhack_cloud/

  line and Python API by the datalad-dataverse extension, and standard research software engineering aspects of scientic software such as unit tests, continuous integration, and documentation. From a research data management and user perspective, this development equips DataLad users with the ability to programatically create Dataverse datasets (containers for research data and their metadata on Dataverse) from DataLad datasets (DataLad's Git-repository-based core data structure) in dierent usage modes. Subsequently, DataLad dataset contents, its version history, or both can be published to the Dataverse dataset via a `datalad push' command. Furthermore, published DataLad datasets can be consumed from Dataverse with a datalad clone call. A mode parameter congures whether Git version history, version controlled le content, or both are published and determines which of several representations the Dataverse dataset takes. A proof-of-principle implementation for metadata annotation allows users to supply metadata in JSON format, but does not obstruct later or additional manual metadata annota-tion via Dataverse's web interface. Overall, this project delivered the groundwork for further extending and streamlining data deposition and consumption in the DataLad ecosystem. With DataLad-Dataverse interoperability, users gain easy additional means for data publication, archival, distribution, and retrieval. Post-Brainhack development aims to mature the current alpha version of the software into an initial v0.1 release and distribute it via standard Python package indices.

Figure 3 :

 3 Figure 3: A video compilation of brain explosions can be seen at https://youtu.be/_5ZDctWv5X4.

  neuroimaging pipelines: FSL 5.0.9[START_REF] Patenaude | A bayesian model of shape and appearance for subcortical brain segmentation[END_REF], FreeSurfer 6.0.0(Fischl, 2012), and ASHS 2.0.0 PMC-T1 atlas[START_REF] Xie | Automated segmentation of medial temporal lobe subregions on in vivo t1-weighted mri in early stages of alzheimer's disease[END_REF]. We ran the three pipelines on T1 images from 15 subjects from the Prevent-AD Alzheimer's dataset (Tremblay-Mercier et al., 2021), composed of cognitively healthy participants between the ages of 55-88 years old that are at risk of developing Alzheimer's Disease. We ran MRIQC (Esteban et al., 2017) -a tool for performing automatic quality control and extracting quality measures from MRI scans -on the 15 T1 scans and obtained Image Quality Metrics (IQMs) from them.We then found the correlations between the IQMs and the pairwise inter-pipeline discrepancy of the left hippocampal volumes for each T1 scan.

Figure 4 :

 4 Figure 4: Plots showing the association between left hippocampal volume discrepancies and MRIQC's EFC quality measure for each of the pipeline pairings.

  is to provide a public codebase that reproduces the 70 pipelines chosen by the 70 teams of the NARPS study[START_REF] Botvinik-Nezer | Variability in the analysis of a single neuroimaging dataset by many teams[END_REF]. The project is public and the code hosted on GitHub at https:// github.com/Inria-Empenn/narps_open_pipelines. This project initially emerged from the idea of creating an open repository of fMRI data analysis pipelines (as used by researchers in the eld) with the broader goal to study and better understand the impact of analytical variability. NARPS a manyanalyst study in which 70 research teams were asked to analyze the same fMRI dataset with their favorite pipeline was identied as an ideal usecase as it provides a large array of pipelines created by dierent labs. In addition, all teams in NARPS provided extensive (textual) description of their pipelines using the COBIDAS[START_REF] Nichols | Best practices in data analysis and sharing in neuroimaging using MRI[END_REF] guidelines.

  to assess the success of the reproductions. At the OHBM Brainhack 2022, our goal was to improve the accessibility and reusability of the database, to facilitate new contributions and to reproduce more pipelines. We focused our eorts on the rst two goals. By trying to install the computing environment of the database, contributors provided feedback on the instructions and on specic issues they faced during the installation. Two major improvements were made for the download of the necessary data: the original fMRI dataset and the original results (statistic maps stored in Neur-oVault) were added as submodules to the GitHub repository. Finally, propositions were made to facilitate contributions: the possibility to use of the Girae toolbox (van Mourik, n.d.) for contributors that are not familiar with NiPype (Gorgolewski, 2017) and the creation of a standard template to reproduce a new pipeline.

  or functions is disputed[START_REF] Siddiqi | Causal mapping of human brain function[END_REF][START_REF] Weber | Functional neuroimaging can support causal claims about brain function[END_REF]. On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain[START_REF] Price | The evolution of cognitive models: From neuropsychology to neuroimaging and back[END_REF]. Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (Larivière et al., /neurocausal.github.io/), we aim to build an online platform and community that allows open sharing, storage, and synthesis of clinical (meta) data crucial for the development of modern, transdiagnostic, accessible, and replicable (i.e., FAIR: Findability, Accessibility, Interoperability, and Reusability) neuropsychology. The project is organized into two infrastructural stages: rst, published peer-reviewed papers will be scrapped to collect already available (meta)data; second, our platform will allow direct uploading of clinical (de-identied) brain maps and their corresponding metadata. The meta-analysis pipeline developed for the rst stage of the project is inspired by and built upon the functionalities of NeuroQuery (Dockès et al., 2020), a successful large-scale neuroimaging meta-analytic platform. The rst stage is the development of the code base allowing (1) downloading and ltering of neuropsychological papers, (2) extraction of reported brain lesion locations and their conversion into a common reference space (3) extraction of clinical and behavioral symptoms and their translation into a common annotation scheme, (4) learning the causal mapping between the neural and neuropsychological information gathered. The second stage of the study aims at creating an online platform that allows for the direct uploading of clinical brain maps and their corresponding metadata. The platform will provide a basic automated preprocessing and a data-quality check pipeline, ensuring that all the ethical norms regarding patient privacy are met. The platform will automatically extract and synthesize key data to ultimately create probabilistic maps synthesizing transdiagnostic information on symptom-structure mapping, which will be dynamically updated as more data are gathered. The nature of the project requires expertise in dierent elds (from clinical neuroscience to computer science) in order to overcome both technical and theoretical challenges. The OHBM Brainhack 2022 gave us the opportunity to set the rst stones. In small subteams, we worked on developing three key building blocks: (1) the input ltering pipeline to ensure the downloaded papers are neuropsychological in nature and oer causal symptomstructure mapping; (2) the extraction of key terms occurrences in the text as to assess which neural space is reported (as they will have to be converted to a common one), (3) the curation of clinical ontology mapping specic neuropsychological batteries and tasks to the cognitive term(s) they touch upon.As we keep tackling our roadmap (Figure1), we believe our eorts will help promote open science practices in clinical neuroscience to the benet of both the neuroscientic and the clinical communities.

Figure 5 :

 5 Figure 5: NeuroCausal: The future of neuropsychology, i.e. brain lesions-symptom mapping, will be transdiagnostic, open, and FAIR: we set out to provide the eld with an open-source platform fostering storage, sharing, synthesis, and metaanalysis of clinical data.

Figure 6 :

 6 Figure 6: Left: Current version of the documentation homepage; Right: Physiopy Contributors

Figure 7 :

 7 Figure 7: Validation of implementation using PyMC. (A) Posterior distributions of region-level behavior eects using brms. (B) Posterior distributions of region-level behavior eects using PyMC. (C) Posterior probabilities of the voxel-level eects being positive or negative, obtained using PyMC (plotted using Nilearn and overlaid in green with the NeuroQuery (Dockès et al., 2020) map for the term emotional faces).

Figure 8 :

 8 Figure 8: Previously, most VASO sequences provided unsorted image series of MRI contrasts. This was not BIDS compatible and could suffer from gradient non-linearity artifacts in the scanner's MR-reconstruction pipeline. In Brainhack 2022, we adapted the SIEMENS reconstruction and to sort volume series by fMRI contrasts. This is BIDS compatible and does not require non-linearity corrections.

  Group instituted a position to have a dedicated person for the hybridisation process. The other challenge was to welcome newcomers into this heavily project-development-oriented event. While newcomers managed to collaborate with projects and self-organise to learn open science related skills, this integration of pre-event train track and beginner friendly process will benet from more attention.

  In the previous Brainhack[START_REF] Gau | Brainhack: Developing a culture of open, inclusive, communitydriven neuroscience[END_REF], we extended the existing 3D-MOSAIC functor that was previously developed by Benedikt Poser and Philipp Ehses. This functor had been previously used to sort volumes of images by dimensions of echo-times, by RF-channels, and by magnitude and phase signals. In this Brainhack, we successfully extended and validated this functor to also support the dimensionality of SETs (that is representing BOLD and VASO contrast).We are happy to share the compiled SIEMENS ICE (Image Calculation Environment) functor that does this sorting. Current VASO users, who want to upgrade their reconstruction pipeline to get the MOSAIC sorting feature too, can reach out to Renzo Huber (RenzoHuber@gmail.com) or Rüdiger Stirnberg (Ruediger.Stirnberg@dzne.de).

		The	'raw'	NIfTI	converted	time-
		series	are	not	BIDS	compatible	(see
		https://github.com/bids-standard/bids-
		specication/issues/1001).
	. Us-	The order of odd and even BOLD and VASO
	ing the GPU-based nuts_numpyro sampler (Phan,	image TRs is unprincipled, making the order-
	Pradhan & Jankowiak, 2019) with default settings,	ing dependent on the specic implementation
	we were able to draw 2,000 posterior samples of	of NIfTI converters.
	the mean eect parameter for each of the 14,752 voxels. Sampling four chains took 23 minutes on four Nvidia Tesla V100 GPUs. The resulting posterior probabilities are shown in Figure Figure 7c overlaid with the meta-analytic	Workarounds with 3D distortion correction, res-ults in interpolation artifacts. Alternative work-arounds without MOSAIC decorators result in un-necessarily large data sizes.
	map for the term emotional faces obtained from
	NeuroQuery (Dockès et al., 2020). The posterior
	probability map is consistent with meta-analytic
	results, showing strong statistical evidence in visual
	cortex and amygdala voxels. The posterior prob-
	ability maps also reveal numerous other clusters
	of strong statistical evidence for both positive and
	negative eects.
	This implementation extension shows that large
	multilevel models are approaching feasibility, sug-
	gesting an exciting new avenue for statistical ana-
	lysis of neuroimaging data. Next steps will be to
	investigate how to interpret and report these pos-
	terior maps, and to try more complex models that
	include additional model terms.
		Furthermore, Remi Gau, generated a tem-
		plate dataset that exemplies how one could to
		store layer-fMRI VASO data. This includes all
		the meta data for `raw' and `derivatives'. Link
	4.14 MOSAIC for VASO fMRI	to this VASO fMRI BIDS demo: https://gin.g-node.org/RemiGau/ds003216/src/bids_demo.
	Renzo (Laurentius) Huber, Remi Gau, Rüdiger Stirnberg,
	Philipp Ehses, Ömer Faruk Gülban, Benedikt A. Poser
	Vascular Space Occupancy (VASO) is a func-
	tional magnetic resonance imaging (fMRI) method
	that is used for high-resolution cortical layer-
	specic imaging (Huber, Finn et al., 2021). Cur-
	rently, the most popular sequence for VASO at
	modern SIEMENS scanners is the one by Stirnberg
	and Stöcker (2021) from the DZNE in Bonn, which
	is employed at more than 30 research labs world-
	wide. This sequence concomitantly acquires fMRI
	BOLD and blood volume signals. In the SIEMENS'
	reconstruction pipeline, these two complementary
	fMRI contrasts are mixed together within the same
	time series, making the outputs counter-intuitive
	for users. Specically:

Acknowledgments : The authors would like to thank Eric Earl, Samuel Guay, Jerome Dockès, Bertrand Thirion, Jean Baptiste Poline, Yaroslav Halchenko, Sara El-Gebali and the whole Open Life Science team for their help and support. Acknowledgements Computation has been performed on the HPC for Research cluster of the Berlin Institute of Health. Acknowledgements: We thank Chris Rodgers for instructions on how to overwrite existing reconstruction binaries on the SIEMENS scanner without rebooting. We thank David Feinberg, Alex Beckett and Samantha Ma for helping in testing the new reconstruction binaries at the Feinbergatron scanner in Berkeley via remote scanning. We thank Maastricht University Faculty of Psychology and Neuroscience for supporting this project with 2.5 hours of 'development scan time'.