麻豆区

Ariel Rokem, University of Washington
Cyril Pernet, Copenhagen University Hospital, Rigshospitalet
Guiomar Niso, Cajal Institute of the Spanish National Research Council
Yaroslav Halchenko, Dartmouth University
Robert Oostenveld, Donders Institute for Brain, Cognition and Behaviour

BIDS received the Prize for its longstanding community-driven approach to standardizing neuroimaging data, resulting in widespread adoption. BIDS has played a crucial role in numerous global data sharing initiatives, serving as a model for other standards. The Prize Selection Committee also commended the organization for its dedication to the broader neuroscience community and ongoing endeavors to extend BIDS to encompass additional data modalities.

Data from neuroimaging experiments can be arranged in many different ways, but in the absence of a standard, they are organized differently between institutions and even within a lab. This leads to misunderstandings and errors, as well as inefficient use of resources. In addition, it results in poor reproducibility even within the lab where data were collected. The听听addresses these challenges through a simple, easy-to-adopt way to organize neuroimaging data. BIDS is a community-led standard for organizing, describing and sharing neuroimaging data. In addition to a specification document which describes the standard, it includes applications and tools that make it easy for researchers to incorporate the standard into their current workflows, maximizing reproducibility, data sharing opportunities and supporting good scientific practices. The widespread adoption of BIDS has had major impact on research, with more than 300 contributors around the world, 100 centres and projects relying on the standard, MRI data from more than 45,000 individuals shared in BIDS format through various platforms, and more than 1,500 citations since 2016.听By removing barriers to data sharing, BIDS is enabling a plethora of projects that rely on open-source data around the world.

The Brain Tumour Segmentation Challenge for Sub-Saharan Africa - Maruf Adewole,听, Lagos, Nigeria

The Prize was awarded for Maruf Adewole鈥檚 significant leadership in initiatives aimed at enhancing research capacity in under-resourced communities and his multifaceted contributions to Open Science, spanning various methodologies and encompassing pillars such as open data sharing, large-scale collaborations, and training.

The Brain Tumour Segmentation (BraTS) Challenge has been running for more than a decade but has never featured data from underserved regions such as Sub-Saharan Africa (SSA). In collaboration with the Consortium for Advancement of MRI Education and Research in Africa (), Maruf Adewole led BraTS-SSA, the first open access magnetic resonance imaging (MRI) dataset of brain tumour cases from Sub-Saharan Africa. This project provided the opportunity to include SSA population in global efforts to create artificial intelligence tools to improve brain tumour detection and treatment planning. By aggregating and curating a diverse dataset from African diagnostics centers, it has provided researchers with a valuable resource to better understand the unique aspects of brain tumours in African populations. This dataset has the power to enhance diagnostic accuracy, treatment planning, and therapeutic outcomes, ultimately improving the quality of care for patients in Africa and other resource-constrained environments whose peculiarities mirrors Africa.

The Prize was awarded for Hao-Ting's numerous contributions to open-source software enabling open and reproducible neuroimaging, and her continuous involvement in both the local and global Open Science communities.

This project presents a new denoising benchmark for functional MRI data that can be repeatedly executed for users of the popular open preprocessing software fMRIPrep. This benchmark introduces the first denoising assessment of connectomes using a contemporary software framework. It boasts an open workflow, from dataset to software implementation. Notably, the project prioritizes the software's lifecycle and community benefits over individual authorship, exemplified by the incorporation into the existing, open, and widely used software library Nilearn, rather than creating a separate software package. With the aim of furnishing guidance to the fMRIprep user community and underscoring the significance of ongoing research method evaluation, this work lays the groundwork for a reproducible research infrastructure. The preprint published on the Neurolibre Preprint Service facilitates future continuous assessment and demonstrates the potential of Neurolibre鈥檚 applications to reproducible research. The benchmark project further served as prototypes of Brain Imaging Data Structure applications (BIDS-App) for generating machine-learning-ready time series and connectomes.

听is an online community of computational neuroscientists whose mission is to foster inclusive global interactions for learning, mentorship, networking, and professional development. Made up of both the Neuromatch Academy and Neuromatch Conference, the project provides open access educational resources and infrastructure to enable thousands of underrepresented scientists to break through barriers in their career paths.

Many scientists around the world cannot participate in scientific discourse due to barriers in financing, education, and closed networks among well-funded labs. Neuromatch reduces or removes these barriers by providing always-affordable, pay-what-you-can education, networking, and scientific communication opportunities. Through Neuromatch programs, new researchers can create a network of loose and strong ties, receive specialized and multidisciplinary scientific training, get experience in presenting scientific research, and be given the tools and experience to function as both collaborative and independent researchers.

Together, the Academy and Conference has served 20,000 participants from 105 different countries. The popularity of their programs is shown in their YouTube channels which have more than 10,000 subscribers and 68,000 hours of watch time. Courses at Stanford University, Harvard University, Queen鈥檚 University and others are using Neuromatch materials.

Only three years into her PhD in the lab of Bratislav Misic at The Neuro, Justine Hansen has already spearheaded two promising Open Science projects, which have opened new avenues regarding the organization of the human brain.

The current lack of open, whole-brain receptor distribution data prevents the study of how multiple neurotransmitter systems relate to brain function. To overcome this, Hansen compiled PET data from 1,238 healthy individuals to construct a whole-brain atlas of 19 receptors and transporters. She and an international team of researchers then mapped this receptor atlas to brain structure and function and published the results on听听which has been cited 25 times so far. She also made the data openly available on听.

Her second project, neuromaps, is a collaboration with researchers from The Neuro, the University of Pennsylvania, the Douglas Research Centre and the National Institute of Mental Health. It brings together more than forty existing brain maps in one place to help scientists find correlations between patterns across different brain regions, spatial scales, modalities and brain functions. It provides a standardized space to view each map in comparison to each other, and assesses the statistical significance of these comparisons, to help researchers distinguish meaningful correlations. This compiled dataset was made publicly available, as well as the software required to replicate these analyses. Since publishing neuromaps in听听the paper has been downloaded more than 2,100 times and its Twitter thread shared more than 100 times.

As a PhD candidate in the lab of Nikola Stikov affiliated with Montreal鈥檚 脡cole Polytechnique and the Montreal Heart Institute, Agah Karakuzu has helped develop four Open Science applications in brain imaging. The first, qMRI-BIDS, brings听听to quantitative MRI data. By keeping track of how the data were produced, qMRI-BIDS improves reproducibility and allows independent labs to reuse the data. Currently qMRI-BIDS provides open access to thousands of scientists around the globe for incorporating qMRI into their research.

qMRLab is an open-source toolbox for data simulation, analysis, and visualization to create the central component of a transparent qMRI workflow.

VENUS听is software that provides a workflow to process MRI data that can be shared openly regardless of the machine manufacturer. Karakuzu has received more than 13 invitations to present VENUS to researchers in various disciplines, two of them being keynotes.

The last project,听, is a powerful preprint server for reproducible Jupyter notebooks for neuroscience that seamlessly integrates data, code, runtime, text and figures. Upon acceptance, notebooks can be freely modified and re-executed through the web by anyone, offering a fully reproducible, 鈥渓ibre鈥� path from data to figures.

YCharOS is a collaboration between Peter McPherson鈥檚 lab at The Neuro and the Structural Genomics Consortium, itself a public-private partnership based in Europe, the United States and Canada.

Inconsistent antibody performance is a significant challenge for researchers and sits at the heart of the reproducibility crisis observed in biomedical research. To address this unmet need, YCharOS performs head-to-head comparisons of commercially available antibodies to the same target protein and publishes the results for use by scientists around the world.

鈥淭he results of this trusted open environment are rigorous antibody characterization data shared without restriction that can guide the research community to select the most appropriate antibodies for their needs,鈥� says McPherson. 鈥淲e believe our efforts will catalyze discoveries.鈥�

The project has now characterized 349 antibodies for 31 proteins linked to neurological disease. It has identified high-quality antibodies for each protein, which will be used to reveal their disease relevance.

McPherson says they will use the prize money to expand their antibody characterization platform to novel key antibody-based applications needed by the scientific community.

鈥淥ne important application is immunohistochemistry, which allows one to detect proteins in human tissues. The funds will help us to purchase new reagents and small equipment necessary for the initial optimization of the assay.鈥�

The International Trainee Prize winner, Fabian F. Voigt, is a postdoctoral researcher at Harvard University. He developed an open-source, light-sheet microscope with greater capabilities than commercially available models, called the 鈥渕esoSPIM鈥�. Light-sheet microscopes are used to image clear brain samples. This allows researchers to map neurons and their connections in whole mouse brains without having to slice the sample into thin sections. Interested researchers can download all the required parts lists, instructions and software to set up and operate such a microscope via mesospim.org. Since 2019, 14 such instruments have become operational across the globe and 16 peer-reviewed publications have been published with mesoSPIM data.

Voigt says he intends to use the funds to attend in-person conferences to spread the word about the project and listen to the needs of current and prospective mesoSPIM users.

鈥淒eveloping an instrumentation idea into a well-documented reproducible open hardware project is a long and painstaking process that takes many years,鈥� says Voigt. 鈥淎long the road, I received quite a few comments doubting that the project would succeed. It feels great to see the effort recognized in this way!鈥�

Sara Larivi猫re, the Canadian Trainee Prize winner, has been awarded for the ENIGMA Toolbox, a centralized, continuously updated repository of hundreds of meta-analytical neuroimaging datasets. ENIGMA includes data from a range of disorders as well as an efficient codebase to visualize, analyze, and relate any neuroimaging findings to multiple scales of brain organization.

The toolbox gives scientists the means and knowledge to explore molecular, histological, and network correlates of brain disorders, and aims to facilitate and homogenize advanced analyses of magnetic resonance imaging (MRI) datasets around the globe.

鈥淚 was pleasantly surprised,鈥� Lariviere says about learning she won. 鈥淚 know some of the other trainees who were competing for this award and they have all made fantastic contributions to Open Science, including high quality open access datasets and data processing pipelines which I frequently use myself. Knowing that I was competing against high-calibre candidates, I can't help but feel extremely grateful and honored to have received this award.鈥�