brainlife

#digitalbrainproject #neuroscience #artificialintelligence #openscience #callforprojects #research #ai #brain | Fondation Adolphe de Rothschild

The Rothschild Foundation Hospital is excited to launch the Digital Brain Project, a $5M collaborative research initiative to build an open source foundational model of the brain through open-access neural data.  Apply by May 15th for up to $500k in funding. Help crack the neural code 👉digitalbrainproject.org  Our objective is to develop a standardized, publicly available dataset of human brain recordings to accelerate fundamental research in cognitive neuroscience, AI system development, and clinical health. Our independent, multidisciplinary Scientific Committee oversees data collection, ethical review, privacy, and open science practices of our project — setting standards for large-scale brain research. The Digital Brain Project is conceived as an open collaborative infrastructure, at the intersection of neuroscience, artificial intelligence, and data science. It aims to accelerate data development, a key bottleneck to developing a foundational AI model of the human brain.  Key Focus Areas: 1️⃣ Brain recording data related to visual processing, language functions and cognitive actions 2️⃣ Rigorous quality, privacy, and ethical control, along with procedures for standardizing data across sites 3️⃣ Support for teams selected by an international multidisciplinary Scientific Committee Call for applications: Research teams working in neuroscience, neuroimaging, or artificial intelligence are invited to submit their applications by May 15, 2026. Selected teams will receive up to $500k USD funding to support their work. We appreciate our Scientific Committee (Lune Bellec Anne-Marie Kermarrec Arthur Mensch Russell Poldrack Lucia Melloni Jose Alain Sahel) for their independent scientific leadership, and partners Université de Montréal and AI at Meta for collaborating on this exciting project. #DigitalBrainProject #Neuroscience #ArtificialIntelligence #OpenScience #Callforprojects #Research #AI #Brain --------------------------------------------------------------  Pierre Bourdillon Julie Boyle Elisa Cascardi Jean-Rémi King Guillaume Le Hénanff Amélie Yavchitz Julien Gottsmann  Fabrice VERRIELE  Charlotte Cardin-Taillia

Data Hosting & Sharing: The Open MEG Archive (OMEGA)

Welcome to the Open MEG Archive (OMEGA) The Open MEG Archive (OMEGA) is the fruit of a collaboration between the McConnell Brain Imaging Centre (BIC) of the Montreal Neurological Institute-Hospital

DANDI

DANDI: Distributed Archives for Neurophysiology Data Integration The DANDI platform is supported by the BRAIN Initiative for publishing, sharing, and processing neurophysiology data. The archive accepts cellular neurophysiology data including electrophysiology, optophysiology, and behavioral time-series, and images from immunostaining experiments. The platform is now available for data upload and distribution. The storage of data in the archive is also supported by the Amazon Opendata program. The data in the archive can be browsed using the Data Portal. For detailed instructions on how to interact (view, upload, download, process) with DANDI click here.

The brain in the machine: How AI could help explain how we think | IBM

Scientists are using large AI models to predict patterns of brain activity at scale, a development that researchers say is pushing neuroscience toward a new kind of digital imaging.

Project Overview ‹ NeuroDatasets Tool – MIT Media Lab

NeuroDatasets is tool, where one can browse and find all available neuroscience datasets for all major modalities: EEG, MRI, MEG, NIRS, PET and more.  &nb…

GitHub - AIM-KannLab/BrainIAC | Giovanni Giulietti

One of the biggest hurdles in neuroimaging AI has always been the scarcity of labeled data, especially for rare conditions. But a recent study published in Nature Neuroscience introduces a potential solution: BrainIAC (Brain Imaging Adaptive Core). Unlike traditional models that are trained for one specific task, BrainIAC is a "foundation model", a large-scale AI model trained on vast amounts of data that can perform many different tasks and be adapted to specific applications. It was pretrained on a massive dataset of 48965 brain structural MRI sequences (T1w, T2w, FLAIR, T1CE) using self-supervised learning, allowing it to understand the "big picture" of brain anatomy across different ages and conditions. It has been tested across seven diverse and clinically meaningful applications:  - brain age prediction - IDH mutation classification - Mild Cognitive Impairment (MCI) classification - diffuse glioma overall survival prediction - MR sequence classification - time-to-stroke prediction - tumor segmentation  BrainIAC consistently outperformed traditional supervised learning approaches and other medical-specific models. The real "magic" happens in low-data scenarios. While traditional AI usually needs thousands of examples, BrainIAC can adapt to new tasks using only a few samples.  BrainIAC can be used for structural brain MRI analysis through two main methods: - Web Usage (BrainIAC platform): the easiest way to use BrainIAC, providing a user-friendly interface to upload structural brain MRI data and run inference. - Local usage (GitHub repository): for more advanced and customized analysis. It allows complete control over the data preprocessing, model training, and inference pipelines. It requires Python 3.9+ and NVIDIA GPU with CUDA 11.0+. 🔗 BrainIAC paper:    https://lnkd.in/dq6Gprgt 🔗 BrainIAC web platform https://lnkd.in/dpreadMp 🔗 BrainIAC GitHub repo: https://lnkd.in/duwSEg4V #MedicalAI #Neuroscience #HealthTech #BrainIAC #Radiology #Innovation #DeepLearning #PrecisionMedicine

Neuropeek — Accelerating the Neuro-AI convergence

Where brain data becomes shared knowledge. A community-driven platform federating neuroscience datasets, models, and tools to accelerate discovery.

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King | 34 comments

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI | 34 comments on LinkedIn

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King | 34 comments

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI | 34 comments on LinkedIn

NeuralBench: A Unifying Framework to Benchmark NeuroAI Models | Hubert Banville

🧠 NeuralBench is now open source. Today we're releasing NeuralBench, a unified framework for benchmarking foundation models of brain activity, developed by the Brain & AI team at FAIR, Meta. 💻 Code: https://lnkd.in/dNJsgBgM 📄 White paper: https://lnkd.in/dvWMg7rx Brain foundation models are starting to show positive transfer to a range of downstream tasks, from brain-computer interfacing to clinical classification. But systematically evaluating them is hard: heterogeneous preprocessing pipelines, input structures, and adaptation methodologies make results difficult to compare. Most prior work also focuses on a narrow set of downstream tasks. NeuralBench addresses this by defining each task end-to-end with config files (data source, preprocessing, splits, optimiser, metrics, architecture) so all models can be evaluated on the same footing. What's in our first release, NeuralBench-EEG v1.0: ⚡ 36 EEG tasks across 94 public datasets, spanning motor imagery, clinical classification, cognitive decoding, and phenotype prediction. 🤖  Task-specific deep learning architectures (EEGNet, Deep4, EEGConformer, CTNet, ...) benchmarked side-by-side with recent EEG foundation models (BENDR, LaBraM, BIOT, CBraMod, LUNA, REVE). 🧩 Extensible to other neuroimaging modalities: the framework already runs MEG and fMRI tasks, leveraging our NeuralSet ecosystem for accessing brain imaging data and the broader neuroscientific software stack. 📜 Released under the MIT license. Help us make it better. Through our white paper, we invite the community to contribute new tasks, datasets, and models, especially for fMRI, MEG, and iEEG. The long-term goal is a fully unified benchmark across neuroimaging tasks and modalities. 🙏🙏🙏 This was a big team effort with Stéphane d'Ascoli, Simon Dahan, Jérémy RAPIN, Marlène Careil, Yohann Benchetrit, Jarod Lévy, Saarang P., Antoine Ratouchniak, Lucy (Mingfang) Zhang, Elisa Cascardi, Katie Begany, Teon Brooks, and Jean-Rémi King. Special thanks to Alexandre Gramfort, Thomas Moreau, Arnaud Delorme, Bruno A. and Pierre Guetschel for feedback and support. #Neuroscience #AI #NeuroAI #Python #OpenSource

CatalystNeuro - Neurophysiology Data & Software Solutions

CatalystNeuro empowers neuroscience labs with data standardization, NWB conversions, spike sorting pipelines, and open-source software tools for neurophysiology research.

Neuroscientists challenge NIH’s proposed data-access policy

The changes would restrict the sharing of human neuroimaging, transcriptomic and genetic data.

Karavela

Using next-gen magnetic resonance, Karavela is collecting the largest dataset of the human brain's dynamics to enhance clinical trials with AI and crack brain decoding.