DANDI

brainlife

The platform allows to analyze Magnetic Resonance Imaging (MRI), electroencephalography (EEG) and magnetoencephalography (MEG) data. Data can either be uploaded from local computers or imported from public archives such as OpenNeuro.org. Over 400+ data processing Apps are available on brainlife.io to build custom data workflows. Thousands of jobs can be submitted using shared clusters or on users' compute resource. Users can perform group-level statistical analysis or apply machine learning methods using Jupyter notebooks. A single record containing the entire data workflow (from raw data to published figures) can be published addressed by with a unique digital object identifier (DOI).

Dandelion Science — from digital brains to therapies

Dandelion uses NeuroAI for precision neuromodulation.

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…

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

#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

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.

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.

facebookresearch/neuroai

Python suite for neuroscience research across all modalities.

Neuroscientists challenge NIH’s proposed data-access policy

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

#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

#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

Precision Medicine in Neuroscience: Tools, Translation, and Implementation: A Workshop

Join the web’s most supportive community of creators and get high-quality tools for hosting, sharing, and streaming videos in gorgeous HD with no ads.

🚀🚀🚀 Excited to share that our Brain&AI team is releasing NeuralSet! 🚀🚀🚀 A fast, simple, and scalable package for Neuro-AI: 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄… | Jarod Lévy | 12 comments

🚀🚀🚀 Excited to share that our Brain&AI team is releasing NeuralSet! 🚀🚀🚀 A fast, simple, and scalable package for Neuro-AI: 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy As a PhD student working extensively with neuro recordings and AI models, I find this library to be a game-changer. This has been the common backbone for the past several years of our projects (TribeV2, DynaDiff, Brain2Qwerty & more!). This library turns raw neuro-recordings into AI-ready tensors. It enables:  - Event-driven processing with typed, validated DataFrames. - Multimodal extractors for fMRI, EEG, MEG, EMG, and iEEG + text, image, audio, and video. - Torch-native segmentation for efficient batching. - Caching and remote compute through exca. Huge thanks to everyone on the team who made this release possible. If you're working on brain decoding, encoding models, or foundation models for neuroscience, don’t miss this! 🧠 Jean-Rémi King 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 Pierre Orhan Antoine Ratouchniak Joséphine Raugel Andrea Elena Santos Revilla Alexis Thual Lucy (Mingfang) Zhang Jérémy RAPIN | 12 comments on LinkedIn

E11 Bio | Moonshot Neuroscience

An FRO building scalable single-cell brain circuit mapping.

BrainWave: A Brain Signal Foundation Model for Clinical Applications

Neural electrical activity is fundamental to brain function, underlying a range of cognitive and behavioral processes, including movement, perception, decision-making, and consciousness. Abnormal patterns of neural signaling often indicate the presence of underlying brain diseases. The variability among individuals, the diverse array of clinical symptoms from various brain disorders, and the limited availability of diagnostic classifications, have posed significant barriers to formulating reliable model of neural signals for diverse application contexts. Here, we present BrainWave, the first foundation model for both invasive and non-invasive neural recordings, pretrained on more than 40,000 hours of electrical brain recordings (13.79 TB of data) from approximately 16,000 individuals. Our analysis show that BrainWave outperforms all other competing models and consistently achieves state-of-the-art performance in the diagnosis and identification of neurological disorders. We also demonstrate robust capabilities of BrainWave in enabling zero-shot transfer learning across varying recording conditions and brain diseases, as well as few-shot classification without fine-tuning, suggesting that BrainWave learns highly generalizable representations of neural signals. We hence believe that open-sourcing BrainWave will facilitate a wide range of clinical applications in medicine, paving the way for AI-driven approaches to investigate brain disorders and advance neuroscience research.