arXiv Machine Learning

A Scaling Study for fMRI Foundation Models

The study investigates how data volume, model size, and training duration affect the performance of fMRI foundation models. Using over 200 datasets and 10,000 GPU‑hours, the authors find that larger models benefit more from additional data, and that at a fixed compute budget, increasing data yields greater gains than enlarging the model. By selecting optimal combinations of data, size, and duration, they produce models that outperform existing fMRI foundation models on out‑of‑distribution tasks while requiring less pretraining compute.

arXiv Computer Vision

Sep 24

A generalizable structural brain MRI foundation model built through dual-priority federated pretraining

BrainFedFM is a structural brain MRI foundation model that was federatively pretrained on 164,707 3‑D scans from 42 sites using a dual‑priority approach that emphasizes informative anatomical regions locally and prioritizes site contributions globally. The model outperformed seven baseline models—including four centralized foundation models—across 20 downstream tasks (classification, regression, segmentation), achieving a mean rank of 1.68 and a 50% performance gain, especially in classification and regression and among underrepresented populations. These results demonstrate the model’s generalizability and show that federated pretraining can effectively develop neuroimaging foundation models without pooling raw images.

By Zhen Yu, Yang Liu, Xiahai Zhuang, Qingchao Chen

computer-vision benchmarks safety

arXiv Computer Vision

1d ago

Natural Image Autoencoder-Based fMRI Representations for Trait and State Prediction

arXiv:2609.34167v2 Announce Type: replace Abstract: Foundation models pre-trained on large-scale fMRI datasets have shown strong downstream performance, but at substantial data and computation cost....

By Juhyeon Park, Yeonwoo Kim, Peter Yongho Kim, Yansen Wang, Mingqing Xiao, Dongqi Han, Dongsheng Li, Taesup Moon

llms fine-tuning benchmarks

arXiv AI

1d ago

Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders

arXiv:2609.37642v1 Announce Type: new Abstract: Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learn...

By Giovanni Marraffini (UNITO), Victoria Shevchenko (UNITO), Carlo Alberto Barbano (UNITO), Demian Wassermann (MIND)

rag

arXiv Machine Learning

Jun 5

Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation

arXiv:2606. 06345v1 Announce Type: cross Abstract: Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes.

By Yohann Benchetrit, Marl\`ene Careil, Simon Dahan, Hubert Banville, St\'ephane d'Ascoli, Jean-R\'emi King

arXiv AI

Jun 4

The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail

arXiv:2606. 04010v1 Announce Type: cross Abstract: Brain foundation models (BFMs) are self-supervised Transformers pretrained on fMRI data.

By Giovanni Marraffini, Gabriel Mahuas, Trinidad Borrell, Victoria Shevchenko, Demian Wassermann

llms fine-tuning benchmarks

arXiv AI

Jun 16

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

arXiv:2604. 18827v2 Announce Type: replace-cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision.

By Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert, Goirik Chakrabarty, Hasan A. Bedel, Paul G. Fahey, Yongrong Qiu, Marissa A. Weis, Michaela Vystr\v{c}ilov\'a, Taliah Muhammad, Lydia Ntanavara, Rachel E. Froebe, Kayla Ponder, Zheng Huan Tan, Emin Orhan, Erick Cobos, Sophia Sanborn, Katrin Franke, Fabian H. Sinz, Alexander S. Ecker, Andreas S. Tolias

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Aug 4

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models

arXiv:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.

By Dingkun Liu, Yuheng Chen, Zhu Chen, Zhenyao Cui, Yaozhi Wen, Jiayu An, Jingwei Luo, Dongrui Wu

fine-tuning benchmarks

arXiv AI

Jul 21

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis

arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.

By Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander

computer-vision fine-tuning benchmarks

arXiv AI

3d ago

Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation

The paper investigates combining a domain‑specific self‑supervised task—voxel‑level brain age prediction—with a general task—image inpainting—to pretrain models for brain MRI segmentation. A multitask pretraining framework jointly optimizes both objectives, yielding representations that outperform single‑task pretraining and training from scratch on three segmentation benchmarks (multiple sclerosis lesions, ischemic stroke lesions, and cortical structures). The study demonstrates that integrating domain‑specific and general self‑supervised tasks benefits the development of generalizable neuroimaging foundation models.

By Tasneem Nasser, Susanne Schmid, Roberto Souza, Naser El-Sheimy

computer-vision fine-tuning

arXiv Computer Vision

3d ago

FlatClip: A Geometry-Aware Surface-Level Baseline for fMRI Representation Learning

arXiv:2609.31204v1 Announce Type: cross Abstract: Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models ar...

By Mo Wang, Wenhao Ye, Zihan Ning, Jiayu Zuo, Junfeng Xia, Hongkai Wen, Quanying Liu

benchmarks

arXiv Machine Learning

Jul 10

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning

arXiv:2603. 12478v2 Announce Type: replace-cross Abstract: Multimodal instruction tuning is often compute-inefficient because training budgets are spread across large mixed image-video pools whose utility is highly uneven.

By Rujie Wu, Haozhe Zhao, Hai Ci, Yizhou Wang

fine-tuning multimodal benchmarks

arXiv AI

Aug 7

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.

By Omar Coser, Antonio Orvieto, Paolo Soda, Loredana Zollo

llms robotics multimodal benchmarks

Related stories