arXiv AI
Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders
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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.
arXiv Machine Learning
Sep 24
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 Machine Learning
Sep 1
Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
arXiv:2608.30418v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction,...
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....
arXiv Computer Vision
Sep 14
Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce
arXiv:2609.12834v1 Announce Type: new Abstract: Modelling how a disease progresses over time requires longitudinal imaging cohorts, which are scarce and small, whereas cross-sectional data -- one ima...
arXiv Machine Learning
Jul 9
STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning
arXiv:2607. 06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and psychiatric burden, and EEG is an attractive substrate for it because it is cheap, portable, and temporally rich.