arXiv AI November 6, 2026 By Carlo Alberto Barbano (MIND), Demian Wassermann (MIND)

BrainPFN: Amortized Brain-Behaviour Prediction via Prior-Data Fitted Networks

BrainPFN is a Prior Data Fitted Network that predicts behavioral phenotypes from functional MRI data without needing per-score or per-cohort refitting. It is meta-trained on a domain-specific prior derived from about 7,000 real functional connectivity matrices, generating one billion synthetic training samples. In tests across four independent cohorts, BrainPFN outperforms Kernel Ridge Regression on small-sample predictions and rivals larger foundation models, all while using a modest, publicly available training pool.

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arXiv AI

Sep 30

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

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,...

By Ce Ju, Antoine Collas, Florent Bouchard, Bertrand Thirion

benchmarks

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 Machine Learning

Jun 2

Bayesian meta-learning for modeling Alzheimer's disease progression

arXiv:2606. 02228v1 Announce Type: cross Abstract: Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment.

By Clara Hoffmann, Nadja Klein

arXiv Computer Vision

Sep 22

Evaluating the Generalization of Neuroimaging Foundation Models on African Brain MRI

arXiv:2609.23983v1 Announce Type: new Abstract: Neuroimaging foundation models pretrained on large, predominantly western cohorts are increasingly proposed as general-purpose backbones for brain MRI...

By Oluwatobi Iyanuoluwa Akinmuleya, Olatokun Shamsudeen Akano, Samuel Danquah Ankapong, Olamide Lawal, Toufiq Musah

arXiv AI

Sep 1

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

arXiv:2506.15626v3 Announce Type: replace-cross Abstract: $\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust...

By Vincent Roca, Marc Tommasi, Paul Andrey, Aur\'elien Bellet, Markus D. Schirmer, Hilde Henon, Laurent Puy, Julien Ramon, Gr\'egory Kuchcinski, Martin Bretzner, Renaud Lopes

safety