FlatClip: Reusing Image Foundation Models for fMRI Representation Learning via Cortical Flatmaps

arXiv:2609.31204 · cs.CE, cs.CV, q-bio.NC · Submitted 2026-09-25 · Read on arXiv

cs.CE, cs.CV, q-bio.NC

Submitted: 2026-09-25

Updated: 2026-10-15

Terminology

• Accuracy — 20× in this paper · explained in 1 episode
• Foundation Model — 19× in this paper · explained in 3 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Alignment — 10× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Feature Extraction — 7× in this paper · explained in 2 episodes
• Representation Learning — 6× in this paper · explained in 2 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Linear probe — 3× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Human Connectome Project — 2× in this paper
• Loss Function — 2× in this paper · explained in 2 episodes
• Natural Scenes Dataset — 2× in this paper
• Active Regions — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• SLIM-Brain: A Data- and Training-Efficient Foundation Model for fMRI Data Analysis
• Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining
• BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
• Scaling Vision Transformers for Functional MRI with Flat Maps
• DINOv2: Learning Robust Visual Features without Supervision
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

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