# Site Is Decodable Before Pretraining: Negative Controls for Probing Frozen Brain-MRI Foundation Models

arXiv: https://arxiv.org/abs/2608.10295 · HTML: https://mlsanity.com/paper/2608.10295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Designing and Interpreting Probes with Control Tasks](https://mlsanity.com/paper/1909.03368/) (2019)
- [Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection](https://mlsanity.com/paper/2004.07667/) (2020)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [On the Challenges and Perspectives of Foundation Models for Medical Image Analysis](https://mlsanity.com/paper/2306.05705/) (2023)
- [Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking](https://mlsanity.com/paper/2409.19407/) (2024)
- [Detect and Correct Bias in Multi-Site Neuroimaging Datasets](https://mlsanity.com/paper/2002.05049/) (2020)
- [Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects](https://mlsanity.com/paper/1910.04597/) (2019)
- [BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation](https://mlsanity.com/paper/2406.10395/) (2024)
- [Scanner Invariant Representations for Diffusion MRI Harmonization](https://mlsanity.com/paper/1904.05375/) (2019)

## Built on by (most cited first, 1 of 1)

- [Dataset-Specific Encoding of Image Content Underlies Cross-Dataset Failure in Brain MRI Foundation Models](https://mlsanity.com/paper/2610.13027/) (2026)

## Abstract (arXiv)

Brain foundation models are often tested with a probe. The model is frozen, a simple classifier is trained on its output, and the classifier's accuracy is taken as a sign of what pretraining learned. We show that this reading can be wrong unless two controls are reported with it. We probed three frozen 3-D brain-MRI models at five depths, on two independent multi-site cohorts (ABIDE-I and ABIDE-II). The site where a scan was acquired could be predicted at about 0.9, on a scale where 0 is chance and 1 is perfect. No clinical or demographic target came close. An untrained copy of the same model predicted site just as well on both cohorts. Untrained models of three architectures, each built with three random seeds, did the same on ABIDE-I. The raw image, shrunk to 12x12x12 voxels and given to no model at all, predicted site at 0.95. Pretraining did not create the site signal. It was already in the scans. We also looked for any clinical target on which pretraining beats an untrained model, and found no reliable case. Adjusting for age, sex and diagnosis left site prediction almost unchanged, and so did a 4.6-fold increase in the number of voxels. Resting-state fMRI from the same people showed the same pattern. Site was the most predictable attribute of brain connectivity (0.81), and it survived five untrained layers while age did not. We recommend reporting two controls with every probe of a brain foundation model: running the probe again on the untrained model, and on the raw input. We release the code for both.