Batch Effects In Brain Foundation Model Embeddings
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913036938248192 |
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| author | Tao, Ye Baker, Bradley T. Wu, Yu Sarwate, Anand D. Panta, Sandeep Plis, Sergey Calhoun, Vince D. |
| author_facet | Tao, Ye Baker, Bradley T. Wu, Yu Sarwate, Anand D. Panta, Sandeep Plis, Sergey Calhoun, Vince D. |
| contents | Foundation models show strong potential for large-scale, high-dimensional biomedical applications, yet their ability to capture relevant neurobiological characteristics remains underexplored. We systematically evaluate embeddings from two neuroimaging foundation models, BrainLM and SwiFT, across multi-site fMRI datasets using a comprehensive evaluation framework. Our results show that foundation model embeddings encode substantial batch-related variability, often dominating diagnosis-related information across heterogeneous datasets. We further investigate how harmonization, applied to reduce batch effects, influences these embeddings. In addition, we find that BrainLM prefers to capture fine-grained regional activity, whereas SwiFT tends to represent interactions between regions, consistent with their respective model architectures. Our study highlights the importance of accounting for batch effects in foundation models and motivates future work on disentangling biologically meaningful signals from acquisition-related variability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14441 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Batch Effects In Brain Foundation Model Embeddings Tao, Ye Baker, Bradley T. Wu, Yu Sarwate, Anand D. Panta, Sandeep Plis, Sergey Calhoun, Vince D. Signal Processing Foundation models show strong potential for large-scale, high-dimensional biomedical applications, yet their ability to capture relevant neurobiological characteristics remains underexplored. We systematically evaluate embeddings from two neuroimaging foundation models, BrainLM and SwiFT, across multi-site fMRI datasets using a comprehensive evaluation framework. Our results show that foundation model embeddings encode substantial batch-related variability, often dominating diagnosis-related information across heterogeneous datasets. We further investigate how harmonization, applied to reduce batch effects, influences these embeddings. In addition, we find that BrainLM prefers to capture fine-grained regional activity, whereas SwiFT tends to represent interactions between regions, consistent with their respective model architectures. Our study highlights the importance of accounting for batch effects in foundation models and motivates future work on disentangling biologically meaningful signals from acquisition-related variability. |
| title | Batch Effects In Brain Foundation Model Embeddings |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.14441 |