Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
arXiv:2605.28990v1 Announce Type: new Abstract: Functional magnetic resonance imaging (fMRI) is a powerful tool for investigating human brain function. However, the high cost of data acquisition and the inherent subjectivity of psychiatric rating scales often lead to datasets with small sample sizes and variable label quality, especially when targeting a specific neurological condition. Combined with the inherently high dimensionality of fMRI data, these limitations substantially increase the risk of model overfitting. Recent years have seen growing interest in developing fMRI foundation model
Advances in self-supervised learning and foundation models are enabling new approaches to complex data types like fMRI, addressing long-standing limitations in neuroimaging research.
Developing robust, task-invariant functional representations from fMRI can unlock more precise and scalable understanding of brain function, crucial for AI applications and medical diagnostics.
This new methodology could enable more accurate and generalizable AI models for neurological conditions, potentially reducing diagnostic subjectivity and data acquisition costs.
- · AI researchers in neuroimaging
- · Healthcare diagnostics
- · Pharmaceutical R&D
- · Patients with neurological conditions
- · Traditional fMRI analysis methods
- · Diagnostic approaches reliant on subjective rating scales
Improved early detection and personalized treatment strategies for neurological disorders will emerge.
Reduced dependence on large, costly labeled fMRI datasets will democratize neuroimaging research and applications.
Generalized brain activity models could form the basis for advanced brain-computer interfaces or even AI consciousness research.
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Read at arXiv cs.LG