RE-CONFIRM framework evaluates biomarker robustness in brain foundation models
A new framework called RE-CONFIRM has been launched to assess how reliable potential biomarkers found through deep learning techniques, like brain foundation models, are for neurological disorders. Research published on arXiv looked at five large datasets related to Autism Spectrum Disorder, Attention-deficit Hyperactivity Disorder, and Alzheimer's Disease. The results suggest that the usual performance metrics fall short in evaluating biomarker reliability. RE-CONFIRM's metrics revealed that just tweaking these models doesn't sufficiently address regional hub impacts, highlighting the need for more comprehensive evaluations when analyzing dynamic functional connectivity.
Key facts
- RE-CONFIRM is a framework for evaluating robustness of biomarker candidates from deep learning models
- Tested on five datasets for ASD, ADHD, and Alzheimer's Disease
- Standard performance metrics are insufficient for biomarker robustness evaluation
- Fine-tuning foundation models fails to capture regional hub effects
- Study published on arXiv with ID 2604.22018
- Focuses on dynamic functional connectivity in brain disorders
- Brain foundation models show zero- or few-shot generalization but biomarkers need evaluation
- RE-CONFIRM provides new metrics for robustness assessment
Entities
Institutions
- arXiv