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Hertie Institute for AI in Brain Health , University of Tübingen , Germany
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Conditional flow models frequently hallucinate non-additive scientific interactions, even when overall prediction accuracy appears high.
Acoustic noise, even when not diagnostically informative, can systematically manipulate Alzheimer's predictions in SSL models, revealing a critical vulnerability in current speech-based assessments.
Layer selection for speech-based PD detection is more about the dataset than the model architecture, revealing a critical flaw in current approaches.
Zero-shot generation of fMRI dynamics for unseen cognitive tasks could revolutionize experimental neuroscience by enabling in-silico testing of novel hypotheses.
SSL embeddings, typically superior for speech tasks, surprisingly lose their edge to hand-crafted acoustic features when classifying mild cognitive impairment from speech, challenging assumptions about representation learning in clinical applications.