Objective.Brain-to-text systems have recently achieved impressive performance when trained on single-participant data, but remain limited by uninvestigated cross-subject generalization.Approach.We present the first neural-to-phoneme decoder trained jointly on the two largest intracortical speech datasets (Willettet al2023Nature6201031-6; Cardet al2024New Engl. J. Med.391609-18), introducing day- and dataset-specific affine transforms to align neural activity into a shared space. Additionally, a...

Music is a universal phenomenon that influences human experiences across cultures. We investigate whether music can be decoded from human brain activity measured with fMRI, by modeling mappings between neural data and latent representations of musical stimuli. Our approach integrates functional and anatomical alignment techniques to facilitate cross-subject decoding. Starting from the GTZan fMRI dataset, where five participants listened to 540 musical tracks from 10 genres, we used the CLAP...

In everyday behaviour, the ability to stop an already initiated action is critical for ensuring both your safety and that of others; for example, when stopping a reaching movement towards a hot stove-top after realising it is hot. Neuroscientific evidence points towards the critical role of several regions in the right prefrontal cortex in the coordination and execution of this response inhibition-specifically the right inferior frontal gyrus (rIFG) and the right dorsolateral prefrontal cortex...

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours that usually present with symptoms related to catecholamine excess. However, a small subset may secrete cytokines such as interleukin-6 (IL-6), leading to atypical systemic manifestations and delayed recognition of a paraneoplastic inflammatory syndrome. We report the case of a middle-aged woman with a previously diagnosed non-secreting cervical paraganglioma who developed fever of unknown origin (FUO), anaemia and...

CONCLUSIONS: This study demonstrates that general-purpose LLMs, operating without specialized model training or fine-tuning, can effectively serve as intelligent agents for automated radiotherapy TP, specifically addressing the BAO problem. This flexible and scalable framework has the potential to enhance clinical decision-making workflows in radiotherapy. Future research directions include exploring more comprehensive and clinically nuanced reward functions and extending the methodology to...