用于脑植入患者神经解码的侵入性神经生理学与全脑连接组学

Brain-computer interface research can inspire closed-loop neuromodulation therapies, promising spatiotemporal precision for the treatment of brain disorders. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for invasive brain signal decoding from neural implants does not exist. Here we develop a platform that integrates brain signal decoding with magnetic resonance imaging connectomics and demonstrate its use across 123 h of invasively recorded brain data from 73 neurosurgical patients treated with brain implants for movement disorders, depression and epilepsy. We introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the United States, Europe and China. We reveal network targets for emotion decoding in left prefrontal and cingulate circuits in deep brain stimulation patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our study highlights the clinical use of brain signal decoding for deep brain stimulation and provides methods that allow for rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neurotherapies in response to the individual needs of patients.