Merk Timon, Köhler Richard, Peterson Victoria, Lyra Laura, Vanhoecke Jonathan, Chikermane Meera, Binns Thomas, Li Ningfei, Walton Ashley, Bush Alan, Sisterson Nathan, Busch Johannes, Lofredi Roxanne, Habets Jeroen, Huebl Julius, Zhu Guanyu, Yin Zixiao, Zhao Baotian, Merkl Angela, Bajbouj Malek, Krause Patricia, Faust Katharina, Schneider Gerd-Helge, Horn Andreas, Zhang Jianguo, Kühn Andrea, Richardson R Mark, Neumann Wolf-Julian
Charité Universitätsmedizin Berlin.
Instituto de Matemática Aplicada del Litoral IMAL.
Res Sq. 2023 Sep 20:rs.3.rs-3212709. doi: 10.21203/rs.3.rs-3212709/v1.
Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.
脑机接口（BCI）提供了前所未有的时空精度，这将极大地扩展多种脑部疾病的治疗方式。要利用这种精度，就需要通过机器学习从脑信号中解码动态患者状态，但目前尚不存在用于识别和推进新型临床BCI方法的标准化框架。在此，我们开发了一个将脑信号解码与连接组学相结合的平台，并在来自73名接受运动障碍、抑郁症和癫痫治疗的神经外科患者的123小时侵入性记录脑数据中展示了其效用。首先，我们引入了基于连接组学的运动解码器，该解码器可在美国、欧洲和中国的帕金森病和癫痫患者队列中进行推广。接下来，我们揭示了患有重度抑郁症的DBS患者左前额叶和扣带回回路中用于情绪解码的网络靶点。最后，我们展示了在癫痫的响应性神经刺激中改善癫痫发作检测的机会。我们的平台为精准医学方法提供了快速、高精度的解码，能够根据患者的个体需求动态调整神经调节疗法。
