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情感脑机接口作为响应性精神刺激的使能技术。

Affective Brain-Computer Interfaces As Enabling Technology for Responsive Psychiatric Stimulation.

作者信息

Widge Alik S, Dougherty Darin D, Moritz Chet T

机构信息

Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, USA, and Harvard Medical School, Boston, MA, USA ; Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.

Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, USA, and Harvard Medical School, Boston, MA, USA.

出版信息

Brain Comput Interfaces (Abingdon). 2014 Apr 1;1(2):126-136. doi: 10.1080/2326263X.2014.912885.

Abstract

There is a pressing clinical need for responsive neurostimulators, which sense a patient's brain activity and deliver targeted electrical stimulation to suppress unwanted symptoms. This is particularly true in psychiatric illness, where symptoms can fluctuate throughout the day. Affective BCIs, which decode emotional experience from neural activity, are a candidate control signal for responsive stimulators targeting the limbic circuit. Present affective decoders, however, cannot yet distinguish pathologic from healthy emotional extremes. Indiscriminate stimulus delivery would reduce quality of life and may be actively harmful. We argue that the key to overcoming this limitation is to specifically decode volition, in particular the patient's intention to experience emotional regulation. Those emotion-regulation signals already exist in prefrontal cortex (PFC), and could be extracted with relatively simple BCI algorithms. We describe preliminary data from an animal model of PFC-controlled limbic brain stimulation and discuss next steps for pre-clinical testing and possible translation.

摘要

对于响应性神经刺激器存在迫切的临床需求,这种刺激器能够感知患者的大脑活动并进行有针对性的电刺激,以抑制不良症状。在精神疾病中尤其如此,因为症状可能在一天中波动。情感脑机接口(BCI)可从神经活动中解码情感体验,是针对边缘系统回路的响应性刺激器的候选控制信号。然而,目前的情感解码器尚无法区分病理性与健康的极端情绪。不加区分地进行刺激会降低生活质量,甚至可能产生积极的危害。我们认为,克服这一限制的关键在于专门解码意志,特别是患者进行情绪调节的意图。这些情绪调节信号已经存在于前额叶皮层(PFC)中,可以通过相对简单的BCI算法提取。我们描述了来自PFC控制边缘脑刺激动物模型的初步数据,并讨论了临床前测试的下一步以及可能的转化。

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