EPFL开发脑网络解码芯片LogSync
EPFL研究人员开发了植入式神经片上系统LogSync,用于记录脑活动,并提取反映不同脑区振荡强度及其同步性的生物标志物。机器学习算法利用这些网络层面的信号区分认知和情感状态,并生成刺激触发信号。研究人员使用既往精神科实验的人类脑记录,以及明尼苏达大学开展的大鼠实验对系统进行评估;报道未提及在人类中植入LogSync设备的临床试验。该芯片尺寸约为4×2毫米。Shoaran表示,协同设计的硬件和算法使系统功耗保持在200微瓦以下。相关成果发表于《IEEE Journal of Solid-State Circuits》。
报道指出,许多现有闭环脑深部刺激系统依赖单一脑区的信号,而与精神科疾病相关的临床信息可能分布于多个相互作用的脑网络中。LogSync将该实验室此前的工作扩展至网络层面的生物标志物和自适应解码,但目前验证仍属临床前阶段。
EPFL researchers have developed an implantable neural chip that tracks communication across brain regions, identifying cognitive and emotional states in real time.
Deep brain stimulation (DBS) is a therapeutic technique that delivers electrical pulses to specific areas of the brain via an implant to modulate neural activity. It is already widely used for movement disorders such as Parkinson’s disease, and researchers are increasingly exploring its potential for severe psychiatric illnesses including major depression, post-traumatic stress disorder, and obsessive-compulsive disorder.
However, most existing DBS systems operate in what engineers call an “open loop” manner: stimulation is delivered according to predefined settings rather than continuously adapting to the patient’s brain activity. Even among closed-loop, or adaptive systems, many rely on signals recorded from a single brain region. This is a major limitation for psychiatric disorders, where clinically relevant information may be distributed across networks of interacting brain regions.
“Our work is based on the growing body of research suggesting that psychiatric disorders arise more from disruptions in communication between brain regions than from dysfunction of any single region,” explains Mahsa Shoaran, head of the Integrated Neurotechnologies Lab in EPFL’s School of Engineering and Neuro-X Institute.
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