Inducing sleep through neural stimulation

Sleep eludes us all at times, but some of us suffer more than most. Sleep disorders affect the majority of adults at some point in their lives, but diagnosing and treating them can be difficult. Most brain scans use electroencephalography to collect data, which lacks the resolution necessary to identify the specific neural circuits that regulate sleep.

Duan et al. probed brain activity in mice using implantable microelectrode arrays (MEAs). Their device consists of two MEAs with 16 recording contacts and a dedicated stimulation site, allowing for simultaneous neural signal readout and targeted electrical stimulation.

With this device, the researchers were able to measure neural differences between sleep and wake cycles and identify key neurons in the medial preoptic area of the brain that have significantly different response mechanisms to sleep and wake. Using the stimulation site of their MEA, the authors targeted these neurons to induce sleep in their subjects.

“Based on recorded firing signatures of sleep responsive neurons, we carried out multiple preliminary tests to select proper deep brain stimulation frequencies,” said author Jinping Luo. “Real-time electrophysiology tracked neuronal responses in the preoptic area. Using these optimized parameters, we could consistently drive mice into sleep-like conditions.”

With the neuron populations the team identified along with the protocol to trigger sleep, the researchers believe this could one day form the foundation of a useful therapeutic tool for treating sleep disorders in humans.

“We plan to develop more microelectrode arrays capable of multi-brain-region recording,” said Luo. “Combined with histological and genetic tools, we will trace their circuit projections to refine sleep-wake models and further optimize deep brain stimulation parameters.”

Source: “Microelectrode array-based detection and modulation of sleep-wake neuroinformation in the mouse preoptic area,” by Yiming Duan, Qianli Jia, Jinping Luo, Yu Wang, Yu Liu, Xingcheng Lu, Jin Shan, Jiangbei Cao, Yulong Ma, Weidong Mi, Xiaoying Zhang, and Xinxia Cai, Nanotechnology and Precision Engineering (2026). The article can be accessed at https://doi.org/10.1063/5.0328124 .