Flexible Deep-Brain Probe for High-Fidelity Multi-Scale Recording of Epileptic Network Dynamics

Epilepsy is a complex neurological disorder characterized by abnormal neural synchronization and interactions between local foci and global brain networks during seizures. Understanding seizure mechanisms across multiple scales is essential for advancing our understanding of epileptic network dynamics and guiding personalized treatment strategies. However, neural recording technologies are limited by insufficient spatial resolution, signal fidelity, and the inability to simultaneously capture network- and cellular-level dynamics. To address these limitations, we developed a high-density, flexible deep-brain probe with excellent mechanical compliance and wideband recording capabilities, enabling high-fidelity recordings of high-frequency oscillations (HFOs, 80-500 Hz) and action potentials (APs). Using a pentylenetetrazol (PTZ)-induced epilepsy model, we identified distinct spatiotemporal dynamics of HFOs and APs across epileptic stages, indicating that CA3 plays a key role in seizure onset, while CA1 is crucial for propagation. AP-HFO coupling analysis further uncovered neuronal heterogeneity, offering insights into the diverse roles of neurons in epileptic networks. This study highlights the potential of a flexible deep-brain probe for advancing epilepsy research and guiding personalized therapeutic interventions.

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Publication Details

Journal
Micromachines
Published
2025-05-30
DOI
https://doi.org/10.3390/mi16060661
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Flexible Deep-Brain Probe for High-Fidelity Multi-Scale Recording of Epileptic Network Dynamics

Zhitao Zhou, Ziyi Zhu, Dujuan Zou, Guangdong Zhou et al.
4 links
Micromachines
Neuroscience and Neural Engineering
article

Flexible Deep-Brain Probe for High-Fidelity Multi-Scale Recording of Epileptic Network Dynamics

Zhitao Zhou, Ziyi Zhu, Dujuan Zou, Guangdong Zhou, Huiran Yang, Xiaoling Wei, Lin Yang, Zhenyu Liang, Yan Zhang, Liuyang Sun, Yanyan Nie
article en

Abstract

Epilepsy is a complex neurological disorder characterized by abnormal neural synchronization and interactions between local foci and global brain networks during seizures. Understanding seizure mechanisms across multiple scales is essential for advancing our understanding of epileptic network dynamics and guiding personalized treatment strategies. However, neural recording technologies are limited by insufficient spatial resolution, signal fidelity, and the inability to simultaneously capture network- and cellular-level dynamics. To address these limitations, we developed a high-density, flexible deep-brain probe with excellent mechanical compliance and wideband recording capabilities, enabling high-fidelity recordings of high-frequency oscillations (HFOs, 80-500 Hz) and action potentials (APs). Using a pentylenetetrazol (PTZ)-induced epilepsy model, we identified distinct spatiotemporal dynamics of HFOs and APs across epileptic stages, indicating that CA3 plays a key role in seizure onset, while CA1 is crucial for propagation. AP-HFO coupling analysis further uncovered neuronal heterogeneity, offering insights into the diverse roles of neurons in epileptic networks. This study highlights the potential of a flexible deep-brain probe for advancing epilepsy research and guiding personalized therapeutic interventions.

MicromachinesVol. 16(6)
Chinese Academy of Sciences (CN), State Key Laboratory of Transducer Technology (CN), Shanghai Institute of Microsystem and Information Technology (CN), Fiberhome Technology Group (China) (CN), University of Chinese Academy of Sciences (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Shanghai Rising-Star Program, Youth Innovation Promotion Association, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
Openalex Percentile: Top 87%
Neuroscience and Neural Engineering
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