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<p>High-density, long-term stable decoding of whole-brain function is crucial for advancing basic neuroscience research and developing neural disorder therapies. However, two major challenges remain: the lack of scalable interfaces capable of long-term, multi-regional recordings and the limited generalizability of existing decoding algorithms across days and individuals. Here, we developed an integrated platform that achieves accurate, stable, and generalizable decoding of behavioral states (resting, roaming, feeding, and flash) with up to 89% accuracy. This platform combines multi-region flexible probes (MRFPs), enabling distributed recordings from 128 sites across eight brain regions over months, with a Conformer-based deep learning framework optimized for brain-wide neural dynamics. Comparative analyses demonstrate that distributed sampling, particularly from five or more regions, markedly enhances decoding performance over concentrated electrode configurations. Furthermore, the platform supports robust generalization across days and individuals without retraining, providing a practical solution for longitudinal and large-scale behavioral neuroscience studies. These results establish a foundation for stable, high-fidelity multi-region electrophysiology and offer a generalizable approach for decoding internal states from complex neural dynamics.</p><p></p>

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A multi-region flexible neural interface for behavioral state decoding in freely moving mice

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

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  • Ye Tian,
  • Gen Li,
  • Haoyang Su,
  • Luyue Jiang,
  • Yunfu Luo,
  • Yingkang Yang,
  • Lei Huang,
  • Jiazhi Li,
  • Shuang Jin,
  • Peijie Chen,
  • Yiming Gao,
  • Yike Xiang,
  • Yi Wei,
  • Yifei Ye,
  • Liuyang Sun

摘要

High-density, long-term stable decoding of whole-brain function is crucial for advancing basic neuroscience research and developing neural disorder therapies. However, two major challenges remain: the lack of scalable interfaces capable of long-term, multi-regional recordings and the limited generalizability of existing decoding algorithms across days and individuals. Here, we developed an integrated platform that achieves accurate, stable, and generalizable decoding of behavioral states (resting, roaming, feeding, and flash) with up to 89% accuracy. This platform combines multi-region flexible probes (MRFPs), enabling distributed recordings from 128 sites across eight brain regions over months, with a Conformer-based deep learning framework optimized for brain-wide neural dynamics. Comparative analyses demonstrate that distributed sampling, particularly from five or more regions, markedly enhances decoding performance over concentrated electrode configurations. Furthermore, the platform supports robust generalization across days and individuals without retraining, providing a practical solution for longitudinal and large-scale behavioral neuroscience studies. These results establish a foundation for stable, high-fidelity multi-region electrophysiology and offer a generalizable approach for decoding internal states from complex neural dynamics.