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<p>We investigated the impact of visual states on basal ganglia oscillatory biomarkers, comparing local field potentials (LFPs) dynamics between Parkinson’s disease (PD) and dystonia and developing a decoding model for state identification. Simultaneous LFPs recordings from the subthalamic nucleus (STN) or globus pallidus internus (GPi), and cortex were obtained from 18 PD and 18 dystonia patients. In the eyes-closed state, theta and alpha power increased in the basal ganglia, with stronger coherence to the central cortex, more pronounced in the STN than in the GPi. Machine learning models identified the eyes-closed state with 88% accuracy for STN and 77% for GPi. The sensorimotor STN and GPi were most informative. The present findings provide proof-of-concept that basal ganglia LFPs can reliably predict a physiological state, highlighting the potential influence of physiological oscillatory activity on pathological bands and its relevance for adaptive stimulation paradigms.</p>

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Decoding the impact of visual states on adaptive deep brain stimulation feedback signals in movement disorders

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Guan-Yu Zhu,
  • Timon Merk,
  • Konstantin Butenko,
  • Zi-Xiao Yin,
  • Ying-Chuan Chen,
  • Ning-Fei Li,
  • Thomas Binns,
  • Ruo-Yu Ma,
  • Ting-Ting Du,
  • Yu-Ye Liu,
  • Hu-Tao Xie,
  • Lin Shi,
  • An-Chao Yang,
  • Fan-Gang Meng,
  • Andrea A. Kühn,
  • Jian-Guo Zhang,
  • Wolf-Julian Neumann

摘要

We investigated the impact of visual states on basal ganglia oscillatory biomarkers, comparing local field potentials (LFPs) dynamics between Parkinson’s disease (PD) and dystonia and developing a decoding model for state identification. Simultaneous LFPs recordings from the subthalamic nucleus (STN) or globus pallidus internus (GPi), and cortex were obtained from 18 PD and 18 dystonia patients. In the eyes-closed state, theta and alpha power increased in the basal ganglia, with stronger coherence to the central cortex, more pronounced in the STN than in the GPi. Machine learning models identified the eyes-closed state with 88% accuracy for STN and 77% for GPi. The sensorimotor STN and GPi were most informative. The present findings provide proof-of-concept that basal ganglia LFPs can reliably predict a physiological state, highlighting the potential influence of physiological oscillatory activity on pathological bands and its relevance for adaptive stimulation paradigms.