Instituto Cajal, CSIC, Madrid, 28002, Spain.
Psychological Sciences, Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA.
Commun Biol. 2024 Mar 4;7(1):211. doi: 10.1038/s42003-024-05871-w.
The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.
对尖波涟漪的研究增进了我们对记忆功能的理解，而其在癫痫等神经疾病中的改变被认为是功能障碍的生物标志物。尖波涟漪表现出多样的波形和特征，仅凭光谱方法无法充分描述。在这里，我们描述了一个用于自动检测和分析这些事件的机器学习模型工具包。这些源自众包黑客马拉松的机器学习架构能够捕捉到在清醒和睡眠状态下在小鼠背侧海马体中记录的大量涟漪特征。当应用于来自猕猴海马体的数据时，这些模型能够进行通用检测并揭示跨物种的共享特征。我们在此提供了一个用户友好的开源模型工具包，用于模型使用和扩展，这有助于加速和标准化尖波涟漪的分析，降低其在生物医学应用中的采用门槛。
