Sousa Bruno Monteiro de, de Oliveira Eliezyer Fermino, Beraldo Ikaro Jesus da Silva, Polanczyk Rafaela Schuttenberg, Leite João Pereira, Lopes Aguiar Cleiton
Graduate Program in Biological Sciences: Physiology and Pharmacology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil.
Laboratory of Molecular and Behavioral Neuroscience (LANEC), Federal University of Minas Gerais, Belo Horizonte, Brazil.
J Neural Eng. 2022 Aug 12;19(4). doi: 10.1088/1741-2552/ac857b.
. Sharp wave-ripples (SWRs, 100-250 Hz) are oscillatory events extracellularly recorded in the CA1 subfield of the hippocampus during sleep and quiet wakefulness. Many studies employed closed-loop strategies to either detect and abolish SWRs within the hippocampus or manipulate other relevant areas upon ripple detection. However, the code and schematics necessary to replicate the detection system are not always available, which hinders the reproducibility of experiments among different research groups. Furthermore, information about performance is not usually reported. Here, we sought to provide an open-source, validated ripple detector for the scientific community.. We developed and validated a ripple detection plugin integrated into the Open Ephys graphical user's interface. It contains a built-in movement detector based on accelerometer or electromyogram data that prevents false ripple events (due to chewing, grooming, or moving, for instance) from triggering the stimulation/manipulation device.. To determine the accuracy of the detection algorithm, we first carried out simulations in MATLAB with real ripple recordings. Using a specific combination of detection parameters (amplitude threshold of 5 standard deviations above the mean, time threshold of 10 ms, and root mean square block size of 7 samples), we obtained a 97% true positive rate and 2.48 false positives per minute. Next, an Open Ephys plugin based on the same detection algorithm was developed, and a closed-loop system was set up to evaluate the round trip (ripple onset-to-stimulation) latency over synthetic data. The lowest latency obtained was 34.5 ± 0.5 ms. The embedded movement monitoring was effective in reducing false positives and the plugin's flexibility to detect pathological events was also verified.. Besides contributing to increased reproducibility, we anticipate that the developed ripple detector plugin will be helpful for many closed-loop applications in the field of systems neuroscience.
尖锐波涟漪（SWRs，100 - 250赫兹）是在睡眠和安静觉醒期间于海马体CA1子区域细胞外记录到的振荡事件。许多研究采用闭环策略来检测并消除海马体内的SWRs，或者在检测到涟漪后操纵其他相关区域。然而，复制检测系统所需的代码和原理图并非总是可用，这阻碍了不同研究团队之间实验的可重复性。此外，通常也不会报告有关性能的信息。在此，我们旨在为科学界提供一个经过验证的开源涟漪探测器。
我们开发并验证了一个集成到Open Ephys图形用户界面中的涟漪检测插件。它包含一个基于加速度计或肌电图数据的内置运动检测器，可防止虚假的涟漪事件（例如由于咀嚼、梳理或移动）触发刺激/操纵设备。
为了确定检测算法的准确性，我们首先在MATLAB中使用真实的涟漪记录进行模拟。通过使用特定的检测参数组合（高于平均值5个标准差的幅度阈值、10毫秒的时间阈值和7个样本的均方根块大小），我们获得了97%的真阳性率和每分钟2.48次假阳性。接下来，开发了一个基于相同检测算法的Open Ephys插件，并建立了一个闭环系统来评估合成数据上的往返（涟漪开始到刺激）延迟。获得的最低延迟为34.5 ± 0.5毫秒。嵌入式运动监测在减少假阳性方面是有效的，并且该插件检测病理事件的灵活性也得到了验证。
除了有助于提高可重复性之外，我们预计所开发的涟漪探测器插件将对系统神经科学领域的许多闭环应用有所帮助。
