Division of Neurology, MEG Center, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA.
Department of Neurosurgery, Saint Louis University St. Louis, MO, USA.
Front Neuroinform. 2014 May 21;8:57. doi: 10.3389/fninf.2014.00057. eCollection 2014.
Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation's memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low- (170 Hz) and high-frequency (70200 Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 h by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2-3 days and used 1-2 TB storage, the new approach can quantify epileptic abnormalities in both low- and high-frequency ranges at source levels, using much less time and computer memory.
最近的研究揭示了高频脑信号（>70 Hz）的重要性。高频信号分析的一个挑战是，高频脑信号的时频表示的大小可能超过 1 太字节（TB），这超出了典型计算机工作站内存（<196GB）的上限。本研究的目的是开发一种新方法，以在单个自动化和多功能界面中提供更高的高频脑磁图（MEG）信号检测灵敏度，而不是更传统的、时间密集型的视觉检查方法，这些方法可能需要数天时间。为了实现这一目标，我们开发了一种新方法，即累积源成像，定义为在一段时间内对源活动的体积求和。该方法在源水平上分析低频（170 Hz）和高频（70200 Hz）范围内的信号。为了从传感器空间的 MEG 信号中提取有意义的信息，将信号分解为代表每个可能的传感器对的时空模式的通道-通道矩阵（CxC）。开发了一种新算法，并通过计算用于体积源成像的最佳 CxC 和源位置-方向权重来进行测试，从而最小化多源干扰并降低计算成本。该新方法已在 C/C++中实现，并使用从临床癫痫患者记录的 MEG 数据进行了测试。实验数据的结果表明，累积源成像可以在使用大约 10GB 计算机内存的情况下，在 12.7 小时内有效总结和可视化 MEG 记录。与传统的方法相比，传统的方法需要 2-3 天时间，并且使用 1-2TB 的存储空间，该新方法可以在源水平上定量低频频带和高频频带的癫痫异常，使用的时间和计算机内存要少得多。
