Geometric classification of brain network dynamics via conic derivative discriminants.
Geometric classification of brain network dynamics via conic derivative discriminants.
复制标题
通过圆锥导数判别式对大脑网络动力学进行几何分类。
DOI：
10.1016/j.jneumeth.2018.06.019
复制
发表时间：
2018
影响因子：
3
通讯作者：
Ching,ShiNung
中科院分区：
文献类型：
--
作者：
Singh,MatthewF;Braver,ToddS;Ching,ShiNung
背景在过去的十年中，模式解码技术使神经科学家在绘制与功能和认知相关的神经表征方面提高了解剖特异性。动态模式特别令人感兴趣，揭示结构化时空节律性大脑活动的频域方法的激增和成功证明了这一点。然而，这种方法的一个缺点是需要估计光谱功率，这限制了分类的时间分辨率。新方法我们提出了一种替代方法，可以对具有高时间保真度的动态模式进行分类。该方法的关键特征是将时间序列数据转换为时间导数。通过这样做，动态编码的信息可以根据导数信号的相空间中的几何图案来揭示。结果我们针对这个问题导出了一个几何分类器，它简化为协方差方面的简单计算。我们通过模拟数据展示了该技术的相对优点和缺点，并通过隐性空间注意力的脑电图数据集对其性能进行了基准测试。我们揭示了隐性空间注意力的时间进程，并通过解剖学映射分类器权重，揭示了其视网膜主题组织。与现有方法的比较我们特别强调了该方法与现有基准相比提供强大的组级分类性能的能力，同时提供与经典的基于光谱的技术互补的信息。相对于基于频谱的技术，还检查了该方法对噪声的鲁棒性和敏感性。结论所提出的分类技术能够以高时间分辨率解码动态模式，其性能优于基准方法，并有利于解剖推理。
BackgroundOver the past decade, pattern decoding techniques have granted neuroscientists improved anatomical specificity in mapping neural representations associated with function and cognition. Dynamical patterns are of particular interest, as evidenced by the proliferation and success of frequency domain methods that reveal structured spatiotemporal rhythmic brain activity. One drawback of such approaches, however, is the need to estimate spectral power, which limits the temporal resolution of classification.New methodWe propose an alternative method that enables classification of dynamical patterns with high temporal fidelity. The key feature of the method is a conversion of time-series data into temporal derivatives. By doing so, dynamically-coded information may be revealed in terms of geometric patterns in the phase space of the derivative signal.ResultsWe derive a geometric classifier for this problem which simplifies into a straightforward calculation in terms of covariances. We demonstrate the relative advantages and disadvantages of the technique with simulated data and benchmark its performance with an EEG dataset of covert spatial attention. We reveal the timecourse of covert spatial attention and, by mapping the classifier weights anatomically, its retinotopic organization.Comparison with existing methodWe especially highlight the ability of the method to provide strong group-level classification performance compared to existing benchmarks, while providing information that is complementary with classical spectral-based techniques. The robustness and sensitivity of the method to noise is also examined relative to spectral-based techniques.ConclusionThe proposed classification technique enables decoding of dynamic patterns with high temporal resolution, performs favorably to benchmark methods, and facilitates anatomical inference.