Laboratory for Cognitive and Computational Neuroscience, Canter for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Madrid, Spain. Departamento de Psicologia Experimental, Procesos Psicologicos y Logopedia, Faculty of Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Madrid, Spain.
J Neural Eng. 2018 Oct;15(5):056011. doi: 10.1088/1741-2552/aacfe4. Epub 2018 Jun 28.
Despite the increase in calculation power over the last few decades, the estimation of brain connectivity is still a tedious task. The high computational cost of the algorithms escalates with the square of the number of signals evaluated, usually in the range of thousands. In this work we propose a re-formulation of a widely used algorithm that allows the estimation of whole brain connectivity in much smaller times.
We start from the original implementation of phase locking value (PLV) and re-formulated it in a computationally very efficient way. What is more, this formulation stresses its strong similarity with coherence, which we used to introduce two new metrics insensitive to zero lag synchronization: the imaginary part of PLV (iPLV) and its corrected counterpart (ciPLV).
The new implementation of PLV avoids some highly CPU-expensive operations and achieves a 100-fold speedup over the original algorithm. The new derived metrics were highly robust in the presence of volume conduction. Moreover, ciPLV proved capable of ignoring zero-lag connectivity, while correctly estimating nonzero-lag connectivity.
Our implementation of PLV makes it possible to calculate whole-brain connectivity in much shorter times. The results of the simulations using ciPLV suggest that this metric is ideal to measure synchronization in the presence of volume conduction or source leakage effects.
尽管在过去几十年中计算能力有所提高，但脑连接的估计仍然是一项繁琐的任务。算法的计算成本随着评估信号数量的平方而增加，通常在数千个范围内。在这项工作中，我们提出了一种广泛使用的算法的重新表述，该算法允许在更短的时间内估计整个大脑的连接。
我们从原始的锁相值 (PLV) 实现开始，并以非常有效的方式对其进行了重新表述。更重要的是，这种表述强调了它与相干性的强烈相似性，我们利用这种相似性引入了两个新的对零延迟同步不敏感的度量：PLV 的虚部 (iPLV) 及其校正对应物 (ciPLV)。
PLV 的新实现避免了一些非常耗费 CPU 的操作，并实现了原始算法 100 倍的加速。新的衍生指标在体积传导存在的情况下具有高度的稳健性。此外，ciPLV 能够忽略零延迟连接，同时正确估计非零延迟连接。
我们对 PLV 的实现使得在更短的时间内计算整个大脑的连接成为可能。使用 ciPLV 进行的模拟结果表明，该指标非常适合在存在体积传导或源泄漏效应的情况下测量同步。
