Chemla Sandrine, Muller Lyle, Reynaud Alexandre, Takerkart Sylvain, Destexhe Alain, Chavane Frédéric
Aix-Marseille Université, Centre National de la Recherche Scientifique (CNRS), UMR-7289 Institut de Neurosciences de la Timone, Marseille, France.
Salk Institute for Biological Studies, Computational Neurobiology Laboratory, La Jolla, California, United States.
Neurophotonics. 2017 Jul;4(3):031215. doi: 10.1117/1.NPh.4.3.031215. Epub 2017 May 19.
Voltage-sensitive dye imaging (VSDI) is a key neurophysiological recording tool because it reaches brain scales that remain inaccessible to other techniques. The development of this technique from to the behaving nonhuman primate has only been made possible thanks to the long-lasting, visionary work of Amiram Grinvald. This work has opened new scientific perspectives to the great benefit to the neuroscience community. However, this unprecedented technique remains largely under-utilized, and many future possibilities await for VSDI to reveal new functional operations. One reason why this tool has not been used extensively is the inherent complexity of the signal. For instance, the signal reflects mainly the subthreshold neuronal population response and is not linked to spiking activity in a straightforward manner. Second, VSDI gives access to intracortical recurrent dynamics that are intrinsically complex and therefore nontrivial to process. Computational approaches are thus necessary to promote our understanding and optimal use of this powerful technique. Here, we review such approaches, from computational models to dissect the mechanisms and origin of the recorded signal, to advanced signal processing methods to unravel new neuronal interactions at mesoscopic scale. Only a stronger development of interdisciplinary approaches can bridge micro- to macroscales.
电压敏感染料成像(VSDI)是一种关键的神经生理学记录工具,因为它能够达到其他技术难以企及的大脑尺度。这项技术从[具体起点]发展到用于行为中的非人类灵长类动物,这多亏了阿米拉姆·格林瓦尔德(Amiram Grinvald)长期且富有远见的工作才得以实现。这项工作为神经科学界带来了巨大益处,开辟了新的科学视角。然而,这项前所未有的技术在很大程度上仍未得到充分利用,许多未来的可能性有待VSDI去揭示新的功能运作。该工具未被广泛使用的一个原因是信号本身的复杂性。例如,该信号主要反映阈下神经元群体反应,并且与尖峰活动没有直接关联。其次,VSDI能够获取皮质内的循环动力学,而这种动力学本质上很复杂,因此难以处理。因此,需要计算方法来增进我们对这一强大技术的理解并实现其最佳应用。在这里,我们回顾这些方法,从剖析记录信号的机制和起源的计算模型,到揭示介观尺度上新的神经元相互作用的先进信号处理方法。只有更强大的跨学科方法发展才能弥合微观到宏观的尺度。