用快速神经电阻抗技术和深度阵列成像大鼠脑回路活动

Adam Fitchett, J. Fabbri, Yaoxing Hu, J. Cange, Karolina Kozeniauskaite, K. Shepard, D. Holder, K. Aristovich
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引用次数: 0

摘要

在神经回路的“介观”水平上,很少有专门用于神经科学的技术。快速神经电阻抗断层扫描(fnEIT)是一种新型成像技术,具有可负担性、便携性和高空间$(\sim 100 \ \mu \mathrm{m})$和时间(1ms)分辨率。具有深度阵列的fnEIT为研究动物模型大脑中的电路动力学提供了机会。然而,目前的深度阵列几何形状并没有针对这种成像模式进行优化。它们具有小而紧密的高阻抗电极,不能为高分辨率EIT图像重建提供足够的信噪比。它们的活动范围也非常有限。有必要开发适合于fnEIT的深度阵列,并在电路神经科学的代表性设置中评估其性能。在本研究中,我们优化了fnEIT深度阵列的几何结构,并探讨了成像大鼠大脑丘脑皮质回路活动的前景。优化结果与假设一致,即小而密集的电极不适合fnEIT。利用优化的几何结构进行的体内实验表明,fnEIT可以以足够高的分辨率对丘脑皮质回路的活动进行成像,从而看到活动从特定的丘脑核传播到体感觉皮层的特定区域。这预示着fnEIT作为一种电路神经科学技术的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Imaging Circuit Activity in the Rat Brain with Fast Neural EIT and Depth Arrays
Few techniques are specialized for neuroscience at the “mesoscopic” level of neural circuits. Fast neural electrical impedance tomography (fnEIT) is a novel imaging technique that offers affordability, portability, and high spatial $(\sim 100 \ \mu \mathrm{m})$ and temporal (~1 ms) resolution. fnEIT with depth arrays offers the opportunity to study the dynamics of circuits in the brains of animal models. However, current depth array geometries are not optimized for this imaging modality. They feature small, closely packed electrodes with high impedance that do not provide sufficient SNR for high resolution EIT image reconstruction. They also have a highly limited range. It is necessary to develop depth arrays suitable for fnEIT and evaluate their performance in a representative setting for circuit neuroscience. In this study, we optimized the geometry of depth arrays for fnEIT, and then investigated the prospects of imaging thalamocortical circuit activity in the rat brain. Optimization was consistent with the hypothesis that small, closely spaced electrodes were not suitable for fnEIT. In vivo experiments with the optimized geometry then showed that fnEIT can image thalamocortical circuit activity at a high enough resolution to see the activity propagating from specific thalamic nuclei to specific regions of the somatosensory cortex. This bodes well for fnEIT's potential as a technique for circuit neuroscience.
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