海马网络轴突对模式θ突发刺激的反应具有较低的活性,从EC到DG的最初较高的刺突序列相似性和从CA1到EC的轴突的后来相似性。

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-12 DOI:10.1088/1741-2552/acf68a
Ruiyi Chen, Yash Shashank Vakilna, Samuel Brandon Lassers, William C Tang, Gregory Brewer
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引用次数: 1

摘要

客观的解码每个海马亚区的记忆功能涉及对每个海马亚网络如何处理输入刺激的广泛理解。Theta burst刺激(TBS)概括了增强海马回路突触的自然大脑刺激。TBS通常应用于一束轴突,以测量下游亚区域的即时反应,如氨角1(CA1)。然而,人们对刺激反应的网络处理知之甚少,尤其是因为分区之间的单个轴突传输是不可访问的。方法为了解决这些局限性,我们在微电极阵列上对海马网络进行了逆向工程,该阵列由具有互连微流体通道的MEMS四腔装置分隔。微型隧道可以监测单个轴突的传输,而在活体切片中是无法实现的。四个腔分别植入内嗅皮层(EC)、齿状回(DG)、CA1和CA3神经元。将图案化的TBS递送至EC海马网关。利用棘突时间的Jaccard距离度量对每个亚区域的诱发棘突模式相似性进行量化。主要结果。我们发现,网络亚区对不同的刺激模式产生了独特的轴突反应。单点和多点刺激引起网络中轴突尖峰的不同信息路由。从CA3到CA1的轴突的空间上最相似的输出反映了CA3复发网络内的自联想。此外,对于0.2s的重复刺激,轴突与DG之间的尖峰模式相似性从高水平转移到10s间隔的重复,轴突与CA1之间的相似性更大。这种时间依赖性反应表明CA3编码的时间信息和轴突将信息传递给CA1。意义重大。我们的设计和询问方法首次深入了解了结构化海马网络四个亚区之间的信息传递差异,以及亚区水平上对刺激反应的动态模式变化实现概率模式分离和新颖性检测。
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Hippocampal network axons respond to patterned theta burst stimulation with lower activity of initially higher spike train similarity from EC to DG and later similarity of axons from CA1 to EC.

Objective. Decoding memory functions for each hippocampal subregion involves extensive understanding of how each hippocampal subnetwork processes input stimuli. Theta burst stimulation (TBS) recapitulates natural brain stimuli which potentiates synapses in hippocampal circuits. TBS is typically applied to a bundle of axons to measure the immediate response in a downstream subregion like the cornu ammonis 1 (CA1). Yet little is known about network processing in response to stimulation, especially because individual axonal transmission between subregions is not accessible.Approach. To address these limitations, we reverse engineered the hippocampal network on a micro-electrode array partitioned by a MEMS four-chambered device with interconnecting microfluidic tunnels. The micro tunnels allowed monitoring single axon transmission which is inaccessible in slices orin vivo. The four chambers were plated separately with entorhinal cortex (EC), dentate gyrus (DG), CA1, and CA3 neurons. The patterned TBS was delivered to the EC hippocampal gateway. Evoked spike pattern similarity in each subregions was quantified with Jaccard distance metrics of spike timing.Main results. We found that the network subregion produced unique axonal responses to different stimulation patterns. Single site and multisite stimulations caused distinct information routing of axonal spikes in the network. The most spatially similar output at axons from CA3 to CA1 reflected the auto association within CA3 recurrent networks. Moreover, the spike pattern similarities shifted from high levels for axons to and from DG at 0.2 s repeat stimuli to greater similarity in axons to and from CA1 for repetitions at 10 s intervals. This time-dependent response suggested that CA3 encoded temporal information and axons transmitted the information to CA1.Significance. Our design and interrogation approach provide first insights into differences in information transmission between the four subregions of the structured hippocampal network and the dynamic pattern variations in response to stimulation at the subregional level to achieve probabilistic pattern separation and novelty detection.

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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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