Distinct Inhibitory Circuits Orchestrate Cortical beta and gamma Band Oscillations
不同的抑制环路协调皮层 beta 和 gamma 波段振荡
Graphical Abstract 图表摘要
Keywords 关键词
Introduction 导言
Information processing in the brain relies on a dynamic interplay among neuronal populations with various rhythmic activities. Characteristic neuronal oscillatory activities vary profoundly across different behavioral states (Steriade et al., 1993), and they are tightly correlated with distinct sensory (Gray and Singer, 1989), motor (Sanes and Donoghue, 1993), and cognitive functions (O’Keefe and Dostrovsky, 1971; Fries et al., 2001). Abnormal or defective neuronal oscillations at specific frequency bands in certain brain areas have often been described in conjunction with human neurological or psychiatric disorders, such as Parkinson’s disease (Lalo et al., 2008) and schizophrenia (Uhlhaas and Singer, 2010).
大脑中的信息处理依赖于神经元群与各种节律活动之间的动态相互作用。神经元振荡活动的特征在不同的行为状态下有很大的不同（ Steriade et al., 1993 ），它们与不同的感觉（ Gray and Singer, 1989 ）、运动（ Sanes and Donoghue, 1993 ）和认知功能（ O’Keefe and Dostrovsky, 1971; Fries et al., 2001 ）紧密相关。某些脑区特定频段的神经元振荡异常或缺陷常常与人类神经或精神疾病有关，如帕金森病（ Lalo et al., 2008 ）和精神分裂症（ Uhlhaas and Singer, 2010 ）。
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Science. 1993; 262:679-685
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Gray, C.M. ∙ Singer, W.
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Proc. Natl. Acad. Sci. USA. 1989; 86:1698-1702
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Sanes, J.N. ∙ Donoghue, J.P.
Oscillations in local field potentials of the primate motor cortex during voluntary movement
Proc. Natl. Acad. Sci. USA. 1993; 90:4470-4474
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Fries, P. ∙ Reynolds, J.H. ∙ Rorie, A.E. ...
Modulation of oscillatory neuronal synchronization by selective visual attention
Science. 2001; 291:1560-1563
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O’Keefe, J. ∙ Dostrovsky, J.
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Uhlhaas, P.J. ∙ Singer, W.
Abnormal neural oscillations and synchrony in schizophrenia
Nat. Rev. Neurosci. 2010; 11:100-113
大脑中的信息处理依赖于神经元群与各种节律活动之间的动态相互作用。神经元振荡活动的特征在不同的行为状态下有很大的不同（ Steriade et al., 1993
69.
Steriade, M. ∙ McCormick, D.A. ∙ Sejnowski, T.J.
Thalamocortical oscillations in the sleeping and aroused brain
Science. 1993; 262:679-685
25.
Gray, C.M. ∙ Singer, W.
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
Proc. Natl. Acad. Sci. USA. 1989; 86:1698-1702
65.
Sanes, J.N. ∙ Donoghue, J.P.
Oscillations in local field potentials of the primate motor cortex during voluntary movement
Proc. Natl. Acad. Sci. USA. 1993; 90:4470-4474
21.
Fries, P. ∙ Reynolds, J.H. ∙ Rorie, A.E. ...
Modulation of oscillatory neuronal synchronization by selective visual attention
Science. 2001; 291:1560-1563
54.
O’Keefe, J. ∙ Dostrovsky, J.
The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat
Brain Res. 1971; 34:171-175
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Lalo, E. ∙ Thobois, S. ∙ Sharott, A. ...
Patterns of bidirectional communication between cortex and basal ganglia during movement in patients with Parkinson disease
J. Neurosci. 2008; 28:3008-3016
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Uhlhaas, P.J. ∙ Singer, W.
Abnormal neural oscillations and synchrony in schizophrenia
Nat. Rev. Neurosci. 2010; 11:100-113
Previous animal studies in vitro (Whittington and Traub, 2003; Bartos et al., 2007) and in vivo (Klausberger and Somogyi, 2008; Sohal et al., 2009; Cardin et al., 2009; Royer et al., 2012; Stark et al., 2013; Fukunaga et al., 2014; Siegle et al., 2014; Veit et al., 2017), together with computational modeling (Freeman, 1972; Wang and Buzsáki, 1996; Tiesinga and Sejnowski, 2009; Buzsáki and Wang, 2012), have strongly suggested that GABAergic interneurons (INs) are among the major players in generating or regulating the temporal structure of neuronal oscillation. In many brain circuits, INs exhibit a rich diversity in their molecular, morphological, and electrophysiological properties (Markram et al., 2004; Klausberger and Somogyi, 2008; Rudy et al., 2011), as well as synaptic connectivity (Pfeffer et al., 2013; Jiang et al., 2015). Although it is tempting to think that a given IN subtype governs one distinct oscillatory rhythm, such a one-to-one relationship has rarely been observed experimentally (Klausberger and Somogyi, 2008). For instance, in the hippocampus, spikes of different IN subtypes were found to lock to different phases of a particular band oscillation (Klausberger et al., 2003), and parvalbumin (PV)-expressing inhibitory neurons were found to be critically involved in the generation of both theta (4- to 8-Hz) (Buzsáki, 2002; Stark et al., 2013) and gamma (30- to 80-Hz) rhythms (Cardin et al., 2009; Sohal et al., 2009). Moreover, a recent study revealed an essential role of another major IN subtype, somatostatin (SOM)-expressing cells, in generating a narrow 20- to 40-Hz band oscillation in the neocortex (Veit et al., 2017, in which the frequency band was termed as a gamma band). Generally, it has been proposed that interplays between interconnected distinct IN subtypes and excitatory pyramidal (principal) cells (PCs) is critical for generating complex rhythmic activities (Vierling-Claassen et al., 2010; Lisman and Jensen, 2013; Womelsdorf et al., 2014), but the underlying circuitry mechanism remains largely unclear.
以往的体外（ Whittington and Traub, 2003; Bartos et al., 2007 ）和体内（ Klausberger and Somogyi, 2008; Sohal et al., 2009; Cardin et al., 2009; Royer et al., 2012; Stark et al., 2013; Fukunaga et al., 2014; Siegle et al., 2014; Veit et al., 2017 ）动物研究以及计算建模（ Freeman, 1972; Wang and Buzsáki, 1996; Tiesinga and Sejnowski, 2009; Buzsáki and Wang, 2012 ）都强烈表明，GABA 能中间神经元（INs）是产生或调节神经元振荡时间结构的主要参与者之一。在许多脑回路中，INs 在分子、形态和电生理特性（ Markram et al., 2004; Klausberger and Somogyi, 2008; Rudy et al., 2011 ）以及突触连接（ Pfeffer et al., 2013; Jiang et al., 2015 ）方面表现出丰富的多样性。虽然人们很容易认为特定的 IN 亚型支配着一种独特的振荡节律，但这种一一对应的关系很少在实验中被观察到 ( Klausberger and Somogyi, 2008 )。例如，在海马中，不同 IN 亚型的尖峰被发现锁定在一个特定波段振荡的不同阶段（ Klausberger et al., 2003 ），而表达抑制性神经元的 parvalbumin（PV）被发现关键地参与了θ（4-8-Hz）（ Buzsáki, 2002; Stark et al., 2013 ）和γ（30-80-Hz）节律的产生（ Cardin et al., 2009; Sohal et al., 2009 ）。此外，最近的一项研究揭示了另一种主要的 IN 亚型--表达体生长抑素（SOM）的细胞--在新皮层产生 20-40-Hz 窄带振荡中的重要作用（ Veit et al., 2017 ，其中该频带被称为伽马频带）。一般认为，不同 IN 亚型和兴奋性锥体（主）细胞（PC）之间的相互影响是产生复杂节律活动的关键（ Vierling-Claassen et al., 2010; Lisman and Jensen, 2013; Womelsdorf et al., 2014 ），但其潜在的电路机制在很大程度上仍不清楚。
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Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
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Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
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Cortical gamma band synchronization through somatostatin interneurons
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以往的体外（ Whittington and Traub, 2003; Bartos et al., 2007
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Bartos, M. ∙ Vida, I. ∙ Jonas, P.
Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks
Nat. Rev. Neurosci. 2007; 8:45-56
78.
Whittington, M.A. ∙ Traub, R.D.
Interneuron diversity series: inhibitory interneurons and network oscillations in vitro
Trends Neurosci. 2003; 26:676-682
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
23.
Fukunaga, I. ∙ Herb, J.T. ∙ Kollo, M. ...
Independent control of gamma and theta activity by distinct interneuron networks in the olfactory bulb
Nat. Neurosci. 2014; 17:1208-1216
36.
Klausberger, T. ∙ Somogyi, P.
Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations
Science. 2008; 321:53-57
62.
Royer, S. ∙ Zemelman, B.V. ∙ Losonczy, A. ...
Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition
Nat. Neurosci. 2012; 15:769-775
66.
Siegle, J.H. ∙ Pritchett, D.L. ∙ Moore, C.I.
Gamma-range synchronization of fast-spiking interneurons can enhance detection of tactile stimuli
Nat. Neurosci. 2014; 17:1371-1379
67.
Sohal, V.S. ∙ Zhang, F. ∙ Yizhar, O. ...
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Nature. 2009; 459:698-702
68.
Stark, E. ∙ Eichler, R. ∙ Roux, L. ...
Inhibition-induced theta resonance in cortical circuits
Neuron. 2013; 80:1263-1276
72.
Veit, J. ∙ Hakim, R. ∙ Jadi, M.P. ...
Cortical gamma band synchronization through somatostatin interneurons
Nat. Neurosci. 2017; 20:951-959
9.
Buzsáki, G. ∙ Wang, X.-J.
Mechanisms of gamma oscillations
Annu. Rev. Neurosci. 2012; 35:203-225
20.
Freeman, W.J.
Waves, pulses and the theory of neural masses
Prog. Theor. Biol. 1972; 2:87-165
70.
Tiesinga, P. ∙ Sejnowski, T.J.
Cortical enlightenment: are attentional gamma oscillations driven by ING or PING?
Neuron. 2009; 63:727-732
76.
Wang, X.J. ∙ Buzsáki, G.
Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model
J. Neurosci. 1996; 16:6402-6413
36.
Klausberger, T. ∙ Somogyi, P.
Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations
Science. 2008; 321:53-57
47.
Markram, H. ∙ Toledo-Rodriguez, M. ∙ Wang, Y. ...
Interneurons of the neocortical inhibitory system
Nat. Rev. Neurosci. 2004; 5:793-807
63.
Rudy, B. ∙ Fishell, G. ∙ Lee, S. ...
Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons
Dev. Neurobiol. 2011; 71:45-61
31.
Jiang, X. ∙ Shen, S. ∙ Cadwell, C.R. ...
Principles of connectivity among morphologically defined cell types in adult neocortex
Science. 2015; 350:aac9462
57.
Pfeffer, C.K. ∙ Xue, M. ∙ He, M. ...
Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Nat. Neurosci. 2013; 16:1068-1076
36.
Klausberger, T. ∙ Somogyi, P.
Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations
Science. 2008; 321:53-57
37.
Klausberger, T. ∙ Magill, P.J. ∙ Márton, L.F. ...
Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo
Nature. 2003; 421:844-848
8.
Buzsáki, G.
Theta oscillations in the hippocampus
Neuron. 2002; 33:325-340
68.
Stark, E. ∙ Eichler, R. ∙ Roux, L. ...
Inhibition-induced theta resonance in cortical circuits
Neuron. 2013; 80:1263-1276
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
67.
Sohal, V.S. ∙ Zhang, F. ∙ Yizhar, O. ...
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Nature. 2009; 459:698-702
72.
Veit, J. ∙ Hakim, R. ∙ Jadi, M.P. ...
Cortical gamma band synchronization through somatostatin interneurons
Nat. Neurosci. 2017; 20:951-959
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Lisman, J.E. ∙ Jensen, O.
The θ-γ neural code
Neuron. 2013; 77:1002-1016
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Vierling-Claassen, D. ∙ Cardin, J.A. ∙ Moore, C.I. ...
Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons
Front. Hum. Neurosci. 2010; 4:198
80.
Womelsdorf, T. ∙ Valiante, T.A. ∙ Sahin, N.T. ...
Dynamic circuit motifs underlying rhythmic gain control, gating and integration
Nat. Neurosci. 2014; 17:1031-1039
The mammalian primary visual cortex (V1) generates rich forms of neuronal oscillation, which are thought to underlie the processing of spatiotemporal information carried by visual inputs (Butts et al., 2007; Jurjut et al., 2011). Low-frequency band (<10-Hz) oscillations could serve as temporal references for information coding (Montemurro et al., 2008; Kayser et al., 2012), whereas faster oscillations in beta and gamma frequency bands could be important for visual attention (Engel et al., 2001; Fries et al., 2001) and feature selection (Gray and Singer, 1989) or binding (Engel and Singer, 2001). These oscillatory activities have been observed in the V1 across different species, including the monkey (Livingstone, 1996; Gieselmann and Thiele, 2008), cat (Gray and Singer, 1989), and mouse (Nase et al., 2003; Niell and Stryker, 2010; Chen et al., 2015; Perrenoud et al., 2016; Saleem et al., 2017; Veit et al., 2017). In comparison to the cat and monkey, the mouse V1 has nearly the same basic visual functions, as manifested by similar receptive field structures and tunings to distinct spatial (e.g., orientation) and temporal features of visual inputs (Niell and Stryker, 2008; Huberman and Niell, 2011). Due to the availability of efficient (opto-)genetic tools for identifying and manipulating specific neuronal types in transgenic animals, mice have been widely used to elucidate differential functions of different IN subtypes in the neocortex (Markram et al., 2004; Rudy et al., 2011; Madisen et al., 2012; Roux et al., 2014).
哺乳动物的初级视觉皮层（V1）会产生丰富的神经元振荡，这被认为是处理视觉输入所携带的时空信息的基础（ Butts et al., 2007; Jurjut et al., 2011 ）。低频段（<10-Hz）振荡可作为信息编码的时间参考（ Montemurro et al., 2008; Kayser et al., 2012 ），而β和γ频段的较快振荡可能对视觉注意（ Engel et al., 2001; Fries et al., 2001 ）和特征选择（ Gray and Singer, 1989 ）或结合（ Engel and Singer, 2001 ）非常重要。在不同的物种中，包括猴（ Livingstone, 1996; Gieselmann and Thiele, 2008 ）、猫（ Gray and Singer, 1989 ）和小鼠（ Nase et al., 2003; Niell and Stryker, 2010; Chen et al., 2015; Perrenoud et al., 2016; Saleem et al., 2017; Veit et al., 2017 ）的 V1 中都观察到了这些振荡活动。与猫和猴相比，小鼠的 V1 具有几乎相同的基本视觉功能，表现为相似的感受野结构和对视觉输入的不同空间（如方向）和时间特征的调谐（ Niell and Stryker, 2008; Huberman and Niell, 2011 ）。由于有了高效的（光学）遗传工具来识别和操纵转基因动物的特定神经元类型，小鼠已被广泛用于阐明新皮层中不同 IN 亚型的不同功能 ( Markram et al., 2004; Rudy et al., 2011; Madisen et al., 2012; Roux et al., 2014 )。
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Proc. Natl. Acad. Sci. USA. 1989; 86:1698-1702
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What can mice tell us about how vision works?
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Rudy, B. ∙ Fishell, G. ∙ Lee, S. ...
Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons
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哺乳动物的初级视觉皮层（V1）会产生丰富的神经元振荡，这被认为是处理视觉输入所携带的时空信息的基础（ Butts et al., 2007; Jurjut et al., 2011
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Modulation of oscillatory neuronal synchronization by selective visual attention
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Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
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Temporal binding and the neural correlates of sensory awareness
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Gieselmann, M.A. ∙ Thiele, A.
Comparison of spatial integration and surround suppression characteristics in spiking activity and the local field potential in macaque V1
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Livingstone, M.S.
Oscillatory firing and interneuronal correlations in squirrel monkey striate cortex
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Gray, C.M. ∙ Singer, W.
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
Proc. Natl. Acad. Sci. USA. 1989; 86:1698-1702
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Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
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Nase, G. ∙ Singer, W. ∙ Monyer, H. ...
Features of neuronal synchrony in mouse visual cortex
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Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
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Perrenoud, Q. ∙ Pennartz, C.M.A. ∙ Gentet, L.J.
Membrane potential dynamics of spontaneous and visually evoked gamma activity in V1 of awake mice
PLoS Biol. 2016; 14:e1002383
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Saleem, A.B. ∙ Lien, A.D. ∙ Krumin, M. ...
Subcortical Source and Modulation of the Narrowband Gamma Oscillation in Mouse Visual Cortex
Neuron. 2017; 93:315-322
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Veit, J. ∙ Hakim, R. ∙ Jadi, M.P. ...
Cortical gamma band synchronization through somatostatin interneurons
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Huberman, A.D. ∙ Niell, C.M.
What can mice tell us about how vision works?
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Niell, C.M. ∙ Stryker, M.P.
Highly selective receptive fields in mouse visual cortex
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Madisen, L. ∙ Mao, T. ∙ Koch, H. ...
A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing
Nat. Neurosci. 2012; 15:793-802
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Markram, H. ∙ Toledo-Rodriguez, M. ∙ Wang, Y. ...
Interneurons of the neocortical inhibitory system
Nat. Rev. Neurosci. 2004; 5:793-807
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Roux, L. ∙ Stark, E. ∙ Sjulson, L. ...
In vivo optogenetic identification and manipulation of GABAergic interneuron subtypes
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Rudy, B. ∙ Fishell, G. ∙ Lee, S. ...
Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons
Dev. Neurobiol. 2011; 71:45-61
In the rodent V1, SOM and PV neurons are two major molecularly distinct subtypes of cortical IN, and they differ substantially in their intrinsic spiking properties (Hu et al., 2011; Lazarus and Huang, 2011; Miao et al., 2016), synaptic connectivity (Markram et al., 2004; Pfeffer et al., 2013; Karnani et al., 2016), and visual functions (Ma et al., 2010; Lee et al., 2012; Wilson et al., 2012; Cottam et al., 2013; Fu et al., 2014). In the present study, we examined how cortical SOM and PV neurons are involved in orchestrating different oscillatory activities in the V1, by performing extracellular recordings of local field potentials (LFPs) as well as spikes of these cells in awake head-fixed transgenic mice genetically specific for the two IN subtypes. Our results suggest that SOM and PV cells preferentially drive low-frequency (5- to 30-Hz) and high-frequency (20- to 80-Hz) band oscillations, respectively, and that both IN subtypes are required for generating visually induced beta oscillation, where their contributions differ.
在啮齿动物 V1 中，SOM 神经元和 PV 神经元是大脑皮层 IN 的两种主要的分子不同亚型，它们在固有尖峰特性（ Hu et al., 2011; Lazarus and Huang, 2011; Miao et al., 2016 ）、突触连接（ Markram et al., 2004; Pfeffer et al., 2013; Karnani et al., 2016 ）和视觉功能（ Ma et al., 2010; Lee et al., 2012; Wilson et al., 2012; Cottam et al., 2013; Fu et al., 2014 ）方面存在很大差异。在本研究中，我们通过对清醒的头固定转基因小鼠的局部场电位（LFP）以及这些细胞的尖峰进行细胞外记录，研究了皮层 SOM 和 PV 神经元如何参与协调 V1 中的不同振荡活动。我们的研究结果表明，SOM 和 PV 细胞分别优先驱动低频（5-30-Hz）和高频（20-80-Hz）波段振荡，而且这两种 IN 亚型都需要产生视觉诱导的贝塔振荡，它们的贡献有所不同。
29.
Hu, H. ∙ Ma, Y. ∙ Agmon, A.
Submillisecond firing synchrony between different subtypes of cortical interneurons connected chemically but not electrically
J. Neurosci. 2011; 31:3351-3361
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Lazarus, M.S. ∙ Huang, Z.J.
Distinct maturation profiles of perisomatic and dendritic targeting GABAergic interneurons in the mouse primary visual cortex during the critical period of ocular dominance plasticity
J. Neurophysiol. 2011; 106:775-787
48.
Miao, Q. ∙ Yao, L. ∙ Rasch, M.J. ...
Selective maturation of temporal dynamics of intracortical excitatory transmission at the critical period onset
Cell Rep. 2016; 16:1677-1689
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Karnani, M.M.M. ∙ Jackson, J. ∙ Ayzenshtat, I. ...
Cooperative subnetworks of molecularly similar interneurons in mouse neocortex
Neuron. 2016; 90:86-100
47.
Markram, H. ∙ Toledo-Rodriguez, M. ∙ Wang, Y. ...
Interneurons of the neocortical inhibitory system
Nat. Rev. Neurosci. 2004; 5:793-807
57.
Pfeffer, C.K. ∙ Xue, M. ∙ He, M. ...
Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Nat. Neurosci. 2013; 16:1068-1076
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Cottam, J.C.H. ∙ Smith, S.L. ∙ Häusser, M.
Target-specific effects of somatostatin-expressing interneurons on neocortical visual processing
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Fu, Y. ∙ Tucciarone, J.M. ∙ Espinosa, J.S. ...
A cortical circuit for gain control by behavioral state
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Lee, S.-H. ∙ Kwan, A.C. ∙ Zhang, S. ...
Activation of specific interneurons improves V1 feature selectivity and visual perception
Nature. 2012; 488:379-383
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Ma, W.P. ∙ Liu, B.H. ∙ Li, Y.T. ...
Visual representations by cortical somatostatin inhibitory neurons--selective but with weak and delayed responses
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Wilson, N.R. ∙ Runyan, C.A. ∙ Wang, F.L. ...
Division and subtraction by distinct cortical inhibitory networks in vivo
Nature. 2012; 488:343-348
在啮齿动物 V1 中，SOM 神经元和 PV 神经元是大脑皮层 IN 的两种主要的分子不同亚型，它们在固有尖峰特性（ Hu et al., 2011; Lazarus and Huang, 2011; Miao et al., 2016
29.
Hu, H. ∙ Ma, Y. ∙ Agmon, A.
Submillisecond firing synchrony between different subtypes of cortical interneurons connected chemically but not electrically
J. Neurosci. 2011; 31:3351-3361
40.
Lazarus, M.S. ∙ Huang, Z.J.
Distinct maturation profiles of perisomatic and dendritic targeting GABAergic interneurons in the mouse primary visual cortex during the critical period of ocular dominance plasticity
J. Neurophysiol. 2011; 106:775-787
48.
Miao, Q. ∙ Yao, L. ∙ Rasch, M.J. ...
Selective maturation of temporal dynamics of intracortical excitatory transmission at the critical period onset
Cell Rep. 2016; 16:1677-1689
33.
Karnani, M.M.M. ∙ Jackson, J. ∙ Ayzenshtat, I. ...
Cooperative subnetworks of molecularly similar interneurons in mouse neocortex
Neuron. 2016; 90:86-100
47.
Markram, H. ∙ Toledo-Rodriguez, M. ∙ Wang, Y. ...
Interneurons of the neocortical inhibitory system
Nat. Rev. Neurosci. 2004; 5:793-807
57.
Pfeffer, C.K. ∙ Xue, M. ∙ He, M. ...
Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Nat. Neurosci. 2013; 16:1068-1076
15.
Cottam, J.C.H. ∙ Smith, S.L. ∙ Häusser, M.
Target-specific effects of somatostatin-expressing interneurons on neocortical visual processing
J. Neurosci. 2013; 33:19567-19578
22.
Fu, Y. ∙ Tucciarone, J.M. ∙ Espinosa, J.S. ...
A cortical circuit for gain control by behavioral state
Cell. 2014; 156:1139-1152
41.
Lee, S.-H. ∙ Kwan, A.C. ∙ Zhang, S. ...
Activation of specific interneurons improves V1 feature selectivity and visual perception
Nature. 2012; 488:379-383
45.
Ma, W.P. ∙ Liu, B.H. ∙ Li, Y.T. ...
Visual representations by cortical somatostatin inhibitory neurons--selective but with weak and delayed responses
J. Neurosci. 2010; 30:14371-14379
79.
Wilson, N.R. ∙ Runyan, C.A. ∙ Wang, F.L. ...
Division and subtraction by distinct cortical inhibitory networks in vivo
Nature. 2012; 488:343-348
Results 成果
All electrophysiological recordings were performed in the V1 layers 2/3–4 of different transgenic mice described below. To achieve selective expression of light-activated inhibitory proton pump archaerhodopsin-3 (Arch; Chow et al., 2010) or excitatory cation channel channelrhodopsin (ChR2; Boyden et al., 2005; Li et al., 2005) in either SOM- or PV-expressing INs, Som-IRES-Cre and Pvalb-IRES-Cre transgenic mice were crossed with lines Ai35 (Rosa-CAG-LSL-ss-Arch-eGFP-ER2-WPRE), Ai27 (Rosa-CAG-LSL-ChR2(H134R)-tdTomato-WPRE), or Ai32 (Rosa-CAG-LSL-ChR2(H134R)-EYFP-WPRE). The bred mice used in this study include SOM::Ai35 (SOM-Arch), PV::Ai35 (PV-Arch), SOM::Ai27, and SOM::Ai32 (SOM-ChR2), as well as PV::Ai27 and PV::Ai32 (PV-ChR2).
所有电生理记录都是在下述不同转基因小鼠的 V1 2/3-4 层进行的。为了在 SOM 或 PV 表达的 IN 中选择性表达光激活抑制性质子泵 archaerhodopsin-3 （Arch； Chow et al., 2010 ）或兴奋性阳离子通道 rhodopsin（ChR2； Boyden et al., 2005; Li et al., 2005 ），Som-IRES-Cre 和 Pvalb-IRES-Cre 转基因小鼠与品系 Ai35（Rosa-CAG-LSL-ss-Arch-eGFP-ER2-WPRE）、Ai27（Rosa-CAG-LSL-ChR2(H134R)-tdTomato-WPRE）或 Ai32（Rosa-CAG-LSL-ChR2(H134R)-EYFP-WPRE）杂交。本研究中使用的育种小鼠包括 SOM::Ai35 (SOM-Arch)、PV::Ai35 (PV-Arch)、SOM::Ai27 和 SOM::Ai32 (SOM-ChR2)，以及 PV::Ai27 和 PV::Ai32 (PV-ChR2)。
14.
Chow, B.Y. ∙ Han, X. ∙ Dobry, A.S. ...
High-performance genetically targetable optical neural silencing by light-driven proton pumps
Nature. 2010; 463:98-102
6.
Boyden, E.S. ∙ Zhang, F. ∙ Bamberg, E. ...
Millisecond-timescale, genetically targeted optical control of neural activity
Nat. Neurosci. 2005; 8:1263-1268
42.
Li, X. ∙ Gutierrez, D.V. ∙ Hanson, M.G. ...
Fast noninvasive activation and inhibition of neural and network activity by vertebrate rhodopsin and green algae channelrhodopsin
Proc. Natl. Acad. Sci. USA. 2005; 102:17816-17821
所有电生理记录都是在下述不同转基因小鼠的 V1 2/3-4 层进行的。为了在 SOM 或 PV 表达的 IN 中选择性表达光激活抑制性质子泵 archaerhodopsin-3 （Arch； Chow et al., 2010
14.
Chow, B.Y. ∙ Han, X. ∙ Dobry, A.S. ...
High-performance genetically targetable optical neural silencing by light-driven proton pumps
Nature. 2010; 463:98-102
6.
Boyden, E.S. ∙ Zhang, F. ∙ Bamberg, E. ...
Millisecond-timescale, genetically targeted optical control of neural activity
Nat. Neurosci. 2005; 8:1263-1268
42.
Li, X. ∙ Gutierrez, D.V. ∙ Hanson, M.G. ...
Fast noninvasive activation and inhibition of neural and network activity by vertebrate rhodopsin and green algae channelrhodopsin
Proc. Natl. Acad. Sci. USA. 2005; 102:17816-17821
Optogenetic Tagging and Manipulation of Cortical SOM and PV INs
光遗传标记和操纵皮层 SOM 与 PV INs
We first performed immunohistological experiments to assess the efficiency and specificity of optogenetic protein expression in the V1 of SOM-Arch/ChR2 and PV-Arch/ChR2 mice. The results showed that, on average, more than ∼70% of SOM or PV cells expressed either Arch or ChR2 (70%–95% and 60%–80%, respectively), along with high-level expression specificity in the desired IN subtypes (Figures 1A, 1C, 1E, and 1G; Figures S1A–S1F; both 11 animals for the Som-IRES-Cre and PV-IRES-Cre mice).
我们首先进行了免疫组织学实验，以评估光遗传蛋白在 SOM-Arch/ChR2 和 PV-Arch/ChR2 小鼠 V1 中表达的效率和特异性。结果显示，平均有超过 70% 的 SOM 或 PV 细胞表达 Arch 或 ChR2（分别为 70%-95% 和 60%-80%），并且在所需的 IN 亚型中具有高水平的表达特异性（图 1A、1C、1E 和 1G；图 S1A-S1F；Som-IRES-Cre 和 PV-IRES-Cre 小鼠均为 11 只）。
我们首先进行了免疫组织学实验，以评估光遗传蛋白在 SOM-Arch/ChR2 和 PV-Arch/ChR2 小鼠 V1 中表达的效率和特异性。结果显示，平均有超过 70% 的 SOM 或 PV 细胞表达 Arch 或 ChR2（分别为 70%-95% 和 60%-80%），并且在所需的 IN 亚型中具有高水平的表达特异性（图 1A、1C、1E 和 1G；图 S1A-S1F；Som-IRES-Cre 和 PV-IRES-Cre 小鼠均为 11 只）。
Figure 1 Optogenetic Manipulation and Identification of Spiking Activity of SOM and PV Cells
图 1 光遗传操纵和 SOM 与 PV 细胞尖峰活动的识别
图 1 光遗传操纵和 SOM 与 PV 细胞尖峰活动的识别
In the V1 of awake head-fixed animals, we observed that yellow (589-nm) or blue (473-nm) laser pulses, applied via an optical fiber (with a core diameter of 50 μm) placed on the cortical surface and near the recording electrode (distance <200 μm), were effective in suppressing or elevating spiking activity of specific IN subtypes through the activation of Arch (Figures 1A–1D) or ChR2 (Figures 1E–1H), respectively, which is consistent with previous studies using the same transgenic mice (Madisen et al., 2012; Stark et al., 2013). Laser-induced changes in spiking activity of cortical cells were monitored by recording with a multiple-microwire array (MMA), and only those units exhibiting nearly identical waveforms during the laser “on” and “off” periods (correlation value r > 0.9) were included for further analysis (see STAR Methods and Figure 1A, right). We also categorized the recorded units into narrow-spike (NS) units (putative cortical INs; n = 162 units) and wide-spike (WS) units (mostly excitatory PCs; n = 156), based on the widely used criteria of spike waveform features: the peak-to-trough latency, the ratio of peak and trough amplitudes, and the width of half peak (Figures 1A and 1B; Figures S1H and S1I; see Barthó et al., 2004; Niell and Stryker, 2010; and Stark et al., 2013).
在清醒的头固定动物的 V1 中，我们观察到黄色（589-nm）或蓝色（473-nm）激光脉冲通过放置在皮层表面和记录电极附近（距离小于 200 μm）的光纤（光纤芯直径为 50 μm）施加、这与之前使用相同转基因小鼠进行的研究（ Madisen et al., 2012; Stark et al., 2013 ）一致。通过多微线阵列（MMA）记录监测激光诱导的皮层细胞尖峰活动变化，只有那些在激光 "开 "和 "关 "期间波形几乎相同的单元（相关值 r > 0.9）才被纳入进一步分析（见 STAR 方法和图 1A 右）。我们还根据广泛使用的尖峰波形特征标准：峰-谷潜伏期、峰-谷振幅比和半峰宽度，将记录的单元分为窄尖峰（NS）单元（推测的皮质 IN；n = 162 个单元）和宽尖峰（WS）单元（主要是兴奋性 PC；n = 156 个单元）（图 1A 和 1B；图 S1H 和 S1I；见 Barthó et al., 2004; Niell and Stryker, 2010 和 Stark et al., 2013 ）。
46.
Madisen, L. ∙ Mao, T. ∙ Koch, H. ...
A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing
Nat. Neurosci. 2012; 15:793-802
68.
Stark, E. ∙ Eichler, R. ∙ Roux, L. ...
Inhibition-induced theta resonance in cortical circuits
Neuron. 2013; 80:1263-1276
2.
Barthó, P. ∙ Hirase, H. ∙ Monconduit, L. ...
Characterization of neocortical principal cells and interneurons by network interactions and extracellular features
J. Neurophysiol. 2004; 92:600-608
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
68.
Stark, E. ∙ Eichler, R. ∙ Roux, L. ...
Inhibition-induced theta resonance in cortical circuits
Neuron. 2013; 80:1263-1276
在清醒的头固定动物的 V1 中，我们观察到黄色（589-nm）或蓝色（473-nm）激光脉冲通过放置在皮层表面和记录电极附近（距离小于 200 μm）的光纤（光纤芯直径为 50 μm）施加、这与之前使用相同转基因小鼠进行的研究（ Madisen et al., 2012; Stark et al., 2013
46.
Madisen, L. ∙ Mao, T. ∙ Koch, H. ...
A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing
Nat. Neurosci. 2012; 15:793-802
68.
Stark, E. ∙ Eichler, R. ∙ Roux, L. ...
Inhibition-induced theta resonance in cortical circuits
Neuron. 2013; 80:1263-1276
2.
Barthó, P. ∙ Hirase, H. ∙ Monconduit, L. ...
Characterization of neocortical principal cells and interneurons by network interactions and extracellular features
J. Neurophysiol. 2004; 92:600-608
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
68.
Stark, E. ∙ Eichler, R. ∙ Roux, L. ...
Inhibition-induced theta resonance in cortical circuits
Neuron. 2013; 80:1263-1276
As shown by the example recordings from the SOM-Arch and PV-Arch mice in Figures 1A and 1C, delivery of yellow laser pulses (5–30 mW, 4-s duration, 12- to 20-s intervals) to induce the Arch-mediated hyperpolarizing currents caused similar effects on the baseline spiking activity of the recorded cells: a sustained decrease of baseline spiking rate in the light-stimulated NS cell and simultaneous increases of spiking rate in other NS and WS cells rapidly after the onset of laser stimulation (note that a significant reduction was indicated by ∗p < 0.05 by Wilcoxon two-sided signed-rank test). We referred to those units showing significant suppressive responses that were time-locked to laser stimulation as optogenetically tagged SOM or PV cells (t-SOM or t-PV cells; p < 0.01, permutation test, light on versus light off; see STAR Methods and Royer et al., 2012). With this opto-tagging method, we identified 6 t-SOM and 8 t-PV cells from recordings in 19 SOM-Arch and 24 PV-Arch mice, respectively, and the mean firing rate of tagged INs was reduced during laser stimulation by >3 Hz and by >25% of the baseline level (Figures 1B and 1D, green/red). The extent of laser-induced suppression of t-SOM or t-PV cells was largely the same during early and late stimulation windows (Figures S1M and S1N). We further found that laser-induced suppression of Arch-expressing SOM or PV cells was accompanied by an increase in spike rate in nearly half of the non-tagged NS and WS cells (black dots, non-tagged, p < 0.01; gray dots, p > 0.01; Figures 1B and 1D).
如图 1A 和 1C 中 SOM-Arch 小鼠和 PV-Arch 小鼠的记录示例所示，发射黄色激光脉冲（5-30 mW，持续时间 4 秒，间隔 12-20 秒）诱导 Arch 介导的超极化电流对记录细胞的基线尖峰活动产生了类似的影响：光刺激的 NS 细胞的基线尖峰率持续下降，而其他 NS 和 WS 细胞的尖峰率在激光刺激开始后迅速上升（注： ∗ p < 0.05）。我们将那些显示出与激光刺激时间锁定的显著抑制性反应的单元称为光标记 SOM 或 PV 细胞（t-SOM 或 t-PV 细胞；p < 0.01，排列检验，开灯与关灯；见 STAR 方法和 Royer et al., 2012 ）。通过这种光标记方法，我们分别从 19 只 SOM-Arch 小鼠和 24 只 PV-Arch 小鼠的记录中鉴定出了 6 个 t-SOM 细胞和 8 个 t-PV 细胞，在激光刺激期间，标记 INs 的平均发射率降低了>3 Hz，基线水平降低了>25%（图 1B 和 1D，绿色/红色）。在早期和晚期刺激窗口，激光诱导的 t-SOM 或 t-PV 细胞的抑制程度基本相同（图 S1M 和 S1N）。我们进一步发现，激光诱导抑制 Arch 表达的 SOM 或 PV 细胞的同时，近一半的非标记 NS 和 WS 细胞的尖峰率也增加了（黑点，非标记，p < 0.01；灰点，p > 0.01；图 1B 和 1D）。
62.
Royer, S. ∙ Zemelman, B.V. ∙ Losonczy, A. ...
Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition
Nat. Neurosci. 2012; 15:769-775
如图 1A 和 1C 中 SOM-Arch 小鼠和 PV-Arch 小鼠的记录示例所示，发射黄色激光脉冲（5-30 mW，持续时间 4 秒，间隔 12-20 秒）诱导 Arch 介导的超极化电流对记录细胞的基线尖峰活动产生了类似的影响：光刺激的 NS 细胞的基线尖峰率持续下降，而其他 NS 和 WS 细胞的尖峰率在激光刺激开始后迅速上升（注： ∗ p < 0.05）。我们将那些显示出与激光刺激时间锁定的显著抑制性反应的单元称为光标记 SOM 或 PV 细胞（t-SOM 或 t-PV 细胞；p < 0.01，排列检验，开灯与关灯；见 STAR 方法和 Royer et al., 2012
62.
Royer, S. ∙ Zemelman, B.V. ∙ Losonczy, A. ...
Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition
Nat. Neurosci. 2012; 15:769-775
Similarly, based on the time-locked increase of spiking activity elicited by blue laser-induced ChR2 excitation (5–30 mW, 4-s pulses), we identified 23 t-SOM cells and 30 t-PV cells from recordings in 21 SOM-ChR2 and 26 PV-ChR2 mice (p < 0.01, with rate increase >2 Hz and >50% of the baseline level; Figures 1E–1H), respectively. Moreover, we also applied 1-ms pulses of blue laser (at 30 mW) to test the stimulus-associated spike latency (Kvitsiani et al., 2013), and we found short latencies (5.55 ± 1.61 and 4.69 ± 2.18 ms for t-SOM and t-PV, respectively, mean ± SD) and low spike jitters (1.47 ± 0.62 ms and 1.48 ± 0.73 ms for t-SOM and t-PV, respectively) of laser-elicited spikes in ChR2-expressing SOM and PV cells (Figures 7A and 7E). The latter result further proves the reliability of opto-tagging with ChR2. Meanwhile, we also found that light activation of ChR2-expressing SOM or PV cells substantially inhibited spiking of about half of the non-tagged NS and WS cells, with comparable extents of inhibition (Figures 1E–1H). We noted that 3 of 38 t-PV cells showed wider spike waveforms, and this portion is similar to what has been reported previously (Kvitsiani et al., 2013; see STAR Methods).
同样，基于蓝色激光诱导 ChR2 激发（5-30 mW，4 秒脉冲）引起的尖峰活动的时间锁定增加，我们从 21 只 SOM-ChR2 小鼠和 26 只 PV-ChR2 小鼠的记录中分别鉴定出 23 个 t-SOM 细胞和 30 个 t-PV 细胞（p < 0.01，速率增加大于 2 Hz 和大于基线水平的 50%；图 1E-1H）。此外，我们还使用 1 毫秒的蓝色激光脉冲（30 毫瓦）来测试刺激相关的尖峰潜伏期（ Kvitsiani et al., 2013 ），结果发现潜伏期很短（t-SOM 和 t-SOM 分别为 5.55 ± 1.61 和 4.69 ± 2.18 毫秒，平均±标清）和较低的尖峰抖动（t-SOM 和 t-PV 分别为 1.47±0.62 毫秒和 1.48±0.73 毫秒）（图 7A 和 7E）。后一结果进一步证明了用 ChR2 进行光标记的可靠性。与此同时，我们还发现，光激活表达 ChR2 的 SOM 或 PV 细胞大大抑制了约一半未标记的 NS 和 WS 细胞的尖峰脉冲，抑制程度相当（图 1E-1H）。我们注意到，38 个 t-PV 细胞中有 3 个表现出更宽的尖峰波形，这部分与之前的报道类似（ Kvitsiani et al., 2013 ；见 STAR 方法）。
38.
Kvitsiani, D. ∙ Ranade, S. ∙ Hangya, B. ...
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Nature. 2013; 498:363-366
38.
Kvitsiani, D. ∙ Ranade, S. ∙ Hangya, B. ...
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Nature. 2013; 498:363-366
同样，基于蓝色激光诱导 ChR2 激发（5-30 mW，4 秒脉冲）引起的尖峰活动的时间锁定增加，我们从 21 只 SOM-ChR2 小鼠和 26 只 PV-ChR2 小鼠的记录中分别鉴定出 23 个 t-SOM 细胞和 30 个 t-PV 细胞（p < 0.01，速率增加大于 2 Hz 和大于基线水平的 50%；图 1E-1H）。此外，我们还使用 1 毫秒的蓝色激光脉冲（30 毫瓦）来测试刺激相关的尖峰潜伏期（ Kvitsiani et al., 2013
38.
Kvitsiani, D. ∙ Ranade, S. ∙ Hangya, B. ...
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Nature. 2013; 498:363-366
38.
Kvitsiani, D. ∙ Ranade, S. ∙ Hangya, B. ...
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Nature. 2013; 498:363-366
Thus, we established reliable approaches to identify specific IN subtypes and manipulate their spiking activity in the V1 in vivo. Our results also indicate that cortical SOM and PV cells may reciprocally modulate their spike activity in vivo, a form of dis-inhibitory regulation in part through direct inhibitory connections between these two IN subtypes in the neocortex (Pfeffer et al., 2013; Xu et al., 2013; Miao et al., 2016).
因此，我们建立了可靠的方法来识别特定的 IN 亚型，并在体内操纵它们在 V1 中的尖峰活动。我们的研究结果还表明，大脑皮层的 SOM 和 PV 细胞可以在体内相互调节它们的尖峰活动，这是一种非抑制性调节，部分是通过新皮层中这两种 IN 亚型之间的直接抑制性连接实现的 ( Pfeffer et al., 2013; Xu et al., 2013; Miao et al., 2016 )。
48.
Miao, Q. ∙ Yao, L. ∙ Rasch, M.J. ...
Selective maturation of temporal dynamics of intracortical excitatory transmission at the critical period onset
Cell Rep. 2016; 16:1677-1689
57.
Pfeffer, C.K. ∙ Xue, M. ∙ He, M. ...
Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Nat. Neurosci. 2013; 16:1068-1076
81.
Xu, H. ∙ Jeong, H.Y. ∙ Tremblay, R. ...
Neocortical somatostatin-expressing GABAergic interneurons disinhibit the thalamorecipient layer 4
Neuron. 2013; 77:155-167
因此，我们建立了可靠的方法来识别特定的 IN 亚型，并在体内操纵它们在 V1 中的尖峰活动。我们的研究结果还表明，大脑皮层的 SOM 和 PV 细胞可以在体内相互调节它们的尖峰活动，这是一种非抑制性调节，部分是通过新皮层中这两种 IN 亚型之间的直接抑制性连接实现的 ( Pfeffer et al., 2013; Xu et al., 2013; Miao et al., 2016
48.
Miao, Q. ∙ Yao, L. ∙ Rasch, M.J. ...
Selective maturation of temporal dynamics of intracortical excitatory transmission at the critical period onset
Cell Rep. 2016; 16:1677-1689
57.
Pfeffer, C.K. ∙ Xue, M. ∙ He, M. ...
Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Nat. Neurosci. 2013; 16:1068-1076
81.
Xu, H. ∙ Jeong, H.Y. ∙ Tremblay, R. ...
Neocortical somatostatin-expressing GABAergic interneurons disinhibit the thalamorecipient layer 4
Neuron. 2013; 77:155-167
Preferential Correlation of PV Cell Spiking with Spontaneous gamma Activity
光伏细胞尖峰与自发伽马活动的优先相关性
Using the above established optogenetic approaches, we next examined how SOM and PV cells contribute to rhythmic activities in the V1. We performed MMA recording in layers 2/3–4 of awake head-fixed mice using the air-floated styrofoam ball setup shown in Figure 2A, which was used in previous studies (Dombeck et al., 2007; Niell and Stryker, 2010), including our own (Chen et al., 2015). Computer-generated visual stimuli were presented to the animal by a cathode ray tube (CRT) monitor (covering a 90 × 75-degree visual field; Figure 2A). During blank stimulation (ambient luminance of 30 cd/m2), neuronal spike rates and the power spectrogram of LFP in spontaneous activity changed characteristically when the animal started to run after a period of being stationary (determined by the relative motion speed [r.s.] calculated from consecutive video frames; Figure 2B, bottom). Population results showed that, in the running state, LFP power of the 1- to 20-Hz frequency band (peak frequency: 4.3 ± 1.7 Hz, mean ± SD) significantly decreased compared with the stationary state, while that of the 40- to 70-Hz gamma band (peak frequency: 54.4 ± 4.2 Hz) increased (n = 274 recordings in 61 mice; Figure 2C; Figures S2A and S2B). Such characteristic locomotion-induced changes of spontaneous LFPs, especially at the gamma band, are consistent with previous studies (Niell and Stryker, 2010; Chen et al., 2015; Saleem et al., 2017).
利用上述成熟的光遗传学方法，我们接下来研究了 SOM 和 PV 细胞如何促进 V1 的节律活动。我们使用图 2A 所示的气浮泡沫塑料球装置在清醒的头固定小鼠的第 2/3-4 层进行了 MMA 记录，该装置曾用于之前的研究 ( Dombeck et al., 2007; Niell and Stryker, 2010 )，包括我们自己的研究 ( Chen et al., 2015 )。计算机生成的视觉刺激通过阴极射线管（CRT）显示器（覆盖 90 × 75 度的视野；图 2A）呈现给动物。在空白刺激期间（环境亮度为 30 cd/m 2 ），当动物在静止一段时间后开始奔跑时（根据连续视频帧计算的相对运动速度 [r.s.] 确定；图 2B 底部），神经元尖峰率和自发活动中 LFP 的功率谱图发生了特征性变化。群体结果显示，与静止状态相比，在奔跑状态下，1-20Hz 频段的 LFP 功率（峰值频率：4.3 ± 1.7 Hz，平均值±标度）明显下降，而 40-70Hz 伽马频段的 LFP 功率（峰值频率：54.4 ± 4.2 Hz）则有所上升（61 只小鼠的 274 条记录；图 2C；图 S2A 和 S2B）。运动诱导的自发 LFPs 的这种特征性变化，尤其是伽马频段的变化，与以前的研究一致（ Niell and Stryker, 2010; Chen et al., 2015; Saleem et al., 2017 ）。
16.
Dombeck, D.A. ∙ Khabbaz, A.N. ∙ Collman, F. ...
Imaging large-scale neural activity with cellular resolution in awake, mobile mice
Neuron. 2007; 56:43-57
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
64.
Saleem, A.B. ∙ Lien, A.D. ∙ Krumin, M. ...
Subcortical Source and Modulation of the Narrowband Gamma Oscillation in Mouse Visual Cortex
Neuron. 2017; 93:315-322
利用上述成熟的光遗传学方法，我们接下来研究了 SOM 和 PV 细胞如何促进 V1 的节律活动。我们使用图 2A 所示的气浮泡沫塑料球装置在清醒的头固定小鼠的第 2/3-4 层进行了 MMA 记录，该装置曾用于之前的研究 ( Dombeck et al., 2007; Niell and Stryker, 2010
16.
Dombeck, D.A. ∙ Khabbaz, A.N. ∙ Collman, F. ...
Imaging large-scale neural activity with cellular resolution in awake, mobile mice
Neuron. 2007; 56:43-57
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
64.
Saleem, A.B. ∙ Lien, A.D. ∙ Krumin, M. ...
Subcortical Source and Modulation of the Narrowband Gamma Oscillation in Mouse Visual Cortex
Neuron. 2017; 93:315-322
Figure 2 Stronger Correlation of PV Cell Spiking with Spontaneous gamma Oscillation
图 2 光伏细胞尖峰与自发伽马振荡的更强相关性
图 2 光伏细胞尖峰与自发伽马振荡的更强相关性
We further found that the population spike rates of excitatory PCs (WS units) as well as inhibitory t-SOM and t-PV cells significantly increased during the running state compared with the stationary state (PC, p < 10−11; t-SOM, p = 0.009; t-PV, p < 0.001; Figure 2D). Interestingly, the mean spontaneous rates of PCs and t-SOM and t-PV cells were all positively correlated with gamma band LFP power, while their correlations with theta band LFP power varied: a negative correlation for PC cells, positive correlation for t-PV cells, and no significant correlation for t-SOM cells (Figure 2E). Furthermore, we calculated the spike-LFP pairwise phase consistency (PPC), a measure of phase synchronization of unit spikes to LFP (Vinck et al., 2010), and we found that a large fraction of recorded PCs (n = 80 of 108) and t-PV cells (n = 25 of 35) as well as all the recorded t-SOM cells (n = 17) exhibited significantly increased PPC in the theta band activity when the power of theta band LFP was increased (ΔPPC at theta: PPCspikes with high theta power – PPCspikes with low theta power; Figures 2F–2H, left, marked units with p < 0.01; Figures S3A–S3D). There was no significant difference in the change of PPC in the theta band among the three cell populations (Figure 2I, left). Some of the recorded PCs (n = 30 of 108), t-SOM cells (n = 9 of 17), and t-PV cells (n = 21 of 35) had significantly increased PPC in the gamma band when the power of gamma band LFP was elevated, particularly during the transition from stationary state to running state (ΔPPC in gamma; Figures 2F–2H, right, marked units with p < 0.01; Figures S3F–S3I). However, the cumulative distribution of ΔPPC in the gamma band suggested that the spiking of PV cells had a higher phase synchronization with LFP gamma activity than that of PCs and SOM cells (Figure 2I, right; PV versus PC, p = 0.039; PV versus SOM, p = 0.025, Kolmogorov-Smirnov test). Moreover, all modulated PCs, SOM cells, and PV cells preferred to fire near the trough of theta cycles (Figure 2J, left; Figure S3E), but at leftward-shifted phases of gamma activity (Figure 2J, right; Figure S3J).
我们进一步发现，与静止状态相比，兴奋性 PCs（WS 单元）以及抑制性 t-SOM 和 t-PV 细胞的群体尖峰率在奔跑状态下显著增加（PC，p < 10 −11 ；t-SOM，p = 0.009；t-PV，p < 0.001；图 2D）。有趣的是，PC、t-SOM 和 t-PV 细胞的平均自发率都与伽马波段 LFP 功率呈正相关，而它们与θ波段 LFP 功率的相关性却各不相同：PC 细胞呈负相关，t-PV 细胞呈正相关，而 t-SOM 细胞则无明显相关性（图 2E）。此外，我们还计算了尖峰与 LFP 成对相位一致性（spike-LFP pairwise phase consistency，PPC），这是衡量单位尖峰与 LFP 相位同步性的指标（ Vinck et al., 2010 ）。我们发现，当 theta 波段 LFP 功率增加时，大部分记录到的 PC 细胞（n = 108 个中的 80 个）和 t-PV 细胞（n = 35 个中的 25 个）以及所有记录到的 t-SOM 细胞（n = 17 个）在 theta 波段活动中表现出显著增加的 PPC（ΔPPC at theta：PPC spikes with high theta power - PPC spikes with low theta power ；图 2F-2H，左侧，标有 p < 0.01 的单位；图 S3A-S3D）。三个细胞群在θ波段的 PPC 变化无明显差异（图 2I 左）。当伽玛波段 LFP 功率升高时，记录到的一些 PC（108 个中的 30 个）、t-SOM 细胞（17 个中的 9 个）和 t-PV 细胞（35 个中的 21 个）在伽玛波段的 PPC 显著增加，特别是从静止状态过渡到运行状态时（ΔPPC in gamma；图 2F-2H，右，标记单位，P＜0.01；图 S3F-S3I）。 然而，ΔPPC 在伽马波段的累积分布表明，与 PC 和 SOM 细胞相比，PV 细胞的尖峰与 LFP 伽马活动具有更高的相位同步性（图 2I，右；PV 与 PC 相比，p = 0.039；PV 与 SOM 相比，p = 0.025，Kolmogorov-Smirnov 检验）。此外，所有受调控的 PCs、SOM 细胞和 PV 细胞都喜欢在θ周期的波谷附近发射（图 2J 左；图 S3E），但在γ活动的左移阶段发射（图 2J 右；图 S3J）。
75.
Vinck, M. ∙ van Wingerden, M. ∙ Womelsdorf, T. ...
The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization
Neuroimage. 2010; 51:112-122
我们进一步发现，与静止状态相比，兴奋性 PCs（WS 单元）以及抑制性 t-SOM 和 t-PV 细胞的群体尖峰率在奔跑状态下显著增加（PC，p < 10 −11 ；t-SOM，p = 0.009；t-PV，p < 0.001；图 2D）。有趣的是，PC、t-SOM 和 t-PV 细胞的平均自发率都与伽马波段 LFP 功率呈正相关，而它们与θ波段 LFP 功率的相关性却各不相同：PC 细胞呈负相关，t-PV 细胞呈正相关，而 t-SOM 细胞则无明显相关性（图 2E）。此外，我们还计算了尖峰与 LFP 成对相位一致性（spike-LFP pairwise phase consistency，PPC），这是衡量单位尖峰与 LFP 相位同步性的指标（ Vinck et al., 2010
75.
Vinck, M. ∙ van Wingerden, M. ∙ Womelsdorf, T. ...
The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization
Neuroimage. 2010; 51:112-122
All these in vivo recording results clearly indicate that PV cells tend to fire spikes more tightly correlated with locomotion-modulated spontaneous gamma activity than SOM cells or excitatory PCs in the mouse V1, while spiking of all three cell populations shows similar correlations with theta activity. These findings also support the general notion that PV cells can play a pivotal role in regulating cortical gamma oscillation (Klausberger and Somogyi, 2008; Cardin et al., 2009; Sohal et al., 2009).
所有这些活体记录结果都清楚地表明，在小鼠 V1 中，与 SOM 细胞或兴奋性 PC 相比，PV 细胞倾向于发射与运动调节的自发伽马活动更紧密相关的尖峰脉冲，而所有这三种细胞群的尖峰脉冲都与θ活动显示出相似的相关性。这些发现也支持了 PV 细胞在调节大脑皮层伽马振荡中起关键作用的一般观点（ Klausberger and Somogyi, 2008; Cardin et al., 2009; Sohal et al., 2009 ）。
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
36.
Klausberger, T. ∙ Somogyi, P.
Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations
Science. 2008; 321:53-57
67.
Sohal, V.S. ∙ Zhang, F. ∙ Yizhar, O. ...
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Nature. 2009; 459:698-702
所有这些活体记录结果都清楚地表明，在小鼠 V1 中，与 SOM 细胞或兴奋性 PC 相比，PV 细胞倾向于发射与运动调节的自发伽马活动更紧密相关的尖峰脉冲，而所有这三种细胞群的尖峰脉冲都与θ活动显示出相似的相关性。这些发现也支持了 PV 细胞在调节大脑皮层伽马振荡中起关键作用的一般观点（ Klausberger and Somogyi, 2008; Cardin et al., 2009; Sohal et al., 2009
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
36.
Klausberger, T. ∙ Somogyi, P.
Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations
Science. 2008; 321:53-57
67.
Sohal, V.S. ∙ Zhang, F. ∙ Yizhar, O. ...
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Nature. 2009; 459:698-702
Differential Correlation of Spiking of Distinct Cell Types with Visually Induced beta and gamma Activity
不同细胞类型的尖峰与视觉诱导的 β 和 γ 活动的差异相关性
Our previous study (Chen et al., 2015) showed that, in the V1 of adult awake mice (wild-type), sinusoidal drifting gratings (2-s duration, spatial frequency: 0.04 cycle/degree, temporal frequency: 3 Hz) in full visual field reliably induced an elevation of beta band (20- to 40-Hz) and 65- to 80-Hz high gamma band activity but a suppression of 50- to 65-Hz baseline gamma band activity. We thus further examined how SOM and PV INs were involved in the visually induced changes of beta and gamma activities. First, in the transgenic mice, similar visually induced changes in cortical beta and gamma activities could be reliably observed in both stationary and running states: a persistent increase in the beta band activity (Δpower peaked at the frequency of 24.6 ± 6.6 Hz, mean ± SD), a reduction in the (50- to 65-Hz) baseline gamma band activity (peaked at the frequency of 57.8 ± 4.6 Hz), and an increase in the (65- to 80-Hz) high gamma band activity (peaked at the frequency of 71.4 ± 5.2 Hz) (Figures 3A and 3B; Figures S2C and S2D; total of 120 recordings in 44 transgenic mice). The visually induced increase of beta band activity was more apparent in the stationary state, while the induced suppression of baseline gamma band activity was more significant in the running state (Figure 3B). The latter observation could be attributed to a relatively higher baseline gamma band activity during the running state. Moreover, the described visually induced changes did not depend on orientations of the grating stimulus (Figure S4). Since trends in the two behavioral states were similar, we pooled the data from the two states in the following analyses (separated results for the two states are provided in Figure S5).
我们之前的研究（ Chen et al., 2015 ）表明，在成年清醒小鼠（野生型）的 V1 中，全视野正弦漂移光栅（持续时间 2 秒，空间频率：0.04 周期/度，时间频率：3 赫兹）能可靠地诱导β波段（20-40-Hz）和 65-80-Hz 高γ波段活动的增强，但抑制 50-65-Hz 基线γ波段活动。因此，我们进一步研究了 SOM 和 PV INs 是如何参与视觉诱导的β和γ活动变化的。首先，在转基因小鼠中，无论是静止状态还是奔跑状态，都能可靠地观察到视觉诱导的大脑皮层β和γ活动的类似变化：β波段活动持续增加（Δpower 在频率为 24.6 ± 6.6 Hz，平均 ± SD），（50 至 65 Hz）基线伽玛频段活动减少（峰值频率为 57.8 ± 4.6 Hz），（65 至 80 Hz）高伽玛频段活动增加（峰值频率为 71.4 ± 5.2 Hz）（图 3A 和 3B；图 S2C 和 S2D；44 只转基因小鼠共 120 次记录）。视觉诱导的β波段活动增加在静止状态下更为明显，而诱导的基线γ波段活动抑制在奔跑状态下更为显著（图 3B）。后一种观察结果可能是由于在奔跑状态下基线伽玛波段活动相对较高。此外，所描述的视觉诱导变化并不取决于光栅刺激的方向（图 S4）。由于两种行为状态的趋势相似，我们在接下来的分析中将两种状态的数据集中起来（两种状态的单独结果见图 S5）。
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
我们之前的研究（ Chen et al., 2015
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
Figure 3 Differential Correlations between the Spiking of PCs, SOM Cells, and PV Cells with Visually Induced beta and gamma Oscillations
图 3 PC、SOM 细胞和 PV 细胞的尖峰与视觉诱导的 β 和 γ 振荡之间的差异相关性
图 3 PC、SOM 细胞和 PV 细胞的尖峰与视觉诱导的 β 和 γ 振荡之间的差异相关性
Most of the recorded excitatory PCs (WS units, n = 74) and inhibitory t-PV cells showed grating-induced transient increases in spike rates, followed by either an increase or decrease in rate in various cells during the 2-s stimulation (Figure 3C). In contrast, nearly all t-SOM cells exhibited a consistent rate increase during stimulation (Figure 3C, bottom). Accordingly, the induced mean rate increase of t-SOM cells was highest among the three cell populations (Figure 3E). Furthermore, only about half of the recorded PCs (n = 34 of 74 units) and t-PV cells (n = 14 of 30) showed significant increases of PPC in the beta band during visual stimulation (ΔPPC = PPCgrating − PPCblank; beta-modulated units with p < 0.01 are marked by short black lines, permutation test; Figure 3D; Figures S3K, S3L, and S5 for separated states). In comparison, PPC in the beta band increased significantly in nearly all the recorded t-SOM cells (n = 10 of 11 cells; p < 0.01; Figure 3D, bottom; Figure S5). Thus, at the population level, SOM cells showed substantially larger visually induced increase of beta band PPC than PCs and PV cells (Figure 3F). These results consistently suggest that spiking of SOM cells is highly correlated with visually induced beta oscillation in the V1.
大多数记录到的兴奋性 PCs（WS 单元，n = 74）和抑制性 t-PV 细胞的尖峰率都显示出光栅诱导的瞬时增加，随后在 2 秒钟的刺激过程中，不同细胞的尖峰率或增加或减少（图 3C）。相比之下，几乎所有的 t-SOM 细胞在刺激过程中都表现出一致的速率增加（图 3C 底部）。因此，t-SOM 细胞的诱导平均速率增加在三个细胞群中最高（图 3E）。此外，只有大约一半记录到的 PCs（74 个单元中的 34 个）和 t-PV 细胞（30 个单元中的 14 个）在视觉刺激期间显示出贝塔波段 PPC 的显著增加（ΔPPC = PPC grating - PPC blank ；p < 0.01 的贝塔调制单元用短黑线标记，进行 permutation 检验；图 3D；图 S3K、S3L 和 S5 中的分离状态）。相比之下，在几乎所有记录到的 t-SOM 细胞中，β 波段的 PPC 都显著增加（11 个细胞中的 10 个；P＜0.01；图 3D，底部；图 S5）。因此，在群体水平上，SOM 细胞显示的视觉诱导的 beta 波段 PPC 增加幅度远远大于 PCs 和 PV 细胞（图 3F）。这些结果一致表明，SOM 细胞的尖峰与视觉诱导的 V1 β 振荡高度相关。
大多数记录到的兴奋性 PCs（WS 单元，n = 74）和抑制性 t-PV 细胞的尖峰率都显示出光栅诱导的瞬时增加，随后在 2 秒钟的刺激过程中，不同细胞的尖峰率或增加或减少（图 3C）。相比之下，几乎所有的 t-SOM 细胞在刺激过程中都表现出一致的速率增加（图 3C 底部）。因此，t-SOM 细胞的诱导平均速率增加在三个细胞群中最高（图 3E）。此外，只有大约一半记录到的 PCs（74 个单元中的 34 个）和 t-PV 细胞（30 个单元中的 14 个）在视觉刺激期间显示出贝塔波段 PPC 的显著增加（ΔPPC = PPC grating - PPC blank ；p < 0.01 的贝塔调制单元用短黑线标记，进行 permutation 检验；图 3D；图 S3K、S3L 和 S5 中的分离状态）。相比之下，在几乎所有记录到的 t-SOM 细胞中，β 波段的 PPC 都显著增加（11 个细胞中的 10 个；P＜0.01；图 3D，底部；图 S5）。因此，在群体水平上，SOM 细胞显示的视觉诱导的 beta 波段 PPC 增加幅度远远大于 PCs 和 PV 细胞（图 3F）。这些结果一致表明，SOM 细胞的尖峰与视觉诱导的 V1 β 振荡高度相关。
We also noted that, in several PCs (n = 6) and t-PV INs (n = 7), PPC was significantly decreased in the baseline gamma band or increased in the high gamma band (gamma-modulated units as marked by short cyan lines in Figure 3D), where the mean changes of PPC in both gamma bands were much more significant in the gamma-modulated t-PV cells than in the PCs (Figures 3G and 3H). On the other hand, only one t-SOM cell showed a slight decrease of PPC in the baseline gamma band (Figures 3D, 3G, and 3H).
我们还注意到，在一些 PCs（n = 6）和 t-PV INs（n = 7）中，PPC 在基线伽玛频段显著下降或在高伽玛频段显著上升（图 3D 中用短青色线标出的伽玛调制单元），其中伽玛调制 t-PV 细胞的 PPC 在两个伽玛频段的平均变化比 PCs 更显著（图 3G 和 3H）。另一方面，只有一个 t-SOM 细胞的 PPC 在基线伽玛波段略有下降（图 3D、3G 和 3H）。
我们还注意到，在一些 PCs（n = 6）和 t-PV INs（n = 7）中，PPC 在基线伽玛频段显著下降或在高伽玛频段显著上升（图 3D 中用短青色线标出的伽玛调制单元），其中伽玛调制 t-PV 细胞的 PPC 在两个伽玛频段的平均变化比 PCs 更显著（图 3G 和 3H）。另一方面，只有一个 t-SOM 细胞的 PPC 在基线伽玛波段略有下降（图 3D、3G 和 3H）。
Taken together, our recordings from opto-tagged IN subtypes directly demonstrate that spiking activity of SOM and PV cells is preferentially correlated with cortical beta and gamma oscillations, respectively.
总之，我们对光标记 IN 亚型的记录直接证明，SOM 和 PV 细胞的尖峰活动分别与大脑皮层的 beta 和 gamma 振荡优先相关。
总之，我们对光标记 IN 亚型的记录直接证明，SOM 和 PV 细胞的尖峰活动分别与大脑皮层的 beta 和 gamma 振荡优先相关。
Stimulus Size Dependence of SOM Cell Spiking and Cortical beta Oscillation
SOM 细胞尖峰和皮层 beta 振荡的刺激大小依赖性
Because SOM cells in the V1 exhibit a distinct monotonic increase of their spike rates evoked by visual stimuli with increasing spatial sizes (Adesnik et al., 2012), we further investigated how t-SOM cells are involved in the stimulus size-dependent changes of induced beta/gamma oscillations, which were reported previously (Chen et al., 2015; Veit et al., 2017). In our in vivo recording, when increasing the overall size of the drifting-grating stimulus from 0.5× to 3× receptive field (rf), evoked spike rates of t-SOM cells consistently increased monotonically (n = 5; p = 0.002, Kruskal-Wallis ANOVA test; Figures 4A–4D, top, and Figure 4E), but that of both PCs (WS units, n = 13, p = 0.001) and other NS-INs (n = 9, p = 0.003) significantly decreased when compared with rates at the stimulus size of 1× rf (Figure 4E). Meanwhile, increasing the stimulus size also enhanced visually induced beta band activity (peak frequency: 25.6 ± 4.9 Hz, mean ± SD; p = 0.044), but it suppressed the baseline gamma band activity (peak frequency: 56.4 ± 1.9 Hz; n = 8 recordings in 8 mice; p = 0.042; Figures 4A–4D, bottom, and Figure 4F). However, there was no significant elevation of the power of induced high gamma band activity when changing the stimulus size (p = 0.502; Figure 4F, red). The corresponding changes of SOM cell-spiking activity and beta oscillation power with increasing stimulus sizes further suggest that SOM cells could be a major player in orchestrating cortical beta band activity.
由于 V1 中的 SOM 细胞在视觉刺激的空间大小增加时表现出明显的单调性尖峰率增加（ Adesnik et al., 2012 ），我们进一步研究了 t-SOM 细胞是如何参与刺激大小依赖性诱导的β/γ振荡变化的（ Chen et al., 2015; Veit et al., 2017 ）。在我们的体内记录中，当漂移光栅刺激的总大小从 0.5 倍增加到 3 倍感受野（rf）时，t-SOM 细胞的诱发尖峰率持续单调增加（n = 5; p = 0.002, Kruskal-Wallis ANOVA 检验；图 4A-4D, top 和图 4E），但与刺激大小为 1×rf 时的速率相比，PCs（WS 单元，n = 13，p = 0.001）和其他 NS-INs （n = 9，p = 0.003）的速率均显著下降（图 4E）。同时，刺激大小的增加也增强了视觉诱导的β波段活动（峰值频率：25.6 ± 4.9 Hz，平均值±标准差；p = 0.044），但抑制了基线γ波段活动（峰值频率：56.4 ± 1.9 Hz；n = 8 只小鼠的 8 次记录；p = 0.042；图 4A-4D，底部和图 4F）。然而，当改变刺激大小时，诱导的高伽马带活动的功率并没有明显提高（p = 0.502；图 4F，红色）。随着刺激大小的增加，SOM 细胞尖峰活动和β振荡功率的相应变化进一步表明，SOM 细胞可能是协调大脑皮层β波段活动的主要参与者。
1.
Adesnik, H. ∙ Bruns, W. ∙ Taniguchi, H. ...
A neural circuit for spatial summation in visual cortex
Nature. 2012; 490:226-231
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
72.
Veit, J. ∙ Hakim, R. ∙ Jadi, M.P. ...
Cortical gamma band synchronization through somatostatin interneurons
Nat. Neurosci. 2017; 20:951-959
由于 V1 中的 SOM 细胞在视觉刺激的空间大小增加时表现出明显的单调性尖峰率增加（ Adesnik et al., 2012
1.
Adesnik, H. ∙ Bruns, W. ∙ Taniguchi, H. ...
A neural circuit for spatial summation in visual cortex
Nature. 2012; 490:226-231
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
72.
Veit, J. ∙ Hakim, R. ∙ Jadi, M.P. ...
Cortical gamma band synchronization through somatostatin interneurons
Nat. Neurosci. 2017; 20:951-959
Figure 4 Effects of Visual Stimulus Size on Neuronal Spiking and Oscillatory Activity
图 4 视觉刺激大小对神经元尖峰和振荡活动的影响
图 4 视觉刺激大小对神经元尖峰和振荡活动的影响
Differential Effects of Inactivating SOM and PV INs on Spontaneous Cortical Dynamics
失活 SOM 和 PV INs 对自发皮层动力学的不同影响
To directly test how SOM and PV cells contribute to the generation of oscillations in distinct bands in the cortex, we performed selective optogenetic inactivation of either IN subtype in pertinent transgenic mice. In SOM-Arch mice, selective suppression of SOM cells, with 4-s yellow laser pulses, substantially suppressed spontaneous low-frequency band (<30-Hz) activity, which was accompanied by a slight increase of 40- to 80-Hz gamma band activity, during both running and stationary states (Figures 5A and 5B; Figures S2E and S2F; n = 21 recordings in 7 mice). Because the effects of SOM cell inactivation were quantitatively comparable between the two behavioral states, we again pooled the data from both states in the following analyses. Suppressing SOM cells significantly reduced the coupling of theta-beta as well as theta-gamma activity (Figure 5C), as measured by the modulation index of phase-amplitude cross-frequency coupling (CFC; see STAR Methods and Canolty et al., 2006). These results indicate that spontaneous beta/gamma activity tends to de-correlate with the theta activity when inactivating local SOM cells. Accordingly, SOM cell inactivation also preferentially decreased the phase synchronization (PPC) of spiking of all three cell populations to spontaneous low-frequency (<30-Hz) band activity (Figure 5D; putative PC-WS units, n = 27; putative IN-NS units, n = 19; t-SOM cells, n = 6). Thus, all these results support an important role of SOM cells in maintaining spontaneous low-frequency band oscillations.
为了直接测试 SOM 和 PV 细胞如何促进大脑皮层不同波段振荡的产生，我们在相关转基因小鼠体内对其中一种 IN 亚型进行了选择性光遗传失活。在 SOM-Arch 小鼠中，用 4 秒的黄色激光脉冲选择性抑制 SOM 细胞，大大抑制了自发的低频段（<30-Hz）活动，与此同时，在奔跑和静止状态下，40-80-Hz 的伽马频段活动略有增加（图 5A 和 5B；图 S2E 和 S2F；7 只小鼠的 21 次记录）。由于 SOM 细胞失活对两种行为状态的影响在数量上具有可比性，因此我们在下面的分析中再次将两种状态的数据集中起来。抑制 SOM 细胞可显著降低θ-β和θ-γ活动的耦合（图 5C），这是用相位-振幅跨频耦合（CFC；见 STAR 方法和 Canolty et al., 2006 ）的调制指数测量的。这些结果表明，当局部 SOM 细胞失活时，自发的 beta/gamma 活动往往与 theta 活动去相关。相应地，SOM 细胞失活也会优先降低所有三个细胞群尖峰与自发低频（<30-Hz）带活动的相位同步（PPC）（图 5D；推定 PC-WS 单元，n = 27；推定 IN-NS 单元，n = 19；t-SOM 细胞，n = 6）。因此，所有这些结果都支持 SOM 细胞在维持自发低频带振荡中的重要作用。
11.
Canolty, R.T. ∙ Edwards, E. ∙ Dalal, S.S. ...
High gamma power is phase-locked to theta oscillations in human neocortex
Science. 2006; 313:1626-1628
为了直接测试 SOM 和 PV 细胞如何促进大脑皮层不同波段振荡的产生，我们在相关转基因小鼠体内对其中一种 IN 亚型进行了选择性光遗传失活。在 SOM-Arch 小鼠中，用 4 秒的黄色激光脉冲选择性抑制 SOM 细胞，大大抑制了自发的低频段（<30-Hz）活动，与此同时，在奔跑和静止状态下，40-80-Hz 的伽马频段活动略有增加（图 5A 和 5B；图 S2E 和 S2F；7 只小鼠的 21 次记录）。由于 SOM 细胞失活对两种行为状态的影响在数量上具有可比性，因此我们在下面的分析中再次将两种状态的数据集中起来。抑制 SOM 细胞可显著降低θ-β和θ-γ活动的耦合（图 5C），这是用相位-振幅跨频耦合（CFC；见 STAR 方法和 Canolty et al., 2006
11.
Canolty, R.T. ∙ Edwards, E. ∙ Dalal, S.S. ...
High gamma power is phase-locked to theta oscillations in human neocortex
Science. 2006; 313:1626-1628
Figure 5 Inactivating SOM and PV Cells Differentially Regulates Spontaneous Oscillations
图 5 使 SOM 和 PV 细胞失活可对自发振荡产生不同的调节作用
图 5 使 SOM 和 PV 细胞失活可对自发振荡产生不同的调节作用
In PV-Arch mice, optogenetically suppressing PV cell activity during spontaneous activity resulted in increased power in a broad frequency range, more significantly in the low-frequency band (<30 Hz) (n = 119 recordings in 30 mice; Figures 5E and 5F; Figures S2G and S2H). The observed power changes during stationary and running states were similar except that the power increase in the 10–60 Hz range was slightly more pronounced in the stationary state (Figure 5F). Suppressing PV cell activity also elevated the coupling (CFC) strength of theta-beta and theta-gamma activity (Figure 5G). This might be attributed to the fact that PCs, SOM cells, and PV cells all preferentially fired spikes at the troughs of the theta cycle when PV cells were partially suppressed (Figure 5E, raw trace). Accordingly, the suppression of PV cells significantly increased the PPC of spikes of all three cell types to the low-frequency band activity (<30 Hz) (Figure 5H; PC-WS units, n = 37; IN-NS units, n = 50; t-PV cells, n = 8). Thus, these results suggest that local PV cells exert a critical role in preventing a high synchronization of neuronal spiking at low-frequency ranges, supporting the idea that PV cell activity stabilizes the network state in a regime of balanced excitation and inhibition (Haider et al., 2006; Xue et al., 2014).
在 PV-Arch 小鼠中，自发活动期间光遗传抑制 PV 细胞活动会导致宽频率范围内的功率增加，低频段（<30 Hz）的功率增加更为显著（30 只小鼠的 119 次记录；图 5E 和 5F；图 S2G 和 S2H）。静止和奔跑状态下观察到的功率变化相似，只是静止状态下 10-60 Hz 范围内的功率增加略微明显（图 5F）。抑制光伏细胞活动也会提高θ-β和θ-γ活动的耦合（CFC）强度（图 5G）。这可能是由于当 PV 细胞受到部分抑制时，PCs、SOM 细胞和 PV 细胞都优先在θ周期的波谷处发射尖峰（图 5E，原始轨迹）。相应地，抑制 PV 细胞会显著增加所有三种细胞类型的尖峰对低频带活动（<30 Hz）的 PPC（图 5H；PC-WS 单元，n = 37；IN-NS 单元，n = 50；t-PV 细胞，n = 8）。因此，这些结果表明，局部 PV 细胞在防止低频范围内神经元尖峰的高度同步化方面发挥了关键作用，支持了 PV 细胞活动将网络状态稳定在兴奋和抑制平衡机制中的观点 ( Haider et al., 2006; Xue et al., 2014 )。
26.
Haider, B. ∙ Duque, A. ∙ Hasenstaub, A.R. ...
Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition
J. Neurosci. 2006; 26:4535-4545
82.
Xue, M. ∙ Atallah, B.V. ∙ Scanziani, M.
Equalizing excitation-inhibition ratios across visual cortical neurons
Nature. 2014; 511:596-600
在 PV-Arch 小鼠中，自发活动期间光遗传抑制 PV 细胞活动会导致宽频率范围内的功率增加，低频段（<30 Hz）的功率增加更为显著（30 只小鼠的 119 次记录；图 5E 和 5F；图 S2G 和 S2H）。静止和奔跑状态下观察到的功率变化相似，只是静止状态下 10-60 Hz 范围内的功率增加略微明显（图 5F）。抑制光伏细胞活动也会提高θ-β和θ-γ活动的耦合（CFC）强度（图 5G）。这可能是由于当 PV 细胞受到部分抑制时，PCs、SOM 细胞和 PV 细胞都优先在θ周期的波谷处发射尖峰（图 5E，原始轨迹）。相应地，抑制 PV 细胞会显著增加所有三种细胞类型的尖峰对低频带活动（<30 Hz）的 PPC（图 5H；PC-WS 单元，n = 37；IN-NS 单元，n = 50；t-PV 细胞，n = 8）。因此，这些结果表明，局部 PV 细胞在防止低频范围内神经元尖峰的高度同步化方面发挥了关键作用，支持了 PV 细胞活动将网络状态稳定在兴奋和抑制平衡机制中的观点 ( Haider et al., 2006; Xue et al., 2014
26.
Haider, B. ∙ Duque, A. ∙ Hasenstaub, A.R. ...
Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition
J. Neurosci. 2006; 26:4535-4545
82.
Xue, M. ∙ Atallah, B.V. ∙ Scanziani, M.
Equalizing excitation-inhibition ratios across visual cortical neurons
Nature. 2014; 511:596-600
Differential Roles of SOM and PV INs in Regulating Visually Induced beta and gamma Oscillations
SOM 和 PV INs 在调节视觉诱导的 beta 和 gamma 振荡中的不同作用
We next examined how SOM and PV cells could differentially regulate visually induced beta and gamma oscillations. To this end, we compared the full-field grating-induced beta and gamma oscillations recorded before and during yellow laser illumination (5–30 mW, 6-s pulses) in SOM-Arch mice. Suppressing SOM cell activity selectively reduced the power increase of the beta band LFP (Δpower = [powergrating− powerblank]/powerblank; p < 10−3, compared with control without laser stimulation), but it did not affect the stimulus-induced changes observed in the baseline (50- to 65-Hz) gamma band and high (65- to 80-Hz) gamma band activity (Figures 6A and 6B). Moreover, there was a ubiquitous reduction in the strength of phase-amplitude coupling of induced beta and baseline and high gamma activities to the theta activity (Figure 6C), similar to that observed for spontaneous activity (Figure 5C). Among the beta-modulated cells (shown in Figure 3D; 12 WS-PCs and 4 NS-INs), the visually induced increase in their beta band PPC was selectively reduced by optogenetic suppression of SOM cells (Figure 6D; p = 0.003). Since very few gamma-modulated PCs or PV cells were observed in this experiment, we were unable to analyze their PPC changes in the baseline and high gamma bands. Nevertheless, the above results provide a piece of causal evidence that the spiking of SOM cells is particularly important for generating visually induced beta oscillation, but not gamma activity, in the V1.
接下来，我们研究了 SOM 和 PV 细胞如何以不同方式调节视觉诱导的 beta 和 gamma 振荡。为此，我们比较了 SOM-Arch 小鼠在黄色激光照射（5-30 mW，6 秒脉冲）之前和照射过程中记录的全场光栅诱导的β和γ振荡。与没有激光刺激的对照组相比，抑制 SOM 细胞的活动选择性地降低了β波段 LFP 的功率增加（Δpower = [power grating - power blank ]/power blank ; p < 10 −3 ），但并不影响在基线（50-至 65-Hz）伽马波段和高（65-至 80-Hz）伽马波段活动中观察到的刺激诱导变化（图 6A 和 6B）。此外，诱导的β、基线和高γ活动与θ活动的相位-振幅耦合强度普遍降低（图 6C），这与自发活动中观察到的情况类似（图 5C）。在β调制细胞（如图 3D 所示；12 个 WS-PCs 和 4 个 NS-INs）中，SOM 细胞的光遗传抑制选择性地降低了视觉诱导的β带 PPC 增加（图 6D；p = 0.003）。由于本实验中观察到的伽马调制 PC 或 PV 细胞很少，我们无法分析它们在基线和高伽马波段的 PPC 变化。尽管如此，上述结果还是提供了一个因果关系证据，即 SOM 细胞的尖峰脉冲对于在 V1 中产生视觉诱导的 beta 振荡（而非 gamma 活动）尤为重要。
接下来，我们研究了 SOM 和 PV 细胞如何以不同方式调节视觉诱导的 beta 和 gamma 振荡。为此，我们比较了 SOM-Arch 小鼠在黄色激光照射（5-30 mW，6 秒脉冲）之前和照射过程中记录的全场光栅诱导的β和γ振荡。与没有激光刺激的对照组相比，抑制 SOM 细胞的活动选择性地降低了β波段 LFP 的功率增加（Δpower = [power grating - power blank ]/power blank ; p < 10 −3 ），但并不影响在基线（50-至 65-Hz）伽马波段和高（65-至 80-Hz）伽马波段活动中观察到的刺激诱导变化（图 6A 和 6B）。此外，诱导的β、基线和高γ活动与θ活动的相位-振幅耦合强度普遍降低（图 6C），这与自发活动中观察到的情况类似（图 5C）。在β调制细胞（如图 3D 所示；12 个 WS-PCs 和 4 个 NS-INs）中，SOM 细胞的光遗传抑制选择性地降低了视觉诱导的β带 PPC 增加（图 6D；p = 0.003）。由于本实验中观察到的伽马调制 PC 或 PV 细胞很少，我们无法分析它们在基线和高伽马波段的 PPC 变化。尽管如此，上述结果还是提供了一个因果关系证据，即 SOM 细胞的尖峰脉冲对于在 V1 中产生视觉诱导的 beta 振荡（而非 gamma 活动）尤为重要。
Figure 6 Differential Regulation of Visually Induced Oscillations by SOM and PV Cells
图 6 SOM 和光电池对视觉诱导振荡的不同调节作用
图 6 SOM 和光电池对视觉诱导振荡的不同调节作用
Optogenetic suppression of PV cells in PV-Arch mice, however, generated different effects on visually induced oscillations. First, LFP power at the low-frequency band (<10 Hz) was significantly enhanced after suppressing PV cell activity (1–10 Hz; p = 0.002, light off versus light on; n = 14 recordings in 10 mice; Figures 6E and 6F). Second, the grating-induced increase of beta power was reduced. Third, the grating-induced increase of high gamma power (peak frequency ∼70 Hz) was also significantly attenuated, while the induced reduction of baseline gamma power was not affected (Figures 6E and 6F). The CFC analysis further indicated that the coupling of theta-beta, theta-baseline gamma, as well as theta- high gamma activity (Figure 6G) was increased. Accordingly, suppressing PV cell activity not only reversed the grating-induced reduction of the low-frequency band PPC (peak frequency, 5 Hz; p < 10−3) but also decreased the induced increase of beta band PPC (p = 0.003, tested around peak frequency 30 Hz, only the beta-modulated 14 WS-PCs and 6 NS-INs were compared; Figure 6H). Again, no gamma-modulated cells were observed, so gamma band ΔPPC between light off and on conditions could not be compared. Together, these results indicate that PV cells also strongly modulate visually induced beta oscillations, in addition to their well-known role in regulating spontaneous and induced gamma oscillations (Cardin et al., 2009; Sohal et al., 2009; Buzsáki and Wang, 2012; Figures 5E, 5F, 6E, and 6F).
然而，光遗传抑制 PV-Arch 小鼠的 PV 细胞对视觉诱导的振荡产生了不同的影响。首先，在抑制 PV 细胞活动后，低频段（<10 Hz）的 LFP 功率显著增强（1-10 Hz；p = 0.002，关灯与开灯；n = 14 次记录，10 只小鼠；图 6E 和 6F）。其次，光栅诱导的贝塔功率增加减少了。第三，光栅诱导的高γ功率（峰值频率∼70 Hz）的增加也显著减弱，而诱导的基线γ功率的减少不受影响（图 6E 和 6F）。CFC 分析进一步表明，θ-β、θ-基线伽马以及θ-高伽马活动（图 6G）的耦合增加了。因此，抑制 PV 细胞活动不仅能逆转光栅诱导的低频段 PPC 的减少（峰值频率，5 Hz；p < 10 −3 ），还能减少诱导的 beta 频段 PPC 的增加（p = 0.003，在峰值频率 30 Hz 附近测试，只比较了 beta 调制的 14 个 WS-PCs 和 6 个 NS-INs；图 6H）。同样，没有观察到伽马调制细胞，因此无法比较光关闭和光开启条件下的伽马带ΔPPC。总之，这些结果表明，除了众所周知的调节自发和诱导伽马振荡的作用外（ Cardin et al., 2009; Sohal et al., 2009; Buzsáki and Wang, 2012 ；图 5E、5F、6E 和 6F），PV 细胞还强烈调节视觉诱导的β振荡。
9.
Buzsáki, G. ∙ Wang, X.-J.
Mechanisms of gamma oscillations
Annu. Rev. Neurosci. 2012; 35:203-225
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
67.
Sohal, V.S. ∙ Zhang, F. ∙ Yizhar, O. ...
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Nature. 2009; 459:698-702
然而，光遗传抑制 PV-Arch 小鼠的 PV 细胞对视觉诱导的振荡产生了不同的影响。首先，在抑制 PV 细胞活动后，低频段（<10 Hz）的 LFP 功率显著增强（1-10 Hz；p = 0.002，关灯与开灯；n = 14 次记录，10 只小鼠；图 6E 和 6F）。其次，光栅诱导的贝塔功率增加减少了。第三，光栅诱导的高γ功率（峰值频率∼70 Hz）的增加也显著减弱，而诱导的基线γ功率的减少不受影响（图 6E 和 6F）。CFC 分析进一步表明，θ-β、θ-基线伽马以及θ-高伽马活动（图 6G）的耦合增加了。因此，抑制 PV 细胞活动不仅能逆转光栅诱导的低频段 PPC 的减少（峰值频率，5 Hz；p < 10 −3 ），还能减少诱导的 beta 频段 PPC 的增加（p = 0.003，在峰值频率 30 Hz 附近测试，只比较了 beta 调制的 14 个 WS-PCs 和 6 个 NS-INs；图 6H）。同样，没有观察到伽马调制细胞，因此无法比较光关闭和光开启条件下的伽马带ΔPPC。总之，这些结果表明，除了众所周知的调节自发和诱导伽马振荡的作用外（ Cardin et al., 2009; Sohal et al., 2009; Buzsáki and Wang, 2012
9.
Buzsáki, G. ∙ Wang, X.-J.
Mechanisms of gamma oscillations
Annu. Rev. Neurosci. 2012; 35:203-225
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
67.
Sohal, V.S. ∙ Zhang, F. ∙ Yizhar, O. ...
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Nature. 2009; 459:698-702
Thus, the above results clearly suggest that cortical SOM cells are particularly important for generating visually induced beta oscillation whereas PV cells are ubiquitously involved in modulating the induced beta and high gamma activities in visual cortical circuits.
因此，上述结果清楚地表明，皮层 SOM 细胞对产生视觉诱导的 beta 振荡尤为重要，而 PV 细胞则普遍参与调节视觉皮层回路中诱导的 beta 和高伽马活动。
因此，上述结果清楚地表明，皮层 SOM 细胞对产生视觉诱导的 beta 振荡尤为重要，而 PV 细胞则普遍参与调节视觉皮层回路中诱导的 beta 和高伽马活动。
SOM and PV INs Preferentially Pace Low- and High-Frequency Band Activities, Respectively
SOM 和 PV INs 分别优先跟踪低频带和高频带活动
Our above results also implicate that the spiking of cortical SOM and PV cells tends to drive low- and high-frequency band oscillations, respectively. To directly test this notion, we applied trains of blue laser pulses (30 mW, 1-ms duration, 30–50 repetitions) to rhythmically activate either cortical SOM or PV cells at frequencies ranging from 1 to 200 Hz in SOM-ChR2 or PV-ChR2 mice. LFP and spike units were simultaneously recorded with a glass micropipette (to avoid the known photoelectric artifacts when using metal electrodes, see Han et al., 2009). In SOM::ChR2 mice, single-laser pulses reliably drove ChR2-expressing SOM cells to fire spikes that faithfully time-locked to the laser pulses (median latency = 5.3 ms, median spike jitter = 1.4 ms; Figure 7A). Besides that, single pulses also elicited a slow LFP response (median rising and decay times of 10.5 and 66.3 ms, respectively; Figure 7B; Figures S6C and S6F). The evoked LFP amplitudes were dependent on the laser power and could be fully blocked by a specific GABAA receptor antagonist bicuculline (5 mM, applied via epipia infusion; Figure 7B). This result indicated that transient activation of ChR2-expressing SOM cells could elicit a slow inhibition in local circuits of the V1. As shown by an example recording in Figure 7C, applying the laser pulses at 5 or 40 Hz generated resonant LFPs whose power peaked at 5 or 40 Hz (relative to the baseline), respectively. By testing various frequencies up to 200 Hz, we found that rhythmically activating SOM cells was most effective in producing resonant circuit activities in the 5–30 Hz range (Figure 7D; Figures S6C and S6F; note the use of normalized power for each activation frequency).
上述结果还表明，大脑皮层 SOM 和 PV 细胞的尖峰突触往往会分别驱动低频和高频段振荡。为了直接验证这一观点，我们在 SOM-ChR2 或 PV-ChR2 小鼠体内应用一连串蓝色激光脉冲（30 毫瓦，持续时间 1 毫秒，重复 30-50 次），以 1-200 赫兹的频率有节奏地激活皮层 SOM 或 PV 细胞。用玻璃微量移液管同时记录 LFP 和尖峰单元（以避免使用金属电极时产生已知的光电伪影，见 Han et al., 2009 ）。在 SOM::ChR2 小鼠中，单激光脉冲可靠地驱动了表达 ChR2 的 SOM 细胞发射尖峰，这些尖峰忠实地与激光脉冲时间锁定（中位潜伏期=5.3 毫秒，中位尖峰抖动=1.4 毫秒；图 7A）。除此之外，单脉冲也会引起缓慢的 LFP 反应（中位上升和衰减时间分别为 10.5 和 66.3 毫秒；图 7B；图 S6C 和 S6F）。诱发的 LFP 振幅取决于激光功率，并可被特异性 GABA A 受体拮抗剂比库库林（5 mM，通过外膜输注；图 7B）完全阻断。这一结果表明，瞬时激活表达 ChR2 的 SOM 细胞可在 V1 的局部回路中引起缓慢抑制。如图 7C 中的记录示例所示，以 5 或 40 Hz 的频率施加激光脉冲会产生共振 LFP，其功率分别在 5 或 40 Hz（相对于基线）达到峰值。通过测试高达 200 Hz 的各种频率，我们发现有节奏地激活 SOM 细胞对产生 5-30 Hz 范围内的共振电路活动最为有效（图 7D；图 S6C 和 S6F；注意每个激活频率都使用了归一化功率）。
28.
Han, X. ∙ Qian, X. ∙ Bernstein, J.G. ...
Millisecond-timescale optical control of neural dynamics in the nonhuman primate brain
Neuron. 2009; 62:191-198
上述结果还表明，大脑皮层 SOM 和 PV 细胞的尖峰突触往往会分别驱动低频和高频段振荡。为了直接验证这一观点，我们在 SOM-ChR2 或 PV-ChR2 小鼠体内应用一连串蓝色激光脉冲（30 毫瓦，持续时间 1 毫秒，重复 30-50 次），以 1-200 赫兹的频率有节奏地激活皮层 SOM 或 PV 细胞。用玻璃微量移液管同时记录 LFP 和尖峰单元（以避免使用金属电极时产生已知的光电伪影，见 Han et al., 2009
28.
Han, X. ∙ Qian, X. ∙ Bernstein, J.G. ...
Millisecond-timescale optical control of neural dynamics in the nonhuman primate brain
Neuron. 2009; 62:191-198
Figure 7 SOM and PV Cells Preferentially Pace Narrow Low-Frequency Band and Wide High-Frequency Band Activities, Respectively
图 7 SOM 和 PV 电池分别优先跟进窄低频段和宽高频段活动
图 7 SOM 和 PV 电池分别优先跟进窄低频段和宽高频段活动
In PV-ChR2 mice, single 1-ms laser pulses also reliably elicited spikes of ChR2-expressing PV cells immediately after each laser pulse (median spike latency = 4.7 ms, median jitter = 1.6 ms; Figure 7E). In contrast to SOM cell activation, transient activation of PV cells generated faster inhibition-dependent LFP responses (median rising time = 5.8 ms; median decay time constant = 23.5 ms; also blocked by bicuculline; Figure 7F; Figures S6I and S6L). Moreover, rhythmic activation of PV cells at various frequencies up to 200 Hz most effectively drove resonant LFP activity in the 20–80 Hz range, which was significantly broader and at a higher frequency band than that driven by the rhythmic activation of SOM cells (Figures 7G and 7H; Figures S6I and S6L). The frequency range of PV cell-driven cortical LFPs in awake mice is comparable to that previously reported in anesthetized mice (Cardin et al., 2009).
在 PV-ChR2 小鼠中，单个 1 毫秒激光脉冲也能可靠地在每个激光脉冲后立即引起表达 ChR2 的 PV 细胞的尖峰（尖峰潜伏期中位数 = 4.7 毫秒，抖动中位数 = 1.6 毫秒；图 7E）。与 SOM 细胞激活不同的是，PV 细胞的瞬时激活会产生更快的抑制依赖性 LFP 反应（中位上升时间 = 5.8 ms；中位衰减时间常数 = 23.5 ms；也被双谷氨酸阻断；图 7F；图 S6I 和 S6L）。此外，以高达 200 Hz 的不同频率有节律地激活 PV 细胞可最有效地驱动 20-80 Hz 范围内的共振 LFP 活动，与有节律地激活 SOM 细胞所驱动的 LFP 活动相比，共振 LFP 活动的频带明显更宽更高 （图 7G 和 7H；图 S6I 和 S6L）。清醒小鼠皮层 LFPs 由 PV 细胞驱动的频率范围与之前报道的麻醉小鼠的频率范围相当（ Cardin et al., 2009 ）。
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
在 PV-ChR2 小鼠中，单个 1 毫秒激光脉冲也能可靠地在每个激光脉冲后立即引起表达 ChR2 的 PV 细胞的尖峰（尖峰潜伏期中位数 = 4.7 毫秒，抖动中位数 = 1.6 毫秒；图 7E）。与 SOM 细胞激活不同的是，PV 细胞的瞬时激活会产生更快的抑制依赖性 LFP 反应（中位上升时间 = 5.8 ms；中位衰减时间常数 = 23.5 ms；也被双谷氨酸阻断；图 7F；图 S6I 和 S6L）。此外，以高达 200 Hz 的不同频率有节律地激活 PV 细胞可最有效地驱动 20-80 Hz 范围内的共振 LFP 活动，与有节律地激活 SOM 细胞所驱动的 LFP 活动相比，共振 LFP 活动的频带明显更宽更高 （图 7G 和 7H；图 S6I 和 S6L）。清醒小鼠皮层 LFPs 由 PV 细胞驱动的频率范围与之前报道的麻醉小鼠的频率范围相当（ Cardin et al., 2009
12.
Cardin, J.A. ∙ Carlén, M. ∙ Meletis, K. ...
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Nature. 2009; 459:663-667
Thus, by systematically comparing the resonant LFPs driven by rhythmic activation of cortical SOM or PV cells, we demonstrate that these two local inhibitory cell populations preferentially entrain the narrow low-frequency (5- to 30-Hz) and wide high-frequency (20- to 80-Hz) oscillations in the V1, respectively.
因此，通过系统比较大脑皮层 SOM 或 PV 细胞节律性激活所驱动的共振 LFP，我们证明了这两个局部抑制性细胞群分别优先抑制 V1 中的窄低频（5-30-Hz）和宽高频（20-80-Hz）振荡。
因此，通过系统比较大脑皮层 SOM 或 PV 细胞节律性激活所驱动的共振 LFP，我们证明了这两个局部抑制性细胞群分别优先抑制 V1 中的窄低频（5-30-Hz）和宽高频（20-80-Hz）振荡。
Discussion 讨论
By recording specific subtypes of IN and manipulating their spiking activity in the neocortex of awake mice, we have revealed the differential and cooperative actions of inhibitory SOM and PV cells in driving cortical beta and gamma oscillations. Spiking of SOM cells is particularly responsible for spontaneous low-frequency band activity and visually induced beta oscillation in a stimulus size-dependent manner, while that of PV cells mainly drives gamma activity. These functions of the two IN subtypes are similar in either the running or stationary state. Moreover, PV cells are also important for maintaining a dynamic network activity balance to enhance the signal-to-noise ratio (SNR) of visually induced cortical beta and gamma activity. Given the existence of direct or indirect connections between cortical SOM and PV cells, our findings could provide a circuit mechanism by which the two local INs interact to exert distinct and coordinated functions in orchestrating cortical oscillations.
通过记录清醒小鼠新皮层中特定亚型的 IN 并操纵它们的尖峰活动，我们揭示了抑制性 SOM 细胞和 PV 细胞在驱动皮层β和γ振荡中的差异和合作作用。SOM 细胞的尖峰刺激主要负责自发低频带活动和视觉诱导的β振荡，其方式与刺激大小有关，而 PV 细胞的尖峰刺激则主要驱动γ活动。这两种 IN 亚型在奔跑或静止状态下的功能相似。此外，PV 细胞对于维持动态网络活动平衡以提高视觉诱导的大脑皮层 beta 和 gamma 活动的信噪比（SNR）也很重要。鉴于大脑皮层 SOM 和 PV 细胞之间存在直接或间接的联系，我们的发现可能提供了一种回路机制，通过这种机制，这两种局部 IN 相互作用，在协调大脑皮层振荡中发挥不同的协调功能。
通过记录清醒小鼠新皮层中特定亚型的 IN 并操纵它们的尖峰活动，我们揭示了抑制性 SOM 细胞和 PV 细胞在驱动皮层β和γ振荡中的差异和合作作用。SOM 细胞的尖峰刺激主要负责自发低频带活动和视觉诱导的β振荡，其方式与刺激大小有关，而 PV 细胞的尖峰刺激则主要驱动γ活动。这两种 IN 亚型在奔跑或静止状态下的功能相似。此外，PV 细胞对于维持动态网络活动平衡以提高视觉诱导的大脑皮层 beta 和 gamma 活动的信噪比（SNR）也很重要。鉴于大脑皮层 SOM 和 PV 细胞之间存在直接或间接的联系，我们的发现可能提供了一种回路机制，通过这种机制，这两种局部 IN 相互作用，在协调大脑皮层振荡中发挥不同的协调功能。
It has been shown that the dynamics of cortical activity and sensory functions are strongly dependent on the brain state (Steriade et al., 1993; Haider et al., 2013; Niell and Stryker, 2010; Fu et al., 2014; Polack et al., 2013). In the present study using awake mice, we show that neuronal oscillations at theta (2- to 8-Hz) and gamma (40- to 70-Hz) bands are predominant in spontaneous LFPs in the V1 and are modulated by behavioral state. A locomotion-dependent augmentation of cortical gamma oscillation was previously observed in non-anesthetized mice (Niell and Stryker, 2010; Chen et al., 2015). Additionally, we show that full-field (∼75-degree) grating stimulation induces characteristic changes of cortical dynamics, including a substantial increase in beta activity (20–40 Hz) and an apparent reduction in the baseline gamma (50- to 65-Hz) and a slight increase in the high gamma (65- to 80-Hz) activity (Figure 3). The induced changes in beta and baseline gamma bands have been consistently observed in the V1 of awake mice (Niell and Stryker, 2010; Saleem et al., 2017; Veit et al., 2017, where the beta activity was termed as low gamma activity). However, in these previous studies, the augmented high gamma activity during visual stimulation was not clearly observed. Moreover, in anesthetized mice, visually induced changes of cortical dynamics were evident only for beta (20- to 30-Hz) or low gamma (30- to 50-Hz) oscillations, but not for high gamma activity (Welle, 2010; Nase et al., 2003).
研究表明，大脑皮层活动和感觉功能的动态与大脑状态密切相关（ Steriade et al., 1993; Haider et al., 2013; Niell and Stryker, 2010; Fu et al., 2014; Polack et al., 2013 ）。在本研究中，我们用清醒的小鼠证明了θ（2-至 8-Hz）和γ（40-至 70-Hz）波段的神经元振荡在 V1 的自发 LFPs 中占主导地位，并受行为状态的调节。以前曾在非麻醉小鼠身上观察到皮层伽马振荡的运动依赖性增强（ Niell and Stryker, 2010; Chen et al., 2015 ）。此外，我们还发现全场（∼75 度）光栅刺激会诱导大脑皮层动态的特征性变化，包括β活动（20-40 赫兹）的大幅增加、基线伽马（50-65 赫兹）的明显减少以及高伽马（65-80 赫兹）活动的轻微增加（图 3）。在清醒小鼠的 V1 中持续观察到了β和基线伽玛波段的诱导变化（ Niell and Stryker, 2010; Saleem et al., 2017; Veit et al., 2017 ，其中β活动被称为低伽玛活动）。然而，在以前的这些研究中，并没有清楚地观察到视觉刺激时增强的高伽马活动。此外，在麻醉小鼠中，视觉引起的大脑皮层动态变化只在β（20-30-Hz）或低γ（30-50-Hz）振荡中明显，而在高γ活动中却不明显（ Welle, 2010; Nase et al., 2003 ）。
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Welle, C.G. (2010). Gamma oscillations in the mouse primary visual cortex as an endophenotype of schizophrenia. PhD thesis (University of Pennsylvania).研究表明，大脑皮层活动和感觉功能的动态与大脑状态密切相关（ Steriade et al., 1993; Haider et al., 2013; Niell and Stryker, 2010; Fu et al., 2014; Polack et al., 2013
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Inhibition dominates sensory responses in the awake cortex
Nature. 2013; 493:97-100
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Niell, C.M. ∙ Stryker, M.P.
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Neuron. 2010; 65:472-479
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Polack, P.-O. ∙ Friedman, J. ∙ Golshani, P.
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Nat. Neurosci. 2013; 16:1331-1339
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Steriade, M. ∙ McCormick, D.A. ∙ Sejnowski, T.J.
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Science. 1993; 262:679-685
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Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
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Sci. Rep. 2015; 5:17847
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Niell, C.M. ∙ Stryker, M.P.
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Neuron. 2010; 65:472-479
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Niell, C.M. ∙ Stryker, M.P.
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Neuron. 2010; 65:472-479
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Saleem, A.B. ∙ Lien, A.D. ∙ Krumin, M. ...
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Veit, J. ∙ Hakim, R. ∙ Jadi, M.P. ...
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Welle, C.G. (2010). Gamma oscillations in the mouse primary visual cortex as an endophenotype of schizophrenia. PhD thesis (University of Pennsylvania).Recent studies suggested that origins of cortical baseline and high gamma activities could be the visual thalamus (Saleem et al., 2017) and local cortex (Chen et al., 2015), respectively. This notion is supported by our finding that cortical PV cells differentially regulate these two specific gamma band activities (Figure 6F). However, visually induced changes of both cortical beta and high gamma activities have rarely been observed in higher mammals, including the cat (Gray and Singer, 1989) and monkey (Livingstone, 1996; Gieselmann and Thiele, 2008). In the cat or monkey V1, induced oscillatory activity is often in a wide gamma range (30–80 Hz), and its power and peak frequency are modulated by visual input features, such as orientation (Gray and Singer, 1989), contrast (Ray and Maunsell, 2010), and spatial size (Gieselmann and Thiele, 2008; Zhang and Li, 2013), as well as by the top-down attentional signals (Engel et al., 2001). In agreement with the dependence on spatial size, we also observed that larger size gratings (at 2× or 3× rf size) enhanced cortical oscillations at separate beta and gamma bands, while smaller size gratings (0.5× or 1× rf) increased the wide gamma band (30- to 80-Hz) activity (Figure 4). We note that other studies in higher mammals and rodents used different sizes of visual stimuli (Gray and Singer, 1989; Gieselmann and Thiele, 2008; Zhang and Li, 2013; Chen et al., 2015; Vinck and Bosman, 2016). As such, a future comparative study is necessary to clearly understand whether specific patterns of induced oscillations in the V1 are more dependent on the stimulus feature or species.
最近的研究表明，皮层基线和高伽马活动的起源可能分别是视觉丘脑（ Saleem et al., 2017 ）和局部皮层（ Chen et al., 2015 ）。我们发现皮层 PV 细胞对这两个特定伽玛波段的活动有不同的调节作用（图 6F），这也支持了这一观点。然而，在高等哺乳动物中，包括猫（ Gray and Singer, 1989 ）和猴（ Livingstone, 1996; Gieselmann and Thiele, 2008 ），很少观察到视觉诱导的大脑皮层β和高γ活动的变化。在猫或猴的 V1 中，诱导振荡活动通常在较宽的伽马范围内（30-80 Hz），其功率和峰值频率受视觉输入特征的调节，如方向（ Gray and Singer, 1989 ）、对比度（ Ray and Maunsell, 2010 ）和空间大小（ Gieselmann and Thiele, 2008; Zhang and Li, 2013 ），以及自上而下的注意信号（ Engel et al., 2001 ）。与空间大小的依赖性一致，我们还观察到，较大尺寸的光栅（2 倍或 3 倍 rf 尺寸）增强了大脑皮层在不同β和γ波段的振荡，而较小尺寸的光栅（0.5 倍或 1 倍 rf）则增强了宽γ波段（30-80-Hz）的活动（图 4）。我们注意到，其他对高等哺乳动物和啮齿动物的研究使用了不同大小的视觉刺激（ Gray and Singer, 1989; Gieselmann and Thiele, 2008; Zhang and Li, 2013; Chen et al., 2015; Vinck and Bosman, 2016 ）。因此，未来有必要进行比较研究，以清楚地了解 V1 中诱导振荡的特定模式是否更依赖于刺激特征或物种。
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最近的研究表明，皮层基线和高伽马活动的起源可能分别是视觉丘脑（ Saleem et al., 2017
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Saleem, A.B. ∙ Lien, A.D. ∙ Krumin, M. ...
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Neuron. 2017; 93:315-322
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
25.
Gray, C.M. ∙ Singer, W.
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
Proc. Natl. Acad. Sci. USA. 1989; 86:1698-1702
24.
Gieselmann, M.A. ∙ Thiele, A.
Comparison of spatial integration and surround suppression characteristics in spiking activity and the local field potential in macaque V1
Eur. J. Neurosci. 2008; 28:447-459
44.
Livingstone, M.S.
Oscillatory firing and interneuronal correlations in squirrel monkey striate cortex
J. Neurophysiol. 1996; 75:2467-2485
25.
Gray, C.M. ∙ Singer, W.
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
Proc. Natl. Acad. Sci. USA. 1989; 86:1698-1702
59.
Ray, S. ∙ Maunsell, J.H.R.
Differences in gamma frequencies across visual cortex restrict their possible use in computation
Neuron. 2010; 67:885-896
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Gieselmann, M.A. ∙ Thiele, A.
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Eur. J. Neurosci. 2008; 28:447-459
83.
Zhang, L. ∙ Li, B.
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Engel, A.K. ∙ Fries, P. ∙ Singer, W.
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Nat. Rev. Neurosci. 2001; 2:704-716
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Sci. Rep. 2015; 5:17847
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Gieselmann, M.A. ∙ Thiele, A.
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Eur. J. Neurosci. 2008; 28:447-459
25.
Gray, C.M. ∙ Singer, W.
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Proc. Natl. Acad. Sci. USA. 1989; 86:1698-1702
74.
Vinck, M. ∙ Bosman, C.A.
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Front. Syst. Neurosci. 2016; 10:35
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Zhang, L. ∙ Li, B.
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PLoS ONE. 2013; 8:e64492
Accumulating evidence has suggested that, among different subtypes of cortical INs, the SOM cell tends to transmit slow signals due to its distinctive intrinsic properties: relatively wide spikes, slow membrane time constants, and slow and weak inhibitory outputs with strong short-term facilitation (Beierlein et al., 2003; Hu et al., 2011; Lazarus and Huang, 2011; Kvitsiani et al., 2013; Miao et al., 2016). Consistently, we showed in the present study that a phasic activation of SOM cells produced a slow inhibitory field potential (Figure 7B). The latter slow inhibitory response may partially account for their preferential entrainment in the 5- to 30-Hz frequency range (Figure 7D), and it may suggest an important role of SOM cells in the regulation of spontaneous low-frequency band activity (Figures 2 and 5). A recent study (Veit et al., 2017) and the present study both provide direct evidence that local SOM cells are particularly important for the generation of a visually induced, size-dependent oscillation in a 15- to 40-Hz band (peak frequency at 20–30 Hz), despite that the different terminologies of gamma versus beta band were used (Figures 3 and 6). Our study provides additional in vivo evidence for correlative increases of evoked spike rate of SOM cells and induced beta activity along with increasing stimulus sizes (Figure 4). Moreover, because SOM cells form inhibitory synapses on apical dendrites of PCs (Markram et al., 2004), functionally they are thought to facilitate the spatial summation or feature integration of visual inputs (Adesnik et al., 2012), possibly through switching from rate-coding (by exerting the surround suppression) to temporal-coding (by enhancing the PC spiking synchronization in the beta range and across relatively wide cortical areas; see Veit et al., 2017) strategies.
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Although PV cells tend to transmit fast signals (Figure 7; Beierlein et al., 2003; Hu et al., 2011; Lazarus and Huang, 2011; Kvitsiani et al., 2013; Miao et al., 2016) and generate cortical gamma band activity (Figures 2, 3, 4, 5, 6, and 7; Cardin et al., 2009; Sohal et al., 2009; Buzsáki and Wang, 2012), our results directly suggest that cortical PV cells also strongly modulate low-frequency band activity, including the induced beta oscillation. Suppressing PV cell activity could result in exceptionally synchronized spontaneous activities across a broad frequency band (most significant in the frequencies <30 Hz) and in the strength of cross-frequency coupling between theta activity and beta/gamma activity (Figure 5). The highly synchronized activity that emerged after the suppression of cortical PV cells resembles a mild local seizure, and it is reminiscent of the known role of local PV cells in gating the propagation of seizure activity (Cammarota et al., 2013). Such seizure-like activity was not observed when SOM cell activity was suppressed in our experiments. Interestingly, our results also indicate that the emergence of seizure-like activity does not reduce the absolute LFP power in the beta band, but it significantly reduces the grating-induced increase of beta band power (Figure 6). In accordance with these findings, PV cells are thought to play an important role in stabilizing the local network in an excitation and inhibition-balanced regime through their perisomatic fast/shunting inhibition (Haider et al., 2006; Xue et al., 2014; Borg-Graham et al., 1998; Zhang et al., 2013), therefore establishing a basis for the visual induction of beta and gamma oscillations (Figures 3 and 6). Moreover, it is worth noting that cortical PV cells show differential changes in their spiking phase synchronization (PPC) to the induced beta and gamma activity (Figure 3), suggesting that there could be functionally distinct subgroups in the molecularly identified PV cell population.
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Front. Comput. Neurosci. 2013; 7:56
Unlike the PV cell, cortical SOM cells rarely form self-inhibitory synapses between each other, instead sending their inhibitory output to other IN subtypes, including PV cells, thus forming dis-inhibitory synapses (Pfeffer et al., 2013; Miao et al., 2016). Our simultaneous in vivo recordings of t-SOM or t-PV cells with other non-tagged putative INs (NS units) suggest a reciprocal regulation between SOM and PV cells in the V1 (Figure 1). This regulation may cause the observed modulation on spontaneous or visually induced beta and gamma activity by these two IN subtypes (Figures 5 and 6). Recent studies have also suggested that, in local neocortical circuits, a population of vasoactive intestinal peptide (VIP)-expressing INs, which preferentially innervate neighboring INs rather than excitatory PCs (Pfeffer et al., 2013; Kepecs and Fishell, 2014), provides major dis-inhibitory input to SOM and PV cells. In the mouse V1, VIP cells were major recipients of afferent behavior-related modulatory inputs and mediated modulations of ongoing gamma activity as well as gain of visual responses by locomotion, primarily through the above dis-inhibitory circuit (Niell and Stryker, 2010; Polack et al., 2013; Fu et al., 2014). Thus, it is likely that this neocortical dis-inhibitory circuit could be fundamental for the regulation of cortical activity dynamics, but how it contributes remains to be further examined in future studies.
48.
Miao, Q. ∙ Yao, L. ∙ Rasch, M.J. ...
Selective maturation of temporal dynamics of intracortical excitatory transmission at the critical period onset
Cell Rep. 2016; 16:1677-1689
57.
Pfeffer, C.K. ∙ Xue, M. ∙ He, M. ...
Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Nat. Neurosci. 2013; 16:1068-1076
35.
Kepecs, A. ∙ Fishell, G.
Interneuron cell types are fit to function
Nature. 2014; 505:318-326
57.
Pfeffer, C.K. ∙ Xue, M. ∙ He, M. ...
Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Nat. Neurosci. 2013; 16:1068-1076
22.
Fu, Y. ∙ Tucciarone, J.M. ∙ Espinosa, J.S. ...
A cortical circuit for gain control by behavioral state
Cell. 2014; 156:1139-1152
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Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
58.
Polack, P.-O. ∙ Friedman, J. ∙ Golshani, P.
Cellular mechanisms of brain state-dependent gain modulation in visual cortex
Nat. Neurosci. 2013; 16:1331-1339
In conclusion, we demonstrate that SOM and PV INs preferentially drive slow and fast oscillations in the sensory cortex, respectively, and shape the sensory input-induced beta and gamma activities cooperatively. The balance of inhibition mediated by these two IN populations is important for maintaining normal cortical dynamics. Our findings provide a detailed cellular mechanism for the generation or regulation of cortical beta and gamma oscillations specifically. Because the emergence of beta-gamma oscillations in the V1 is dependent on both sensory inputs (Gieselmann and Thiele, 2008; Ray and Maunsell, 2010; Zhang and Li, 2013) and modulation from higher cortices (Fries et al., 2001; Engel et al., 2001), our findings may offer a circuit basis for understanding how the bottom-up and top-down signals interact to control the cortical dynamics that underlie visual perception and behaviors.
24.
Gieselmann, M.A. ∙ Thiele, A.
Comparison of spatial integration and surround suppression characteristics in spiking activity and the local field potential in macaque V1
Eur. J. Neurosci. 2008; 28:447-459
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Ray, S. ∙ Maunsell, J.H.R.
Differences in gamma frequencies across visual cortex restrict their possible use in computation
Neuron. 2010; 67:885-896
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Zhang, L. ∙ Li, B.
Surround modulation characteristics of local field potential and spiking activity in primary visual cortex of cat
PLoS ONE. 2013; 8:e64492
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Engel, A.K. ∙ Fries, P. ∙ Singer, W.
Dynamic predictions: oscillations and synchrony in top-down processing
Nat. Rev. Neurosci. 2001; 2:704-716
21.
Fries, P. ∙ Reynolds, J.H. ∙ Rorie, A.E. ...
Modulation of oscillatory neuronal synchronization by selective visual attention
Science. 2001; 291:1560-1563
STAR★Methods
Key Resources Table
| REAGENT or RESOURCE | SOURCE | IDENTIFIER |
|---|---|---|
| Antibodies | ||
| Goat IgG anti-Somatostatin (SOM) | Santa Cruz | RRID:AB_2302603 |
| Rabbit IgG anti-Parvalbumin (PV) | Swant | RRID: AB_10000344 |
| Alexa Fluor 488 Donkey anti Rabbit or Goat IgG | Thermo Fisher Scientific | RRID:AB_2535792 |
| Alexa Fluor 594 Donkey anti Rabbit or Goat IgG | Thermo Fisher Scientific | RRID: AB_2534102 |
| Alexa Fluor 594 Donkey anti-Rabbit IgG | Thermo Fisher Scientific | RRID:AB_141637 |
| Alexa Fluor 594 Donkey anti-Goat IgG | Thermo Fisher Scientific | RRID:AB_2534105 |
| Chemicals | ||
| Isoflurane | RWD Life Science | R510-22 |
| Bicuculline Methobromide | Tocris | Cat. No. 0109/10 |
| Cyanoacrylate adhesives | Sigma-Aldrich | Z105902 |
| Kwik-Sil | WPI Inc | Item#: KWIK-SIL |
| Experimental Models: Organisms/Strains | ||
| Mouse: Ai35 | Jackson Laboratory | Jax No. 012735 |
| Mouse: Ai32 | Jackson Laboratory | Jax No. 012569 |
| Mouse: Ai27 | Jackson Laboratory | Jax No. 012567 |
| Mouse: SOM(SST)-Cre | Jackson Laboratory | Jax No. 013044 |
| Mouse: PV-Cre | Jackson Laboratory | Jax No. 008069 |
| Software and Algorithms | ||
| MATLAB 2015b | Mathworks | |
| Offline Sorter | Plexon | |
| Other | ||
| Ni-Cr alloy microwires | California Fine Wire | CFW 100188 |
| Tungsten wires | California Fine Wire | CFW 100211 |
| CerebusT M Data Acquisition System | Blackrock Microsystems | |
| Multclamp 700B | Molecular Devices | |
| Laser (473/589 nm) | Changchun Industry Laser | |
| Laser shutter | Lambda Photometrics | SRS474 |
| Master 8 | A.M.P.I. | |
Contact for Reagent and Resource Sharing
Further information and requests for reagents may be directed to and will be fulfilled by the Lead Contact, Dr. Xiaohui Zhang (xhzhang@bnu.edu.cn).
Experimental Model and Subject Details
All experiments were performed in accordance with protocols approved by the Animal Research Advisory Committee of State Key Laboratory of Cognitive Neuroscience & Learning at Beijing Normal University (IACUC-BNUNKLCNL-2013-10) and Institute of Neuroscience, Chinese Academy of Sciences (Ref. NO. NA-100418). Different types of transgenic mice at ages of 2-8 months (no preference on the animal sex) were used in the present study. Three optogenetic-tool knock-in lines were used in our study: Ai35 (Rosa-CAG-LSL-ss-Arch-eGFP-ER2-WPRE; Jackson Laboratory, Jax No. 012735), Ai27 (Rosa-CAG-LSL-ChR2(H134R)-tdTomato- WPRE; Jax No. 012567) and Ai32 (Rosa-CAG -LSL-ChR2(H134R)-EYFP-WPRE; Jax No. 012569; Madisen et al., 2012). The first one is engineered for high level and Cre-dependent transgenic expression of yellow light-activated proton pump archaerhodopsin-3 (Arch, see ref. Chow et al., 2010) and the last two are for expression of blue light-activated cation channel channelrhodopsin (ChR2, see refs. Boyden et al., 2005; Li et al., 2005). Interneuronal subtype-specific expression of Arch or ChR2 was achieved by crossing these three optogenetic tool lines with the Som-IRES-Cre (by J. Z. Huang at CSHL; Jax No. 013044) and Pvalb-IRES-Cre (by S Arbor at FMI, Jax No. 008069) lines, respectively, to generate six types of transgenic mice: SOM::Ai35 (SOM-Arch), PV::Ai35 (PV-Arch), SOM::Ai27 or Ai32 (SOM-ChR2) and PV::Ai27 or Ai32 (PV-ChR2). All transgenic mice belong to the C57BL/6J strain and were reared on a 12/12 hr light/dark cycle to the adult age. In total, 25 SOM::Ai35, 13 SOM::Ai27 and 12 SOM::Ai32 transgenic mice were used to optogenetically inactivate or activate cortical inhibitory SOM cells in the V1. Another 30 PV::Ai35, 20 PV::Ai27 and 8 PV::Ai32 mice were used to inactivate or activate cortical PV cells.
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Madisen, L. ∙ Mao, T. ∙ Koch, H. ...
A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing
Nat. Neurosci. 2012; 15:793-802
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Chow, B.Y. ∙ Han, X. ∙ Dobry, A.S. ...
High-performance genetically targetable optical neural silencing by light-driven proton pumps
Nature. 2010; 463:98-102
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Boyden, E.S. ∙ Zhang, F. ∙ Bamberg, E. ...
Millisecond-timescale, genetically targeted optical control of neural activity
Nat. Neurosci. 2005; 8:1263-1268
42.
Li, X. ∙ Gutierrez, D.V. ∙ Hanson, M.G. ...
Fast noninvasive activation and inhibition of neural and network activity by vertebrate rhodopsin and green algae channelrhodopsin
Proc. Natl. Acad. Sci. USA. 2005; 102:17816-17821
Method Details
Surgery
Prior to electrophysiological recording, the animal was anesthetized by the inhalation of isoflurane (1%–3% in oxygen) and then implanted a stainless-steel head-plate with dental acrylic as described in the previous study (Chen et al., 2015). Animal was then allowed to recover and habituate to our spherical treadmill setup (Chen et al., 2015). After 2-3 training sessions (10-15 min per each), the animal quickly learned to stand still, run and even to occasionally groom. On the day of recording, the animal was anesthetized with isoflurane and restrained in a stereotaxic apparatus again. Body temperature was kept at 37°C by a homeostatically controlled heating pad (RWD Life Science). A craniotomy (0.5-1 mm in diameter) was made over the primary visual cortex (∼3 mm lateral lambda). The brain surface was covered with 1.2% low melting temperature agarose (Sigma) in saline, and then the head-plate opening was again filled with silicone elastomer (Kwik-Sil, WPI). After the surgery, the animal was allowed to recover from the anesthesia for 1-2 hours before the recording on the treadmill setup. The dura was removed immediately before the insertion of recording electrodes.
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
in vivo electrophysiology
Local field potentials (LFPs) and spikes were recorded mostly from the layers 2-4 neurons (about 200-500 μm below the cortical surface) in the V1 with the custom-made electrode array, which composed of one to six tetrodes, or stereotrodes, or single-electrodes (impedance 0.2-0.5 MΩ) spaced by 200-300 μm intervals. The tetrode or stereotrode was constructed by twisting together 4 or 2 tungsten wires (0.0005” diameter, California Fine Wire Company, CFW 100211). The Ni-Cr alloy microwires in 0.0013” or 0.001” diameter (California Fine Wire Company, Stablohm 675, CFW 100188) were used for making the single-electrodes. In addition to using the uncoated stainless steel wire inserted in the V1 as a common reference, another reference electrode (same as the recording electrode) in agarose was connected to the tissue 1-2 mm above the V1 surface to further reduce the background noise. For recording the laser light pulses-evoked LFP resonant activity (Figure 7), a glass micropipette, filled with the normal saline (2-5 MΩ), was used to avoid the potential transient photoelectric noise (Han et al., 2009). To maximally reduce the artifact of the line noise (50 Hz), the electrode headstage and adaptor were wrapped with tin foil and grounded.
28.
Han, X. ∙ Qian, X. ∙ Bernstein, J.G. ...
Millisecond-timescale optical control of neural dynamics in the nonhuman primate brain
Neuron. 2009; 62:191-198
Electric signals from metal wire electrodes were recorded by the CerebusT M Data Acquisition System (Blackrock Microsystems), while that recorded by the glass micropipette were first amplified by the amplifier Multi-Clamp 700B (Molecular Devices) and then transferred to the CerebusTM system. LFPs were band-pass filtered in the range of 1-500 Hz and sampled at 2 kHz. Spiking signals were band-pass filtered in the range of 250-7500 Hz and sampled at 30 kHz. For the spike detection, the signal threshold was set at about 6 times of the noise level (root mean square: RMS). LFPs and spikes were saved to the computer hard-disk for further offline analysis.
Behavior monitoring
The behavior of the animal on the spherical treadmill setup was captured (25 frames/s) by a digital camera above it. The relative speed (r.s., in arbitrary unit) of the animal motion was estimated based on the pixels’ luminance intensity changes of consecutive video frames (down sample to 5 frames/s). The animal’s behavior states were classified to stationary and running states, during which the values of r.s. were less and larger than the global mean value, respectively. Details of the calculation were described in our previous study (Chen et al., 2015).
13.
Chen, G. ∙ Rasch, M.J. ∙ Wang, R. ...
Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period
Sci. Rep. 2015; 5:17847
Visual stimulation
Visual stimuli were generated with a PC computer containing a NVIDIA GeForce GT430 graphics board and displayed on a cathode-ray tube (CRT) monitor (Sony CPD-G520, 40.5 × 30.5 cm, 800 × 600 resolution, refresh rate 120 Hz, mean luminance 30 cd/m2) placed ∼20 cm in front of the animal (covering 90° × 75° of the visual field) and centered on its midline. Luminance nonlinearities were gamma corrected. We mapped the receptive fields (rf) using sparse 2D noise stimuli, in which a white square was flashed on a black background at each of the 8 × 8 positions in a pseudorandom sequence. After the mapping of receptive fields, we slightly modified the position of the monitor to make sure the receptive fields were located at the center of it. Full-field standard sinusoidal drifting gratings (covering ∼75° visual angle, 100% contrast, 0.04 cycles per degree, temporal frequency 3 Hz, 2 s per trial, inter-stimulus interval ∼2 s, gray blank with mean luminance) were presented at 12 directions (separated by 30°) in a pseudorandom sequence (12 repetitions for each direction) to characterize the visually induced oscillatory activities.
For examining the dependence of induced oscillations on stimulus size, 4 drifting gratings were presented in the size of 0.5x, 1x, 2x and 3x of the rf of recorded LFP, as shown in the Figure 4.
Optogenetic inactivation and activation
The 589 nm yellow laser and 473 nm blue laser was used to activate the light-activated Arch and ChR2, respectively.
The light from yellow or blue laser (50 mW, Changchun New Industry Laser) was transmitted to the cortical surface near the recording electrode (< 200 μm distance) through an optical fiber (200 μm, core diameter is 50 μm) that was connected to a laser-fiber coupler. A round continuously variable neutral density filter was placed in the light path between the laser and the coupler to adjust the output light intensity at the cortical surface in the range of 0.3-30 mW. In the experiments of applying the 4 or 6 s duration laser lighting, the range of light intensity was often 5-30 mW (measured at the optical fiber opening using a photometer). Although the laser power used to individual animals often varied in the 5-30 mW, an appropriate intensity was used in each experiment to exert apparent light modulation on the LFP activity but not to elicit apparent seizure activity and affect long-term stable recording of LFP and neuronal spikes. Moreover, as shown by Figure S1K, laser intensity within this range show similar level of light modulation to LFP activity (as measured on theta activity), and the modulation effect at 5-10 mW laser is ∼60% of maximum at 30 mW. In the experiments of applying 1 ms laser pulse stimuli, the intensity was set at 30 mW.
In the optogenetic stimulation experiments, LFP and neuronal spiking activity showed significant light induced changes when laser was on, and could quickly recover to the normal activity states after each trial of the laser stimulation. Using similar laser stimulation in the wild-type C57 mice, we did not observe any significant neuronal activity changes in the V1 when the laser was on, suggesting that the laser lighting does not activate the retina-to-V1 visual pathway in our experimental conditions. The on and off of laser lighting to cortical surface were set with a laser shutter (SRS474, Lambda Photometrics) under the control of TTL pulses generated from the Master 8 (A.M.P.I.). In the experiments of examining effects of inactivating or activating one subtype of cortical inhibitory IN on spontaneous cortical dynamics, 4 s light pulses (duration 4 s, inter pulse interval ∼20 s, repetitions 20-30) were applied to the cortical surface in the absence of visual stimulus (gray background luminance). To measure the LFP resonant activity during the rhythmic activation of one subtype of cortical inhibitory IN at different frequencies, 3 s-length trains of 1 ms blue laser pulses (frequency varied in the range of 1 to 200 Hz, 30-50 repetitions) were used. In the experiments with visual stimuli, laser light stimulation began 3 s before the onset of visual stimulation (2 s drifting gratings) and lasted for 6 s. The preceding laser on can avoid the known transient photoelectric artifacts on recorded LFP and spiking activities elicited by visual stimuli. Meanwhile, this laser lighting protocol could allow us to specifically examine the effects of inactivating one IN subtype on the visually induced relative changes (grating versus blank) of oscillatory activities by comparing that under light on and off conditions. Trials with and without the laser stimulation were alternated and repeated for 36-72 times in a typical experiment.
By varying the distance between the recording electrode and the optic fiber, we estimated what the effective distance of laser lighting is to affect local population neural activity (LFP power spectrum). After examining the relationship between light (with largest intensity of 30 mW) induced LFP power changes (theta band) and the optic fiber-electrode distances (horizontal), we found that the light effect was largely restricted in V1 area (half spatial width of half maximum of light effect is about 0.4 mm, Figure S1L). We also test how light intensity affected the light induced LFP power changes (Figure S1K). So that, light in a range of 5-30 mW was usually used (varied case by case) considering both its efficiency and the potential contamination to the stability or signal-to-noise ratio of neural activity.
In addition to examine the effects of optogenetically inactivating SOM or PV cells on local population oscillatory activities, we also compared the effects of activating (4 s long pulse stimulation) SOM or PV cells in SOM::Ai27/32 or PV::Ai27/32 mice. The results showed that activating SOM or PV substantially silenced the spiking activity of other types of neurons and decreased the oscillatory activities in all frequency bands (Figure S6). There was no substantial difference between activating SOM and activating PV. Thus, the persistent activation method could provide no more information about how SOM and PV differentially regulate local population oscillatory activities when compared with the inactivation method in the present study.
Immunohistochemistry
Different types of transgenic mice were deeply anesthetized with pentobarbital (i.p. injection) after the recording, and then perfused transcardially with the cold saline followed by 4% paraformaldehyde (w/v) in PBS (0.01 M). The brain was dissected and post-fixed for 2 h at 4°C. After fixation, the brain was placed in 30% sucrose (w/v) in PBS solution overnight at 4°C. The occipital part of brain (containing the V1) was sectioned into 20 μm coronal slices using a cryostat (Leica). The sections were treated with blocking solution (10% Bovine Serum Albumin in PBS with 0.5% Triton X-100) for 1 h at 20°C and then incubated with primary antibodies (Goat IgG anti-SOM, MAB353, Santa Cruz 1:200; Rabbit IgG anti-PV, PVG-214, Swant 1:1000) diluted in the blocking solution overnight at 4°C.
Slices were then washed with PBS for 3 × 10 min and incubated with the second antibody (Alexa Fluor 488 or 594 Donkey anti Rabbit IgG 1:1000, Invitrogen) for 2 h at 20°C. Sections were then washed three times with PBS, followed by incubation in 4’,6-diamidino-2-phenylindole (DAPI) staining solution for 10 min. After another round of three 5-min washings in PBS, the stained sections were mounted onto glass slides, air-dried and coverslipped with Mounting Medium (Vector). All the fluorescence staining signals in brain sections were viewed and acquired by a Nikon A1 confocal microscope using a 10x objective and under the scanning mode of 1 × 3 jigsawed and 2 μm-step Z stack. All confocal images were acquired as a TIFF file, and analyzed with the ImageJ software (http://rsbweb.nih.gov/ij/). The percentage of cortical PV or SOM cells that expressed endogenous fluorescence proteins was counted from the randomly selected 3-5 sections for each animal, and the presented average values were measured from 2-4 animals for each examined transgenic line. Data were presented as mean ± s.e.m.
Spike sorting
Raw spike waveforms were sorted into single units (presumptive neurons) by manually tracing boundaries between three-dimensional clusters based on waveform features: the peak amplitude, the first principle component and the waveform energy using the Offline Sorter (Plexon). Clusters that have no clear separation with noise were excluded. Inter spike intervals and autocorrelation functions were inspected for all putative units. If in which the absolute refractory period (2 ms) was violated, the unit was also excluded. All units were further classified (analyzed by custom written MATLAB scripts), by fitting a Gaussian mixture model with two mixture components, as the wide spike (WS) and narrow-spike (NS) units based on their waveform features: the peak-trough latency, the half-peak width and the peak-trough ratio (Barthó et al., 2004; Niell and Stryker, 2010; Stark et al., 2013; related results see Figures S1G–S1J).
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Barthó, P. ∙ Hirase, H. ∙ Monconduit, L. ...
Characterization of neocortical principal cells and interneurons by network interactions and extracellular features
J. Neurophysiol. 2004; 92:600-608
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
68.
Stark, E. ∙ Eichler, R. ∙ Roux, L. ...
Inhibition-induced theta resonance in cortical circuits
Neuron. 2013; 80:1263-1276
Light evoked spike responses and cell type identification
The peri-stimulus time histogram (PSTH) of spikes was used to present the light evoked neuronal responses. Time bins of 20 and 1 ms of PSTHs were used to measure laser light-evoked changes of neuronal spike rates for stimuli of 4-6 s and 1 ms laser pulses, respectively, in the transgenic mice. To quantitatively estimate the significance of light-evoked changes of neuronal firing rate, we used the following statistical tests: the permutation test (Royer et al., 2012) for the 4-6 s laser pulses and the Stimulus-Associated spike Latency Test (SALT, Kvitsiani et al., 2013) for the 1 ms pulses. In the permutation test, the mean firing rate difference (Δf) between control (without) and the (with) laser lighting trials was calculated, and both kind of trials were shuffled for 1000 times to calculate the corresponding (Δfs 1:1000). The p value was estimated from the rank of Δf in the Δfs 1:1000 distribution: If less than 10 values from Δfs 1:1000 were higher (lower) than Δf, that is, the p value was less than 0.01 (10/1000), indicating that the light evoked firing rate increase (decrease) is significant. In the SALT, which is designed to test the significance of changes of spike timing relative to the onset of light stimulus, the light-activated neurons were identified based on the short spike latency and low jitter. In these methods, we also adopted the Pearson’s correlation coefficient (r) to compare the waveforms of units recorded before and during the laser lighting, and only those units showing no substantial changes in their waveforms were included in further data analysis. Using the above statistical methods, we identified 29 SOM cells in the SOM-Arch and SOM-ChR2 mice and 38 PV cells in the PV-Arch and PV-ChR2 mice, which met the statistical criterion of p < 0.01 and r > 0.9. We noted that 3 out of all 38 tagged PV cells showed broader spike waveforms, and this portion is similar to that reported previously in recording from neocortex in vivo (Kvitsiani et al., 2013). However, relatively broader spikes recorded from tagged PV cells can be caused by some other technical factors, such as the electrode position adjacent to recorded cells or the low rate of leaking expression of optogenetic proteins in PCs in PV-Cre:ChR2/Arch transgenic mice. Given only small portion of tagged PV cells showed broad spikes, we have regarded them as putative PV cells also in our study, partly due to there is no effective way to further identifying the cell type nature of these cells.
62.
Royer, S. ∙ Zemelman, B.V. ∙ Losonczy, A. ...
Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition
Nat. Neurosci. 2012; 15:769-775
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Kvitsiani, D. ∙ Ranade, S. ∙ Hangya, B. ...
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Nature. 2013; 498:363-366
38.
Kvitsiani, D. ∙ Ranade, S. ∙ Hangya, B. ...
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Nature. 2013; 498:363-366
Spectral analysis
The power spectrum of LFP signals typically fall-off proportional to 1/f. To reduce the dynamic range and thus reduce the power leakage from the lower frequencies into the higher frequencies during spectral estimation, we whitened the LFP signals. In the pre-whitening process, a low-order (order = 3) autoregressive (AR) spectrum estimation was used, which could reduce the dynamic range but without fitting specific structural features of the data (Niell and Stryker, 2010; Mitra and Pesaran, 1999). We used the Levinson Durbin recursion method to fit the AR model, and the coefficients (Ak) of this process were then used to filter the original time series data (Xt). The residuals:
49.
Mitra, P.P. ∙ Pesaran, B.
Analysis of dynamic brain imaging data
Biophys. J. 1999; 76:691-708
53.
Niell, C.M. ∙ Stryker, M.P.
Modulation of visual responses by behavioral state in mouse visual cortex
Neuron. 2010; 65:472-479
(1)
We first normalized the filtered LFP signals (1-200 Hz) by calculating the z-score and performing the pre-whitening as described above. The power spectrum of the spontaneous LFP was computed using the multi-taper estimation method in MATLAB with the chronux package (http://chronux.org), using 5 s data segments and 3-5 tapers (TW = 3, K = 5). Then, we smoothed the spectrum by using the “locfit” function in the chronux toolbox after removing the 50 Hz line noise in some mice to reduce the variation but without affecting the features. In the time-frequency spectral analysis, a 0.5 s sliding window at a 0.02 s step was used to calculate continuous power spectrograms of the spontaneous LFPs of 1-10 min length and the visual stimuli- or light-induced LFPs of 4-12 s length (including the blank). The wavelet analysis (by “cwt” function in MATLAB wavelet toolbox) was also used to calculate power spectrogram for the Figures 5A and 5E, Figures S2E and S2G to enhance the temporal resolution. The power at each frequency and time point was finally normalized by the mean power across all frequency bands (1-90 Hz) and the total period (normalized power). Continuous LFP amplitude/power in specific frequency bands (theta 2-8 Hz, gamma 40-70 Hz) for Figure 2 and Figure S3 data were estimated by using the Hilbert transform after band-pass filtering (as described below) the raw signal.
Cross-frequency coupling (CFC)
The previous method described by Canolty et al., (2006) was used to assess the phase-amplitude CFC. The LFP was first band-pass filtered using an infinite impulse response (IIR) Elliptic filter by means of the “filtfilt” function (MATLAB). Then the Hilbert transform (in the Signal Processing Toolbox) was used to compute the instantaneous beta (15-30 Hz) or gamma (40-70 Hz) amplitudes, AG(t), and theta (2-8 Hz) phase, ΦT(t). A composite complex-valued signal:
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Canolty, R.T. ∙ Edwards, E. ∙ Dalal, S.S. ...
High gamma power is phase-locked to theta oscillations in human neocortex
Science. 2006; 313:1626-1628
(2)
(3)
(4)
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Esghaei, M. ∙ Daliri, M.R. ∙ Treue, S.
Attention decreases phase-amplitude coupling, enhancing stimulus discriminability in cortical area MT
Front. Neural Circuits. 2015; 9:82
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Esghaei, M. ∙ Daliri, M.R. ∙ Treue, S.
Attention decreases phase-amplitude coupling, enhancing stimulus discriminability in cortical area MT
Front. Neural Circuits. 2015; 9:82
LFP phase preference of spikes
LFP was band-pass filtered as described above and then the theta (T), beta (B) or gamma (G) phase series: ΦT(t), ΦB(t) or ΦG(t) was estimated using the Hilbert transform function. Similar to the phase-amplitude CFC, here the LFP amplitude: AG(t) was replaced by the spike train: S(t) to calculate the phase preference of spikes. A composite complex-valued signal:
(5)
(6)
Spike-LFP pairwise phase consistency (PPC)
The LFP, which was filtered in the range of 1-200 Hz, and spike train of a unit (mean firing rate > 1 Hz) were used to calculate the PPC (Vinck et al., 2010). The sample estimate of the PPC was defined as:
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Vinck, M. ∙ van Wingerden, M. ∙ Womelsdorf, T. ...
The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization
Neuroimage. 2010; 51:112-122
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Oostenveld, R. ∙ Fries, P. ∙ Maris, E. ...
FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data
Comput. Intell. Neurosci. 2011; 2011:156869
Two groups of PPC of spikes occurred when the theta or gamma band LFP power was high or low during spontaneous activity was compared (Figure 2). The difference of them was:
(9)
(10)
(11)
Light evoked resonant circuit activity
The 1-ms single laser pulse-evoked average of LFPs was calculated to reflect the field responses to the transient optogenetic activation of one type of inhibitory IN. To test the resonation property, the evoked LFP power change (Powerlight/Powerbaseline; Powerlight: power of 3 s length LFP segments with rhythmic light stimulation; Powerbaseline: power of 3 s length LFP segments without rhythmic light stimulation) at each light stimulation frequency was calculated based on the above spectrum analysis (multi-taper power estimation method in the chronux package). The power changes at each frequency were then normalized to their maximum to get the resonation tuning curve (Figure 7).
VEP, spike rate, receptive field and orientation tuning
Visual evoked potentials (VEPs) were recorded primarily in the layers 2-4 and calculated as the visual stimulus triggered LFPs’ average. Negative peak amplitudes of the VEPs were measured for stimulation of the drifting grating or the sparse 2D noise. Only those recordings with VEP amplitudes > 100 μV were included in further analyses. PSTHs of spikes fired around the visual stimulation were also measured to calculate the baseline mean firing rate (2 s blank), the visually evoked transient peak rate (within 100 ms after the stimulus onset) and the mean rate of induced persistent spiking (Figure 3). To estimate the size of receptive fields, VEP evoked by bright square flash in each grid and the spike triggered stimulus average in pre-spike frames were calculated. A global measure (S, 1-circular variance, see ref. Ringach et al., 2002) of the orientation tuning curve was computed:
60.
Ringach, D.L. ∙ Shapley, R.M. ∙ Hawken, M.J.
Orientation selectivity in macaque V1: diversity and laminar dependence
J. Neurosci. 2002; 22:5639-5651
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Quantification and Statistical Analysis
Data are presented as mean ± SEM. unless otherwise stated in text and figure legends. Statistical significance (p values) were computed by following methods in MATLAB (Mathworks): permutation test (Royer et al., 2012), Wilcoxon two-sided signed rank test, Spearman two-tailed rank correlation test, Kruskal-Wallis ANOVA test, Kolmogorov-Smirnov test and Stimulus-Associated spike Latency Test (SALT, Kvitsiani et al., 2013). More details of all performed statistical tests and the number of cells, recording sites and mice are described in text and figure legends.
62.
Royer, S. ∙ Zemelman, B.V. ∙ Losonczy, A. ...
Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition
Nat. Neurosci. 2012; 15:769-775
38.
Kvitsiani, D. ∙ Ranade, S. ∙ Hangya, B. ...
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Nature. 2013; 498:363-366
Data and Software Availability
Data analyses were performed with custom written scripts in MATLAB (Mathworks). The spike sorting software is the Offline Sorter from Plexon. Requests for data and MATLAB scripts used in the present study can be directed to the lead author (xhzhang@bnu.edu.cn).
Acknowledgments
We thank Drs. J. Huang (CSHL) and H. Zeng (Allen Institute) for kindly providing Som-IRES-Cre mice as well as Ai35, Ai27, and Ai32 mice, respectively; Dr. Y. Liu for technical supports to the laser stimulation setup; and Dr. B. Li (CSHL) for critical reading on the manuscript. The work was supported by the grants from the State Key Research Program of China (2011CBA00404 to X. Zhang and 2014CB846101 to M.J.R.), the Interdisciplinary Research Funds of Beijing Normal University (to X. Zhang), and the MIT Greater China for Innovation Fund (to Y.L. and X. Zhang). H.W.T. was supported by a grant of the National Natural Science Foundation of China (31628007) and in part by NIH grants (EY019049 and EY025722).
Author Contributions
X. Zhang, G.C., and H.W.T. conceived the research. X. Zhang, G.C., and Y.L. designed the experiments. G.C. and Y.Z. performed the in vivo physiology experiments and data analysis. M.J.R., X. Zhao, X. Zhang, and Y.L. performed additional data analysis. X.L. and Q.Y. performed the immunohistochemistry experiments. X. Zhang, G.C., and M.J.R. wrote the manuscript and all authors commented on the writing.
Declaration of Interests
The authors declare no competing interests.
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