Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex
Sensory perceptions are modulated by the context in which they are experienced . In primary sensory areas , neuronal responses to sensory inputs are also modulated by behavioral states , including level of arousal , attention and locomotion ( Iriki et al . , 1996; Petersen and Crochet , 2013; Bennett et al . , 2014; McGinley et al . , 2015 ) . In vivo recordings in awake mice have shown that locomotion modulates the response properties of neurons in the primary visual cortex ( V1 ) , resulting in an increased gain of excitatory neuron responses to visual stimuli ( Niell and Stryker , 2010; Keller et al . , 2012; Bennett et al . , 2013; Polack et al . , 2013; Saleem et al . , 2013; Erisken et al . , 2014; Reimer et al . , 2014 ) . However , the neuronal circuits underlying this response modulation are unclear . Recent studies have revealed that a specific subclass of inhibitory neurons , expressing vasoactive intestinal peptide ( VIP ) , strongly increase their activity during locomotion ( Fu et al . , 2014; Reimer et al . , 2014; Jackson et al . , 2016 ) . VIP neurons mainly inhibit a second class of inhibitory neurons , expressing somatostatin ( SST; Figure 1A; Pfeffer et al . , 2013; Jiang et al . , 2015; Urban-Ciecko and Barth , 2016 ) . It has been proposed that cholinergic activation of VIP neurons during locomotion would inhibit SST neurons , alleviating inhibition onto excitatory neurons and , as a consequence , increase the gain of excitatory neuron visual responses ( Figure 1B; Fu et al . , 2014 ) . However , a previous study has reported an increase of SST spiking activity in layer 2/3 during locomotion ( Polack et al . , 2013 ) , an observation that challenges the hypothesis of an SST-cell mediated disinhibitory circuit . The aforementioned recordings of SST neuronal activity were acquired in different sensory contexts , either in darkness or during the presentation of visual stimuli . One hypothesis that would explain the discrepancies between these results is that V1 neuronal responses to locomotion are context-dependent . 10 . 7554/eLife . 14985 . 003Figure 1 . Imaging locomotion responses of excitatory and inhibitory neurons in mouse V1 . ( A ) Schematic of the connectivity between pyramidal neurons ( Pyr ) and subtypes of inhibitory neurons , vasoactive intestinal peptide ( VIP ) , somatostatin ( SST ) and parvalbumin ( PV ) expressing neurons , established from in vitro studies in V1 ( Pfeffer et al . , 2013; Jiang et al . , 2015 ) . ( B ) Proposed disinhibition model: locomotion activates VIP neurons through cholinergic ( ACh ) inputs , SST neurons are inhibited , which leads to a disinhibition of Pyr neurons and an increase in the gain of visual responses during locomotion ( Fu et al . , 2014 ) . ( C ) Experimental set-up for two-photon calcium imaging in V1 of awake-behaving mice . Mice are head-fixed and can run freely on a cylindrical treadmill either during the presentation of a visual stimulus ( oriented gratings ) or in darkness . ( D ) Confocal images of 50 µm thick coronal sections showing cell type specific GCaMP6f expression in VIP , SST and PV-positive inhibitory neurons as well as in CaMKII-positive excitatory populations . Boundaries between cortical layers are indicated . ( E ) Left panel , in vivo two-photon images of VIP , SST and PV neurons labelled with GCaMP6f; cortical depth of imaging is indicated . Right panel , example calcium transients ( ΔF/F0 , coloed traces ) of single VIP , SST and PV neurons , imaged in darkness and during visual stimulation with oriented gratings ( grey bar above trace ) , and aligned with the corresponding running speed ( cm/s , black traces ) . Scale bars on images , 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 14985 . 003 In this study , we tested this hypothesis by directly comparing the locomotion responses of excitatory and inhibitory neurons in darkness and during visual stimulation . We used two-photon calcium imaging to monitor the activity of excitatory neurons as well as of three non-overlapping populations of inhibitory neurons ( VIP , SST and parvalbumin [PV] neurons ) in layer 2/3 and layer 4 of V1 in awake behaving mice . Our results show that during visual stimulation these three classes of interneurons increase their activity with locomotion , challenging the model of a disinhibitory circuit mediated through SST neurons . We found that locomotion affects the activity of inhibitory circuits differently in darkness and during visual stimulation , revealing a context-dependent , cell type specific response to locomotion in V1 . The highest proportion of context-dependent responses to locomotion was found among SST neurons , which play a central role in V1 microcircuits . We suggest alternative mechanisms of how locomotion modulates the neuronal activity in V1 , highlighting the dynamic nature of interneurons function that strongly depends on the behavioral context of the animal .
Isoluminant grey screen stimulation is commonly used to record so called ‘spontaneous activity’ of neurons in the visual cortex . Since our results showed different locomotion responses in the dark and during the presentation of drifting gratings , we tested whether this difference was due to the presence of patterned visual stimuli or , more simply , to the presence of light ( Figure 2—figure supplement 1 ) . We quantified the amplitude of fluorescence changes during stationary and locomotion periods in all three contexts: darkness , grey screen and drifting gratings . We did not find any significant difference for any of the inhibitory populations ( VIP , SST and PV neurons ) between the two types of visual stimulation ( gratings vs grey screen; Figure 2—figure supplement 1C ) . For excitatory neurons , we found a lower LMI during the presentation of a grey screen than during drifting grating presentation ( mean of median LMI: 0 . 17 ± 0 . 02 grey versus 0 . 19 ± 0 . 02 visual stimulation; p=0 . 033 , n = 12 , Kruskal–Wallis test; Figure 2—figure supplement 1C ( i ) ) . Locomotion responses for each type of visual stimulus ( gratings vs grey screen ) were still significantly higher than during darkness ( mean of median LMI: 0 . 07 ± 0 . 02 dark versus 0 . 17 ± 0 . 02 grey; p=0 . 007 , n = 12 , Kruskal–Wallis test ) ( Figure 2—figure supplement 1C ( i ) ) . These results indicate that , during visual stimulation and independently of the presence of patterned visual stimuli , excitatory , VIP , SST and PV neurons show increased activity during locomotion . While comparisons of a neuronal population’s LMI distribution ( Figure 2C ) indicates how , on average , that cell type is modulated by locomotion in different sensory contexts , it does not provide information about the context-dependent responses of single neurons . For instance , the average LMI could be the same in darkness and during visual stimulation even though individual neurons may have large changes in their LMI , which cancel out when considering the population as a whole . In order to show the diversity of locomotion responses within each neuronal subtype , we examined the LMI value in darkness versus during visual stimulation for each neuron ( Figure 3A ) . Neurons near the identity line show context-independent locomotion responses ( similar LMI in darkness and during visual stimulation ) , while the other neurons changed their response to locomotion from one context to another ( context-dependent responses ) . We first quantified this diversity by calculating the difference between the LMI value during visual stimulation and the LMI value in darkness for each neuron ( Figure 3B ) . These results confirmed that VIP neurons displayed mainly context-independent locomotion responses ( Figure 3B ( ii ) , narrow distribution , centered around 0 ) , while locomotion responses of SST neurons were mainly context dependent ( Figure 3B ( iii ) , broad distribution shifted towards positive values ) . Both excitatory and PV neuronal populations included a diversity of locomotion responses ( broad distributions ) . To quantify the proportions of context-independent and context-dependent neurons in each cell type , we first determined the variability of the locomotion responses for each context by comparing neuronal responses across odd and even locomotion periods ( Figure 3—figure supplement 1; see Materials and methods ) . We found high correlation values for all neuronal populations , both in darkness and during visual stimulation ( 0 . 676 < R < 0 . 944; p<0 . 0001 ) , indicating a general low variability of the responses across different locomotion periods in both contexts . We determined the proportion of context-dependent neurons meeting two criteria: i ) with a response that was significantly different across contexts ( neurons distance from the identity line in Figure 3A , to estimate the error on the LMI in both dark and stimulated conditions for each neuron , bootstrapping was employed ( see Materials and methods ) ) , and ii ) with low variability of locomotion responses ( Figure 3—figure supplement 1 ) . 10 . 7554/eLife . 14985 . 008Figure 3 . Context-dependent responses to locomotion of individual excitatory and inhibitory neurons in layer 2/3 . ( A ) Left panels , scatter plots of the locomotion modulation index ( LMI ) of individual neurons in darkness versus during visual stimulation ( gratings ) with an associated Pearson correlation coefficient ( R-values ) . Context-dependent ( CD; red ) and context-independent ( CI; blue ) locomotion responsive neurons are highlighted . Context dependency was defined for each neuron by its distance from the identity line and the variability of its locomotion responses ( see Materials and methods and Figure 3—figure supplement 1 ) . Neurons that were either non-responsive to locomotion or responded unreliably are shown as open circles . Right panels , percentages of context-dependent ( CD ) and context-independent ( CI ) neurons for each neuronal subtype . Note the high proportion of CI VIP neurons ( 66% ) , the high proportion of CD SST neurons ( 49% ) , and the diversity of both PV and excitatory ( Exc ) neurons . ( B ) Histograms of the difference between the LMI value in darkness and during visual stimulation ( LMIStim-LMIDark ) for each neuronal population . Negative values indicate increased responses to locomotion in darkness compared with visual stimulation , positive numbers indicate increased responses to locomotion during visual stimulation , and numbers close to 0 ( within red lines; –0 . 2 < LMIStim-LMIDark < 0 . 2 ) indicate context-independent responses . DOI: http://dx . doi . org/10 . 7554/eLife . 14985 . 00810 . 7554/eLife . 14985 . 009Figure 3—figure supplement 1 . Variability of locomotion responses in darkness and during visual stimulation . ( A–B ) Scatter plots of LMI values calculated for each neuron from their responses during even and odd locomotion and stationary periods , in darkness ( A ) and during visual stimulation ( oriented gratings ) ( B ) . Individual periods of locomotion separated by stationary periods ( epochs ) were divided into odd and even epochs and mean LMIs were calculated for each context ( darkness and visual stimulation ) . Pearson correlation coefficient R-values are shown for each scatter plot . Note the high correlation values for all neuronal populations both in darkness and during visual stimulation ( p<0 . 001 ) . Red lines delineate 5% of the neurons with the highest variability , as measured by the difference in LMI during odd and even epochs . Context-dependent ( red ) and context-independent ( blue ) locomotion responsive neurons identified in Figure 3A are highlighted . DOI: http://dx . doi . org/10 . 7554/eLife . 14985 . 00910 . 7554/eLife . 14985 . 010Figure 3—figure supplement 2 . Representative examples of calcium transients ( ΔF/F0 ) of context-independent and context-dependent neurons , in darkness and during visual stimulation with oriented gratings ( grey bar above trace ) . The corresponding running speed is shown below each trace ( cm/s , black ) . Exc , excitatory neuron . DOI: http://dx . doi . org/10 . 7554/eLife . 14985 . 010 These results confirm that most VIP neurons were context-independent , remaining locomotion-responsive in both sensory contexts ( 66% ) , with only 17% of neurons showing context-dependent responses ( Figure 3A ( ii ) , Figure 3—figure supplement 2 ) . The proportion of context-dependent neurons was the highest among SST neurons , with 49% of neurons showing context-dependent responses to locomotion ( Figure 3A ( iii ) , Figure 3—figure supplement 2 ) . Both excitatory and PV neurons had approximately the same proportion of context-dependent neurons ( 22% for excitatory and 25% for PV neurons ) ( Figure 3A ( i ) , ( iv ) ) . Finally , we tested whether context-dependent neurons differ from context-independent ones with regard to the following characteristics: percentage of visually responsive neurons , orientation selectivity and direction selectivity . We did not find any significant difference in any neuronal population ( comparisons between context-dependent and context independent neurons for each cell type , OSI , p>0 . 261; DSI p>0 . 093 , Kruskal–Wallis test ) , suggesting that the mechanisms underlying the modulation of locomotion responses differ from those determining the selectivity of visual responses . Layer 2/3 neurons receive sensory information from excitatory neurons in layer 4 , the main thalamo-recipient layer , as well as top-down information from higher cortical areas ( Niell , 2015 ) . In addition , these neurons receive subcortical inputs from the dorsal lateral geniculate nucleus as well as neuromodulatory inputs ( Polack et al . , 2013; Fu et al . , 2014; Lee et al . , 2014 ) . Context-dependent locomotion responses of layer 2/3 neurons may thus come from one of these distinct inputs or from a combination of them . By using the same approach as for layer 2/3 neurons , we recorded locomotion responses in layer 4 neurons ( excitatory n = 331; VIP n = 57; SST n = 74; PV n = 109; in 6 , 4 , 6 and 6 mice , respectively ) . As in layer 2/3 , we used local injections of AAVs into V1 for the Cre-inducible expression of the genetically-encoded calcium indicator GCaMP6f . However , we observed that on average the GCaMP6f labelling in layer 4 was sparser than in layer 2/3 ( Figure 1D ) . Thus , we cannot exclude that we preferentially labelled subtypes of layer 4 neurons in which transduction efficiency with these AAV vectors would be higher . The quantification of locomotion responses showed no significant difference between layer 2/3 and layer 4 neurons , in any cell type , both in darkness and during visual stimulation ( Figure 4B , C ) . The results showed a higher mean LMI value for PV neurons in layer 4 ( 0 . 45 ± 0 . 04 ) compared to layer 2/3 ( 0 . 35 ± 0 . 04 ) during visual stimulation . However , this did not reach significance; p=0 . 058 , Mann-Whitney U-test ) . In addition , the results showed similar proportions of context-dependent responses in layer 4 as described in layer 2/3 ( Figure 4A , see also Figure 3A; context-dependent neurons: Exc , L2/3: 22% , L4: 17%; VIP , L2/3: 17% , L4: 26%; SST , L2/3: 49% , L4: 42%; PV , L2/3: 25% , L4: 23% ) . 10 . 7554/eLife . 14985 . 011Figure 4 . Locomotion responses of individual inhibitory and excitatory neurons in V1 cortical layer 4 . ( A ) Scatter plots of locomotion modulation index ( LMI ) of individual neurons in darkness versus during visual stimulation ( oriented gratings ) , with associated Pearson correlation coefficient ( R-values ) for excitatory ( Exc; n = 331 ) , VIP ( n = 57 ) , SST ( n = 74 ) , and PV ( n = 109 ) neurons . Context-dependent ( red ) and context-independent ( blue ) locomotion responsive neurons are highlighted . Context dependency per neuron was defined by its distance from the identity line and its variability to locomotion periods ( see Materials and methods ) . Neurons that were either non-responsive to locomotion or responded unreliably are shown as open circles . ( B ) Mean of the median LMI per animal and s . e . m . for layer 2/3 ( L2/3 ) as well as layer 4 ( L4 ) , in darkness ( Dark , black bars ) and during visual stimulation ( Stim , coloed bars ) for Exc ( L2/3 , n = 12; L4 , n = 6 ) , VIP ( L2/3 , n = 12; L4 , n = 4 ) , SST ( L2/3 , n = 11; L4 , n = 6 ) , and PV ( L2/3 , n =13; L4 , n =6 ) mice . Within each cell type , there was no significant difference ( n . s . , p>0 . 05 , Mann-Whitney U test ) between the median LMI across layers in either context ( darkness: Exc , p=0 . 151; VIP , p=0 . 521; SST , p=0 . 350; PV , p=0 . 966; visual stimulation: Exc , p=0 . 750; VIP , p=0 . 133; SST , p=0 . 961; PV , p=0 . 058; ( C ) Histograms of the difference between the LMI value in darkness and during visual stimulation ( LMIStim-LMIDark ) for each cell type . Negative values indicate increased responses to locomotion in the dark compared with visual stimulation , positive numbers indicate increased responses to locomotion during visual stimulation , and numbers close to 0 ( within red lines; –0 . 2 < LMIStim-LMIDark < 0 . 2 ) indicate context-independent responses . DOI: http://dx . doi . org/10 . 7554/eLife . 14985 . 011
In this study , we used the relative changes in fluorescence of the genetically-encoded calcium indicator GCaMP6f as a reporter of the spiking activity of cortical neurons ( Chen et al . , 2013 ) . For a given fluorescent calcium indicator , the relationship between the amplitude of somatic fluorescence changes and the number of spikes can be affected by a number of factors including the concentration of calcium buffers in the soma , the balance between calcium influx and efflux as well as calcium release from internal stores ( Grienberger et al . , 2012 ) . Consequently , potential confounding factors in the present study would be ( 1 ) different intracellular calcium dynamics in different types of inhibitory neurons as well as ( 2 ) a higher increase of cytosolic free calcium concentration for the same number of spikes during locomotion compared to stationary periods . Considering that neuromodulators can regulate calcium influx ( Fucile , 2004; Shen and Yakel , 2009 ) , this second possibility may result from the action of neuromodulators released during locomotion that would increase the amount of calcium entering the neuron in response to each spike . In that case , for the same number of spikes , the increase in fluorescence of our calcium indicator would be higher during locomotion than during stationary periods . Without an independent readout of the spiking activity for each neuronal type in each behavioral context , we cannot exclude that the relationship between fluorescence transients and the number of spikes differ between different neurons and different contexts . However , the comparison of our results ( mean ΔF/F0 , Figure 2—figure supplement 1B , ‘stim’ column ) with spiking frequencies published in a previous study ( see Supplementary Table 3 of Polack et al . , 2013 ) in mouse V1 strongly suggests that somatic GCaMP6f fluorescence changes do reflect changes in spiking activity related to locomotion . For the same neuronal populations ( layer 2/3 Excitatory , SST and PV neurons; layer 4 Excitatory neurons ) and visual stimulation condition ( drifting gratings ) , both data sets show the same relative change in signal during locomotion compared to stationary periods ( corresponding to an approximate doubling of activity during locomotion for all three cell types ) . This similarity suggests that somatic GCaMP6 fluorescence changes during locomotion do reflect changes in spiking activity , at least in these cell types during visual stimulation . In this study , we found that SST activity increased with locomotion during visual stimulation . This is in line with previous electrophysiological recordings of SST neurons ( Polack et al . , 2013 ) but in contradiction with the current disinhibitory model that relies on the inhibition of SST neurons during locomotion ( Figure 1B; Fu et al . , 2014 ) . Our results provide an explanation for these discrepancies since the aforementioned electrophysiological recordings were acquired during visual stimulation whereas imaging of SST activity was done in the dark ( Fu et al . , 2014 ) . The disinhibitory model was based on the assumption that the locomotion-driven response of SST neurons would be similar in the dark and during visual stimulation ( Fu et al . , 2014 ) . The same assumption was made in the interpretation of membrane potential fluctuations of VIP and SST neurons recorded during the presentation of a blank screen ( Reimer et al . , 2014 ) . While VIP neurons were reliably depolarized during running , the SST population was heterogeneous . The authors distinguished two populations of SST interneurons ( see Supplementary Figure 5C of Reimer et al . , 2014 ) : Type I cells were inhibited by running while Type II cells were depolarized . Importantly , spiking activity of SST neurons was not reported and it is thus not clear how the membrane potential fluctuations relate to spiking activity . Our findings regarding the locomotion responses of SST neurons in darkness are consistent with the previous imaging study performed in similar conditions ( Figure 2—figure supplement 2 of the present study compared to Figure 3 and Figure S3 of Fu et al . , 2014 ) as well as with the heterogeneity of membrane potential fluctuations of SST neurons during locomotion ( Reimer et al . , 2014 ) . We cannot exclude the possibility that a disinhibitory circuit may underlie the activity of a small fraction of neurons in darkness: the majority of VIP neurons increase their activity with locomotion , while a small proportion of SST neurons are inhibited during locomotion , potentially leading to the increase in activity of some pyramidal neurons . However , the results obtained in darkness show that the majority of SST neurons are not responsive to locomotion at all , challenging the generality of a disinhibitory circuit acting through the inhibition of SST neurons . With visual stimulation , the inconsistency of the disinhibitory model is even stronger since the vast majority of SST neurons increase their activity with locomotion ( see Figure 2B ( iii ) ) . Consequently , the results obtained during visual stimulation ( present study and Polack et al . , 2013 ) are incompatible with a model in which VIP neurons disinhibit excitatory neurons by inhibiting SST neurons . Additionally , while the vast majority of VIP neurons are context-independent with regard to their locomotion response , excitatory neurons show significantly increased locomotion responses during visual stimulation compared to darkness conditions . Therefore , the context-dependent responses of excitatory neurons do not result from a disinhibitory circuit initiated by VIP neurons . An appealing aspect of the disinhibitory model was the idea of a canonical circuit for gain modulation of sensory responses ( Pi et al . , 2013 ) . While the connectivity may be canonical , we show that the circuit activity can strongly differ depending on the behavioral context . Therefore , functional properties of inhibitory neurons should not be generalized from one context to the next , and caution should be taken when inferring connectivity from functional recordings obtained in a specific behavioral context . Our results indicate that , in addition to the activation of VIP neurons during locomotion , other pathways are involved in linking locomotion and visual responses in V1 . We suggest that neuromodulatory inputs triggered by locomotion would not only activate VIP neurons through nicotinic acetylcholine receptors as previously shown ( Alitto and Dan , 2012; Arroyo et al . , 2014; Fu et al . , 2014 ) , but would also directly activate PV , SST , and excitatory neurons . Previous work has demonstrated cholinergic facilitation of cortical inhibitory neurons ( Kawaguchi , 1997; Xiang et al . , 1998; Arroyo et al . , 2012; Alitto and Dan , 2012 ) , including SST neurons ( Kawaguchi , 1997; Fanselow et al . , 2008; Xu et al . , 2013; Chen et al . , 2015 ) . Similarly , in vitro studies have shown that norepinephrine can depolarize both excitatory ( McCormick et al . , 1993; Kirkwood et al . , 1999 ) and inhibitory ( Kawaguchi and Shindou , 1998 ) cortical neurons . Finally , in vivo studies have shown that neuromodulatory inputs , cholinergic and noradrenergic , can control the gain and signal-to-noise ratio of V1 excitatory neurons during locomotion ( Pinto et al . , 2013; Polack et al . , 2013; Bennett et al . , 2014; Lee et al . , 2014 ) . We suggest that in darkness , the effect of neuromodulatory inputs remains subthreshold in SST neurons . During visual stimulation , SST neurons are strongly activated and the effect of neuromodulatory inputs becomes suprathreshold . In agreement with the known intra-cortical connectivity in mouse V1 ( Figure 1A; Pfeffer et al . , 2013; Jiang et al . , 2015 ) , our findings support this neuromodulatory hypothesis . In darkness , VIP and PV neurons are activated by locomotion and inhibit SST and excitatory neurons , preventing their activation by locomotion-dependent inputs . During visual stimulation , SST and excitatory neurons are activated: they overcome the intra-cortical inhibition by VIP and PV neurons and become responsive to direct locomotion-dependent inputs . Since SST neurons provide the main intra-cortical input to VIP neurons ( Pfeffer et al . , 2013 ) and are strongly visually-responsive , they likely inhibit VIP neurons ( or a subpopulation of VIP neurons ) during visual stimulation . This is consistent with the decrease in activity of a portion of VIP neurons that was observed during visual stimulation ( Fu et al . , 2014; see also Figure 2—figure supplement 3B ( ii ) . An alternative or complementary hypothesis to the neuromodulatory pathway is that the modulation of visual inputs by locomotion already takes place in subcortical nuclei , such that the thalamo-cortical inputs received by excitatory neurons , and potentially SST neurons , would convey the increased gain of visual responses during locomotion . Indeed , recent studies have shown that projections from the dorsal lateral geniculate nucleus ( Erisken et al . , 2014; Roth et al . , 2016 ) and from the thalamic latero-posterior nucleus ( Roth et al . , 2016 ) to V1 both convey locomotion signals . The diversity of context-dependent responses to locomotion within SST , PV and , to a lesser extent , VIP populations indicates that there are functional sub-types within each of these interneuron populations . Based on a comprehensive analysis of morphological and electrophysiological properties of inhibitory neurons , a recent in vitro study has identified seven distinct types of cortical interneurons in layer 2/3 ( Jiang et al . , 2015 ) . Further , in vivo characterization of the activity of these sub-types will be necessary to identify how these populations relate to the different context-dependent responses identified in the present study .
Three Cre-driver transgenic mice lines were used to label inhibitory interneurons: Sst<tm2 . 1 ( cre ) Zjh> ( SST-Cre ) [RRID:IMSR_JAX:013044] , Pvalb<tm1 ( cre ) Arbr> ( PV-Cre ) [RRID:IMSR_JAX:008069] , Vip<tm1 ( cre ) Zjh> ( VIP-Cre ) [RRID:IMSR_JAX:010908] , all originally obtained from Jackson Laboratory , ME , USA . These lines were cross-bred with Rosa-CAG-LSL-tdTomato [RRID:IMSR_JAX:007914] mice . C57Bl/6 wild type mice ( Jackson Laboratory , ME ) were used for virus injections targeting the expression of GCaMP6 in CaMKII-expressing neurons . Mice were group housed ( typically 2–4 mice ) and both male and female mice were used for the experiments . All procedures were approved by the University of Edinburgh animal welfare committee , and were performed under a UK Home Office project license . Imaging was performed using a custom-built resonant scanning two-photon microscope with a Ti:Sapphire pulsing laser ( Chameleon Vision-S , Coherent , CA , USA; < 70 fs pulse width , 80 MHz repetition rate ) tuned to 920 nm . Using a 40X objective ( 0 . 8 NA , Nikon ) , 600×600 pixel images with a field-of-view of 250 × 250 μm were acquired at 40 Hz with custom-programmed LabVIEW based software ( version 8 . 2; National Instruments , UK ) . We used two-photon calcium imaging in head-fixed mice that ran freely on a cylindrical treadmill ( Figure 1C; Dombeck et al . , 2007 ) . Habituation and imaging started 2–3 weeks after AAV injection . Mice were habituated to head-fixation in the dark for 45 min and began to run freely on a polystyrene cylinder ( 20 cm diameter , on a ball-bearing mounted axis ) . The mice’s running speed on the circular treadmill was continuously monitored using an optical encoder ( E7P , 250cpr , Pewatron , Switzerland ) connected to a data acquisition device ( National Instrument , UK ) with custom-written software in LabView ( National Instrument , UK ) and analyzed in MATLAB ( Mathworks , MA ) . Mice could run freely and spent on average 26 ± 2% of the time running in the dark and 41 ± 2% during visual stimulation ( n = 48 mice , 51 sessions ) . Two-photon imaging was performed at 2–3 focal planes per mouse , at cortical depths between 130 and 350 μm for L2/3 neurons and 350–500 μm for L4 neurons ( cortical layers were confirmed on histological sections , see below ) . Laser power at the brain surface was kept below 50 mW . Mice with excessive brain movement artifacts were excluded . At each focal plane ( n = 100 fields of view ) , 8–12 trials ( 60 s duration ) were acquired in total darkness and 12–20 trials acquired during visual stimulation , with dark and visual stimulation trials randomly interleaved . Visual stimuli were generated using the Psychophysics Toolbox package ( Brainard , 1997 ) for MATLAB ( Mathworks , MA ) and displayed on an LCD monitor ( 51 × 29 cm , Dell , UK ) placed 20 cm from the right eye , covering 104° × 72° of the visual field . Visual stimulation trials consisted of stationary full-field square-wave gratings for 4–5 s and the corresponding drifting phase for 2 s ( 0 . 03 cpd , 1 Hz , 8 equally spaced directions in randomized order , contrast 80% , mean luminance 37 cd/m2 ) . Each trial started and ended with a grey screen ( isoluminance ) . Additional grey screen data were obtained during the presentation of an isoluminant grey screen for 5–15 s preceding the presentation of each oriented grating for 5 s ( 0 . 03 cpd , 1 Hz , 4 equally spaced orientations in randomized order , contrast 80% , mean luminance 37 cd/m2 ) . At the end of the imaging session , red retrograde beads ( Lumafluor , USA ) were injected either at the surface or at 2 different focal planes at which neurons had been imaged . This red labelling was used as a structural landmark in histological sections to confirm which cortical layers had been imaged . Animals were transcardially perfused with 0 . 9% saline and 4% PFA in phosphate buffer ( 0 . 1 M ) . Brains were sliced with a vibratome ( 50 µm thick ) and rinsed in phosphate buffered saline ( PBS ) . The slices were then mounted and counterstained with either DAPI ( Vectashield mounting medium , Vector Labs , UK ) or NeuroTrace 640/660 fluorescent Nissl stain ( 1:2000; RRID:nlx_152414 , Life Technologies , NY ) and coverslipped . Sections were imaged with a confocal microscope ( Nikon A1R , Nikon Instruments , UK ) to define the boundaries of cortical layers and localize the retrograde beads injected at the imaged focal planes in vivo .
Cortical responses to sensory stimuli are modulated by behavioral state . In the primary visual cortex ( V1 ) , visual responses of pyramidal neurons increase during locomotion . This response gain was suggested to be mediated through inhibitory neurons , resulting in the disinhibition of pyramidal neurons . Using in vivo two-photon calcium imaging in layers 2/3 and 4 in mouse V1 , we reveal that locomotion increases the activity of vasoactive intestinal peptide ( VIP ) , somatostatin ( SST ) and parvalbumin ( PV ) -positive interneurons during visual stimulation , challenging the disinhibition model . In darkness , while most VIP and PV neurons remained locomotion responsive , SST and excitatory neurons were largely non-responsive . Context-dependent locomotion responses were found in each cell type , with the highest proportion among SST neurons . These findings establish that modulation of neuronal activity by locomotion is context-dependent and contest the generality of a disinhibitory circuit for gain control of sensory responses by behavioral state .
How we perceive what we see depends on the context in which we see it , such as what we are doing at the time . For example , we perceive a park landscape differently when we are running through it than when we are sitting on a park bench . Behavior can also alter neuronal responses in the brain . Indeed , the neurons in the part of the brain that receives information related to vision ( known as the visual cortex ) respond differently to visual stimuli when an animal is moving compared to when the animal is still . However , while some recent studies revealed that specific types of neurons become more or less responsive during movement , others reported the opposite results . One hypothesis that would explain these contradictory findings would be if the way that behavior , in this case movement , affects neuronal responses also depends on the external context in which the movement happens . Now , Pakan et al . have tested this hypothesis by imaging the activity of different types of neurons in the primary visual cortex of mice that were either running on a treadmill or staying still . The experiments were conducted in two different contexts: in total darkness ( in which the mice could not see ) and in the presence of display screens ( which provided the mice with visual stimulation ) . Pakan et al . confirmed that running does indeed affect the activity of specific neurons in different ways in different contexts . For example , when the mice received visual stimulation , the three main classes of neurons that send inhibitory signals in the visual cortex became more active during running . However , when the mouse ran in the dark , two of these neuron types became more active during running while the third type of neuron was unresponsive . This finding reveals more about the dynamic nature of inhibitory activity that strongly depends on the animal’s behaviour . It also shows how these neurons influence the excitatory neurons in the visual cortex , which send information to the rest of the brain for further processing towards perception . The next step will be to identify what precise mechanism makes these neurons respond differently in unique contexts , and to tease apart how these movement-dependent signals affect the way animals perceive visual stimuli .