Optogenetic techniques for neural inactivation are valuable for linking neural activity to behavior but they have serious limitations in macaques. To achieve powerful and temporally precise neural inactivation, we used an adeno-associated viral (AAV) vector carrying the channelrhodopsin-2 gene under the control of a Dlx5/6 enhancer, which restricts expression to GABAergic neurons. We tested this approach in the primary visual cortex, an area where neural inactivation leads to interpretable behavioral deficits. Optical stimulation modulated spiking activity and reduced visual sensitivity profoundly in the region of space represented by the stimulated neurons. Rebound firing, which can have unwanted effects on neural circuits following inactivation, was not observed, and the efficacy of the optogenetic manipulation on behavior was maintained across >1000 trials. We conclude that this inhibitory cell-type-specific optogenetic approach is a powerful and spatiotemporally precise neural inactivation tool with broad utility for probing the functional contributions of cortical activity in macaques.
A major goal of systems neuroscience is to understand how neural activity mediates behavior. Neural inactivation techniques are central to this endeavor (Wurtz, 2015). However, these techniques can have unintended consequences that complicate data interpretation (Abraham, 2008; Goold and Nicoll, 2010; Goshen et al., 2011; Sokolova and Mody, 2008; Stemmler and Koch, 1999; Turrigiano et al., 1998). For example, by impairing task performance, neural inactivation can cause animals to explore new task strategies for acquiring reward. This change in strategy may change the information flow through neural circuits. To avoid these complications, inactivation methods are needed that can be reversed more quickly than these circuit-level changes can occur.
Optogenetics is the fastest method for reversible neural inactivation currently available. In rodents, optogenetic inactivation has revealed links between neural activity and behavior that would have been difficult to discover with traditional, slower inactivation methods based on injection of pharmacological agents, cortical cooling, or lesioning (Goshen et al., 2011; Hanks et al., 2015; Yartsev et al., 2018). Optogenetic inactivation has already been used in a few pioneering studies to perturb the behavior of macaque monkeys (Acker et al., 2016; Afraz et al., 2015; Cavanaugh et al., 2012; Fetsch et al., 2018). The approach taken in these studies was to reduce neuronal spiking by activating hyperpolarizing opsins (eNpHR, Arch, or Jaws). The directness of this approach facilitates the interpretation of behavioral effects. However, the behavioral effects produced this way have been small, perhaps because most promoters used in viral vectors drive expression in many neuronal types, and suppression of inhibitory neurons may counteract suppression of excitatory neurons.
An alternative approach, which has been successful in rodents, is to selectively activate inhibitory neurons with channelrhodopsin-2 (ChR2) (Cone et al., 2019; Glickfeld et al., 2013; Guo et al., 2014; Khan et al., 2018; McBride et al., 2019). This approach has two advantages. First, it is based on the opening of ion channels, which conduct more ions per photon absorbed than ion pumps. Second, it leverages the dense local connectivity and low synaptic failure rates of GABAergic neurons to suppress long-range excitatory signaling locally and robustly (Isaacson and Scanziani, 2011; Kubota et al., 2015; Packer and Yuste, 2011; Wiegert et al., 2017).
To test the efficacy of this approach for cortical inactivation in macaques, we injected area V1 of three rhesus monkeys with a viral vector containing a cell-type-specific promoter (AAV–mDlx5/6–ChR2) (Dimidschstein et al., 2016). In this study, we confirm the specificity of GABAergic neuronal transduction in macaque cortex and demonstrate that illumination of the injection site modulates spiking activity. We also show that illumination impairs visual sensitivity profoundly, reversibly, and reliably at the receptive fields of the illuminated neurons but not outside. We conclude that optogenetic stimulation of inhibitory neurons is a powerful method for inactivating regions of the macaque monkey brain with high spatial and temporal precision.
A previous study showed that an AAV vector carrying the gene for the fluorescent reporter, GFP, under the control of the mDlx5/6 enhancer, transduced V1 GABAergic neurons in a marmoset with 93% selectivity (Dimidschstein et al., 2016). To determine whether AAV–mDlx5/6–ChR2–mCherry has similar selectivity in macaque, we injected V1 of one animal (monkey 1) and examined the tissue histologically (Figure 1 and Figure 1—figure supplement 1). mCherry-positive cells had non-pyramidal morphologies, consistent with them being GABAergic. Similar histological results with this viral vector have been described in macaques previously (Scerra et al., 2019).
Most mCherry-positive neurons co-expressed parvalbumin (468/543), a marker for 75% of GABAergic neurons in macaque V1 (Van Brederode et al., 1990). This high level of co-expression is consistent with selective transduction of GABAergic neurons and is sufficiently high to suggest that parvalbumin-positive neurons were transduced with particularly high efficiency (p<0.005; binomial test).
To test whether ChR2 expression was sufficiently strong to perturb neural activity, we recorded extracellular spiking responses from single- and multi-units near the injection sites in two other monkeys (monkeys 2 and 3) while they performed a contrast detection task. Most sites were visually driven (46/56, response to a low-contrast Gabor stimulus greater than baseline firing rate; 19/56, p<0.05; Mann-Whitney U test, Figure 2—figure supplement 1). Given our selection criteria, all sites were significantly modulated by optical stimulation (p<0.06; Mann-Whitney U test; see Methods). Some units were excited by optical stimulation (Figure 2A) whereas others were suppressed (Figure 2B). At 38 of the 56 sites, optical stimulation increased spiking. Excitation was prevalent in our dataset because we searched for sites at which optical stimulation produced an audible change in the baseline firing rate (Figure 2C). The mean latency to response was 14±26 (SD) ms and was <5 ms at 11 sites (Figure 2—figure supplement 2A–B). Neurons excited at short latency (<5 ms) presumably expressed ChR2 and suppressed other neurons via synaptic inhibition. The latency of suppression was longer than the latency of excitation, but this comparison is challenging because baseline firing rates were low (Figure 2—figure supplement 2C, Figure 2—figure supplement 3).
Neural activity suppression using halorhodopsins in monkeys is typically followed by a rebound of activity at the termination of optical stimulation (Acker et al., 2016; Fetsch et al., 2018). We did not observe such rebounds with AAV–mDlx5/6–ChR2. We compared average firing rates at 18 suppressed sites in a 50-ms window before and after optical stimulation. At one example site, the pre-laser firing rate exceeded the post-laser firing rate (22 vs. 0 impulses/sec, p<0.001, Wilcoxon signed rank test, Figure 3A), consistent with sustained suppression. At a different example site, the pre-laser firing rate was lower than the post-laser firing rate, consistent with a small rebound (10 vs. 25 impulses/sec, p=0.02, Wilcoxon signed rank test, Figure 3B). Such rebounds were rare; post-laser firing rates exceeded pre-laser firing rates at only 2 of 18 sites (Figure 3C).
Activity at most suppressed sites recovered to baseline levels gradually after laser termination. We measured this recovery time by computing the first time at which the average spike count in a 50-ms sliding window returned to 90% of the pre-laser firing rate. Recovery times ranged from 0 to 215 ms (Figure 3D) with roughly half of the sites recovering within 100 ms (median = 97.5 ms). These data demonstrate that suppression persists several tens of milliseconds after laser termination.
To evaluate the behavioral efficacy of optogenetic stimulation, we trained monkeys 2 and 3 to perform two visually demanding tasks. Reward contingencies were independent of laser stimulation in both tasks.
In the visually guided saccade task, a target appeared inside the receptive fields (RFs) of the stimulated V1 neurons on a random subset of trials and outside on other trials (Figure 4A–B). Data from an example block of trials from each monkey show the main results (Figure 4C–D). On control trials, both monkeys made accurate saccades to most target locations. On laser trials, the monkeys failed to make saccades into the RFs of the optically stimulated neurons. Saccades were unaffected when the target appeared at other locations, indicating that the optogenetic effect was retinotopically specific. On laser trials when the target appeared inside the RFs, monkey 2 typically maintained fixation, and monkey 3 typically made leftward ~10° saccades. Similar behaviors were observed on catch trials in which no target was shown (Figure 4E–F, see Materials and methods). The inaccuracy of saccades made by monkey 3 into the left visual field was likely due to repeated electrode penetrations in the midbrain of this animal that were unrelated to the current experiments (Figure 4—figure supplement 1).
We collected data in 10 sessions from monkey 2 (16 blocks of trials) and 7 sessions from monkey 3 (20 blocks of trials). Within each session, we calculated the distance between saccade end points and target locations. When the target appeared inside the RFs of stimulated neurons, the saccade end points tended to be closer to the target on control trials than on laser trials (p<0.002 for monkey 2, p=0.03 for monkey 3; Wilcoxon signed rank tests). When the target appeared in other locations, the saccade endpoints were similarly close to the target on control and laser trials (p=0.92 for monkey 2, p=0.38 for monkey 3; Wilcoxon signed rank tests; Figure 4—figure supplement 2A–B). Saccade latencies were greater on laser trials than on control trials when targets were inside the RFs (p<0.0001 for monkey 2 and 3; Mann-Whitney U tests; Figure 4—figure supplement 2C–D) but not when targets were elsewhere (p=0.90 for monkey 2 and p=0.41 for monkey 3; Mann-Whitney U tests; Figure 4—figure supplement 2E–F).
To confirm that the deficit in task performance was not purely oculomotor, we trained monkeys 2 and 3 to perform a contrast detection task that required saccades to targets outside of the RFs of the stimulated neurons (Figure 5A–B: see Materials and methods). An example block of trials from each monkey demonstrates the main results. Both monkeys detected the visual stimulus more frequently on control than on laser trials (proportion of hits on control vs. laser trials; p<0.001 for monkey 2 and monkey 3; binomial tests for equality of proportions; Figure 5C–D). This performance deficit was also reflected in psychometric functions (Figure 5C–D inset) and in the contrast values selected by the staircase procedure (Figure 5E–F). Neither monkey was able to detect the visual stimulus with above-chance accuracy on laser trials even at the maximum stimulus contrast achievable. Saccades to the stimulus location were never required, and thus the behavioral effects produced by optical stimulation in this task cannot be explained by an oculomotor deficit.
In one session, the Gabor stimulus location was randomized across trials, confirming the retinotopic specificity of the effect (Figure 6). Additional control experiments confirmed that the monkeys were able to make saccades to both target locations irrespective of optical stimulation (data not shown) and showed that performance on control trials was unaffected by the interleaved laser trials (Figure 6—figure supplement 1).
We collected data in 11 sessions from monkey 2 (69 blocks of trials) and 12 sessions from monkey 3 (81 blocks of trials). In almost every session (10/11 in monkey 2, 11/12 in monkey 3), the proportion of hits on control trials was significantly greater than on laser trials (binomial tests for equality of proportions, p<0.05, Figure 7A–B). An analysis of sensitivity indices (d’) confirmed that this change in performance was consistent with a reduction in sensitivity and inconsistent with a pure change in criterion (Figure 7C–D, Figure 7—figure supplement 1). In most blocks of trials (52/69 in monkey 2 and 63/81 in monkey 3), optical stimulation increased detection thresholds beyond the limits of the display, an event that occurred rarely on control trials (0/63 blocks in monkey 2, 8/81 blocks in monkey 3).
As laser power increased, errors became more common, which caused the staircase procedure to increase the stimulus contrast rapidly (Figure 8A). The magnitude of the behavioral effect increased steeply with laser intensity between 12.8 and 22.3 mW, and it saturated by 30.0 mW (Figure 8B). Behavior on control trials was not significantly affected by changes in laser power (r=−0.15, p=0.78; Spearman’s correlation between d’ on control trials from each block and laser power).
Optogenetic modulations of neural activity were linked to effects on behavior across these trials (r=0.36, p=0.43; Spearman’s correlation between neural laser modulation index and difference in d’ between control and laser trials; Figure 8—figure supplement 1). Pooling data across all blocks of trials reduced the correlation (r=0.16, p=0.23). Pooling the data reduced statistical power due to covariates across blocks of trials that exerted different effects on neurophysiological and behavioral outcomes (e.g. fiber position, stimulus location in the visual field, and quality of neural recordings). These covariates were held fixed in the data shown in Figure 8A–B.
In a previous study, the behavioral effects produced by optogenetic silencing of neurons in area MT using the suppressive opsin, Jaws (red-shifted cruxhalorhospsin), decreased over tens of minutes (Fetsch et al., 2018). To determine whether a similar phenomenon occurred with ChR2-mediated inactivation, we analyzed 4 experimental sessions (28 blocks of trials) from monkey 2 and 5 sessions (33 blocks of trials) from monkey 3. From these sessions, we considered only the subset of blocks with identical laser power.
The behavioral effects we observed were consistently large over the course of ~1000 trials (or ~50 mins). We quantified the behavioral effect as the difference in d’ between laser and control trials within each block. The behavioral effect varied little as a function of block number, (r=0.18, p=0.59; Spearman’s correlation between block number and d’ averaged across sessions; Figure 8C). It was also consistent within individual sessions; linear regression slopes of the behavioral effect as a function of block number in each session did not differ significantly from zero (p=0.57, Student’s t-test). For comparison with previous work (Fetsch et al., 2018), we calculated the behavioral effect in early and late trials within each session. Unlike the previous work, the behavioral effect did not differ between the first 480 trials (4 blocks) and the subsequent trials, suggesting the absence of compensatory changes under the conditions of the current study (p=0.79, Student’s t-test, Figure 8D).
The fast activation and inactivation of neurons afforded by optogenetics has revolutionized our understanding of the nervous systems of rodents and invertebrates. Understanding the primate brain at a similar level of detail is facilitated by optogenetics in the macaque monkey—a model organism with a brain structure similar to humans that can be trained to perform complex behavioral tasks. Rapid activation was already feasible in primates using microsimulation or optogenetics, and now rapid inactivation is too.
We achieved inactivation by stimulating GABAergic neurons in macaque V1 and measured electrophysiological and behavioral consequences. First, we showed that the AAV–mDlx5/6–ChR2 vector targeted ChR2 expression to GABAergic neurons in area V1. Second, we showed that optical stimulation modulated the activity of neurons near the injection site. Third, we showed that optical stimulation impaired visual sensitivity in two behavioral tasks. The reduction in sensitivity was specific to trials in which optical stimulation was delivered and to the RF location of the stimulated neurons, demonstrating the temporal and spatial precision of the inactivation. Laser-induced modulations of neural responses were rapid, rebound activity following light pulses was negligible, and behavioral effects were consistent across ~1000 trials.
Below, we compare the results of our study with those of previous studies that used optogenetic neural inactivation to perturb macaque behavior. We then discuss the effect of optogenetic stimulation of V1 on eye movements and ways in which the method could be improved. Finally, we discuss potential applications of AAV–mDlx5/6–ChR2 for understanding primate brain function.
Four previous studies used optogenetic inactivation to perturb monkey behavior (Acker et al., 2016; Afraz et al., 2015; Cavanaugh et al., 2012; Fetsch et al., 2018). The two studies most similar to ours quantified the effect of neural inactivation on behavior as changes in visual discrimination thresholds on 2AFC tasks (Afraz et al., 2015; Fetsch et al., 2018). In one study, inactivation of inferotemporal cortical neurons raised thresholds for classifying face stimuli on the basis of gender (Afraz et al., 2015). In the other, inactivation of MT cortical neurons biased judgements of visual motion direction (Fetsch et al., 2018). In both cases, threshold changes were smaller than those we observed (~5% vs. >100%, Figure 7).
The threshold changes we observed were large for potentially several reasons. First, we excited ChR2-expressing inhibitory neurons to reduce the spiking of excitatory neurons. Stimulation of a small number of inhibitory neurons can suppress the activity of a large number of excitatory neurons (Wiegert et al., 2017). Second, we used ChR2, which conducts more ions per absorbed photon than ion pumps (Jaws and ArchT). Third, we used higher laser power (4–160 mW vs. ~2 mW and ~12 mW; Figures 4–8). Fourth, we inactivated area V1, an area that is indispensable for the behaviors we studied (Koerner and Teuber, 1973; Merigan et al., 1993; Radoeva et al., 2008). Higher-order visual cortical areas may be sufficiently interconnected to allow one or more areas to compensate for others with regard to the behaviors tested. An intriguing, and now-testable, hypothesis is that the spared visual sensitivity following visual cortical lesions is due to the engagement of slow compensatory mechanisms, not the unmasking of normally functioning pathways (Leopold, 2012).
We interpreted the stimulation-induced change in the monkeys’ performance as a change in sensitivity, and it is inconsistent with a change in criterion alone. Additionally, the brevity and unpredictability of the optical stimulation makes large, consistent, selective changes in criterion on laser trials unlikely. Nevertheless, we cannot rule out the possibility that the optical stimulation affected sensitivity and criterion together (Figure 7—figure supplement 1).
Illumination of ChR2-expressing neurons in area V1 causes monkeys to make saccades into the RFs of the stimulated neurons under some conditions (Jazayeri et al., 2012). This behavior is consistent with the perception of a phosphene (Tehovnik et al., 2003). In our study, however, monkeys rarely made saccades into the RFs of the stimulated neurons, suggesting that they did not experience a phosphene. This result held on trials requiring a saccade to a visual target inside the RFs of the stimulated neurons and on trials in which no target was shown, a condition similar to the key condition in the study of Jazayeri et al., 2012. In principle, detection of the optical stimulation could have provided a cue for acquiring reward in the visually guided saccade task. Having sensed the optical stimulation, and seen no target, the monkey could have increased its reward rate by making a saccade into the RFs of the stimulated neurons. The fact that the monkeys did not behave this way suggests that they were unable to detect the stimulation, or at least were unable to use it to direct saccades. Sensing the optical stimulation would not have been useful for increasing reward rate in the contrast detection task.
We attribute the difference between studies to the population of V1 neurons stimulated. We used a Dlx5/6 enhancer to express ChR2 selectively in inhibitory neurons whereas Jazayeri et al., 2012 used the human synapsin I promoter, which drives expression in both excitatory and inhibitory neurons (Nathanson et al., 2009). One hypothesis is that phosphenes are caused by spikes in a subset of excitatory projection neurons. In this case, pan-GABAergic stimulation would not be expected to produce phosphenes, but stimulation of specific GABAergic subtypes might. For example, stimulation of VIP-expressing neurons might produce a phosphene through disinhibition of excitatory neurons (Cone et al., 2019).
Over the course of this study, monkeys 2 and 3 acquired visual deficits in areas of the visual field corresponding to the regions of V1 inactivated. To ask whether the optogenetic manipulations produced long-lasting visual deficits, we conducted behavioral experiments in monkey 2 twenty-two months after the final optogenetic experiment was conducted (Figure 8—figure supplement 2). Visual sensitivity, assessed by the probability, latency, and accuracy of visually guided saccades, was reduced in the optogenetically manipulated lower-right visual field relative to the unmanipulated upper-left visual field, but the deficit was subtle. We presume that this deficit reflects cortical damage, which could be due to tissue heating by the light, repeated penetrations by optical fibers, or single-unit recordings that were made in this animal for three years prior to commencing the current study. While a comparable behavioral dataset could not be obtained from monkey 3, histological analysis of the calcarine sulcus, where most of the optogenetic manipulations were made in this animal, revealed nothing unusual (e.g. areas of necrosis, burn marks, etc.) (data not shown). They did reveal electrode/optical fiber tracks, the expected gliosis associated with these tracks, and healthy-looking mCherry+ neurons that were similar in morphology and density to those in monkey 1.
The laser power used in the current study spanned a broad range (4–160 mW for 200–300 ms) and, on average, was higher than that used in other studies (Afraz et al., 2015; Cavanaugh et al., 2012; Dai et al., 2014; El-Shamayleh et al., 2017; Fetsch et al., 2018; Gerits et al., 2012; Inoue et al., 2015; Ohayon et al., 2013; Stauffer et al., 2016; Tamura et al., 2017). Given the stimulation parameters we used, (450 nm light conducted through 300 µm-diameter optical fibers), we likely heated tissue near the fiber tip by several °C in many of our experiments (Arias-Gil et al., 2016). However, the consistency of the behavioral effect within individual sessions after repeated optical stimulation argues against acute damage (Figure 8C–D).
The tissue damage produced by optogenetic manipulations can be mitigated by using artificial dura (Nandy et al., 2019; Ruiz et al., 2013) and red-shifted or step-function opsins (Berndt et al., 2009). Artificial dura allows non-invasive optical stimulation of the superficial cortical layers, reducing mechanical damage. Red-shifted opsins are activated by long-wavelength light, which heats tissue less and thus causes less thermal damage than short-wavelength lights do. The neural activity produced by step-function opsins outlast the light pulses required to trigger them, allowing brief, safe light pulses to produce longer lasting stimulation events (Gong et al., 2020).
Optogenetic stimulation of inhibitory neurons using AAV–mDlx5/6–ChR2 facilitates at least three broad categories of experiments. The first category includes experiments in which slow neural inactivation precludes data collection, for example, experiments probing the neural substrate of life-sustaining processes (e.g. breathing) (Baertsch et al., 2018; Simonyan, 2014). Less extreme examples include the inactivation of oculomotor structures that are necessary for stable visual fixation, a simple oculomotor behavior without which more complicated behaviors are difficult to study (Goffart et al., 2012; Krauzlis et al., 2017). Experiments in which inactivation induces compensatory changes in task strategy (Paolini and McKenzie, 1997) or the routing of neural signals also fall in this category (Cowey, 2010; Kinoshita et al., 2019; Leopold, 2012; Mori et al., 2006).
The second class of experiments are those that address questions about the functional significance of spike timing. Monkeys can learn to use signals in sensory cortices at particular times relative to external and internal events to mediate their behavior (Poort et al., 2012; Roelfsema et al., 1998; Seidemann et al., 1998). Just as electrical microstimulation can be used to reveal the contribution of spikes added to sensory representations at specific times, optogenetic inactivation can be used, complementarily, to eliminate spikes. Indeed, optogenetic inactivation was used recently to show that spiking activity in the frontal eye fields of macaques contributes to memory-guided saccades before, during, and after target presentation (Acker et al., 2016). Future studies may reveal differences between the transient and sustained phases of sensory-, decision- and movement-related signals for guiding behavior (Freedman et al., 2001; Hegdé, 2008; Ibos and Freedman, 2017; Roelfsema et al., 2007; Shushruth et al., 2018).
A third class of experiments probes the electrophysiological response properties of inhibitory neurons in vivo (Adesnik et al., 2012; Atallah et al., 2012; Cardin et al., 2009; Scholl et al., 2015; Sohal et al., 2009; Wilson et al., 2017; Wilson et al., 2012). Excitatory and inhibitory neurons within a cortical area have different response properties in mice, cats, and ferrets, a fact that is presumably related to differences in their respective functional contributions (Huang and Paul, 2018). Identification of inhibitory and excitatory neurons in vivo has been challenging in monkeys. The discovery of fast-spiking excitatory neurons in primates undermines the use of extracellularly recorded spike waveforms (Kelly et al., 2019). Optogenetic phototagging of inhibitory neurons, using AAV–mDlx5/6–ChR2 permits electrophysiological identification of neuronal subtypes more decisively (Figure 9).
In summary, the optogenetic approach used in this study holds promise for a finer level of neural circuit interrogation than previously achievable in monkeys. This union of neural inactivation technique and animal model has broad utility for addressing many outstanding questions in systems neuroscience that span the domains of sensation, action and cognition.
Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Gregory D Horwitz (firstname.lastname@example.org).
Three rhesus monkeys (Macaca mulatta) participated in this study (males; 7–14 kg). Two monkeys were surgically implanted with a head-holding device and a recording chamber that provided access to the primary visual cortex (V1). Surgical procedures, experimental protocols, and animal care conformed to the NIH Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at the University of Washington.
Animal husbandry and housing were overseen by the Washington National Primate Research Center. All monkeys had ad-libitum access to biscuits (Fiber Plus Monkey Diet 5049, Lab Diet). Monkeys 2 and 3 had controlled daily access to fresh produce and water. When possible, animals were pair-housed and allowed grooming contact. Cages were washed every other week, bedding was changed every day, and animals were examined by a veterinarian at least twice per year.
During each experiment, monkeys viewed a CRT monitor binocularly with their heads fixed. The viewing distance was 100 cm for monkey 2 and 50 cm for monkey 3. Eye position signals were measured with an optical eye tracker for monkey 2 and a scleral search coil for monkey 3. Behavioral and stimulation timing events and eye position signals were digitized and stored for offline analysis.
Recombinant AAV vectors were produced using a conventional three-plasmid transient transfection of human embryonic kidney cells (HEK293T, female, unauthenticated) with polyethylenimine (25 kDa, Polysciences). The transfer plasmid was pAAV-mDlx-ChR2-mCherry-Fishell-3 (Addgene #83898). Vectors were harvested and purified by ultracentrifugation through an iodixanol gradient column, exchanged into phosphate buffered saline (PBS), and titered using qPCR.
After mapping a track through V1 gray matter using standard extracellular recording techniques in awake fixating monkeys, we advanced an electrode and cannula to the deepest point of the track and began a series of injections. Using a Hamilton syringe attached to a manual pump, we injected 1.0–1.5 µl of AAV vector at each of several locations spaced 500 µm apart along a track (normal to the opercular surface). Each injection was followed by a 2 min wait period after which the electrode and cannula were slowly retracted to the next site. This process was repeated at 9–14 sites, and a total of 14–17 µl was injected along each track. In monkey 2, we injected 14 μL of AAV9–mDlx5/6–ChR2–mCherry (1.5 × 1013 genomes/ml) at each of two opercular sites that were ~2 mm apart. The AAV vector was injected along 4 mm tracks throughout the thickness of the cortex at both sites, in the left hemisphere. In monkey 3, we injected ~17 μL of AAV1–mDlx5/6–ChR2–mCherry (1.0 × 1013 genomes/ml) along a 5 mm track in the first site and 14 μL along a 6.5 mm track in the second site, in the right hemisphere, to target both opercular and calcarine regions of area V1. The two injection tracks were ~1.5 mm apart.
We injected area V1 of monkey 1 with AAV1–mDlx5/6–ChR2-mCherry to examine the specificity of vector transduction. These injections were performed during a surgical procedure while the monkey was anesthetized, and electrophysiological recordings were not made. The monkey recovered from the surgery and was euthanized 45 days later with an overdose of pentobarbital and perfused transcardially with 4% paraformaldehyde (wt/vol). The brain was removed, cryoprotected in 30% sucrose (wt/vol) and 50 μm-thick sections were cut on a sliding microtome. Fluorescence signals from mCherry (primary antibody: 1:250, Clontech 632543, mouse anti-mCherry; secondary antibody: 1:200, Invitrogen Molecular Probe) and parvalbumin (primary antibody: 1:5000, rabbit anti-PV, Swant 27; secondary antibody: 1:200, Invitrogen Molecular Probes) were detected immunocytochemically. Sections were counterstained with DAPI (1:5000, Molecular Probes D-21490) and cover-slipped using a DABCO-based mounting medium.
Three to four weeks after AAV injections in monkeys 2 and 3, we searched for neuronal responses to blue light (450 nm; 33–161 mW) delivered to area V1 via an optical fiber (300 μm outer diameter; Thor Labs) with a beveled tip that eased entry through the dura. A fiber and a glass-coated tungsten electrode (1–3 MΩ FHC) were placed in a common guide tube and lowered independently into the brain by microdrive (Narashige or Alpha-Omega). Extracellular spikes were amplified (1x head-stage), high-pass filtered (250 Hz cutoff), digitized (sampling rate of 40 kHz) and sorted (Plexon MAP system).
Stimulation sites were selected by inserting an electrode into V1 and finding a region with vigorous visual activity and a clearly defined receptive field (RF). The optical fiber was then lowered while repeatedly delivering brief laser pulses. The optical fiber typically lagged the electrode by 100–500 µm. Only sites at which optical stimulation produced an audible change in firing rate were tested.
The laser was developed in-house by the Bioengineering Core at the Washington National Primate Research Center. Light was generated by a laser diode (part # PL TB450B). Light delivery was modulated by modulating the current to the laser diode (digital to analog converter part # AD5683) not by shutter.
Monkeys were trained to make saccades to visual targets 4–17° from the fixation point. Each trial began when the monkey acquired a central fixation point (0.2–0.3° sided square) within a 1.6 × 1.6° electronic window. Then, 13 ms after the central target disappeared, a saccade target (square with sides 0.3–0.4°) was presented. Two to ten target locations, equiangularly spaced at fixed radius, were interleaved within each block of trials. Monkeys were rewarded for making a saccade to the target. On half of the target-present trials at each location, a 300-ms laser pulse was delivered simultaneously with the target presentation (Figure 4B). We interleaved 10–30 catch trials in which no target was presented, and the monkey was rewarded unconditionally. Optical stimulation was delivered on half of the catch trials, immediately after the fixation point disappeared.
Monkeys were trained to detect a Gabor stimulus positioned 4–17° from the fixation point. Each trial began when the monkey acquired the fixation point. Then, after a 520-ms delay, a drifting Gabor stimulus appeared on half of the trials (spatial frequency = 1 cycle/°, temporal frequency = 8 Hz, standard deviation = 0.2°, duration = 200 ms). Immediately after the Gabor stimulus disappeared, a pair of targets appeared along the horizontal meridian, 2° from the fixation point. A saccade to the target on the same side of the screen as the Gabor stimulus was rewarded on Gabor-present trials, and a saccade to the target on the opposite side was rewarded on Gabor-absent trials.
The Gabor stimulus appeared inside the RF of neurons at the recording site in all trials except a few in which retinotopic specificity was tested (Figure 6). Optical stimulation began at the stimulus onset, lasted 300 ms (Figure 5B), and was delivered on half of the Gabor-present and half of the Gabor-absent trials. The monkey typically performed several blocks of trials per session, each consisting of 120 trials. Stimulus strength was adjusted by independent contrast staircase procedures on laser and control trials. Contrast, defined as the difference between the highest and lowest luminance values, divided by the sum of the two, increased by a factor of 1.18–1.33 following an incorrect response and decreased by a factor of 0.75–0.85 following three consecutive correct responses.
All statistical analyses were performed in MATLAB.
In this equation, Φ-1 is the inverse normal cumulative distribution function. Proportions of 0 were replaced with 0.5/n, and proportions of 1 were replaced by 1-0.5/n, where n is the number of Gabor-present (for hits) or Gabor-absent trials (for false alarms) (Stanislaw and Todorov, 1999).
Proportions of correct responses were fit with a cumulative Weibull distribution function by maximizing likelihood assuming binomial error. Fitting was performed using the inbuilt MATLAB fmincon function. Detection threshold was defined as the luminance contrast corresponding to 82% correct.
Metaplasticity: tuning synapses and networks for plasticityNature Reviews Neuroscience 9:387.https://doi.org/10.1038/nrn2356
A viral strategy for targeting and manipulating interneurons across vertebrate speciesNature Neuroscience 19:1743–1749.https://doi.org/10.1038/nn.4430
Mouse primary visual cortex is used to detect both orientation and contrast changesJournal of Neuroscience 33:19416–19422.https://doi.org/10.1523/JNEUROSCI.3560-13.2013
Visual fixation as equilibrium: evidence from superior colliculus inactivationJournal of Neuroscience 32:10627–10636.https://doi.org/10.1523/JNEUROSCI.0696-12.2012
Signal Detection Theory and PsychophysicsNew York: Wiley.
Time course of visual perception: coarse-to-fine processing and beyondProgress in Neurobiology 84:405–439.https://doi.org/10.1016/j.pneurobio.2007.09.001
Saccadic eye movements evoked by optogenetic activation of primate V1Nature Neuroscience 15:1368–1370.https://doi.org/10.1038/nn.3210
Visual field defects after missile injuries to the geniculo-striate pathway in manExperimental Brain Research 18:88–113.https://doi.org/10.1007/BF00236558
Neuronal control of fixation and fixational eye movementsPhilosophical Transactions of the Royal Society B: Biological Sciences 372:20160205.https://doi.org/10.1098/rstb.2016.0205
Primary visual cortex: awareness and blindsightAnnual Review of Neuroscience 35:91–109.https://doi.org/10.1146/annurev-neuro-062111-150356
Detection Theory: A User's GuidePsychology press.
Visual effects of lesions of cortical area V2 in macaquesJournal of Neuroscience 13:3180–3191.https://doi.org/10.1523/JNEUROSCI.13-07-03180.1993
Saccade modulation by optical and electrical stimulation in the macaque frontal eye fieldJournal of Neuroscience 33:16684–16697.https://doi.org/10.1523/JNEUROSCI.2675-13.2013
Neural activity within area V1 reflects unconscious visual performance in a case of blindsightJournal of Cognitive Neuroscience 20:1927–1939.https://doi.org/10.1162/jocn.2008.20139
Optogenetics through windows on the brain in the nonhuman primateJournal of Neurophysiology 110:1455–1467.https://doi.org/10.1152/jn.00153.2013
Optogenetic activation of GABAergic neurons in primate V1 impairs detection performance through indirect effects on excitatory neuronsProgram No 30710 2019 Neuroscience Meeting Planner Chicago, IL: Society for Neuroscience, 2019 Online.
The laryngeal motor cortex: its organization and connectivityCurrent Opinion in Neurobiology 28:15–21.https://doi.org/10.1016/j.conb.2014.05.006
Silencing-induced metaplasticity in hippocampal cultured neuronsJournal of Neurophysiology 100:690–697.https://doi.org/10.1152/jn.90378.2008
Saccadic eye movements evoked by microstimulation of striate cortexEuropean Journal of Neuroscience 17:870–878.https://doi.org/10.1046/j.1460-9568.2003.02489.x
Calcium-binding proteins as markers for subpopulations of GABAergic neurons in monkey striate cortexJournal of Comparative Neurology 298:1–22.https://doi.org/10.1002/cne.902980102
Using perturbations to identify the brain circuits underlying active visionPhilosophical Transactions of the Royal Society B: Biological Sciences 370:20140205.https://doi.org/10.1098/rstb.2014.0205
Joshua I GoldSenior Editor; University of Pennsylvania, United States
Michael SchmidReviewing Editor; Newcastle University, United Kingdom
Michael SchmidReviewer; Newcastle University, United Kingdom
Wim VanduffelReviewer; Laboratory for Neuro-and Psychophysiology, Belgium
In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.
Targeted optogenetic inactivation of neural circuits in non-human primates is essential to clarify specific links between neuronal activity and behaviour. Here the authors capitalise on the recent development of Dlx5/6 enhancer-guided targeting of GABAergic neurons (Dimidschstein et al., 2016) for optogenetic manipulation of macaque primary visual cortex (V1). The authors show how optogenetic targeting of V1 GABAergic neurons modulates V1 neural activity and leads to a substantial, specific impairment in vision guided behaviour.
Decision letter after peer review:
Thank you for submitting your article "Fast and reversible neural inactivation in macaque cortex by optogenetic stimulation of GABAergic neurons" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Michael Schmid as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Joshua Gold as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Wim Vanduffel (Reviewer #2); David Sheinberg (Reviewer #3).
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
De, El-Shamayleh, and Horwitz describe anatomical, electrophysiological and behavioral results of optogenetic deactivation experiments targeting primary visual cortex (V1) in macaques. The authors capitalise on the recent development of Dlx5/6 enhancer-guided targeting of GABAergic neurons (Dimidschstein et al., 2016). Here De et al. used the Dlx5/6 enhancer in combination with a depolarizing opsin (ChR2) to activate inhibitory neurons, with the aim to inactivate the local downstream excitatory neurons. A key advance of this study is the demonstration that a AAV approach for targeting inhibitory neurons that has been shown to work in the marmoset translates to the rhesus monkey. The authors show histological evidence for transduced neurons near the injection site, as evidenced by mCherry expression. Moreover, most of the transduced neurons were PV+, indicating high specificity for inhibitory neurons. At neuron level, they observed both increased (2/3 of the stimulated sites) and suppressed (1/3 of the sites) light-induced activity. Moreover, the monkeys failed to make reliable saccades to targets represented by the stimulated neurons. Finally, the monkeys had severely reduced contrast detection thresholds at these sites. The authors provide compelling results from a combination of histological, electrophysiological and behavioural tests. Particularly the strong behavioural effects advance the field and will be of great interest to a wide audience. Unwanted rebound effects, which are typically present when using alternative hyperpolarizing opsins (e.g. ArchT or Jaws), are largely absent. Overall, the evidence presented is solid, the analyses are sound, the writing is very straightforward and the message is clear. The new research is important, timely and provides an important step forward for the field. However, the reviewers have expressed important concerns that need to be addressed before the manuscript can be accepted for publication.
1) The expression pattern needs to be more fully characterised. The selectivity of the vector seems to be high -i.e. mainly restricted to PV+ interneurons. Yet, the sensitivity is surprisingly low (only 41 neurons are transduced). Would this be a vast underestimation of the real number of the neurons expressing ChR2? It would be good for readers to know the percentage of Parvalbumin neurons that show fluorescence to get a better estimate on the expression sensitivity. There are some recent reports indicating that the threshold for detecting FP expression might be higher than the threshold for the functional gene (Kinoshita et al., 2019). Or do the authors think that the number of neurons expressing ChR2 can be as low as ~40 in order to evoke a clear behavioral effect? Figure 1 suggests a very laminar-specific expression pattern, but the authors explain that this is not typical. Was this slice the only one analyzed? Ideally the reader would like to see an assessment across cortical laminae, but perhaps the authors could show further sections that give a more representative view of the expression pattern. Although seemingly annoying, this may be useful for layer-specific optogenetic deactivations.
2) Characterise more fully electrophysiological responses. Given the relatively long latencies of the optogenetic effect (see Figure 2—figure supplement 2), it is unlikely that these are only first order neurons expressing the opsin which are directly activated by the blue light. How do the authors explain the long latency effects? It would be also interesting to plot the latencies of the cells showing a suppression effect (i.e. the time after stimulation onset that the activity drops significantly below the pre-stimulation firing rate). These latencies should be longer than those of neurons showing an excitatory effect. Estimating the onset of suppression is not trivial, but this could be informative regarding potential direct and indirect effects. Figure 3B also shows suppression for a site with some very bursty responses which seem to drastically inflate the Y-axis (Response). Was this high variability and bursty activity common for suppressed sites? The overall spontaneous rates of many GABAergic cells is fairly high, but it's not clear if that is the case for the population explored here. That spiking increased in >60% of recorded sites is in line with successful targeting of GABAergic interneurons. But what do we learn about these neurons? The authors discuss the potential of photo-tagging in the Discussion and provide one exemplary direction selective unit in Figure 9. But one is left with the question what happened at the other sites? Are they visually responsive?
3) Further aspects should be considered that might have influenced behavioural performance. For the behavioral tasks the authors should probably emphasize that reward contingencies were not dependent on laser delivery. It was also unclear on why the measure used quantifying the effect for the oculomotor task was not simply distance from the target? For this task, the data for Monkey 3 shown in Figure 4 even for the control trials looks like it's not right on the center of the RF location. Does this figure show exactly where the target was presented and how were the eye positions calibrated? In both paradigms opto stimulation occurred at the same time as visual stimulation. Given a visual response latency of 40 ms or more in V1 neurons, at least in theory, the opto stimulus could serve as a cue telling the monkey how to act in order to get reward. It is indicated that the change in contrast detection performance is due to the reduction in sensitivity and not a change in criterion. One cannot conclude that from d-primes only. The c-criterion should also be listed as there can be a change in sensitivity and criterion.
4) Electrophysiological and behavioural measures should be more directly related to each other. There's no obvious reason why these couldn't be done simultaneously. If possible, it would be good to see opto elicted spiking activity from the trials during behavioural testing and to probe whether there is a direct relationship between the strength of spiking and the behavioural effect.
5) Clarify for the detection conditions, how the authors move from the example sessions (Figure 5) to the population data (Figure 7). Some rewording here to make it clear that the comments in the paragraph below are referring to the examples in Figure 5 and not the whole population (which follows in a couple of paragraphs). For the population, the authors should revisit Figure 7 to not include all the blocks, as this conflates the independent sessions (11 and 12) from the blocks, which are clearly not independent. To include all the blocks in Figure 7 is a clear case for pseudo-replication. The population analysis needs to be by session, not block. The authors should also revisit the psychometric fits (examples in Figure 5, e.g.). The laser fits don't look very good – was there some estimate of goodness of fit for these?
6) Clarify details about injection and stimulation procedures (see minor points), including heating induced damage considerations. A concern is in understanding and justifying the need for the large increase in power used to activate the neurons under study. The absolute power levels are on a direct concern if they cause lasting damage to the tissue. On one hand the prolonged efficacy across the session is evidence that effects of greater power did not present an acute problem, but there could be concern that prolonged use in a single site, for example, could lead to irreversible damage. More discussion on the power would be useful.https://doi.org/10.7554/eLife.52658.sa1
1) The expression pattern needs to be more fully characterised. The selectivity of the vector seems to be high -i.e. mainly restricted to PV+ interneurons. Yet, the sensitivity is surprisingly low (only 41 neurons are transduced). Would this be a vast underestimation of the real number of the neurons expressing ChR2? It would be good for readers to know the percentage of Parvalbumin neurons that show fluorescence to get a better estimate on the expression sensitivity.
The reviewers are correct that the number of transduced neurons in Figure 1 is a vast underestimate. Indeed, many more neurons expressed ChR2 in monkey 1 than are shown in the Figure 1. Unfortunately, a nearby injection of an entirely different viral vector that expressed ChR2-eYFP under the control of a different promoter complicated our analyses of selectivity and sensitivity. Transduction by the two vectors is easily distinguished on the basis of their distinct fluorescent protein genes. However, the spectral overlap between the eYFP signal and the green secondary antibody we used to label PV neurons required us to look at sections where the two injections did not overlap. In Figure 1, we show a section of V1 near the edge of the AAV-mDlx5/6-ChR2mCherry transduction zone that lacks ChR2-eYFP expression; this region allows us to estimate selectivity easily but provides an underestimate of sensitivity due to the sparse mCherry label.
To address reviewers’ comments, we have analyzed a substantially larger region in monkey 1 that spans the V1–V2 border (Figure 1—figure supplement 1). We have amplified the AAV-mDlx5/6-ChR2-mCherry signal and PV signal, the latter using a short wavelength secondary antibody that avoids confusion with the ChR2-eYFP signal from the second viral vector. This section had many more ChR2-mCherry+ cells (N=543). In regions of efficient transduction, we estimate that ~50% of parvalbumin+ neurons were transduced.
There are some recent reports indicating that the threshold for detecting FP expression might be higher than the threshold for the functional gene (Kinoshita et al., 2019). Or do the authors think that the number of neurons expressing ChR2 can be as low as ~40 in order to evoke a clear behavioral effect?
The threshold for detecting FP expression may indeed exceed the threshold for functional ChR2 expression. We were able to detect native FP signal in histological sections from monkey 1, suggesting that ChR2 expression was likely sufficient to manipulate spiking activity (monkey 1 was not used in the electrophysiological/ behavioral experiments). To facilitate cell counting, all sections shown in the manuscript were amplified immunohistochemically.
The minimum number of V1 neurons that must be manipulated to cause a behavioral effect is an important issue that our data do not speak to. The number of neurons affected by the light stimulation may depend on the efficiency of AAV transduction, the shape of the optical fiber tip, spread of the laser light, tissue transmissibility, laser power, sensitivity of the behavioral assay, and other factors.
Figure 1 suggests a very laminar-specific expression pattern, but the authors explain that this is not typical. Was this slice the only one analyzed? Ideally the reader would like to see an assessment across cortical laminae, but perhaps the authors could show further sections that give a more representative view of the expression pattern. Although seemingly annoying, this may be useful for layer-specific optogenetic deactivations.
The efficiency of transduction across cortical laminae is determined by in part by where and how the vector injection is made. The AAV injections into monkey 1 were made during a surgical procedure, without electrophysiological guidance, which may explain the concentration of expression in superficial cortical layers. Injections into monkeys 2 and 3 were based on electrophysiological depth measurements and are therefore more likely to have spanned all V1 layers. To provide the reviewers with evidence for expression in deeper layers, we have recently made another injection of AAV-mDlx5/6ChR2-mCherry into area V4 of a monkey that was not used in this study. In that experiment, transduction spanned all of the layers (except for layer 4 which, in our hands, is difficult to transduce efficiently irrespective of the vector injected). Please see Figure 1B of http://www.pnas.org/content/116/52/26195 for the results of that experiment.
2) Characterise more fully electrophysiological responses. Given the relatively long latencies of the optogenetic effect (see Suppl Figure 1), it is unlikely that these are only first order neurons expressing the opsin which are directly activated by the blue light. How do the authors explain the long latency effects?
The long latency effects are likely due to complex network activity within and beyond V1. The existence of these complex interactions means that selective optical stimulation of inhibitory neurons need not necessarily exert a net-inhibitory effect on the circuit.
It would be also interesting to plot the latencies of the cells showing a suppression effect (i.e. the time after stimulation onset that the activity drops significantly below the pre-stimulation firing rate). These latencies should be longer than those of neurons showing an excitatory effect. Estimating the onset of suppression is not trivial, but this could be informative regarding potential direct and indirect effects.
We agree with the reviewers that estimating the onset of suppression is not trivial. Decreases in firing rate are more difficult to detect than increases in firing rate, especially when the baseline firing rate is low, as is often the case in V1. We have done our best to estimate the latency of the optogenetic effect for both activated and suppressed sites. As anticipated by the reviewer, the latencies at suppressed sites were longer than those at activated sites (Figure 2—figure supplement 2C). However, the interpretation of this result is complicated; the expected delay from synaptic transmission is brief relative to the bias produced by estimating a reduction in an already-low firing rate (relative to an increase). We have provided raster plots that i l lust rate laser responses at ever y suppressed site we studied (Figure 2—figure supplement 3).
Figure 3B also shows suppression for a site with some very bursty responses which seem to drastically inflate the Y-axis (Response). Was this high variability and bursty activity common for suppressed sites? The overall spontaneous rates of many GABAergic cells is fairly high, but it's not clear if that is the case for the population explored here.
Suppressed sites were not unusually variable and bursty. The example neuron shown in Figure 3B was selected specifically because its baseline firing rate was high (it also happened to be bursty), which made the suppression effect particularly clear. Most suppressed sites had low baseline firing rates, making suppression less obvious (Figure 2—figure supplement 3). The baseline firing rates of suppressed and activated sites were similar (p=0.87; unpaired t test).
That spiking increased in >60% of recorded sites is in line with successful targeting of GABAergic interneurons. But what do we learn about these neurons? The authors discuss the potential of photo-tagging in the Discussion and provide one exemplary direction selective unit in Figure 9. But one is left with the question what happened at the other sites? Are they visually responsive?
All neurophysiological and behavioral data were collected concurrently except for the data in Figure 9, which was collected during a block of fixation trials. Once we found a site that was modulated by the laser, we focused on documenting the behavioral deficit. The non-stationarity of firing rate apparent in a few of the plots in Figure 2—figure supplement 3 is due to changes in isolation quality.
At most sites—both activated and suppressed by the laser—presentation of the Gabor stimulus evoked a response (Figure 2—figure supplement 1). Of the 56 sites, 46 had elevated responses during visual stimulation, and of those, 19 attained statistical significance (p<0.05, Mann Whitney U test). We report these numbers in the revised manuscript. The weakness of the visual response is expected. We did not tailor the visual stimulus (an achromatic, 1 cycle/° upward-drifting, hor i zontal Gabor pat tern) to the preferences of the neurons at the stimulation site, and the contrast of the Gabor stimulus was usually low because it was adjusted by a staircase procedure to be near psychophysical detection threshold.
3) Further aspects should be considered that might have influenced behavioural performance. For the behavioral tasks the authors should probably emphasize that reward contingencies were not dependent on laser delivery.
We emphasize in the revised manuscript that the reward contingencies were not dependent on laser delivery.
It was also unclear on why the measure used quantifying the effect for the oculomotor task was not simply distance from the target?
Thank you for the suggestion. We have repeated the analysis of saccade-task performance using distance from the end point to the target as suggested by the reviewer (Figure 4—figure supplement 1).
For this task, the data for Monkey 3 shown in Figure 4 even for the control trials looks like it's not right on the center of the RF location. Does this figure show exactly where the target was presented and how were the eye positions calibrated?
We have represented the target locations outside RFs in the revised figure (Figure 4). The figures show the nominal locations of the targets on the screen and calibrated estimates of eye position relative to these locations. Our eye position calibration is imperfect but is reasonably accurate (< 1° error).
Monkey 3 made inaccurate saccades into the left visual field even on some control trials (Figure 4—figure supplement 1, Figure 4E–F). This was likely due to repeated electrode penetrations into the midbrain of this animal, unrelated to the current experiments, that resulted in oculomotor deficits. This animal exhibited a leftward nystagmus that precluded accurate fixation behavior several months before the collection of data presented in this manuscript. During data collection for the current study, this animal developed several blind spots presumably due to cortical damage. The nystagmus is unrelated to the optogenetic manipulations made in this study and therefore unlikely to be of interest to the readers of eLife, but the blind spots are relevant and now discussed in the revised Discussion.
In both paradigms opto stimulation occurred at the same time as visual stimulation. Given a visual response latency of 40 ms or more in V1 neurons, at least in theory, the opto stimulus could serve as a cue telling the monkey how to act in order to get reward.
We agree with the reviewers, and have elaborated on this point in the revised manuscript. Indeed, if the monkey had been able to detect the optical stimulation, he might have been able to use this information in the saccade task to get reward on optical stimulation trials (Figure 4, Figure 4—figure supplement 1A–B). On trials in which optical stimulation was delivered and no target was visible, a saccade into the receptive fields of the stimulated neurons would often have been rewarded. The fact that the monkey did not routinely make saccades to the target in the RFs of the illuminated neurons suggests that he was unable to detect the stimulation, or at least was unable to use it to direct his saccades. In the contrast detection task, the optical stimulation does not provide a cue that is useful for getting a reward. The two possible choices are equally likely to be rewarded on both control and laser stimulation trials.
It is indicated that the change in contrast detection performance is due to the reduction in sensitivity and not a change in criterion. One cannot conclude that from d-primes only. The c-criterion should also be listed as there can be a change in sensitivity and criterion.
We now address this point in the revised manuscript. We can explain the changes in c-criterion and d’ using a model in which the effect of the laser is to make the signal distribution more similar to the noise distribution, and we include a figure for the reviewers illustrating this point (Figure 7—figure supplement 1). We are unable, however, to explain the changes in d’ on the basis of a change in subjective criterion alone; a pure change in criterion does not affect d’. We cannot rule out the possibility that the laser changes the monkeys' subjective criterion and sensitivity, but the brevity and unpredictability of the optical stimulation argues against a large change in criterion.
4) Electrophysiological and behavioural measures should be more directly related to each other. There's no obvious reason why these couldn't be done simultaneously. If possible, it would be good to see opto elicted spiking activity from the trials during behavioural testing and to probe whether there is a direct relationship between the strength of spiking and the behavioural effect.
All of the electrophysiological recordings, with the exception of those in Figure 9, were made while the monkeys were performing the contrast detection task. We clarify this point in the revised manuscript.
We looked for a relationship between the strength of optical stimulation on spiking responses and behavioral effects, and we observed a weak, positive correlation that failed to reach statistical significance (Figure 8—figure supplement 1B).
In interpreting this result, it is important to consider uninteresting reasons for finding such a relationship and also for not finding one. The laser power changed across blocks of trials. Low laser power affects neural responses and behavior weakly, and high laser power affects both strongly, which would be expected to produce a positive correlation. A reason for not detecting such a correlation is that the electrodes recorded only a fraction of the neurons affected by the optical stimulation, and the quality of the neural signal varied from day to day. These two sources of variability (laser power and recording quality), prevent us from accurately estimating the relationship between the electrophysiologically generated response (across all neurons) and the resultant behavioral effect.
Nevertheless, in one session, we manipulated the laser power on seven blocks of trials, keeping the fiber position, electrode position and the spatial location of stimulus fixed. Under these conditions, we were able to observe a clear, positive correlation between neural and behavioral modulation (Figure 8—figure supplement 1A).
5) Clarify for the detection conditions, how the authors move from the example sessions (Figure 5) to the population data (Figure 7). Some rewording here to make it clear that the comments in the paragraph below are referring to the examples in Figure 5 and not the whole population (which follows in a couple of paragraphs).
We have rewritten the confusing passages to clarify the division between the example data in Figure 5 and the population data in Figure 7.
For the population, the authors should revisit Figure 7 to not include all the blocks, as this conflates the independent sessions (11 and 12) from the blocks, which are clearly not independent. To include all the blocks in Figure 7 is a clear case for pseudo-replication. The population analysis needs to be by session, not block.
We have repeated the analysis in Figure 7, treating each session as an independent observation.
The authors should also revisit the psychometric fits (examples in Figure 5, e.g.). The laser fits don't look very good – was there some estimate of goodness of fit for these?
We agree that the fits to the psychometric function data on laser trials are not very good. There are two reasons for this. First, in many blocks, performance increased shallowly with stimulus contrast because of the strong inactivation. Performance on laser trials even at the highest contrast was therefore poor, forcing the psychometric fit to have a shallow slope within the range of contrasts tested. Second, not all stimulus contrasts were probed equally often because of the staircase procedure. This fact can give the appearance of a poor model fit. The fit takes into account the number of stimulus presentations at each contrast. As a result, the model more accurately fits the points that were probed more often. We have replotted the psychometric functions, representing the number of stimulus presentations at each contrast as the size of the corresponding data points (Figures 5–6, Figure 6—figure supplement 1).
The deviance, the measure of fitting error that is minimized in generalized linear models, was actually lower on control trials (median = -20.07) than on laser trials (median = -17.76) because of the flatness of the psychometric function over the range of contrasts we were able to test.
6) Clarify details about injection and stimulation procedures (see minor points), including heating induced damage considerations. A concern is in understanding and justifying the need for the large increase in power used to activate the neurons under study. The absolute power levels are on a direct concern if they cause lasting damage to the tissue. On one hand the prolonged efficacy across the session is evidence that effects of greater power did not present an acute problem, but there could be concern that prolonged use in a single site, for example, could lead to irreversible damage. More discussion on the power would be useful.
We have clarified the details of the injection and stimulation procedures in the revised manuscript.
During some of our initial experiments, we used high laser power because we did not know a priori the laser power needed to induce a behavioral effect. However, we show that a laser power as low as 30 mW is sufficient to achieve a strong behavioral effect (Figure 8B).
Both monkeys currently have scotomas in areas of the visual field corresponding to some of the regions of V1 inactivated. The laser power used in some experiments was unnecessarily high and likely caused thermal damage in the stimulated regions. As pointed out the reviewer, we did not observe any acute change in the behavioral effect over repeated stimulation (Figure 8C–D) but we cannot rule out that heating did not lead to permanent damage. Another likely cause of permanent damage in these experiments is mechanical due to repeated insertions of the 300 µm optical fibers. We have added discussion on these points and potential remedies for future experiments in the revised manuscript.https://doi.org/10.7554/eLife.52658.sa2
- Gregory D Horwitz
- Abhishek De
- Yasmine El-Shamayleh
- Gregory D Horwitz
- Abhishek De
- Abhishek De
- Yasmine El-Shamayleh
- Gregory D Horwitz
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We thank Michael N Shadlen for helpful comments on the manuscript, Elizabeth Buffalo for generous microscope access, and Albert Ng for help with MRI-related software. This work was funded by NIH EY018849 to Gregory D Horwitz, NIH/ORIP grant P51OD010425 to Washington National Primate Research Center, NEI Center Core Grant for Vision Research P30 EY01730 to the University of Washington and R90 DA033461 (Training Program in Neural Computation and Engineering) to Abhishek De.
Animal experimentation: Surgical procedures, experimental protocols and animal care conformed to the NIH Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at the University of Washington (IACUC protocol #4167-01).
- Joshua I Gold, University of Pennsylvania, United States
- Michael Schmid, Newcastle University, United Kingdom
- Michael Schmid, Newcastle University, United Kingdom
- Wim Vanduffel, Laboratory for Neuro-and Psychophysiology, Belgium
- David Sheinberg
© 2020, De et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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Early life adversity (ELA) is associated with increased risk for stress-related disorders later in life. The link between ELA and risk for psychopathology is well established but the developmental mechanisms remain unclear. Using a mouse model of resource insecurity, limited bedding (LB), we tested the effects of LB on the development of fear learning and neuronal structures involved in emotional regulation, the medial prefrontal cortex (mPFC) and basolateral amygdala (BLA). LB delayed the ability of peri-weanling (21 days old) mice to express, but not form, an auditory conditioned fear memory. LB accelerated the developmental emergence of parvalbumin (PV)-positive cells in the BLA and increased anatomical connections between PL and BLA. Fear expression in LB mice was rescued through optogenetic inactivation of PV-positive cells in the BLA. The current results provide a model of transiently blunted emotional reactivity in early development, with latent fear-associated memories emerging later in adolescence.
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