article: Brain stimulation modulates the excitability of neural circuits and drives neuroplasticity. While the local effects of stimulation have been an active area of investigation, the effects on large-scale networks remain largely unexplored. We studied stimulation-induced changes in network dynamics in two macaques. A large-scale optogenetic interface enabled simultaneous stimulation of excitatory neurons and electrocorticographic recording across primary somatosensory (S1) and motor (M1) cortex (Yazdan-Shahmorad et al. , 2016). We tracked two measures of network connectivity, the network response to focal stimulation and the baseline coherence between pairs of electrodes; these were strongly correlated before stimulation. Within minutes, stimulation in S1 or M1 significantly strengthened the gross functional connectivity between these areas. At a finer scale, stimulation led to heterogeneous connectivity changes across the network. These changes reflected the correlations introduced by stimulation-evoked activity, consistent with Hebbian plasticity models. This work extends Hebbian plasticity models to large-scale circuits, with significant implications for stimulation-based neurorehabilitation.
  Many neurological and psychiatric disorders arise from dysfunctional neural dynamics at the network level, which in turn stem from aberrant neural connectivity (Stam, 2014; Wu et al. , 2016; Edwardson et al. , 2013; Yahata et al. , 2016; DeSalvo et al. , 2014; Skudlarski et al. , 2010). The brain shows marked plasticity across a variety of learning and memory tasks (Takeuchi et al. , 2014; Bliss et al. , 2014) and during recovery after brain injury or stroke (Edwardson et al. , 2013; Hara, 2015; Murphy and Corbett, 2009), and many have proposed to take advantage of this innate plasticity to treat neural disorders (Miller et al. , 2010; Jackson et al. , 2006; Lucas and Fetz, 2013; Edwardson et al. , 2013). In principle, brain stimulation protocols can be designed to leverage this plasticity in order to rewire aberrant neural connectivity, potentially curing these disorders. Implementing such treatments requires a better understanding of how stimulation-induced plasticity drives changes in network connectivity and network dynamics. The simple Hebbian model of plasticity (Donald, 1949) and spike-timing dependent versions of it (Bi and Poo, 2001) explain a large body of data on activity-dependent plasticity, including both in vitro (Massobrio et al. , 2015; Abrahamsson et al. , 2016), and in vivo (Andersen et al. , 2017; Feldman, 2012; Shulz and Jacob, 2010) studies. Despite the extensive work studying Hebbian plasticity at the cellular level, it remains unclear how synaptic plasticity leads to large-scale functional reorganization. Recently, several studies have shown large-scale plasticity following brain stimulation that is consistent with Hebbian mechanisms (Jackson et al. , 2006; Lucas and Fetz, 2013; Seeman et al. , 2017; Nishimura et al. , 2013; Rebesco and Miller, 2011; Rebesco et al. , 2010; Song et al. , 2013; Lajoie et al. , 2017). In particular, Fetz and colleagues implemented an activity-dependent stimulation protocol, effectively introducing an artificial connection between two sites in the motor cortex (Jackson et al. , 2006; Lucas and Fetz, 2013). Continuous reinforcement of this artificial connection led to a stable functional change in stimulation-evoked movements, indicating that stimulation induces large-scale plasticity. Recently these results were reproduced in a modeling work at the network level (Lajoie et al. , 2017). Similar results have been observed using open-loop stimulation protocols to induce targeted plasticity between two cortical sites (Rebesco and Miller, 2011; Seeman et al. , 2017). Notably, these papers have reported off-target effects, but these were either interpreted as global changes in excitability or were not explained. Although the results from closed-loop and open-loop experiments suggest large-scale plasticity, the underlying neural network changes remain unexplored. Here, we measure connectivity across sensorimotor cortex and track changes in network connectivity in response to open-loop stimulation. This work takes advantage of a large-scale optogenetic interface (Yazdan-Shahmorad et al. , 2016) that enables us to simultaneously stimulate populations of excitatory neurons while recording large-scale μ-electrocorticography (μ-ECoG) activity across two brain areas, S1 and M1. We first establish and compare two measures of functional connectivity that provide complementary views of the mechanisms of plasticity. Next, we investigate how stimulation impacts connectivity between and within cortical areas. We then test whether stimulation-evoked activity drives large-scale network plasticity in a Hebbian manner. The goal of this work is to investigate large-scale functional reorganization following stimulation, which will inform future neurorehabilitation strategies.
  We quantified inter-area functional connectivity between S1 and M1 using two different measures—one based on the network response to optogenetic stimulation and the other based on spontaneous neural activity. The first measure focused on how the response to optical stimulation propagates through the network. Optogenetic stimulation in S1 and M1 evoked responses across both cortical areas, and we characterized these responses according to their amplitudes and delays (Figure 2A and E). The delays exhibited a bimodal distribution with a 3–6 ms separation between the early and late responses (Figure 2B and F). Classifying the responses across electrodes by delay recovers the spatial separation between the two cortical areas—following a boundary along the central sulcus—with shorter delays in the area being stimulated (S1 or M1; primary responses) and longer delays in the other area (M1 or S1; secondary responses) (Figure 2C and G). The short delay of the secondary responses suggests close functional connectivity between these areas. We quantified this connectivity with the stimulus-evoked response ratio (SERR). SERR was defined as the peak-to-trough amplitude of the secondary responses (A2) normalized by the peak-to-trough amplitude of the primary response (A1), with both responses measured in the high gamma (60–200 Hz) filtered signal (see dashed-line inset boxes in Figure 2, A and E). The SERR is a measure of the connectivity between the site of stimulation and sites in the other cortical area (see Figure 2D and H). The second measure evaluates functional connectivity during spontaneous activity. We focused on the coherence in field potential recordings between electrodes, a widely used measure of connectivity that captures the degree of phase-locking between two signals (Lang et al. , 2012; Bastos and Schoffelen, 2015). We calculated the coherence between the site of stimulation and the secondary sites across different frequency bands. Because SERR and coherence are derived independently from recordings with and without simultaneous stimulation, they might reflect fundamentally different aspects of network connectivity. Therefore, in each experiment we compared these two measures prior to conditioning stimulation. Figure 3A shows two examples comparing SERR to coherence in the theta band (4–8 Hz). To quantify the relationship between these measures, we performed linear regression between them across channels (Figure 3B–C). An example showing a strong relationship between SERR and theta band coherence is shown in Figure 3B. This analysis was repeated for all coherence frequency bands and for all experiments; the mean and standard error of the regression parameters are shown in Figure 3C. The two measures of functional connectivity were highly correlated: across sessions, the distribution of regression slopes was significantly different from 0 (t-test: p=3. 73e-06), and 69 percent of individual experiments showed significant linear regressions (p<0. 05). This finding indicates that at baseline, before any conditioning, the stimulation-evoked response reflects network dynamics across the frequency spectrum. We used a simple stimulation protocol—we delivered 5 ms laser light pulses at a frequency of 5 or 7 Hz at either one or two cortical sites (Figure 4A). For our initial analyses, and unless otherwise noted, when stimulating at two cortical sites, we alternated stimulation between the two light sources to avoid any interference between the evoked responses from each light source. In each experiment, conditioning stimulation was applied for 50 min, and we evaluated functional connectivity every 10 min during blocks of passive baseline recording and active testing (100 light pulses delivered through each laser). We also conducted control sessions with the same passive recording and testing blocks, but with no stimulation during the conditioning blocks. A representative example experiment, shown in Figure 4B, shows that stimulation in M1 leads to significant increases (paired t-test, p=3. 88e−11) in mean SERR. Similar increases in SERR were observed for a majority of experiments (14 out of 18 experiments) across both monkeys (Figure 4C). The increases in connectivity were symmetric between the two cortical areas (Figure 4D). No significant change in mean SERR was observed in control sessions without conditioning stimulation (Figure 4D). Furthermore, the increase in SERR was significantly larger for stimulation sessions than control sessions (unpaired t-test: p=0. 0036) We next asked whether conditioning increased coherence-based measures of inter-area connectivity. We did observe increases in mean theta band (4–8 Hz) coherence following conditioning, as shown in Figure 4E for the same dataset used in Figure 4B (paired t-test, p=0. 03). The increase in theta coherence was significant for the majority of experiments (13 out of 20; Figure 4E), and the effect was localized to the theta band (Figure 4G; paired t-test, p=0. 017; Bonferroni correction for multiple comparisons). We also looked for change in the theta-band power as a result of stimulation and did not see any significant changes, supporting the conclusion that changes in coherence reflect changes in functional connectivity (See Figure 4—figure supplement 2). We showed in Figure 3C above that SERR and coherence are correlated measures of functional connectivity in the baseline condition. Here we ask how this relationship is changed by stimulation. Figure 4H (black lines) replicates the baseline data from Figure 3C, along with new data showing the correlation between these measures at the end of each experiment (green lines) and between their conditioning-induced changes (yellow lines). After conditioning, the cross-channel correlation between these measures significantly decreased (paired t-test, p=6. 3e-10), and the changes in connectivity were uncorrelated (t-test on distribution of regression slopes, p=0. 85). This suggests that the stimulation affects the two measures differently. To further address this, we replicated this analysis for our control (no stimulation) sessions. In this case, we did not see a significant decrease in the correlation between measures after control sessions (paired t-test, p=0. 36; see Figure 4—figure supplement 1). Additionally, a direct comparison between the median decrease in the correlation for stimulation versus control sessions showed a significant difference when all frequencies were considered (p=0. 0025), though the difference was not significant when only compared in the theta band. Together, these results show that the stimulation-induced changes in these two measures of connectivity do not correlate with each other at a fine-scale. This may reflect the fact that the two measures are not functionally equivalent, despite their correlation, i. e. , they capture related, but not identical, aspects of connectivity. As shown in Figure 1B, our stimulation protocol included 5 repetitions of baseline recording, testing, and conditioning. This design allowed us to track connectivity changes across time, after each 10 min increment of conditioning. Additionally, since neural activity was recorded throughout the experiment, we were able to measure changes in SERR during conditioning blocks. Figure 5A shows the evolution of mean inter-area connectivity for both measures in an example session. To account for differences in network connectivity across monkeys and sessions, we also analyzed the changes in each measure with respect to the baseline pre-conditioning measurements (Figure 5B). For this example session (Figure 5A), and on average across all sessions (Figure 5B), there is a trend of increasing connectivity across the session that starts within the first 10 min conditioning block. These increases in inter-area connectivity measured with SERR and coherence (Figure 5B) were correlated across time (Pearson correlation coefficient = 0. 26). Additionally, the rate of strengthening was consistent across conditioning blocks; we detected a significant increase in SERR (p<0. 05) at ~ 4. 5 min of stimulation in 4 out of 5 conditioning blocks (see Figure 5—figure supplement 1 for comparison with control sessions). Note that the stimulation-induced plasticity was not reinforced during baseline recording blocks. Given the length of the conditioning (10 min) and baseline recording (5 min in most experiments) blocks, one might expect that stimulation-induced changes would revert back to baseline. We did observe a significant decrease (paired t-test, p=2. 9e-05) in the SERR between the last 100 pulses of the conditioning block and the following test block, indicating some unlearning of the stimulation-induced connectivity changes. We next evaluated the fine-scale effects of conditioning across the entire network. Since SERR is restricted to connectivity from the stimulation site, this analysis was conducted using only changes in pairwise coherence. Figure 6A shows data from an example session, with connectivity changes represented as a set of heatmaps: the heatmap at each recording site represents the change in pairwise coherence between that electrode and all of the other electrodes. A magnified example heatmap is shown in Figure 6A, right; top panel. This example shows heterogeneous changes in connectivity across the network. We next asked whether such heterogeneous changes are to be expected. A Hebbian plasticity model suggests that the fine-scale changes in connectivity should reflect the statistics of plasticity-inducing activity. In this case, we predicted that correlated stimulation-evoked activity between two cortical sites would strengthen the functional connectivity between them, while uncorrelated or anti-correlated stimulation-evoked activity would weaken their functional connectivity. We quantified stimulation-induced correlations by measuring the pairwise coherence during the conditioning block and subtracting out the coherence calculated during baseline recording (thought to be representative of initial connectivity). We refer to this difference as the stimulus-evoked coherence. Indeed, in the example session of Figure 6A, one can see a similarity between the pattern of changes in baseline coherence across the array (left panel) with the stimulus-evoked coherence (middle panel). To quantify this similarity, we used linear regression to predict changes in baseline coherence across sites based on the stimulus-evoked coherence. Importantly, both of these quantities use the initial baseline coherence as a reference. So to avoid spurious correlations introduced by subtracting the same data from the two regression variables, we split the baseline recording blocks into two intervals and used the data from only one interval for each regressor (See Materials and Methods for more details). Data from the example session in Figure 6A is shown as a scatter plot in Figure 6B, along with the calculated regression parameters. The plot shows a strong correlation between the stimulus-evoked coherence and changes in baseline coherence. This relationship was consistent across our data (Figure 6C): the distribution of linear regression slopes across experimental sessions and monkeys was significant (paired t-test: p=2. 41e-19; 101 out of 105 experimental blocks had significant (p<0. 05) linear regression models). These results support a Hebbian model for network-wide changes in connectivity. To further test this model, we conducted some experimental sessions using a more complex spatio-temporal pattern of optical stimulation. Specifically, by reducing the latency between the two light sources to either 10 or 30 ms (Figure 7A), we introduce interference between evoked responses from the two light sources (Figure 7B). We repeated the analysis of mean inter-area coherence changes (c. f. Figure 4F) for these sessions, and we did not see a consistent increase in mean inter-area connectivity (Figure 7C). Nevertheless, we found that stimulus-evoked coherence continued to predict changes in baseline coherence (paired t-test on the distribution of regression slopes: p=1. 37e-28; linear regression slopes with p<0. 05: 160 out 170 blocks; Figure 7D, see Figure 7—figure supplement 1 for an example session). Furthermore, the regression parameters were similar to those obtained with the simple stimulation patterns (Figure 6C), and both simple and complex stimulation experiments yielded regression slopes that were significantly larger than those obtained from control sessions (long-latency vs. control sessions, ranksum test: p=0. 048; short-latency vs. control sessions, ranksum test: p=0. 037; see Figure 7—figure supplement 2). These results demonstrate a robust relationship between stimulus-evoked correlations and changes in baseline connectivity across the network. These findings are well explained by a Hebbian model of large-scale stimulation-induced plasticity.
  Functional connectivity between M1 and S1 has been demonstrated in fMRI (Matsui et al. , 2011; McGregor and Gribble, 2015), electrophysiology (Iriki et al. , 1989; Yazdan-Shahmorad et al. , 2016; Prsa et al. , 2017), and anatomical studies (Kosar et al. , 1985; Petrof et al. , 2015). To evaluate stimulation-induced changes across these areas we investigated two measures of functional connectivity, one based on stimulation and one based on natural neural processing. Variants of SERR (Seeman et al. , 2017; Feldmeyer and Sakmann, 2000; Klinshov et al. , 2014) and coherence (Bastos and Schoffelen, 2015; Lang et al. , 2012) have been used previously to evaluate functional connectivity. However, to our knowledge they have never been used in combination or compared. In principle, these two measures reflect different aspects of functional connectivity. SERR is a more direct measure of the projections from the stimulation site. Importantly, stimulation likely evokes activity from both terminals and cell bodies located at the stimulation site, though C1V1 does not express well down at the axon (Rajasethupathy et al. , 2015). Secondary responses arise from a combination of synaptic, antridromic, and indirect (network) effects. That said, based on the timing of the evoked responses and the limited expression of C1V1 in axon terminals, we expect the amplitude of secondary responses normalized to primary response amplitudes to reflect synaptic connectivity. Coherence, on the other hand, measures the broader effects of network-wide dynamics. Despite these differences, we saw that the measures are robustly correlated across channels at the beginning of our experiments. This would be expected in a network where stability was maintained with a Hebbian mechanism. In other words, direct connectivity, as reflected in SERR, drives correlated activity, which is reflected in coherence; conversely, correlated activity drives changes in connectivity (see Network effects). These coupled mechanisms lead the network to a stable, steady state (Fox and Stryker, 2017; Toyoizumi et al. , 2014; Zenke and Gerstner, 2017). Notably, however, our conditioning protocol caused the two measures of connectivity to become less correlated across electrodes. This might seem surprising given that conditioning led to a significant mean increase in connectivity between S1 and M1 for both measures and that the changes in coherence were robustly predicted by the stimulus-evoked coherence. However, this decrease in the correlation between the measures after conditioning seems to be limited to high frequencies and may reflect a transient instability of the network due to the stimulation-induced perturbations in connectivity. Previous work has demonstrated that either activity dependent (Jackson et al. , 2006; Lucas and Fetz, 2013; Nishimura et al. , 2013; Rebesco et al. , 2010; Song et al. , 2013) or paired electrical stimulation (Rebesco and Miller, 2011; Seeman et al. , 2017) can lead to plastic changes in both primate and rodent cortex. Here we demonstrated that optogenetic stimulation at even a single location strengthens inter-area functional connectivity between brain areas. A similar observation was reported for one session of single-site electrical stimulation (Seeman et al. , 2017). This result is consistent with spike-timing dependent plasticity (STDP) rules (Bi and Poo, 2001). In particular, since the time difference between the activation of the primary (pre-synaptic) and secondary (post-synaptic) responses to optical stimulation is about 3–6 ms, repetitive stimulation should strengthen the connectivity from the site of stimulation to the other area. While stimulation-induced long-term potentiation (LTP) and STDP have been observed within local circuits (Iriki et al. , 1989; Feldman, 2012; Shulz and Jacob, 2010), and have been posited as the mechanism of stimulation-induced plasticity within M1 (Lajoie et al. , 2017) this is the first study explaining large-scale changes across cortical networks. Notably, conditioning drove increases in coherence between S1 and M1 only in the theta band (4–8 Hz). This result is difficult to interpret because the conditioning stimulation frequency (5 or 7 Hz) itself lies within the range of the theta band. It is possible that Hebbian mechanisms are selectively enhancing connectivity at the conditioning frequency. It’s also possible that theta band coherence best reflects the plastic connections between these areas. Since we did not vary stimulation frequency outside of this range, no strong conclusions can be drawn. The temporal dynamics of both measures reveal a trend of increasing inter-area connectivity throughout the experiment. This trend suggests that neuroplastic changes start almost immediately after the start of stimulation. Interestingly, we observed unlearning of the stimulation-induced changes during passive recording, though some of the stimulation-induced changes persisted. Previous studies have shown that longer conditioning sessions (3–48 hr) result in changes that are stable over timescales comparable to the length of conditioning (Jackson et al. , 2006; Lucas and Fetz, 2013; Seeman et al. , 2017; Nishimura et al. , 2013; Rebesco and Miller, 2011). Further experiments are required to estimate the stability of plasticity following optogenetic stimulation over longer time scales. Given that Hebbian rules have been used extensively to explain synaptic plasticity, we wanted to explore the idea that large-scale stimulation-induced changes are consistent with Hebbian plasticity. The strongest test of the Hebbian model is to compare the correlations directly induced by stimulation with the changes in network correlations observed after conditioning. Our ability to record artifact-free signals during stimulation allowed us to perform this test. We did find a predictive linear relationship between stimulus-evoked coherence and the changes in baseline coherence after conditioning. Furthermore, the model held for both simple and complex spatiotemporal patterns of stimulation-evoked activity. There is a large body of research on sensory cortical plasticity (Merzenich et al. , 1983; Qi et al. , 2011). This study differs in two key dimensions. First, previous work has largely focused on how changes in the statistics of sensory input and/or motor output affect the cortical representation in sensory cortex. In contrast, we have directly manipulated cortical circuits, without a detailed knowledge of the somatotopic map. Also, earlier work studied plasticity on the time scale of days to months, while we are studying changes across minutes. Despite these differences, both appear to support a key role for Hebbian learning in sensorimotor cortical plasticity. Still, a deeper understanding of the relationship between these bodies of work would require a study of intermediate time-frames as well, along with detailed cortical mapping. These are important questions for future work. The large-scale interface used in this study enabled robust estimation of functional connectivity and stimulation-evoked correlations. However, there are several limitations in our experimental setup. First, surface stimulation combined with high blue light absorption and scattering in tissue limits the depth of light penetration (Yizhar et al. , 2011). The ability to target specific cortical layers would help us understand the anatomical basis of the plasticity we observed. Second, surface µECoG recordings reflect a summation of nearby neural activity, making comparisons to explicit synaptic learning rules difficult. Depth recordings would reveal more detailed information about spike timing, the role of different cortical layers, and the relationship between synaptic and large-scale plasticity. Lastly, the µECoG arrays were placed acutely for each experiment. To investigate the long-term effects of stimulation we need stable, chronic recordings. Our results offer a proof-of-concept that optogenetic stimulation can drive predictable changes in network-scale connectivity. The success of optogenetics in NHP represents an important step forward for translational use (Doroudchi et al. , 2011; Han, 2012). The continued rapid progress in the field has the potential to bring cell-type specific neuromodulation therapies to the clinic. However, even where therapeutic approaches ultimately wind up using electrical stimulation, the advantages of optogenetics make it a powerful tool for better understanding the underlying mechanisms of plasticity and, thus, for the development of therapeutic applications of neuromodulation. This framework also provides scientific insight into the mechanisms of neural plasticity. Future efforts should focus on linking the plasticity results presented here with improvements in motor function or sensory perception, which will have important implications for stimulation-based therapies.
  Here, we give a brief summary of our large-scale optogenetic interface (details in (Yazdan-Shahmorad et al. , 2016). We combined three existing techniques to implement a practical, large-scale interface for both manipulation and recording from the surface of the brain in two rhesus macaque monkeys. We used an efficient technique for infusion of the optogenetics viral vector (AAV5. CamKIIa. C1V1 (E122T/E162T). TS. eYFP. WPRE. hGH, 2. 5 × 1012 virus molecules/ml; Penn Vector Core, University of Pennsylvania, PA, USA, Addgene number: 35499) into primary somatosensory (S1) and motor (M1) cortices based on convection-enhanced delivery (CED). We infused 200 μL of virus in four sites (two in M1 and two in S1) in monkey G and 250 μL in five sites (two in M1 and three in S1) of monkey J. Infusion rate started at 1 µl/min and was increased to 5 µl/min. Following infusion we used an artificial dura to protect the brain while maintaining optical access. We verified expression with epifluorescent imaging. To record the evoked responses in both M1 and S1, we used either one or two 96 channel micro-electrocorticography (μ-ECoG) arrays that were designed to allow minimally-attenuated (Ledochowitsch et al. , 2015) optical access. In both animals, we observed reliable light evoked neural responses from the large channel-expressing areas. Optical stimulation was applied using a fiber optic (core/cladding diameter: 62. 5/125 um, Fiber Systems, TX, USA) that was connected to a 488 nm laser (PhoxX 488–60, Omicron-Laserage, Germany) and was positioned above the array (Figure 1). We used a Tucker-Davis Technologies system (FL, USA) for µ-ECoG recording and to control the laser stimulus. We chose the locations of stimulation based on the results of our epifluorescent imaging (see (Yazdan-Shahmorad et al. , 2016) ) and whether we got a physiological response to light stimulation. Based on detailed histological and electrophysiological analyses, (see (Yazdan-Shahmorad et al. , 2016) ) we estimate that there was a uniform distribution of expression around our infusion sites and at the locations that we chose to stimulate. The monkeys were awake sitting in primate chairs for the duration of experiments. To ensure that the monkey remained awake, we provided random reward as well as audio and visual stimuli (displaying a cartoon). In addition, we visually monitored the animal throughout the experiment. Our stimulation and recording paradigms consisted of baseline recording, testing and conditioning blocks (Figure 4A). During baseline recording blocks we collected 5 min or 30 s of baseline activity. In test blocks we delivered 100 light pulses (five msec duration at 5 Hz) to each laser; pulses were alternated between laser sites in blocks of 10 pulses each in order to reduce the effects of habituation. For the conditioning blocks we stimulated through one or two lasers at a frequency of 5 or 7 Hz for 10 min. We repeated this protocol five times for each experiment. Our experiments consisted of single- and two-site stimulation with different laser configurations: i) one or two lasers in M1, ii) one or two lasers in S1 and iii) one laser in M1 and one laser in S1. For two-site stimulation, the delay between the two lasers was 10,30,70 or 100 ms. We also included control sessions in which we kept the structure of passive recording and active testing blocks, but did not stimulate during the conditioning blocks. Monkeys were deeply sedated (per above Surgical procedures) and perfused transcardially with heparinized phosphate buffered saline (PBS) followed by cold 4% paraformaldehyde in phosphate buffer. The brain was extracted and post-fixed in the same fixative for 24 hr at 4°C and then dissected into twelve 6 mm-thick coronal blocks using a custom matrix. After 7–10 days incubation in 30% sucrose, blocks were frozen and cut on a cryostat (Microm, Germany) into 50 μm thick sections. Representative sections were selected from each block and processed for EYFP immunocytochemistry using a free-floating technique. Sections were initially washed in PBS, incubated in 3% hydrogen peroxide in PBS for 10 min to quench endogenous peroxidase activity, then rinsed in two changes of 50% ethanol followed by three changes of PBS for 5 min each. Next we incubated the sections in 5% normal donkey serum in PBS for 1 hr to block non-specific binding. Primary rabbit polyclonal anti-GFP antibody (Abcam, RRID: AB_303395) was diluted 1: 15,000 in PBS containing 0. 01% Triton X-100 and was applied to the tissue for 48 hr at 4°C. Sections were then rinsed in PBS, incubated in biotinylated donkey anti-rabbit antibody, (1: 2,000, Jackson Immunoresearch) for 12 hr at 4°C, rinsed and incubated in ExtrAvidin (Sigma-Aldrich, 1: 5,000) for 5–6 hr in room temperature. Peroxidase was detected using a diaminobenzidine (DAB) chromogenic reagent (Sigma-Aldrich). Sections were rinsed in PBS, mounted on gelatin-coated slides, air dried, dehydrated in graded alcohols, cleared in xylene and coverslipped with D. P. X. mounting media (Sigma-Aldrich). Additional adjacent sections were stained with cresyl violet (Nissl) using standard techniques, to reveal cortical cytoarchitecture. Double immunofluorescence was performed using the similar to above approach although using combinations of primary antibodies from different host species for GFP and for the interneuron markers. Primary antibodies were: rabbit polyclonal anti-GFP antibody (1: 10,000, Abcam, GFP ab290); goat polyclonal anti-GFP antibody (1: 1,000, Abcam, ab5450). These were paired with one of the following primary antibodies: mouse monoclonal antibody for parvalbumin, (1: 1,000, Sigma-Aldrich P3088); mouse monoclonal antibody for calbindin (1: 800 Sigma-Aldrich CB-955); rat monoclonal antibody for somatostatin (1: 200, Millipore MAP354); rabbit polyclonal anti-GAD65/67 (1: 500, Millipore AB1511); a cocktail of mouse monoclonal antibody for GAD67 (1: 500, Millipore MAB5406) and mouse monoclonal antibody for GAD65 (1: 1,000, Sigma-Aldrich G1166). Sections were blocked using normal donkey serum and processed for 4–6 hr in a mix of matching secondary antibodies; all of which were raised in donkey (Thermo Fisher Scientific, 1: 300): anti-rabbit Alexa Fluor 488, anti-goat Alexa Fluor 488 (both for GFP). Interneuron markers were visualized using anti-mouse Alexa Fluor 594, anti-rat Alexa Fluor 594; anti-rabbit Alexa Fluor 594. Sections were mounted using Vectashield (Vector Labs) and imaged using Zeiss LSM510 Meta confocal microscope (Zeiss, Germany) and 90i imaging system equipped with a CCD camera (Nikon, Japan).
summary: From riding a bike to reaching for a cup of coffee, all skilled actions rely on precise connections between the sensory and motor areas of the brain. While sensory areas receive and analyse input from the senses, motor areas plan and trigger muscle contractions. Precisely adjusting the connections between these and other areas enables us to learn new skills, and it also helps us to relearn skills lost as a result of brain injury or stroke. About 70 years ago, a psychologist named Donald Hebb came up with an idea for how this process might occur. He proposed that whenever two neurons are active at the same time, the connection between them becomes stronger. This idea, that ‘cells that fire together, wire together’, became known as Hebb’s rule. Many studies have since shown that Hebb’s rule can explain changes in the strength of connections between pairs of neurons. But can it also explain how connections between entire brain regions become stronger or weaker? New results show that it can. The data were obtained using a technique called optogenetics, in which viruses are used to introduce genes for light-sensitive proteins into neurons. Shining light onto the brain will then activate any cells within that area that contain the resulting proteins. Yazdan-Shahmorad, Silversmith et al. used this technique to activate small regions of either sensory or motor brain tissue in live macaque monkeys. Doing so strengthened the overall connectivity between the two areas. The effects were more variable at the level of smaller brain regions, with some connections becoming weaker rather than stronger. However, Yazdan-Shahmorad, Silversmith et al. show that Hebb’s rule explains most of the observed changes. Many neurological and psychiatric disorders stem from abnormal brain connectivity. Simple forms of brain stimulation are already used to treat certain neurological disorders, such as Parkinson’s disease. Stimulating the brain to induce specific changes in connectivity may ultimately enable us to leverage the brain’s natural learning mechanisms to cure, instead of just treat, these conditions.
keywords: neuroscience
title: Targeted cortical reorganization using optogenetics in non-human primates