Targeted cortical reorganization using optogenetics in non-human primates
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 ) .
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 .
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 .