You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Targeted cortical reorganization using optogenetics in non-human primates}
Article:
Abstract:
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.
Introduction:
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.
Discussion:
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.
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 .