Targeted cortical reorganization using optogenetics in non-human primates

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.

In vivo microstimulation with cathodic and anodic asymmetric waveforms modulates spatiotemporal calcium dynamics in cortical neuropil and pyramidal neurons of male mice

“…If this is the case, then asymmetric cathodic stimulation would also be less effective at eliciting a cortical response in these deeper layers, at least near the threshold current, which agrees with the reduced magnitude change in voltage sensitive dye reported by Bari et al [78]. However, it should be noted that in one study in the motor cortex, anodic leading stimulation had lower electrophysiological thresholds than cathodic leading stimulation in deeper cortical layers [82], but in another anodic current had higher thresholds for eliciting movement in deeper layers [83]. Together, given the diversity of cell types and their fiber arborizations among different brain regions and cortical layers, these results highlight the complexity of the mechanisms behind ICMS and emphasize the importance of characterizing the responses of neuronal subtypes.…”

Intracortical dynamics underlying repetitive stimulation predicts changes in network connectivity

“…To estimate functional connectivity through oscillatory synchrony of two brain regions, we computed coherence between all possible electrode pairs using FieldTrip (ft_connectivityanalysis) 39 . Coherence provides a measure of the phase difference between the paired signals that is unaffected by their amplitudes and has been previously used to estimate functional connectivity 8,40 . To calculate coherence, we divided the pre and post-stimulation resting periods ranging from 5 to 10 minutes into 1s epochs.…”

“…To calculate coherence, we divided the pre and post-stimulation resting periods ranging from 5 to 10 minutes into 1s epochs. We used a multitaper method with 2 Hz spectral smoothing to compute the spectral estimate of each epoch 8,41 . Coherence was calculated as the normalized crossspectral density between the two signals.…”

“…During tetanic stimulation in rat hippocampal slices, synaptic responses dynamically changed as more pulses were applied 7 , and the magnitude of maintenance effects correlated with changes during stimulation. In non-human primates, >5Hz optogenetic stimulation to the sensorimotor cortices progressively increased functional connectivity during stimulation and predicted changes in post-stimulation connectivity 8 . Furthermore, 10Hz magnetic stimulation (rTMS) to cat visual cortices elicited a pulse-wise increase in neural activity, resulting in significantly increased post-stimulation evoked and spontaneous activity 9 .…”

Probing Neural Networks for Dynamic Switches of Communication Pathways

“…Taken together, our results suggest a potential mechanism the human brain might 314 have developed to use the physiological constraints imposed by coupling delays to its 315 computational advantage. They motivate future brain stimulation studies to investigate 316 phase-lagged neural synchronization, e.g., through multi-site transcranial stimulation, or 317 optogenetics in combination with multielectrode recordings [30]. Thereby, the…”