Real-time in vivo optogenetic neuromodulation and multielectrode electrophysiologic recording with NeuroRighter

Abstract

Optogenetic channels have greatly expanded neuroscience’s experimental capabilities, enabling precise genetic targeting and manipulation of neuron subpopulations in awake and behaving animals. However, many barriers to entry remain for this technology – including low-cost and effective hardware for combined optical stimulation and electrophysiologic recording. To address this, we adapted the open-source NeuroRighter multichannel electrophysiology platform for use in awake and behaving rodents in both open and closed-loop stimulation experiments. Here, we present these cost-effective adaptations, including commercially available LED light sources; custom-made optical ferrules; 3D printed ferrule hardware and software to calibrate and standardize output intensity; and modifications to commercially available electrode arrays enabling stimulation proximally and distally to the recording target. We then demonstrate the capabilities and versatility of these adaptations in several open and closed-loop experiments, demonstrate spectrographic methods of analyzing the results, as well as discuss artifacts of stimulation.

Mechanics of mouse ocular motor plant quantified by optogenetic techniques

“…The ability of channelrhodopsins to respond in graded fashion to sinusoidal stimuli has been demonstrated (Laxpati et al 2014;Tchumatchenko et al 2013), but, to our knowledge, this is the first study to investigate the use of the property to probe the dynamics of neural circuitry. Linear control systems analyses and sinusoidal stimuli have played a critical role in explorations of ocular motor physiology, and the optogenetic techniques render it possible, in theory, to inject sinusoidal driving functions in specific neurons at various locations within Close and Luff (1974) 4.4 Ϯ 0.4 -8.9 4.8 Ϯ 0.5 -Values are means Ϯ SD for 8 isolated muscles.…”

Efficacy of nonselective optogenetic control of the medial septum over hippocampal oscillations: the influence of speed and implications for cognitive enhancement

“…Recent developments in the capacity to manipulate hippocampal oscillatory activity through optogenetic manipulation of the septal pacemaker (Laxpati et al. ; Vandecasteele et al. ; Bender et al.…”

“…Previous studies have shown that it is possible to entrain hippocampal oscillations through optogenetic control of the medial septum (MS) in wild‐type rats (Laxpati et al. ), although the efficacy of this entrainment in relation to movement, which has been shown to be an important variable in cell‐selective optogenetics (Vandecasteele et al. ; Bender et al.…”

Closed-Loop and Activity-Guided Optogenetic Control

“…Conditional inhibition of a genetically targeted subpopulation using the simultaneously recorded activity of that subpopulation is another straightforward example. Because fiber photometry readout is a univariate, time-varying scalar, submillisecond processing with a real-time operating system (Sohal et al, 2009; O’Connor et al, 2009; Paz et al, 2013; Krook-Magnuson et al, 2014; Siegle and Wilson, 2014; Stark et al, 2014; Laxpati et al, 2014; Krook-Magnuson et al, 2015) could be used to optogenetically clamp activity in the cells below a target level with minimal intensity inputs (Ahmadian et al, 2011). Because a constant online stream of optical information about how targeted cells are responding to the photoinhibition is available in a single channel, illumination could, in theory, be adjusted in real-time to be no more intense than needed, to adaptively increase to avoid escape from the optical clamp, to silence potential rebound activity if desired, and to reveal which time-varying pattern of inhibition was most effective at achieving these goals (itself useful informative about circuit dynamics).…”

“…Latencies introduced by data acquisition (e.g., scanning-image reconstruction time, frame-grabber latency, operating system limitations), online image processing (e.g., online motion correction, cell ROI application), the optical sensors themselves (e.g., rise time), computational time to choose inputs (e.g., solving a constrained optimization problem), and delays in hardware actuation (e.g., loading images to spatial light modulators, galvanometer or AOD travel and settling time) will likely together sum to milliseconds of delay between detecting an event and delivering a targeted light stimulus (Laxpati et al, 2014). …”