Dopamine Dynamics during Continuous Intracranial Self-Stimulation: Effect of Waveform on Fast-Scan Cyclic Voltammetry Data

Abstract

The neurotransmitter dopamine is heavily implicated in intracranial self-stimulation (ICSS). Many drugs of abuse that affect ICSS behavior target the dopaminergic system, and optogenetic activation of dopamine neurons is sufficient to support self-stimulation. However, the patterns of phasic dopamine release during ICSS remain unclear. Early ICSS studies using fast-scan cyclic voltammetry (FSCV) rarely observed phasic dopamine release, which led to the surprising conclusion that it is dissociated from ICSS. However, several advances in the sensitivity (i.e., the use of waveforms with extended anodic limits) and analysis (i.e., principal component regression) of FSCV measurements have made it possible to detect smaller, yet physiologically relevant, dopamine release events. Therefore, this study revisits phasic dopamine release during ICSS using these tools. It was found that the anodic limit of the voltammetric waveform has a substantial effect on the patterns of dopamine release observed during continuous ICSS. While data collected with low anodic limits (i.e., +1.0 V) support the disappearance of phasic dopamine release observed in previous investigation, the use of high anodic limits (+1.3 V, +1.4 V) allows for continual detection of dopamine release throughout ICSS. However, the +1.4 V waveform lacks the ability to resolve narrowly spaced events, with the best balance of temporal resolution and sensitivity provided by the +1.3 V waveform. Ultimately, it is revealed that the amplitude of phasic dopamine release decays but does not fully disappear during continuous ICSS.

Hitchhiker’s Guide to Voltammetry: Acute and Chronic Electrodes for in Vivo Fast-Scan Cyclic Voltammetry

“… 91 , 94 Alternatively, reference electrodes can be implanted on the day of recordings through a guide cannula. 46 , 95 , 96 …”

Fast-Scan Cyclic Voltammetry: Chemical Sensing in the Brain and Beyond

“…34,37 The first ranked component captures the maximum variance inherent to the training set; and subsequent components describe the remaining variance, in a manner such that all PCs are orthogonal to another. 37,52 Then, concentration dynamics for the targeted species can be predicted in unknown data by projecting the data onto the PCs to extrapolate the contribution of each analyte contained in the training set. This approach can be very effective; however, it struggles when two analytical signals have similar sources of variance (voltammetric features).…”

“…Principal component analysis is considered to be an unsupervised dimensionality reduction technique, because it creates PCs to describe variance in the predictor variables without considering the response. 52,55 By contrast, PLS regression analysis is a supervised dimensionality reduction technique that projects both the predictor and response variables onto a new vector space to find components that maximize the covariance of the projected structures. 55,56 This ‘supervision’ generally allows PLS regression models to describe the training data more efficiently with fewer components.…”

Electrochemistry at the Synapse

“…Early studies using the traditional dopamine waveform found that phasic dopamine release disappeared during the continuous ICSS. Newer studies with an improved waveform coupled with principal component regression reevaluated the original finding and showed that while phasic dopamine release is not required to maintain ICSS behavior, small amounts of dopamine were still released (28). Numerous studies measured cue-evoked dopamine release in response to natural rewards or alcohol, using an audio or visual cue to predict the reward.…”