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The role of negative affect as a motivational factor in animal models of drug addiction has been underexplored in the context of cocaine self-administration. The present investigation studied the relationship between magnitude of affective response and quantity of cocaine consumed in order to clarify the affective components that drive drug use in a preclinical model. Rats self-administered (SA) cocaine 6 h/day for 14 consecutive days while their ultrasonic vocalizations (USVs) were recorded. Animals displayed an increase in 50-kHz call rates (indicating positive affect) when their drug levels were rapidly rising and an increase in 22-kHz call rates (indicating negative affect) when forced to abstain. The rate of 50-kHz calls predicted drug consumption during the 1st week of SA, but not week two. Contrarily, there was a strongly predictive positive association between rate of 22-kHz calls and amount of drug consumed during the 2nd week of SA. Experimental results indicate that after chronic cocaine self-administration, negative affect emerges when animals are deprived of expected drug during withdrawal. Moreover, the increase in USVs indicating negative affect when deprived of drug was directly related to drug intake, concurrent with a decay in the direct relationship between USVs indicating positive affect and drug intake. The present preclinical support for the widely hypothesized shift from positive to negative affect as a salient motivational factor in human drug abuse adds to growing evidence of the unique value of rat USVs for understanding the role of emotion in drug addiction.

The combined development of new technologies for neuronal recordings and the development of novel sensors for recording both cellular activity and neurotransmitter binding has ushered in a new era for the field of neuroscience. Among these new technologies is fiber photometry, a technique wherein an implanted fiber optic is used to record signals from genetically encoded fluorescent sensors in bulk tissue. Fiber photometry has been widely adapted due to its cost-effectiveness, ability to examine the activity of neurons with specific anatomical or genetic identities, and the ability to use these highly modular systems to record from one or more sensors or brain sites in both superficial and deep-brain structures. Despite these many benefits, one major hurdle for laboratories adopting this technique is the steep learning curve associated with the analysis of fiber photometry data. This has been further complicated by a lack of standardization in analysis pipelines. In the present communication, we present pMAT, a ‘photometry modular analysis tool’ that allows users to accomplish common analysis routines through the use of a graphical user interface. This tool can be deployed in MATLAB and edited by more advanced users, but is also available as an independently deployable, open-source application.

SUMMARY Ventral tegmental area (VTA) neurons play roles in reward and aversion. The VTA has, in addition to dopamine neurons, glutamatergic neurons expressing VGluT2. Here, by determining the firing patterns of VTA-VGluT2 neurons expressing channelrhodopsin 2, we identified a major subpopulation of VTA-VGluT2 neurons whose firing rates decreased or were unchanged during sucrose consumption and increased during facial airpuff presentation. We identified a small subpopulation of VTA-VGluT2 neurons whose firing rates increased in response to both rewarding and aversive stimuli. We also found that the changes in firing rate of some VTA-VGluT2 neurons were greater following reward delivery compared with reward omission, whereas others did not differ. We conclude that VTA-VGluT2 neurons are responsive to aversive stimuli, but subpopulations of VTA-VGluT2 neurons are differentially affected by sucrose reward. Reward-responsive subpopulations of VTA-VGluT2 neurons are also divided into those affected by reward expectation alone or the real-time delivery of reward., Graphical abstract

The combined development of new technologies for neuronal recordings and the development of novel sensors for recording both cellular activity and neurotransmitter binding has ushered in a new era for the field of neuroscience. Among these new technologies is fiber photometry, a technique wherein an implanted fiber optic is used to record signals from genetically encoded fluorescent sensors in bulk tissue. Fiber photometry has been widely adapted due to its cost-effectiveness, ability to examine the activity of neurons with specific anatomical or genetic identities, and the ability to use these highly modular systems to record from one or more sensors or brain sites in both superficial and deep-brain structures. Despite these many benefits, one major hurdle for laboratories adopting this technique is the steep learning curve associated with the analysis of fiber photometry data. This has been further complicated by a lack of standardization in analysis pipelines. In the present communication, we present pMAT, a ‘photometry modular analysis tool’ that allows users to accomplish common analysis routines through the use of a graphical user interface. This tool can be deployed in MATLAB and edited by more advanced users, but is also available as an independently deployable, open-source application.

SUMMARY Ventral tegmental area (VTA) neurons play roles in reward and aversion. We recently discovered that the VTA has neurons that co-transmit glutamate and GABA (glutamate-GABA co-transmitting neurons), transmit glutamate without GABA (glutamate-transmitting neurons), or transmit GABA without glutamate (GABA-transmitting neurons). However, the functions of these VTA cell types in motivated behavior are unclear. To identify the functions of these VTA cell types, we combine recombinase mouse lines with INTRSECT2.0 vectors to selectively target these neurons. We find that VTA cell types have unique signaling patterns for reward, aversion, and learned cues. Whereas VTA glutamate-transmitting neurons signal cues predicting reward, VTA GABA-transmitting neurons signal cues predicting the absence of reward, and glutamate-GABA co-transmitting neurons signal rewarding and aversive outcomes without signaling learned cues related to those outcomes. Thus, we demonstrate that genetically defined subclasses of VTA glutamate and GABA neurons signal different aspects of motivated behavior., Graphical Abstract, In Brief Root et al. examine ventral tegmental area (VTA) neurons that release glutamate without GABA, GABA without glutamate, or both glutamate and GABA. Cell types have differential projection densities and unique neuronal activity profiles related to cues predicting rewarding, nonreward, or aversive outcomes and in the receipt of predicted rewards.

The ventral tegmental area (VTA) has dopamine, GABA, and glutamate neurons, which have been implicated in reward and aversion. Here, we determined whether VTA-glutamate or -GABA neurons play a role in innate defensive behavior. By VTA cell-type specific genetic ablation, we found that ablation of glutamate, but not GABA, neurons abolishes escape behavior in response to threatening stimuli. We found that escape behavior is also decreased by chemogenetic inhibition of VTA-glutamate neurons, and detected increases in activity in VTA-glutamate neurons in response to the threatening stimuli. By ultrastructural and electrophysiological analysis, we established that VTA-glutamate neurons receive a major monosynaptic glutamatergic input from the lateral hypothalamic area (LHA) and found that photoinhibition of this input decreases escape responses to threatening stimuli. These findings indicate that VTA-glutamate neurons are activated by and required for innate defensive responses, and that information on threatening stimuli to VTA-glutamate neurons is relayed by LHA-glutamate neurons.