The relative contributions of glutamate and dopamine within the nucleus accumbens to cocaine-induced reinstatement of drug-seeking behavior were assessed. When extinguished cocaine self-administration behavior was reinstated by a cocaine-priming injection, extracellular levels of both dopamine and glutamate were elevated in the nucleus accumbens. However, when yoked cocaine or saline control subjects were administered a cocaine prime, only dopamine levels were elevated. Thus, glutamate increased only when animals reinstated lever pressing, whereas dopamine increased regardless of behavior. The increase in glutamate was not accounted for simply by the act of lever pressing itself, because the cocaine self-administration group still demonstrated elevated glutamate when the levers were withdrawn from the operant chamber. Moreover, reinstatement of lever pressing for food did not elevate extracellular glutamate, indicating that increased glutamate initiated responding selectively for a drug reinforcement. The source of glutamate was shown to be glutamatergic afferents from the prefrontal cortex because inhibiting prefrontal cortical glutamatergic neurons that project to the accumbens prevented the rise in glutamate. Together, these data demonstrate that activation of a glutamatergic projection from the prefrontal cortex to the nucleus accumbens underlies cocaine-primed reinstatement of drug-seeking behavior.

The role of limbic, cortical, and striatal circuitry in a footshock reinstatement model of relapse to cocaine seeking was evaluated. Transient inhibition of the central extended amygdala [CEA; including the central nucleus of the amygdala (CN), ventral bed nucleus of the stria terminalis (BNSTv), and nucleus accumbens shell (NAshell)], ventral tegmental area (VTA), and motor circuitry [including the dorsal prefrontal cortex (PFCd), nucleus accumbens core (NAcore), and ventral pallidum (VP)] blocked the ability of footshock stress to reinstate lever pressing previously associated with cocaine delivery. However, inhibition of the basolateral amygdala, mediodorsal nucleus of the thalamus, or the ventral prefrontal cortex had no effect on drug-seeking behavior. These data suggest that footshock stress activates limbic circuitry of the CEA that, via the VTA, activates motor output circuitry responsible for producing lever press responding. Consistent with this notion, the D 1 /D 2 dopamine receptor antagonist fluphenazine blocked footshock-induced reinstatement when infused into the PFCd. Further, inhibition of the NAshell blocked a footshock-induced increase in dopamine within the PFC and concomitantly blocked reinstatement responding. Also supporting the idea of a CEA-VTA-motor circuit in stress-induced reinstatement of cocaine seeking, inactivation of the PFCd was shown to block stress-induced glutamate release within the NAcore while concurrently inhibiting reinstatement responding. Taken together, these data suggest that footshock activates limbic circuitry in the CEA, which in turn activates a VTA dopamine projection to the PFCd. The rise in dopamine within the PFCd initiates reinstatement via a glutamatergic projection to the NAcore.

Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.

In vivo extracellular recording studies have traditionally shown that dopamine (DA) transiently inhibits prefrontal cortex (PFC) neurons, yet recent biophysical measurements in vitro indicate that DA enhances the evoked excitability of PFC neurons for prolonged periods. Moreover, although DA neurons apparently encode stimulus salience by transient alterations in firing, the temporal properties of the PFC DA signal associated with various behaviors is often extraordinarily prolonged. The present study used in vivo electrophysiological and electrochemical measures to show that the mesocortical system produces a fast non-DA-mediated postsynaptic response in the PFC that appears to be initiated by glutamate. In contrast, short burst stimulation of mesocortical DA neurons that produced transient (Ͻ4 s) DA release in the PFC caused a simultaneous reduction in spontaneous firing (consistent with extracellular in vivo recordings) and a form of DA-induced potentiation in which evoked firing was increased for tens of minutes (consistent with in vitro measurements). We suggest that the mesocortical system might transmit fast signals about reward or salience via corelease of glutamate, whereas the simultaneous prolonged DA-mediated modulation of firing biases the long-term processing dynamics of PFC networks.

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 m spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Key words: effective connectivity; information transfer; microcircuits; rich club; transfer entropy Significance StatementMany studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ϳ70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.