Cholinergic and ghrelinergic receptors and KCNQ channels in the medial PFC regulate the expression of palatability.

The medial prefrontal cortex (mPFC) is a key brain region for the control of consummatory behavior. Neuronal activity in this area is modulated when rats initiate consummatory licking and reversible inactivations eliminate reward contrast effects and reduce a measure of palatability, the duration of licking bouts. Together, these data suggest the hypothesis that rhythmic neuronal activity in the mPFC is crucial for the control of consummatory behavior. The muscarinic cholinergic system is known to regulate membrane excitability and control low-frequency rhythmic activity in the mPFC. Muscarinic receptors (mAChRs) act through KCNQ (Kv7) potassium channels, which have recently been linked to the orexigenic peptide ghrelin. To understand if drugs that act on KCNQ channels within the mPFC have effects on consummatory behavior, we made infusions of several muscarinic drugs (scopolamine, oxotremorine, physostigmine), the KCNQ channel blocker XE-991, and ghrelin into the mPFC and evaluated their effects on consummatory behavior. A consistent finding across all drugs was an effect on the duration of licking bouts when animals consume solutions with a relatively high concentration of sucrose. The muscarinic antagonist scopolamine reduced bout durations, both systemically and intra-cortically. By contrast, the muscarinic agonist oxotremorine, the cholinesterase inhibitor physostigmine, the KCNQ channel blocker XE-991, and ghrelin all increased the durations of licking bouts when infused into the mPFC. Our findings suggest that cholinergic and ghrelinergic signaling in the mPFC, acting through KCNQ channels, regulates the expression of palatability.

The medial prefrontal cortex is crucial for the maintenance of persistent licking and the expression of incentive contrast.

We examined the role of the medial prefrontal cortex (mPFC) in reward processing and the control of consummatory behavior. Rats were trained in an operant licking procedure in which they received alternating access to solutions with relatively high and low levels of sucrose (20 and 4%, w/v). Each level of sucrose was available for fixed intervals of 30 s over 30 min test sessions. Over several days of training, rats came to lick persistently when the high level of sucrose was available and suppressed licking when the low level of sucrose was available. Pharmacological inactivations of the mPFC, specifically the rostral part of the prelimbic area, greatly reduced intake of the higher value fluid and only slightly increased intake of the lower value fluid. In addition, the inactivations altered within-session patterns and microstructural measures of licking. Rats licked equally for the high and low levels of sucrose at the beginning of the test sessions and "relearned" to reduce intake of the low value fluid over the test sessions. Durations of licking bouts (clusters of licks with inter-lick intervals <0.5 s) were reduced for the high value fluid and there were many more brief licking bouts (<1 s) when the low value fluid was available. These effects were verified using an alternative approach (optogenetic silencing using archaerhodopsin) and were distinct from inactivation of the ventral striatum, which simply increased overall intake. Our findings suggest that the mPFC is crucial for the maintenance of persistent licking and the expression of learned feeding strategies.

Identification of the hippocampal input to medial prefrontal cortex in vitro.

To delineate the cellular mechanisms underlying the function of medial prefrontal cortex (mPFC) networks, it is critical to understand how synaptic inputs from various afferents are integrated and drive neuronal activity in this region. Using a newly developed slice preparation, we were able to identify a bundle of axons that contain extraneocortical fibers projecting to neurons in the prelimbic cortex. The anatomical origin and functional connectivity of the identified fiber bundle were probed by in vivo track tracing in combination with optic and whole-cell recordings of neurons in layers 2/3 and 5/6. We demonstrate that the identified bundle contains afferent fibers primarily from the ventral hippocampus but does not include contributions from the mediodorsal nucleus of the thalamus, amygdala, or lateral hypothalamus/medial forebrain bundle. Further, we provide evidence that activation of this fiber bundle results in patterned activity of neurons in the mPFC, which is distinct from that of laminar stimulation of either the deep layers 5/6 or the superficial layer 1. Evoked excitatory postsynaptic potentials are monosynaptic and glutamatergic and exhibit bidirectional changes in synaptic efficacy in response to physiologically relevant induction protocols. These data provide the necessary groundwork for the characterization of the hippocampal pathway projecting to the mPFC.

Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data.

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These "brain-reading" analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.