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.

Encoding and predicting aversive events are critical functions of circuits that support survival and emotional well-being. Maladaptive circuit changes in emotional valence processing can underlie the pathophysiology of affective disorders. The lateral habenula (LHb) has been linked to aversion and mood regulation through modulation of the dopamine and serotonin systems. We have defined the identity and function of glutamatergic (Vglut2) control of the LHb, comparing the role of inputs originating in the globus pallidus internal segment (GPi), and lateral hypothalamic area (LHA), respectively. We found that LHb-projecting LHA neurons, and not the proposed GABA/glutamate co-releasing GPi neurons, are responsible for encoding negative value. Monosynaptic rabies tracing of the presynaptic organization revealed a predominantly limbic input onto LHA Vglut2 neurons, while sensorimotor inputs were more prominent onto GABA/glutamate co-releasing GPi neurons. We further recorded the activity of LHA Vglut2 neurons, by imaging calcium dynamics in response to appetitive versus aversive events in conditioning paradigms. LHA Vglut2 neurons formed activity clusters representing distinct reward or aversion signals, including a population that responded to mild foot shocks and predicted aversive events. We found that the LHb-projecting LHA Vglut2 neurons encode negative valence and rapidly develop a prediction signal for negative events. These findings establish the glutamatergic LHA-LHb circuit as a critical node in value processing.

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.

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.