  Reward processing and derangements thereof, such as drug addiction, involve
the coordinated activity of many brain areas. Prior work has identified many
behavioral, molecular biological and single neuron changes throughout the
mesocorticolimbic system that reflect and drive addictive behavior.
Subpopulations in the ventral tegemental area (VTA) encode positive reward
prediction error, negative reward prediction error, and the magnitude of the
reward. Phasic activity in VTA dopaminergic neurons correlates with hedonic
value. Tonic activity of groups in the dorsomedial prefrontal cortex (dmPFC)
can encode antidepressant states. However, little is known about how drug
addiction might affect population encoding across larger brain regions. Here,
we compare the information content associated with network patterns in naive,
acutely intoxicated and chronically addicted states in a plastic attractor
network. We found that addiction decreases the network's ability to store and
discriminate among patterns of activity. Altered dopaminergic tone flattens the
energy landscape and decreases the entropy associated with each network
pattern. Altered dmPFC activity produces signal-to-noise deficits similar to
computational models of schizophrenia. Our results provide a conceptual
framework for interpreting altered neural population dynamics in
psychopathological states based on information theory. They also suggest a view
of the subtypes of depression as on a continuum of combinations of cortical and
subcortical dysfunction. This suggests that patients who suffer from depression
with psychotic features will have more cortical than mesolimbic dysfunction.
Furthermore, our framework can be applied to other psychiatric illnesses and so
may help us, in general, quantitatively understand psychiatric illnesses as
disorders in the representation and processing of information by distributed
brain networks.
