Project Summary
Negative mood is a common feature of anxiety, depression, bipolar disorder, and schizophrenia, which inflict
immeasurable human suffering along with a combined economic burden of $600 billion in the US each year. The
brain basis of negative affect has been the focus of costly research efforts, but two critical barriers have slowed
scientific discovery. First, there is no mechanistic explanation for how negative affect is caused in the brain. A
solution to this barrier can be found in predictive processing, an emerging paradigm for unifying brain
mechanisms across emotion, cognition, perception, movement, and other psychological domains. Predictive
processing accounts posit that the brain continuously constructs prediction signals to control visceromotor and
motor movements, while copies of these prediction signals anticipate incoming sensory signals from the body
and the external world. Incoming sensory signals are thought to be relayed throughout the brain as prediction
error signals. No study to date has examined negative affect in relation to the dynamics of signal flow within the
specific architectural features of the brain. To surmount this barrier, I will take advantage of a conceptual
innovation from our lab and thirty years of tract-tracing studies in mammals to test the hypothesis that prediction
signals and prediction error signals can be traced across specific layers of cerebral cortex and subcortical
structures. Briefly, prediction signals are thought to originate in deep layers of cortices that have less laminar
development (e.g., anterior midcingulate cortex, aMCC, which is important for visceromotor control and affect)
and arrive to subcortical structures (e.g., hypothalamus, involved in visceromotor control) and primary sensory
cortices (e.g., primary visual cortex, V1). Interoceptive prediction error signals should originate from subcortical
structures (e.g., hypothalamus) and other (exteroceptive) sensory prediction errors should originate in primary
sensory cortices (e.g., V1), arriving to the upper and deep layers, respectively, of cortices with less laminar
development (e.g., aMCC). In human subjects, these hypotheses remain to be tested due to a second barrier:
neuroimaging methods have lacked sufficient spatial resolution to measure activity in deep vs. upper cortical
layers and small subcortical structures. Newly developed ultra-high field (7 Tesla) fMRI techniques have
sufficient resolution to overcome this barrier. With this methodological innovation, I will probe the mechanisms
that cause negative affect in the circuitry outlined above via functional connectivity analyses of a 7T fMRI dataset
our lab has curated. Ninety-two healthy subjects were instructed to anticipate visual or somatosensory stimuli
(prediction period) that were either unpleasant or neutral and then were presented with the stimuli (prediction
error period). In two specific aims, I will 1) measure dynamic prediction signals during negative affect, and 2)
characterize prediction error signals during negative affect. The proposed research promises to deliver a new
paradigm for studying the brain basis of negative affect, with the ultimate goal of developing targeted treatments
for negative mood, a hallmark feature of many mental illnesses.