Sunday, December 8, 2024
12/8/2024
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.
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