Bridging Scales to Understand Endogenous Neuromodulation and its Regulation

Neuromodulatory nuclei detect and transform brain network activity into simpler signals, then send
neurotransmitters back out to large-scale brain networks to change their function. Such nuclei are centrally
implicated in mental disorders and adaptive resilience, and their regulation remains an untapped resource for
interventions. The purpose of this grant is to understand how neuromodulatory nuclei detect and in turn
influence distributed patterns of brain activity to impact behavior. To understand their regulation and effects on
brain function, the investigative team has developed novel neuroimaging, behavioral, and analytic methods.
These methods include: training participants to endogenously self-regulate dopaminergic midbrain, isolating
distinct streams of information in the midbrain over multiple timescales, distinguishing behavioral contexts and
network effects associated with univariate activation in neuromodulatory nuclei, and finally relating midbrain
activation to memory-conducive states in medial temporal lobe memory systems. Our team has recently
developed whole-brain analyses of real-time fMRI during midbrain neurofeedback and machine-learning tools
for characterizing nonlinear latent dynamics from high-dimensional data. Now, with these tools, we can relate
midbrain activation to whole brain states. We hypothesize 1) that distinct distributed spatiotemporal patterns
precede and follow midbrain univariate activation, specify it uniquely among neuromodulatory nuclei, and
distinguish sustained from transient midbrain responses; 2) that the evolution of these patterns over the
training session will predict learning to upregulate midbrain, and 3) that endogenous midbrain regulation will
predict brain and behavioral effects we and others have previously shown to be associated with midbrain
activation and dopamine function. If the aims of this project are achieved, we will have introduced a multi-level
model of the neural states that support midbrain activation, a complement of methods for regulating midbrain
noninvasively, and an improved understanding of its impact on learning and motivated behavior. Reliable
cognitive strategies for dynamically and selectively fine-tuning neural networks to suit behavioral contexts will
lay the foundation for a wide array of interventions across educational and clinical applications.

Grant Number: 5R01MH131667-04
NIH Institute/Center: NIH
Principal Investigator: Rachel Adcock