# Brainstem-forebrain networks and threat computation > **NIH NIH R01** · BOSTON COLLEGE · 2024 · $674,382 ## Abstract Project Summary A cardinal feature of anxiety disorders is exaggerated fear to cues signaling threat. The prevailing view of the neural circuit for fear learning is a division of labor in which the amygdala and associated forebrain regions signal threat probability, and brainstem regions organize fear output. This view is the intellectual foundation for proposals that forebrain threat dysfunction drives exaggerated fear in anxiety disorders. Much more than organizing fear output, my laboratory is showing brainstem networks compute prediction error – a learning signal to drive fear learning. Further, brainstem networks signal threat – a function purported to be specific to the forebrain. This proposal will reveal brainstem networks that signal threat probability, compute prediction error, and organize fear output. Aim 1 will detail the emergence of brainstem threat and behavior signaling dynamics. Female and male rats will receive Neuropixels implant through a complete dorsal-ventral brainstem axis. Thousands of single units will be recorded from 20+ brainstem regions during a fear discrimination procedure that produces selective learning to a threat cue. Firing analyses during the cue period will reveal the emergence of brainstem network threat signaling and tracking of diverse behaviors over fear learning. Aim 2 will establish a framework for brainstem prediction error dynamics. Firing analyses during the outcome period will link brainstem network firing dynamics for prediction error that precede the emergence of fear learning. Aim 3 will reveal forebrain inputs shaping brainstem firing dynamics and fear learning. Rats will receive brainstem Neuropixels implant. AAV2-retro in a brainstem region and cre-casp3 in a forebrain region will be used to delete specific inputs to the brainstem (e.g. prelimibic cortex neurons projecting to the periaqueductal gray). Firing analyses comparing casp3 and control rats will reveal the dependence of brainstem firing dynamics (threat, behavior, and prediction error) on forebrain inputs. Aim 4 will chemogenetically manipulate brainstem firing to diminish prediction error computation and fear learning. Rats will receive brainstem Neuropixels implant. Half will then receive excitatory DREADD infusions in regions flanking the periaqueductal gray – sources of a tonic prediction error organized by firing inhibition. Actuator injections (JH60), but not saline injection, will excite brainstem firing – blocking the tonic prediction error network. Firing analyses will reveal the effects of network interruption on brainstem firing dynamics for threat, behavior, and prediction error. This proposal will extend scientific knowledge by uncovering core threat functions of brainstem networks. The proposal will further reveal how forebrain inputs shape brainstem firing dynamics that signal threat, compute prediction error and organize specific fear behaviors. This knowledge is essential to developing effective anxiety disorder tre... ## Key facts - **NIH application ID:** 10874694 - **Project number:** 5R01MH117791-07 - **Recipient organization:** BOSTON COLLEGE - **Principal Investigator:** MICHAEL A MCDANNALD - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $674,382 - **Award type:** 5 - **Project period:** 2018-09-01 → 2028-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10874694 ## Citation > US National Institutes of Health, RePORTER application 10874694, Brainstem-forebrain networks and threat computation (5R01MH117791-07). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10874694. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*