# Translational studies in humans and mice to test a circuit-level computational model of auditory hallucinations..

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $838,977

## Abstract

Auditory hallucinations (AH) are core symptoms of psychosis for which treatment is often ineffective or poorly
tolerated. A first step towards developing more selective and safer biological interventions is to elucidate AH
mechanisms at a neurobiological circuit level, a level at which AH are currently poorly understood. Here, we
use a novel computational circuit-level model combined with translational experiments in humans and mice to
identify circuit mechanisms underlying AH. Dorsal-striatal dopamine (DA) excess is implicated in AH, and AH
severity correlates with a task behavioral phenotype consisting of increased false alarms (endorsing auditory
sounds that are not present in signal-detection tasks) reported with high confidence. In mice, stimulating DA
release in the dorsal striatum also induces this AH-like phenotype of high-confidence false alarms in a similar
signal-detection task. These findings are consistent with computational models whereby AH result from
exaggerated perceptual prior expectations and suggest a role for their implementation in dorsal striatum.
However, the precise relationships between model-proposed cognitive computations and circuit neurobiology
are unclear. Important gaps include how dorsal-striatal DA and medium spiny neuron activity contribute to
perceptual learning and AH-like percepts, as well as potential additional roles of reward-based processes in
ventral striatum. To address these gaps, we have developed a first-of-its-kind computational corticostriatal
circuit model of AH which recapitulates documented behavioral and neural phenotypes associated with
perceptual and reward tasks, and which additionally generates DA-dependent AH-like false alarms. Informed
by this model, here we will use human data from antipsychotic-free patients with schizophrenia (Aim 1) and
mouse data including a mouse model of genetic risk for schizophrenia (Aim 2), combined with a translational
signal-detection paradigm, to test quantitative predictions from our AH circuit model. Aim 1 (humans) will use
behavior, fMRI and neuromelanin-sensitive MRI to test for distinct contributions of perceptual learning to AH
and their implementation by dorsal-striatal circuits and dopaminergic nigral regions innervating dorsal striatum.
Aim 2 (mice) will use DA sensors, neuronal recordings, and optogenetic stimulation to parse the specific
contributions of dorsal and ventral-striatal DA and medium spiny neurons to perceptual learning and AH-like
false alarms. Exploratory Aim 3 will develop circuit-model extensions incorporating additional circuit elements
(e.g., direct D1 and indirect D2 pathways, cholinergic interneurons) to help further explain circuit mechanisms
of existing D2 and candidate non-D2 antipsychotic drugs. This multidisciplinary project will thus use
translational and computational methods combining the strengths of clinical and preclinical research, and of
theory- and data-driven methods, to advance our knowledge about circuit mechan...

## Key facts

- **NIH application ID:** 10903132
- **Project number:** 1R01MH136672-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Guillermo Horga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $838,977
- **Award type:** 1
- **Project period:** 2024-08-13 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10903132

## Citation

> US National Institutes of Health, RePORTER application 10903132, Translational studies in humans and mice to test a circuit-level computational model of auditory hallucinations.. (1R01MH136672-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10903132. Licensed CC0.

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