# Biophysical modeling as a translational bridge for understanding neural ensemble alterations in schizophrenia.

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $784,835

## Abstract

SUMMARY/ABSTRACT
Schizophrenia is a devastating and burdensome illness that afflicts ~1% of the global population. Cognitive
symptoms are a hallmark of the disease, affecting most individuals with schizophrenia, and being responsible
for the greatest reduction in quality of life. Despite their significant impact, the biological mechanisms of
cognitive deficits remain elusive, in part due to limitations of the experimental approaches typically used to
study them in humans. To overcome these limitations, we propose a novel approach using biophysical
modeling as an explanatory theoretical framework for bridging the translational gap between previous
preclinical work in mouse models of schizophrenia-relevant risk and the proposed work in patients with
schizophrenia. We propose translation of the findings of reduced neuronal ensemble reliability (n-ER) in the
primary visual cortex (V1) as a window into a brain-wide circuit-level alteration in schizophrenia and its
relationship to cognitive deficits. To achieve this, we will use a combined sample of 1,760 individuals, including
healthy individuals, patients with schizophrenia or bipolar disorder and their first-degree relatives, from the
HCP Young Adult, HCP Psychosis, and HCP Early Psychosis projects. Specifically, we will measure voxel
ensemble reliability (v-ER) in humans using resting-state and visual-stimulation fMRI data—akin to calcium
imaging studies in mice— as a theoretically grounded and translational index of excitation-inhibition balance
(E/I) in cortical circuits. First, we aim to develop a biophysical model of V1 constrained by preclinical and basic
neuroscience experiments, and test model predications of neuroimaging measures related to E/I. Second, we
will test for reduced v-ER in patients with schizophrenia—directly translating preclinical findings—and use
biophysical model simulations to identify potential biological mechanisms. Third, we will use the unique sample
characteristics of the HCP Psychosis project (patients and first-degree relatives) to investigate the relationship
between genetic burden for schizophrenia and v-ER. Fourth, given the convergence of cognitive deficits in the
preclinical mouse models, we will examine the relationship between v-ER and cognitive performance. We will
further seek to establish reduced v-ER as a brain-wide mechanism of cognitive deficits by testing for
relationships in cognition across disparate sensory domains. Throughout, we will use well-powered, rigorous,
state-of-the-art fMRI and statistical data-driven methods suitable for large-scale studies and HCP-like fMRI
sequences, including cross-validation and independent confirmation. Together with a strong theoretical
foundation and using biophysical modeling to complement fMRI analyses, this approach will begin to elucidate
the biological mechanisms of cognitive deficits in schizophrenia. In doing so, this project will establish v-ER as
a fully translational neuroimaging measure with the potenti...

## Key facts

- **NIH application ID:** 10780619
- **Project number:** 1R01MH134973-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Kenneth Wengler
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $784,835
- **Award type:** 1
- **Project period:** 2024-04-01 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10780619

## Citation

> US National Institutes of Health, RePORTER application 10780619, Biophysical modeling as a translational bridge for understanding neural ensemble alterations in schizophrenia. (1R01MH134973-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10780619. Licensed CC0.

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