# An integrative computational interrogation of circuit dysfunction inschizophrenia via neural timescales

> **NIH NIH R01** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2022 · $757,102

## Abstract

SUMMARY/ABSTRACT
Schizophrenia is a devastating and burdensome illness the mechanisms of which remain elusive. Contributing
to their elusiveness are a highly complex set of genetic factors, proposed etiological and pathophysiological
pathways, and phenotypic manifestations. To address this complexity, we propose a hybrid method combining
data-driven approaches to large-scale multimodal datasets and theory-driven computational approaches in
order to provide a theoretically constrained framework bridging genetics, development, circuit function,
cognition, and phenomenology of schizophrenia. To that end, and in response to ‘Notice of Special Interest
regarding the Use of Human Connectome Data (HCP) for Secondary Analysis’, we will use data from up to
64,000 individuals, including healthy individuals and patients with schizophrenia and other disorders, from
various HCP-related projects as well as the UK Biobank. We specifically propose measuring intrinsic neural
timescales (INT) from resting-state fMRI data as a theory-driven index of excitation/inhibition (E/I) imbalance in
cortical microcircuits. First, extending our prior work we aim to confirm and further characterize INT alterations
in schizophrenia (widespread trait-like INT reductions and local hierarchy-dependent INT modulations in
relation to psychotic symptoms) and to test their specificity relative to other disorders. Second, we will evaluate
the developmental trajectories of INT and characterize the genetic profile of this fMRI measure and its overlap
with the genetic profile for schizophrenia risk. Third, given the role of E/I ratio in cortical microcircuits in
supporting working-memory computations, we will examine the relationship between INT and working-memory
activation and performance. We will further seek to establish INT as a circuit-level mediator of polygenic risk for
schizophrenia on cognitive deficits. Throughout, we will use well-powered, rigorous, state-of-the-art fMRI and
statistical data-driven methods suitable for large-scale studies and HCP-style fMRI sequences, including cross-
validation and tests of generalizability. Together with a strong theoretical foundation and using biophysical
modeling to complement fMRI analyses, this hybrid—theory- and data-driven—approach will facilitate an
integrated understanding of the circuit-level mechanisms bridging distal genetic-risk factors and proximal
manifestations of schizophrenia. In particular, the combination of cutting-edge cell-type enrichment analyses of
GWAS (which in schizophrenia have suggested converging enrichment in excitatory and inhibitory cortical
cells) and biophysical modeling at the level of cortical microcircuits of interacting excitatory and inhibitory
cellular populations will provide an interpretation of disparate data in terms of convergent cell- and circuit-level
pathways. In doing so, this project will validate a theoretically informative, interpretable, translatable, and
scalable resting-state fMRI meas...

## Key facts

- **NIH application ID:** 10585148
- **Project number:** 1R01MH129395-01A1
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** Guillermo Horga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $757,102
- **Award type:** 1
- **Project period:** 2022-09-15 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10585148

## Citation

> US National Institutes of Health, RePORTER application 10585148, An integrative computational interrogation of circuit dysfunction inschizophrenia via neural timescales (1R01MH129395-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10585148. Licensed CC0.

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