# Computational Modeling Core_Frank

> **NIH NIH P50** · MCLEAN HOSPITAL · 2021 · $209,848

## Abstract

PROJECT SUMMARY (Computational Modeling Core, Core Leader: Frank, Brown University)
The overarching goal of the Computational Modeling Core is to provide a common formal framework that can
quantify dynamic decision processes in approach-avoidance conflict across species in Projects 1-4, including
the impact of neural recordings and manipulations. We leverage hierarchical Bayesian parameter estimation of
the drift diffusion model (HDDM), which captures not only choice proportions for varying reward, aversion, and
conflict, but also the full response time distributions associated with these choices. HDDM facilitates reliable
estimation of decision parameters and their modulation by trial-by-trial variance in neural signals, and supports
Bayesian hypothesis testing for how these parameters may differ as a function of clinical status, brain state, and
manipulations (e.g., nociceptin antagonism, acute/chronic stress, stimulation). We have shown how such
“computational biomarkers” can provide enhanced sensitivity to discriminate between patient conditions and
symptoms relative to traditional measures of behavior and brain activity. We will leverage neural recordings and
stimulation from frontal cortex and basal ganglia across species to assess whether their variability is
parametrically related to motivated evidence accumulation, and whether these signals are altered with neural
manipulations and in clinical populations. Machine learning methods will quantify the degree to which such
quantitative model fitting improves (1) classification of patient condition and brain state relative to the same
methods applied to the raw behavioral and neural data or their summary statistics, and (2) our ability to map
disease course, including suicidality and symptoms. Building on our extensive experience in neural networks
and levels of computation involved in motivated learning and decision making across species, our computational
framework will facilitate not only enhanced sensitivity to discriminate between clinical conditions, but will also
identify hypotheses about the mechanisms involved, which will be tested via causal manipulations using the
same quantitative framework. For example, our preliminary modeling studies indicate that variability in sub-
populations within pregenual cingulate activity in non-human primates affects motivated evidence accumulation,
and that in humans, the same parameter distinguishes MDD vs. healthy subjects and scales with symptoms.
Moreover, these computational biomarkers are critical for predicting whether any individual is in one clinical state
or another, whereas classification based on behavior and/or brain activity alone is at chance levels. The causal
neural and psychological mechanisms of these effects will be further delineated and greatly expanded by utilizing
the same quantitative framework with causal manipulations and more precise temporal recordings.
Contribution to Overall Center Goals & Interactions with Other Center...

## Key facts

- **NIH application ID:** 10142542
- **Project number:** 5P50MH119467-02
- **Recipient organization:** MCLEAN HOSPITAL
- **Principal Investigator:** MICHAEL J. FRANK
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $209,848
- **Award type:** 5
- **Project period:** 2020-04-15 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10142542

## Citation

> US National Institutes of Health, RePORTER application 10142542, Computational Modeling Core_Frank (5P50MH119467-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10142542. Licensed CC0.

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