# Modeling learning under volatility in Borderline Personality Disorder

> **NIH NIH K23** · YALE UNIVERSITY · 2024 · $191,700

## Abstract

Project Summary / Abstract
This is a patient-oriented career development proposal designed to provide the candidate with advanced
training, protected research time, and mentored research experience. The research aims focus on Borderline
Personality Disorder (BPD), which is a serious and debilitating mental illness that affects 2-6% of the
population and increases risk of suicide. Current treatments have limited efficacy and there are no FDA-
approved medications for BPD. The candidate's long-term career goal is to improve the treatment of BPD and
other disorders with significant social symptoms by using computational psychiatry to better predict prognosis
and best treatment match.
To continue her progress toward this goal, the candidate proposes a detailed plan for training, including expert
mentorship and three supervised research aims. The training plan is designed for the candidate to gain
expertise in neuroimaging, computational modelling, and learning theory. The candidate will also build on her
strong foundation in clinical research and treatment of people with Borderline Personality Disorder and in
statistical approaches to data analysis. The mentorship team has extensive expertise in clinical psychiatry
research, fMRI study design and analysis, and computational modelling of learning. They will provide the
candidate with the resources and supervision needed to advance toward her research goals.
These studies leverage recent advances in computational psychiatry. Through three hypothesis-driven
research aims, subject behavior and brain activation will be tested during an interactive social learning task.
These aims test learning under volatility (when the environment is unpredictable) in BPD compared to
BPD+PTSD, PTSD, and trauma-exposed healthy controls. Aim 1 tests the role of anterior cingulate cortex for
signaling volatility, and interaction between amygdala and anterior cingulate in this setting. Aim 2 tests
learning patterns in BPD versus comparator subjects to identify illness-specific phenotypes. Aim 3 tests how
neural and behavioral markers of learning relate to social functioning. Next steps will be to refine a
computational model of learning in BPD to predict prognosis, predict best treatment match for an individual,
and test novel biological treatment targets.
Improving our mechanistic understanding of interpersonal symptoms will improve our clinical treatments and
significantly reduce suffering for millions of people with BPD and their families.

## Key facts

- **NIH application ID:** 10850892
- **Project number:** 5K23MH123760-05
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Sarah K Fineberg
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $191,700
- **Award type:** 5
- **Project period:** 2020-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10850892

## Citation

> US National Institutes of Health, RePORTER application 10850892, Modeling learning under volatility in Borderline Personality Disorder (5K23MH123760-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10850892. Licensed CC0.

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