# Neural Mechanisms of Irritability: A Dimensional Approach with Integration of Multi-Voxel Pattern Analysis and Univariate Analysis

> **NIH NIH R00** · YALE UNIVERSITY · 2021 · $236,443

## Abstract

Chronic, clinically-impairing irritability is a serious health problem that affects 3-5% of youth and predicts both
adult depressive and anxiety disorders and long-term impairment. Current studies on the neural mechanisms
of irritability in youth employ traditional univariate analyses and have limitations: (1) they examine activation
patterns in particular regions, thus using only a small portion of the data and (2) they tend to find small to
moderate effect sizes. In addition, little is known about functional connectivity at rest and its association with
irritability or the ability of neural measures to predict changes in irritability over time. These limitations could be
addressed through research using multi-voxel pattern analysis (MVPA), i.e., by applying machine learning
methods to both resting state and task-based fMRI data. MVPA exploits the full spatial pattern of brain activity
and thus has increased sensitivity compared to conventional univariate, individual-voxel-based general linear
model (GLM) approaches. However, univariate analyses may provide complementary or unique information
relative to MVPA, given its sensitivity to variability and global engagement of task effects between subjects.
The specific aims of this project are to identify patterns of whole-brain activation (Aim 1 & 2a, 2b) and
functional connectivity at rest (Aim 2a, 2b) using a combination of MVPA and univariate approaches to predict
(1) individual differences in irritability in a clinical sample of youth and in healthy youth with varying degrees of
irritability, and (2a) individual differences in irritability and (2b) changes in irritability 6 months later in a
community sample of youth with varying degrees of irritability. This work will provide new insights into the
pathophysiology of multiple mental disorders for which irritability is an important symptom, and it has the
potential to identify biomarkers that can be used to predict the course and prognosis of clinically-impairing
irritability. The long-term goal of the PI is to become an independent NIH-funded investigator with a
neuroscience-focused research program targeting developmental trajectories of irritability. To achieve this
goal, the PI aims to expand her previous training to support both advanced, independent research on resting
state and task-based fMRI and the use of pattern classification techniques, as well as their integration with
univariate methods, applied in samples with varying degrees of irritability. This training objective directly
supports the PI’s research objective, which is to identify the neural mechanisms of irritability and neural
correlates that predict changes in irritability over time using a combination of MVPA and univariate imaging
methods applied to task-based and resting state fMRI data. The central hypothesis, based on the PI’s pilot
data, is that activity patterns during a frustration task, along with connectivity patterns at rest, will predict levels
of, and changes in,...

## Key facts

- **NIH application ID:** 10231183
- **Project number:** 5R00MH110570-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Wan-Ling Tseng
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $236,443
- **Award type:** 5
- **Project period:** 2017-06-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10231183

## Citation

> US National Institutes of Health, RePORTER application 10231183, Neural Mechanisms of Irritability: A Dimensional Approach with Integration of Multi-Voxel Pattern Analysis and Univariate Analysis (5R00MH110570-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10231183. Licensed CC0.

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