# Development and clinical validation of multimodal risk algorithms for predicting future internalizing psychopathology

> **NIH NIH K08** · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · 2020 · $184,535

## Abstract

I am currently Assistant Professor and a licensed clinical psychologist in the Department of Psychiatry at the
University of Vermont. My long-term career goal is to become an independent investigator using novel
strategies in developmental neuroimaging to study mood and anxiety symptomatology from birth to maturity.
Although I have been trained in the analysis of longitudinal structural MRI, I require further training in the
processing and analysis of state-of-the-art multiband neuroimaging data that allows for more sensitive
measures of brain connectivity. I am also lacking expertise with regard to more sophisticated analytic methods
for more fully leveraging large-sample multimodal datasets. Such approaches will enable me to move beyond
conventional univariate statistical analyses and prepare me for future Big Data initiatives. During the proposed
K08 period, my overarching goal is to develop expertise in the application of machine-learning approaches to
multimodal data in order to characterize the most salient psychosocial and brain-based predictors of youth
internalizing psychopathology. To achieve these goals, I am pursuing career development and training
activities in the following areas: 1) assessment and characterization of psychosocial risk factors; 2) theory and
implementation of Big Data methods, including machine learning algorithms and cross-validation strategies; 3)
analysis of multiband multimodal brain imaging data using Human Connectome Project pipelines with the aim
of more comprehensively assessing aspects of cortico-limbic connectivity; 4) independently running my own
neuroimaging research study; and 5) developing and submitting a competitive R01 application. In order to
obtain this expertise, I am proposing training activities at several institutions, including the University of
Vermont, Harvard Medical School, McGill University, and Oregon Health and Science University. The research
project in this K08 proposal aims to produce risk algorithms for a transdiagnostic dimension of
psychopathology, using novel machine learning approaches to leverage two of the largest longitudinal
neuroimaging samples in the world (IMAGEN and the Adolescent Brain Cognitive Development study). These
risk algorithms will subsequently undergo refinement using a new sample of clinic-referred youths that I will
recruit from an outpatient psychiatric clinic in Vermont. As part of the project, I will also test the degree to which
these algorithms predict treatment response. These data will be used as pilot data for my planned R01
application. Given the methods that I am proposing, this project will be able to detect complex non-linear
interactions involving risk factors from a multitude of domains. As a result, this work will inform, and help to
delineate, various etiological pathways that ultimately result in internalizing problems. Most importantly, this
project could inform early identification and targeted intervention strategies during a critical p...

## Key facts

- **NIH application ID:** 10054828
- **Project number:** 1K08MH121654-01A1
- **Recipient organization:** UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
- **Principal Investigator:** Matthew D Albaugh
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $184,535
- **Award type:** 1
- **Project period:** 2020-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10054828

## Citation

> US National Institutes of Health, RePORTER application 10054828, Development and clinical validation of multimodal risk algorithms for predicting future internalizing psychopathology (1K08MH121654-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10054828. Licensed CC0.

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