# Integration of markers across physiologic, behavioral, and self-report levels at baseline and in response to treatment to characterize novel subtypes in youth with ADHD

> **NIH NIH K23** · STANFORD UNIVERSITY · 2020 · $193,536

## Abstract

Project Summary
This Mentored Patient-Oriented Career Development Award (K23) will facilitate Dr. Leikauf’s development into
an independent researcher and child psychiatrist. It will provide the foundation for him to meaningfully improve
strategies for cost-effective personalization of care for ADHD and related emotional disorders in diverse
practice settings through a deeper understanding of the underlying phenotypic heterogeneity of these
disorders. Attention-Deficit/Hyperactivity Disorder (ADHD) is highly prevalent and functionally impairing, yet
phenotypically heterogeneous. Despite the complexity of the phenotype, the diagnosis is most often made
using DSM-based behavioral symptom rating scales during short visits in busy pediatric settings. Additionally,
current treatments are widely used but have not been demonstrated to improve functional outcomes. Currently,
treatment selection involves significant trial-and-error based on caregiver’s subjective report of symptomatic
improvement. Cost-effective, objective measures that would aid in personalized treatment selection and
address the full range of dysfunction for children with ADHD are critically needed. The heterogeneity of the
disorder has limited the development of such tools. Until now, the field has relied on subtypes that have limited
validity and are based exclusively on symptom rating scales. Recent developments in our ability to collect and
analyze multidimensional data for individual subjects provide a new opening for progress in this crucial area.
The project will use two multi-dimensional datasets from completed clinical trials as well as generate data from
a smaller, prospective study. The anticipated outcomes are as follows: 1) identification of more phenotypically
homogeneous groups of children with ADHD at baseline 2) identification of more homogeneous groups based
on creating a multivariate classifier informed by mechanistic response to treatment, and 3) validation of
classification schema. Dr. Leikauf’s specific training goals, in collaboration with his mentorship team, include
acquisition of knowledge, experience, and skills in the following areas relevant to the proposed work: 1)
modern machine-learning statistical techniques that allow reliable inferences to be drawn from high
dimensional data with non-linear relationships between variables; 2) mentored experience conducting a multi-
method/multi-dimensional prospective study with the goal of identifying personalized therapeutic targets 3)
additional translational research/clinical experience to understand what is currently known about ADHD and its
relationship to cognitive dysfunction including working memory, sustained attention, and response inhibition,
and 4) training in acquisition and analysis of EEG. The results should have immediate clinical impact and
provide the foundation for a future prospective personalized therapeutics trial. The experiences in this
proposal, if awarded, will also develop Dr. Leikauf’s abil...

## Key facts

- **NIH application ID:** 10054894
- **Project number:** 1K23MH121650-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** John Leikauf
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $193,536
- **Award type:** 1
- **Project period:** 2020-08-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10054894

## Citation

> US National Institutes of Health, RePORTER application 10054894, Integration of markers across physiologic, behavioral, and self-report levels at baseline and in response to treatment to characterize novel subtypes in youth with ADHD (1K23MH121650-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10054894. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*