# Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2020 · $1,177,059

## Abstract

Project Summary
 Two of the earliest onset, most common, and costly neurodevelopmental disorders in child psychiatry
are Attention Deficit Hyperactivity Disorder (ADHD) and Autism spectrum disorders (ASD). The clinical
heterogeneity and the imprecise nature of their nosological distinctions represents a fundamentally
confounding factor limiting a better understanding of their etiology, prevention, and treatment. In short, simple
design assumptions regarding `homogeneity in samples' in typical and atypical populations may explain the
frequently very small effect sizes in psychopathology research. Clinically, these same assumptions may
account for why treatments often have weak or unpredictable effects.
 Recent developments in the computational sciences, have enabled the implementation of models
sufficiently complex to address the aforementioned situation regarding subpopulations; however, very few tie
the outputs to the specific outcome or questions being asked by the investigator. Under the parent grant, we
developed and published a novel hybrid supervised/unsupervised machine learning method to characterize
biologically relevant heterogeneity in ADHD and/or ASD – the Functional Random Forest (FRF). The hybrid
FRF combines machine learning and graph theoretic analyses in order to identify population subtypes related
to the clinically most important outcomes (in the case, of this proposal, negative valence symptoms) trans-
diagnostically (ASD, ADHD, TD).
 Despite developing the FRF, subtyping results using functional MRI (fMRI) signals have lagged behind
the subtyping of behavioral profiles. In addition, they have yet to become sufficiently sensitive and specific, for
rapid translation into clinical practice. Fortunately, parallel advances in functional neuroimaging, allow for
precision functional mapping of individuals, and can be synergistically combined with the FRF to greatly boost
our ability to subtype and characterize individual patients from fMRI data. Here we combine the FRF with
precision mapping to reveal common variants and individual specificity in global brain organization. The
proposed individual-specific precision mapping moves beyond group averaging approaches, which are
obscuring important inter-individual differences related to distinct pathophysiologies underlying negative
valence across diagnoses (ADHD, ASD, TD).
 Thus, the current proposal aims to apply FRF algorithms to trans-diagnostic (TD, ASD, ADHD)
behavioral and precision functional mapping RSFC data to identify distinct sub-populations across ASD,
ADHD, and TD that relate to negative valence symptom dimensions.

## Key facts

- **NIH application ID:** 9971165
- **Project number:** 2R01MH096773-08A1
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Nico Dosenbach
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,177,059
- **Award type:** 2
- **Project period:** 2012-08-06 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9971165

## Citation

> US National Institutes of Health, RePORTER application 9971165, Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD (2R01MH096773-08A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9971165. Licensed CC0.

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