# Endocrine-mediated pubertal brain network development: Bridging datasets with machine learning

> **NIH NIH K99** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2024 · $124,169

## Abstract

PROJECT SUMMARY
This proposal aims to address the BRAIN Initiative goals of developing and applying technologies for
innovative study of the relationships between biological processes, neural structure, and brain function, to
further our understanding of how sex hormones (e.g., estradiol, testosterone, progesterone) exert organizing
effects on brain development during puberty. Animal and postmortem human research has revealed that these
effects include cytoarchitectonic changes, including apoptosis and changes to dendrite structure, but
technology has limited in vivo study in humans. While neuroimaging has provided insight into associations
between sex hormone levels during puberty and the brain’s macro-scale and functional architecture, this
literature overlooks potential neurobiological mechanisms (e.g., cytoarchitecture changes) and the impact of
phasic versus tonic (i.e., annual versus weekly) hormone changes on brain development. The proposed
human neuroimaging project addresses these gaps by integrating estimates of cytoarchitecture from advanced
biophysical modeling of diffusion-weighted imaging, to complement commonly used macro-scale and
functional architecture measures, and by assessing both sparsely and densely sampled hormone levels using
three independent studies of youth at various stages of pubertal maturation. Furthermore, the proposed
research applies machine learning to facilitate multi-dataset integration and investigation of how hormone
levels are related to correspondence between the brain’s structural and functional architectures. The candidate
has a background in magnetic resonance imaging, including with developmental populations, is experienced in
developing and applying data-driven tools for studying large-scale brain networks, and seeks further training in
neuroendocrinology, machine learning, and diffusion-weighted imaging to become an independent researcher
in this field. During the mentored phase, she will (1) develop and train a model that predicts sex hormone
levels from functional connectomics in a large adolescent dataset, test its accuracy predicting the same and
novel hormones in an independent adolescent dataset, and share the pre-trained model for use by the broader
research community and (2) assess roles of sex hormone levels during puberty in developing structure-
function associations using multivariate, cross-decomposition approaches. Training from leaders in adolescent
neuroendocrinology, machine learning, multi-dataset analysis, and diffusion-weighted imaging will aid in this
work and prepare her for the independent phase, when she will (3) apply the pretrained model to predict (a)
average hormone levels, to further assess the generalizability of identified sex hormone-related brain networks,
and (b) weekly sex hormone levels, to assess the role of phasic hormone changes on the identification of tonic
neurobiological associations. Then, she will (4) apply further cross-decomposition approaches to model ...

## Key facts

- **NIH application ID:** 10782962
- **Project number:** 1K99MH135075-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Katherine Louise Bottenhorn
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $124,169
- **Award type:** 1
- **Project period:** 2024-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10782962

## Citation

> US National Institutes of Health, RePORTER application 10782962, Endocrine-mediated pubertal brain network development: Bridging datasets with machine learning (1K99MH135075-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10782962. Licensed CC0.

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