# Individualized spatial topology in functional neuroimaging

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2020 · $667,005

## Abstract

Project Summary. Neuroimaging is poised to take a substantial leap forward in
understanding the neurophysiological underpinnings of human behavior, due to a
combination of improved analytic techniques and the quality of imaging data. These
advances are allowing researchers to develop population-level multivariate models of
the functional brain representations underlying behavior, performance, clinical status and
prognosis, and other outcomes. Population-based models can identify patterns of brain
activity, or `signatures', that can predict behavior and decode mental states in new
individuals, producing generalizable knowledge and highly reproducible maps. These
signatures can capture behavior with large effect sizes, and can be used and tested
across research groups. However, the potential of such signatures is limited by
neuroanatomical constraints, in particular individual variation in functional brain anatomy.
To circumvent this problem, current models are either applied only to individual
participants, severely limiting generalizability, or force participants' data into anatomical
reference spaces (atlases) that do not respect individual functional topology and
boundaries. Here we seek to overcome this shortcoming by developing new topological
models for inter-subject alignment, which register participants' functional brain maps to
one another. This will increase effective spatial resolution, and more importantly allow us
to explicitly analyze the spatial topology of functional maps make inferences on
differences in activation location and shape across persons and psychological states.
We will test and validate the methods using a purpose-designed experiment (n = 120)
that includes two types of naturalistic narrative experiences (movies and audio stories)
and tasks from three functional domains (pain, emotion, and cognition). The tasks are
designed with several constraints in mind, including: (1) systematic coverage of
cognitive, emotional, and sensory tasks matched in stimulus properties (e.g., stimulus
duration); and (2) multiple levels of task demand within each task, to permit parametric
modeling and prediction of demand levels. We will compare our new methods to existing
methods based on out-of-sample effect sizes in predicting behavior and test-retest
reliability. We will make the analytic methods, software, and dataset available to other
researchers, along with a library of functional reference spaces for multiple psychological
states.

## Key facts

- **NIH application ID:** 9908089
- **Project number:** 5R01EB026549-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Martin Lindquist
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $667,005
- **Award type:** 5
- **Project period:** 2018-07-18 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9908089

## Citation

> US National Institutes of Health, RePORTER application 9908089, Individualized spatial topology in functional neuroimaging (5R01EB026549-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9908089. Licensed CC0.

---

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