. 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.

Public Health Relevance

. We develop new methods for enhancing the development of models that can predict behavior, clinical status, and other outcomes using neuroimaging data. Successful development will help improve the translational impact of neuroimaging. It will also contribute to developing multidisciplinary neuroscience, by promoting the development of neural signatures for specific mental processes in humans with increased precision and specificity.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB026549-02
Application #
9747296
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Duan, Qi
Project Start
2018-07-18
Project End
2022-03-31
Budget Start
2019-04-01
Budget End
2020-03-31
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205