We propose to develop statistical methods for the analysis of the local covariance structure between measurements in multimodal biomedical imaging studies. Despite the rise of multi-modal imaging studies and the proliferation of derived measures available from each modality, very little work has explored relationships between modalities. Notably, brain structure and function evolve dramatically in the context of adolescent brain development, but typical analyses usually evaluate each imaging phenotype separately. We propose to develop tools for measuring relationships between brain imaging phenotypes provided by disparate imaging modalities. Covariance structures at the subject level will allow identification of local relationships between measures, through weighted regressions and singular value decomposition-based techniques. These techniques will be extended to accommodate any number of imaging modalities and incorporate repeated measures. We will use extensive test-retest data provided by the Consortium of Reproducibility and Reliability (CORR) for validation, and to identify coupling measures that are particularly reliable. The rich multi-modal imaging data and associated clinical phenotypes from the Philadelphia Neurodevelopmental Cohort (PNC) will allow us to delineate how inter- modal coupling evolves in normal brain development, and also establish how such relationships are altered in association with psychosis-spectrum symptoms. This proposal builds upon established collaboration between the PIs and capitalizes on their complimentary expertise in imaging statistics, developmental neuroimaging, psychopathology, and intimate familiarity with the PNC dataset. Upon completion, this project will provide powerful new tools for the neuroscience community, as well as novel insights regarding brain development and early psychosis-spectrum symptoms.

Public Health Relevance

The overarching goal of this proposal is to develop new tools for integrative analysis of local relationships among brain imaging measures. Despite the rise of multi-modal imaging studies and the proliferation of derived measures available from each modality, very little work has explored relationships between modalities. The methods developed as part of this proposal will fill this critical gap in the field, and will lead to more sensitive and specific biomarkers of brain pathology and healthy development.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
1R01MH112847-01
Application #
9284987
Study Section
Neuroscience and Ophthalmic Imaging Technologies Study Section (NOIT)
Program Officer
Friedman, Fred K
Project Start
2017-05-10
Project End
2022-03-31
Budget Start
2017-05-10
Budget End
2018-03-31
Support Year
1
Fiscal Year
2017
Total Cost
$451,760
Indirect Cost
$171,164
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N et al. (2018) MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions. J Neuroimaging 28:389-398
Valcarcel, Alessandra M; Linn, Kristin A; Khalid, Fariha et al. (2018) A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis. Neuroimage Clin 20:1211-1221
Reardon, P K; Seidlitz, Jakob; Vandekar, Simon et al. (2018) Normative brain size variation and brain shape diversity in humans. Science 360:1222-1227
Medaglia, John D; Satterthwaite, Theodore D; Kelkar, Apoorva et al. (2018) Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment. Neuroimage 166:293-306
Alexander-Bloch, Aaron F; Shou, Haochang; Liu, Siyuan et al. (2018) On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178:540-551
Yu, Meichen; Linn, Kristin A; Cook, Philip A et al. (2018) Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum Brain Mapp 39:4213-4227
Fortin, Jean-Philippe; Cullen, Nicholas; Sheline, Yvette I et al. (2018) Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167:104-120
Baum, Graham L; Roalf, David R; Cook, Philip A et al. (2018) The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 173:275-286
Xia, Cedric Huchuan; Ma, Zongming; Ciric, Rastko et al. (2018) Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun 9:3003
Rosen, Adon F G; Roalf, David R; Ruparel, Kosha et al. (2018) Quantitative assessment of structural image quality. Neuroimage 169:407-418

Showing the most recent 10 out of 16 publications