Human neuroanatomy is enormously variable across subjects - a factor that limits the power of brain studies to detect effects of interest. While degeneration in subcortical structures and cortical gray matter is manifest in many conditions such as aging, Alzheimer's disease, Huntington's disease, multiple sclerosis and schizophrenia, large studies are needed in order to find robust and stable effects that separate groups. Furthermore drug development becomes highly costly as detecting small reductions in atrophy can take years and hundreds or thousands of subjects. These factors raise the importance of longitudinal studies, in which one acquires data at multiple time points and examines the differences in temporal trajectories. Compared to a cross-sectional approach, the longitudinal design can provide more sensitivity and specificity for examining subtle associations by reducing the confounding effect of between-subject variability. Moreover, a serial assessment can be the only way to unambiguously characterize the effect of interest in a randomized experiment, such as a drug trial. Finally, longitudinal studies provide unique insights into the temporal dynamics of the underlying biological process, such as disease progression. Taking full advantage of a longitudinal design requires the optimization of the computational tools that perform image processing and hypothesis testing. In this project, we propose to design, develop and distribute intrinsically longitudinal image processing and hypothesis testing tools and validate them in the study of a set of neurodegenerative diseases.

Public Health Relevance

The analysis of longitudinal imaging data holds the promise of more accurate computer- aided diagnosis of neurodegenerative disease as well as more effective and efficient quantification of the effects of potential therapeutic interventions. The successful completion of the proposed project would provide a set of accurate, specific and sensitive tools to the thousands of clinicians and researchers that currently use FreeSurfer, improving the power of a wide array of NIH-funded studies to quantify disease and drug effects.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (NOIT)
Program Officer
Liu, Yuan
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Massachusetts General Hospital
United States
Zip Code
Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H et al. (2016) Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease. Neuroimage 134:573-86
Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi et al. (2016) Dopamine D1 signaling organizes network dynamics underlying working memory. Sci Adv 2:e1501672
Sabuncu, Mert R; Ge, Tian; Holmes, Avram J et al. (2016) Morphometricity as a measure of the neuroanatomical signature of a trait. Proc Natl Acad Sci U S A 113:E5749-56
Tisdall, M Dylan; Reuter, Martin; Qureshi, Abid et al. (2016) Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion. Neuroimage 127:11-22
Iglesias, Juan Eugenio; Van Leemput, Koen; Augustinack, Jean et al. (2016) Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases. Neuroimage 141:542-55
Ge, Tian; Nichols, Thomas E; Lee, Phil H et al. (2015) Massively expedited genome-wide heritability analysis (MEGHA). Proc Natl Acad Sci U S A 112:2479-84
Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Aganj, Iman et al. (2015) An algorithm for optimal fusion of atlases with different labeling protocols. Neuroimage 106:451-63
Reuter, Martin; Tisdall, M Dylan; Qureshi, Abid et al. (2015) Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 107:107-15
Beliveau, Vincent; Svarer, Claus; Frokjaer, Vibe G et al. (2015) Functional connectivity of the dorsal and median raphe nuclei at rest. Neuroimage 116:187-95
Iglesias, Juan Eugenio; Augustinack, Jean C; Nguyen, Khoa et al. (2015) A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. Neuroimage 115:117-37

Showing the most recent 10 out of 18 publications