While imaging studies are widely used in clinical practice and research, the number of neuroimaging- based biomarkers is small. For example, in clinical trials of immunomodulatory therapies for MS, the only commonly used imaging biomarkers are the total lesion volume and the number of new and en- hancing lesions. These biomarkers are essential, but do not capture the recovery process of lesions, which is thought to decline in more severe, progressive disease. The partial or complete recovery of lesions may depend both on the ability of the brain to heal and on external factors, such as treat- ment or environmental and behavioral exposures. In this proposal we take the natural next step of proposing imaging biomarkers for MS based on the formation and change of lesions as observed on multi-sequence structural MRIs. To solve this problem we propose to address several general method- ological problems: 1) develop models and methods for the longitudinal analysis of several images of the same brain; 2) identify and estimate the length of history that is necessary to estimate recovery; 3) study the association with known biomarkers of the disease (in this case total volume and number of new and enhancing lesions); 4) develop methods that are robust to changes in imaging protocols that inevitably arise in longitudinal neuroimaging studies; and 5) develop the computational tools that allow for sophisticated methods to be implemented seamlessly in practice. While our scienti?c problem is focused, the proposed statistical methods are general and can be applied to a wide variety of longitu- dinal neuroimaging studies. For example, there are many ongoing longitudinal neuroimaging studies, including the ADNI, AIBL, HBC, and MISTIE, where our methods could be used to study subtle or large changes in lesions or in white and gray matter intensities.
. The project provides statistical analysis methods for quanti?cation of the evolution in the intensity of brain lesions on multi-sequence Magnetic Resonance Imaging (MRI). Methods are motivated by the need to develop new neuroimaging-based biomarkers for multiple sclerosis (MS), but can be applied to other types of brain diseases including stroke, Alzheimer disease, and cancer.
| Leroux, Andrew; Xiao, Luo; Crainiceanu, Ciprian et al. (2018) Dynamic prediction in functional concurrent regression with an application to child growth. Stat Med 37:1376-1388 |
| Muschelli, John; Sweeney, Elizabeth; Crainiceanu, Ciprian M (2018) freesurfer: Connecting the Freesurfer software with R. F1000Res 7:599 |
| Xiao, Luo; Li, Cai; Checkley, William et al. (2018) Fast covariance estimation for sparse functional data. Stat Comput 28:511-522 |
| Caldito, Natalia Gonzalez; Saidha, Shiv; Sotirchos, Elias S et al. (2018) Brain and retinal atrophy in African-Americans versus Caucasian-Americans with multiple sclerosis: a longitudinal study. Brain 141:3115-3129 |
| Smirnova, Ekaterina; Ivanescu, Andrada; Bai, Jiawei et al. (2018) A practical guide to big data. Stat Probab Lett 136:25-29 |
| Dworkin, J D; Linn, K A; Oguz, I et al. (2018) An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions. AJNR Am J Neuroradiol 39:626-633 |
| Chén, Oliver Y; Crainiceanu, Ciprian; Ogburn, Elizabeth L et al. (2018) High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics 19:121-136 |
| Dworkin, J D; Sati, P; Solomon, A et al. (2018) Automated Integration of Multimodal MRI for the Probabilistic Detection of the Central Vein Sign in White Matter Lesions. AJNR Am J Neuroradiol 39:1806-1813 |
| 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 |
Showing the most recent 10 out of 102 publications
