We propose to establish new integrative and data-driven methods for building systems neuroscience models of executive functioning during childhood. We will develop these methods by employing public datasets that contain complementary quantitative biological metrics of genomics, structural and functional neuroimaging and cognitive performance. By studying the multi-dimensional predictors of executive functioning during childhood, the Principal Investigator will gain invaluable protected time that will enhance his career as a big data scientist. The candidate will also gain experience in the new science of integrating imaging with genomics and in the statistical methods necessary to optimally construct sparse and interpretable low-dimensional models from high-dimensional data. We will determine validity by comparing models between training and independent testing datasets. Ultimately, this research will identify a fusion of genomics and imaging predictors that predict executive functioning in normal subjects and how these relationships are modified by environment.
This project will train an accomplished imaging scientist to perform big data science based on new quantitative biomarkers. Using large datasets, this study will reveal mechanisms that may relate to early signs of neuropsychiatric disorders or risk for such disorders. This research will inform prevention and intervention strategies that may improve childhood outcomes.
|Legant, Wesley R; Shao, Lin; Grimm, Jonathan B et al. (2016) High-density three-dimensional localization microscopy across large volumes. Nat Methods 13:359-65|
|Kandel, Benjamin M; Avants, Brian B; Gee, James C et al. (2016) White matter hyperintensities are more highly associated with preclinical Alzheimer's disease than imaging and cognitive markers of neurodegeneration. Alzheimers Dement (Amst) 4:18-27|
|Kandel, Benjamin M; Wang, Danny J J; Gee, James C et al. (2015) Eigenanatomy: sparse dimensionality reduction for multi-modal medical image analysis. Methods 73:43-53|
|Kandel, Benjamin M; Wang, Danny J J; Detre, John A et al. (2015) Decomposing cerebral blood flow MRI into functional and structural components: a non-local approach based on prediction. Neuroimage 105:156-70|
|Dhillon, Paramveer S; Wolk, David A; Das, Sandhitsu R et al. (2014) Subject-specific functional parcellation via prior based eigenanatomy. Neuroimage 99:14-27|