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Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software

PI: Lei Wang PhD and Michael Miller PhD (JHU)

Our lab’s role is to use SchizConnect to retrieve schizophrenia imaging data for pipeline processing; implement the subcortical software developed and hardened at JHU, with remote processing being carried out at MriCloud. The overall project extends and hardens powerful computational anatomy and computer science software to analyze large datasets from neuroimaging studies of neurodevelopmental and neurodegeneration disorders including Huntington’s Disease, Schizophrenia and Attention Deficit Hyperactive Disorders.


  • NIBIB: 1R01EB020062-01A1 NIBIB (Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software) (PI: Miller, MPI: Paulsen, Mostfosky, Wang) - Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software (09/01/2015 - 08/31/2019).

Over the past decade, we have been building, parsing and wrangling systems for extracting neurodegeneration and neurodevelopment biomarkers from high-dimensional magnetic resonance (MR) imagery at 1 mm3 scale which are discriminating. At the same time, large and complex data sets and networks of segmented structures are becoming increasingly available to the research community such as Predict-HD, Track-HD, ADNI, and SchizConnect. Neuroscientists and clinicians are interested in tracking biomarkers which characterize rates of atrophy in anatomical networks, onset of or changepoint times of spread through the networks, and prediction of risk to conversion as determined by clinical symptoms. These wrangling and modeling methods are novel. Our biomarkers are extracted via brain mapping technologies based on diffeomorphometry, the study of morphological change via diffeomorphic tracking of anatomical coordinate systems at the sub millimeter scale. Like stereology, diffeomorphometry discovers high-dimensional features signalling neurodegeneration and neurodevelopment via tight integration of random field based statistical methods via large deviation empirical probability estimators calculated via high-dimensional permutation testing. Family-wise rates are calculated for group comparisons, and have been advanced changepoint modelling allowing us to explicitly estimate the spread of progression of anatomical feature change through the networked structures associated to neurodegeneration - Alzheimer's Disease (AD) and Huntingdon's Disease (HD) and neurodevelopment - Schizophrenia (SZ) and Attention Deficit and Hyperactive Disorder (ADHD). These tools will be disseminated and tested via MriCloud. We will perform three specific aims.
Aim 1 will use our MriCloud architecture to deploy a Multi-Atlas Brain Mapping module for mapping an ontology of approximately 400 structures to T1 and DTI data. The architecture will support many atlases which are matched across a broad range of age from pediatric to geriatric groups, and as well as several diseases.
Aim 2 will deploy a Statistical Shape Diffeomorphometry module consisting of pipelines for a) generating templates of structures from populations of cross-sectional datasets, b) data reduction to templates for cross-sectional and longitudinal geodesic mappings, and c) multiple hypothesis testing procedures based on vertex, Laplace-Beltrami basis functions and PCA basis functions. Users with their own ontology definitions of the subcortical structures will be able to generate population templates and visualize the statistics in template coordinates.
Aim 3 will generate a webportal for users to use modules from Aims 1 and 2 to examine abnormalities in networks of structures such as the striatum, thalamus, amygdala and hippocampus.