This application of administrative supplement is in response to NOT-AG-18-008: Alzheimer's Disease and its related Dementias (AD/ADRD)-focused Administrative supplements for NIH grants that are not focused on Alzheimer's disease. There are three main goals in our R01 project: the development of novel computational algorithms for the reconstruction of fiber orientation distribution (FOD) from multi-shell diffusion imaging data (Aim 1); the validation of FOD-based tractography on mouse imaging and tracer injection data (Aim 2); and development of tools for the computation of fiber bundles of human brains using connectome imaging data (Aim 3). In the first two years of this R01 project, we have made tremendous progresses toward these goals. We have published 8 papers in less than two years. In this administrative supplement, we propose to apply the connectome modeling tools developed in our R01 project to analyze the large-scale diffusion MRI and tau PET imaging data from ADNI3 to study the structural basis of tau pathology propagation in the development of Alzheimer?s disease (AD). While it has long been suggested in Braak stages that neuron-to-neuron propagation of tau pathology along axonal fiber tracts could play a critical role in the development of AD, the in vivo quantification of tau pathology in the brain has only been available recently with the invention of novel ligands such as AV1451 used in ADNI3. By jointly analyzing the diffusion MRI and tau PET imaging data on a large cohort of normal controls (NCs), patients with mild cognitive impairment (MCI) and AD, we aim to study the structural basis of tau pathology propagation and develop more precise imaging marker with diffusion MRI for the early diagnosis of AD. The work we propose here falls within the scope of Aim 1 and Aim 3 of the original R01 project. There are two specific aims in this administrative supplement project. 1. To study the association of fiber bundle connectivity and tau PET burden in the limbic cortical areas. 2. To study the association of tau- connectivity networks and tractography-based structural networks. With the completion of this supplemental project, we will develop a suite of tools for studying the relation of structural connectivity and tau uptake values from PET imaging. We will for the first time provide the tools to compute tau-connectivity network and examine its structural basis based on data from diffusion MRI. All the tools developed in this project will be publicly distributed to the research community on NITRC (www.nitrc.org).
It has long been suggested in the Braak staging of AD that neuron-to-neuron propagation of tau pathology could play a critical role in AD development, but the in vivo quantification of tau pathology in the brain has only been available recently with the invention of tau PET imaging. The computational tools developed in this project will allow the in vivo examination of the structural basis of tau pathology propagation in AD development and the identification of novel imaging markers for the early diagnosis of AD.
| Aydogan, Dogu Baran; Shi, Yonggang (2018) Tracking and validation techniques for topographically organized tractography. Neuroimage 181:64-84 |
| Aydogan, Dogu Baran; Jacobs, Russell; Dulawa, Stephanie et al. (2018) When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity. Brain Struct Funct 223:2841-2858 |
| Gahm, Jin Kyu; Shi, Yonggang; Alzheimer’s Disease Neuroimaging Initiative (2018) Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space. Med Image Anal 46:189-201 |
| Tang, Yuchun; Sun, Wei; Toga, Arthur W et al. (2018) A probabilistic atlas of human brainstem pathways based on connectome imaging data. Neuroimage 169:227-239 |
| Wang, Junyan; Aydogan, Dogu Baran; Varma, Rohit et al. (2018) Modeling topographic regularity in structural brain connectivity with application to tractogram filtering. Neuroimage 183:87-98 |
| Wang, Junyan; Shi, Yonggang (2017) Kernel-Regularized ICA for Computing Functional Topography from Resting-state fMRI. Med Image Comput Comput Assist Interv 10433:373-381 |
| Shi, Y; Toga, A W (2017) Connectome imaging for mapping human brain pathways. Mol Psychiatry 22:1230-1240 |
| Gahm, Jin Kyu; Shi, Yonggang (2017) Holistic Mapping of Striatum Surfaces in the Laplace-Beltrami Embedding Space. Med Image Comput Comput Assist Interv 10433:21-30 |
| Sun, Wei; Amezcua, Lilyana; Shi, Yonggang (2017) FOD Restoration for Enhanced Mapping of White Matter Lesion Connectivity. Med Image Comput Comput Assist Interv 10433:584-592 |
| Wang, Junyan; Aydogan, Dogu Baran; Varma, Rohit et al. (2017) Topographic Regularity for Tract Filtering in Brain Connectivity. Inf Process Med Imaging 10265:263-274 |
