Our research focuses on diffusion magnetic resonance imaging, the only method that can map the connections of the living human brain. Our ongoing research includes NIH R01 projects to enable the analysis of extremely large datasets and to map previously invisible superficial white matter. We are also involved in projects related to analysis of diffusion tractography via fiber clustering, creation of white matter atlases, extraction of machine-learning-relevant features from the white matter, and development of open-source software. We have a longstanding collaboration with Dr. Alexandra J. Golby on topics related to neurosurgical planning.
Harmonizing Multi-Site Diffusion MRI Acquisitions for Neuroscientific Analysis Across Ages and Brain Disorders
NIH R01 MH119222
Diffusion MRI (dMRI) is the only non-invasive method that can map the living human brain’s connections and is critical for understanding mental disorders. Several large studies such as the Human Connectome Project (HCP) and the Adolescent Brain Cognitive Development (ABCD) have collected or are poised to collect diffusion MRI data from over 30,000 subjects. However, an important challenge is that these datasets collected from different scanners cannot be pooled for joint analysis due to large inter-scanner (inter-site) differences, caused by differences in vendor specific software for data reconstruction, the sensitivity of head coils etc. These scanner differences are often larger than the effect sizes observed between groups in psychiatric disorders. A second challenge for large-scale data analysis is the lack of a single consistent ontology-based definition and automated extraction of white matter connections across the lifespan (including neonates and children). A third challenge is the sheer size of the combined dMRI datasets (several terabytes), limiting the ability of researchers to test hypotheses as this requires expertise and complex computational resources for processing, storing, and visualizing such large volumes of data. In this grant, we propose to address these challenges to enable large- scale data-intensive analysis of dMRI data. Specifically, in Aim 1, we propose to develop novel mathematical algorithms to remove scanner-specific differences from data acquired at multiple sites. We will harmonize 10,000 subjects from the ABCD study acquired at 21 different sites, another 10,000 subjects from the HCP initiative spanning the entire lifespan and numerous disease indications and 10,000 subjects from the Healthy Brain Network. All the harmonized datasets (30,000 subjects), will be shared with the community using the NIMH data archive (NDA). In Aim 2, we will develop a formal ontology-based system for defining 189 white matter fascicles using neuroanatomical landmarks known from human and monkey literature on brain connectivity. Our main focus will be to develop novel algorithms for automated and consistent clustering and extraction of these fiber bundles spanning the entire human lifespan including neonates. To enable widespread use without the need for demanding computational resources and technical knowledge, in Aim 3, we will develop a web-based system for real-time 3D viewing and querying of the harmonized data and fascicles (integrating with NIMH data archive infrastructure) for a user-defined selection of subjects from the entire cohort of subjects across different diagnostic categories. Overall, the potential impact of this framework is significant, as it will, for the first time, allow a large-scale data-intensive analysis of dMRI data to study neurodevelopment as well as mental disorders cutting across diagnostic boundaries.
Mapping the Superficial White Matter Connectome of the Human Brain Using Ultra High Resolution Multi-Contrast Diffusion MRI
NIH R01 MH125860
In this 5-year R01 project entitled “Mapping the superficial white matter connectome of the human brain using ultra high resolution multi-contrast diffusion MRI,” we propose to create the first atlas of the human brain’s superficial white matter (SWM) using sub-millimeter ultra high resolution diffusion MRI (dMRI). The SWM is located between the deep white matter and the cortex. It plays an important role in neurodevelopment and aging, and it has been implicated in a large number of diseases including Alzheimer’s, Huntington’s, epilepsy, autism spectrum disorder, schizophrenia, and bipolar disorder. Despite its significance in health and disease, the SWM is vastly underrepresented in current descriptions of the human brain connectome. The SWM contains short, u- shaped association fiber bundles called u-fibers. Multiple challenges have thus far prevented comprehensive mapping of the human brain’s SWM. These challenges include the inadequate spatial resolution of dMRI data, which prevents u-fiber tracing using current tractography methods, as well as the small size, high curvature, and high inter-subject variability of the u-fibers. An additional challenge is the lack of ground truth information. Our understanding of human neuroanatomy relies heavily on the results of invasive tracer studies in monkeys, but the detailed neuroanatomy of the SWM in monkeys has not yet been systematically compiled or analyzed. We propose to address these challenges to create the most comprehensive description of the SWM to date. Our strategy includes using ultra high spatial resolution dMRI acquisitions (~700µm isotropic or better) at multiple echo times (TE), novel dMRI tractography methods designed for tracing u-fibers, anatomically informed machine learning to parcellate the u-fibers, and expert neuroanatomical generation of the SWM connectivity matrix from monkey tracer studies. Furthermore, we will develop a novel ontological framework to organize and name the SWM systems of the monkey and human brains. Overall, these steps will enable robust in-vivo tracing and capturing of inter-subject variability of the SWM of the human brain at an unprecedented spatial resolution. Our proposed deliverables will be the first comprehensive, anatomically curated atlases of the SWM in human and monkey, which will enable the study of the SWM in health and disease. We will publicly release all image data, tractography atlases, monkey connectivity matrices, extracted fascicles, and all software as open source.
Image Features for Brain Phenotypes
NIH P41 EB015902
The Image Features for Brain Phenotypes TR&D is led by Drs. Sandy Wells and Lauren O’Donnell. Toward the long-term goal of developing machine learning systems for clinically assisted reads, the Image Features for Brain Phenotypes TR&D investigates 3D brain image features for description of healthy versus non-healthy brain phenotypes.
Machine Learning for White Matter Atlases
The group investigates methods for machine learning and statistical analysis based on data from diffusion MRI fiber tractography. Our method for fiber clustering is a machine learning technology that enables discovery of thousands of unique white matter brain connections that are found very robustly in large groups of subjects. We refer to this as data-driven white matter parcellation. This technique enables neuroscience and neuroanatomy research, as well as research in neurosurgical planning. Recent studies include investigations of autism using machine learning classification techniques, novel statistical analyses that leverage the geometry of the fiber tracts, and ongoing work to anatomically annotate and publicly release curated white matter fiber cluster atlases.
SlicerDMRI Open-Source Software
Our NIH-funded open-source software, SlicerDMRI, is downloaded 200 times per month and used in multiple brain research studies for diffusion MRI visualization and analysis. Our software for diffusion MRI fiber tractography clustering, white watter wnalysis, is available as open source.