Yogesh Rathi, Ph.D. - US grants

DESCRIPTION (provided by applicant): In this grant application, we propose to develop clinicaly feasible methods for the acquisition and analysis of advanced diffusion magnetic resonance imaging (dMRI) of pediatric patients and apply it to study micro and macro level pathology in attention-deficit hyperactivity-disorder (ADHD). Advanced dMRI techniques can provide details about the layout of white matter pathways in the brain, that are not possible using the current clinical standard of diffusion tensor imaging (DTI). However, these advanced protocols require long scan times and any motion during this time results in artifacts and loss of signal. As a result, dMRI acquisition of children becomes a challenging task, particularly if they are hyperactive (as in ADHD). In this grant application, we propose several novel algorithms for fast acquisition and reconstruction of advanced dMRI protocols. In particular, we will use our multi-slice acquisition protocol (as opposed to the standard single-slice acquisition) along with a scheme to recover dMRI signals from very few measurements. This will dramatically reduce scan time and make it possible to obtain advanced dMRI scans of pediatric patients (in a clinic). We will validate our methods on several test subjects and then apply them to the study of children and adolescents with ADHD. In particular, we will analyze global connectivity properties of the anatomical neural networks in ADHD along with local diffusion based microstructural properties that may be affected due to pathology. Thus, the improvements suggested in this proposal will bring advanced dMRI protocols to the clinic and allow us to quantify micro and macro level abnormalities in patients with any type of psychiatric or neurological disorder.

ABSTRACT Obsessive-compulsive disorder (OCD) is a chronic mental illness affecting 4-7 million people in the US. Patients are impaired in multiple Research Domain constructs. Medication and behavioral therapies yield inadequate symptom relief in 50-70% of patients. As a result, OCD remains a leading worldwide cause of disability, equivalent to more visible disorders such as schizophrenia. Roughly 1/3 of patients are unable to work due to their symptoms and their caregivers report profound, life-impairing stress. More recently, the field has focused on deep brain stimulation (DBS). However, about a third of the patients who undergo DBS receive no meaningful benefit. We propose a primarily retrospective investigation of imaging-based targeting for Deep Brain Stimulation (DBS) for severe obsessive-compulsive disorder (OCD). We aim to increase response rates while testing imaging biomarkers that could guide therapeutic intervention. Our hospital is the highest-volume site in the US using DBS at the ventral capsule/ventral striatum (VC/VS) for the treatment of intractable OCD. A major contributor to imperfect response is that DBS is implanted at standard coordinates across patients, without accounting for variation in VC/VS anatomy. Our investigation will study imaging markers that may target ventral capsule/ventral striatum (VC/VS) for DBS to patient-specific brain circuitry. We hypothesize that clinical response will be related to the degree that the DBS electrical field influences orbitofronto-thalamic fibers and the nucleus accumbens grey matter. If we are successful in identifying a biomarker during this three-year retrospective study, a future prospective clinical trial will target DBS implants to the newly identified structures in a patient-specific fashion.

Abstract( ! 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. !

Abstract 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.