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Basal Ganglia Shape Analysis and Circuitry in Huntington Disease

Grant Number: 1U01NS082085-01
Partners: Michael Miller, PhD, Christopher Ross, PhD, Laurent Younes, PhD, Susumu Mori, PhD, John T Ratnanather, PhD, Andreia Vasconcellos Faria, PhD, John's Hopkins University

This project will use sophisticated methods of statistical shape analysis and tract tracing for analyzing local atrophy in the basal ganglia and tracing circuits from cortex to striatum and from striatum to globus pallid us. The hypothesis are first, statistical shape analysis will enable the detection of local changes in the basal ganglia very early. We predict that atrophy will proceed, at least in part, in concert with the basal ganglia circuitry involving projections from cortex to striatum and then striatum to globus pallid us. If the hypothesis is supported it will be consistent with the idea that therapeutics delivered locally into the striatum may have more widespread beneficial effects in the brain. Conversely, if atrophy does not proceed along basal ganglia circuits then it will suggest that small molecule therapeutics or therapeutics which can be targeted more widely in the brain will be necessary.

Neurobiological Predictors of Huntington Disease

Grant Number: 5R01NS040068-12

PREDICT-HD is an international 30-site observational study of persons at-risk for Huntington disease. PREDICT-HD capitalizes on two unique aspects of Huntington disease among neurodegenerative disorders: The ability to know in advance who will develop the Disease and the knowledge that all affected individuals have the same etiology (a CAG expansion in the huntingtin gene). PREDICT-HD has become part of a world-wide effort and offers an unprecedented opportunity to examine the pathophysiology and neurobiology of early Huntington disease. We seek to maximize the impact of this resource and test new and refined hypotheses to advance clinical trials in Huntington disease. The ultimate goal of PREDICT-HD is to define the neurobiology of Huntington disease sufficiently to allow clinical trials of potential Disease-modifying therapies before at-risk individuals have diagnosable symptoms of the Disease.

Functional Connectivity in Premanifest Huntington Disease

Grant Number: 1U01NS082083-01
Partners: Stephen Rao, PhD, Mark J Lowe, PhD, Ken Sakaie, PhD, Katherine A Koenig, PhD, The Cleveland Clinic Foundation; Deborah L Harrington, PhD, University of New Mexico; Edward Bullmore, PhD, Mikail Rubinov, The University of Cambridge

Cognitive change in the premanifest stage of Huntington disease is a key marker of disease prognosis (Harrington et al., 2012). Cognition depends fundamentally on interactions among multiple brain regions. Hence, it is essential to identify brain networks that are altered in the premanifest stage of Huntington disease, as this knowledge may inform future intervention therapeutics. Connectivity patterns measured from low-frequency fluctuations in the blood oxygen level dependent (BOLD) functional MRI (fMRI) signal during rest have the potential to identify disruptions inintrinsic brain organization in the premanifest stage of Huntington disease. Functional connectivity MRI (fcMRI) based on resting-state BOLD imaging correlates with cognitive abilities and is less prone to practice effects relative to task-activated fMRI, an advantage for longitudinal study designs. Although resting-state fcMRI is a promising biomarker for other diseases, it has received scant attention in the premanifest stage of Huntington disease. The primary goal of this project is to use fcMRI to identify the earliest changes in brain networks in the premanifest stage of Huntington disease and to track them longitudinally.

4D Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington Disease

Grant Number: 8462842
Partners: Guido Gerig, PhD, The University of Utah

The proposed project will develop sophisticated longitudinal 4D shape models and will integrate derived data, training materials and tools with the parent PREDICT-HD project. This project will utilize the PREDICT-HD imaging data and informatics infrastructure to accomplish these goals.


Grant Number: U54 EB005149
Partners: Ron Kikinis, PhD, Tina Kapur PhD, Brigham and Women's Hospital; Stephen R Aylward, PhD, William J Schroeder, PhD, Kitware Inc.; Guido Gerig, PhD, Robert S MacLeod, PhD, Ross Whitaker, PhD, University of Utah; Polina Golland, PhD, Eric W. Grimson, PhD, Massachusetts Institute of Technology; Randy Gollub, PhD, Gregory C Sharp, PhD, Massachusetts General Hospital; Jeffery S Grethe, PhD, University of California, San Diego; Daniel S Marcus, PhD, Washington University in St. Louis; James Miller, PhD, GE Research; Steven D Pieper, PhD, Isomics, Inc.; Martin Syner, PhD, University of North Carolina at Chapel Hill; Allen Tannenbaum, PhD, Georgia Tech; John D Van Horn, PhD, University of California, Los Angeles

PREDICT-HD is studying Huntington disease, a neurodegenerative genetic disorder that affects muscle coordination, behavior and cognitive function, and causes severe debilitating symptoms by middle age. The aims of this DBP capitalize on two unique aspects of Huntington disease among neurodegenerative disorders—the ability to know in advance exactly who will develop the disease and the knowledge that all affected individuals have the same root cause (i.e., a CAG repeat expansion in the Huntington disease gene).

fMRI Resting State Study

Grant Number:

The CSAIL group has a novel graph-based approach to determining changes in functional resting state data between healthy and diseased populations. We want to leverage their work on our Huntington disease patients and they want larger populations to validate their work on.

PET Ancillary Grant

Grant Number: Pilot Study
Partners: Andrew Feiqin, PhD, Vijay Dhawan PhD, David Eidelberg, PhD, Yilong Ma, PhD, Martin Niethammer, PhD, Chris Tang, PhD, The Feinstein Institute for Medical Research

In this study, we propose adding resting state metabolic imaging with FDG PET (to be conducted at baseline and after 1 year) to quantify individual subject HDPP expression at each longitudinal time point in PREDICT-HD participants. We plan to address the following Specific Aims: (1) To validate HDPP in a new cohort of well characterized pHD subjects and to measure the change in its expression over 1 year; (2) To compare the rate of change in HDPP over 1 year to changes in other PREDICT-HD measures including MRI (volumetrics, MHDPP), and clinical measures; and (3) To reproduce and validate a novel brain network associated with HD symptom onset. The ultimate goal of this work is to identify the most sensitive and reliable imaging measure for use in future clinical trials in individuals with preclinical HD.

Imaging and Genetics in Huntington's Disease

Grant Number: 1 U01 NS082074—01A1
Partners: Jessica Turner, PhD, Jingyu Liu, PhD, The Mind Research Network; Elizabeth Aylward, PhD, Seattle Children's Research Institute; James F Gusella, PhD, Center for Human Genetic Research

Preceding the clinical diagnosis of Huntington's disease (HD) there is evidence for decrease in functional abilities in several domains, as well as in brain volumes. What is not understood is the genetic influences which modulate this loss, in the context of having genetic marker for HD> We use the PREDICT-HD data to determine genetic correlates of Brain Structure and loss of function, to reduce the uncertainty in disease progression particularly in subjects whose genetic marker indicates a wide window of multiple decades in which they might develop the clinical diagnosis.