Utilizing MRI and sensorimotor measures to phenotype adrenomyeloneuropathy progression

The National Institute of Neurological Disorders and Stroke awarded the Hugo W. Moser Research Institute at Kennedy Krieger, Inc. $234,360 on September 5, 2026, under the Extramural Research Programs in the Neurosciences and Neurological Disorders program (CFDA 93.853) to identify phenotypic subtypes and disease trajectories in adrenomyeloneuropathy using quantitative sensorimotor measures and spinal cord MRI indices.

The research addresses the lack of sensitive outcome measures and biomarkers that have limited clinical trial success in AMN, the most common form of X-linked adrenoleukodystrophy. The project will obtain cross-sectional sensorimotor outcome measures in 80 individuals with AMN using machine learning cluster analysis to determine distinct phenotypic subtypes and correlate them with spinal cord MRI abnormalities in the dorsal columns and lateral corticospinal tracts. The work will also determine whether longitudinal sensorimotor measures and spinal cord MRI indices change over a two-year period to validate quantitative assessments of AMN progression.

Performance occurs in Baltimore, Maryland. The period of performance extends from the award date through August 31, 2031. This is a Project Grant, a category of assistance funding under the NINDS extramural research program supporting investigator-initiated research in neuroscience and neurological disorders.

Abstract

Adrenomyeloneuropathy (AMN), the most common form of X-linked adrenoleukodystrophy, is an untreatable disorder with a heterogeneous symptom profile and progression rate. Adult males and females experience significant disability due to long-tract distal axonopathy in the spinal cord. Novel therapies are being developed, but the lack of sensitive outcome measures and biomarkers of progression has limited the success of clinical trials. IT was recently shown that quantitative sensorimotor measures can reflect progression in AMN. Despite this, challenges with synthesizing and interpreting meaning across hundreds of data points for these measures has led to trial failure and lack of clarity on how to accurately define change across this rare disease cohort with variable disease course. The overall hypothesis is that phenotypic subtypes and trajectories of disease can be identified in AMN and localized to MRI indices of damage in spinal cord white matter tracts. This hypothesis will be investigated through the following specific aims: aim 1. Obtain cross-sectional sensorimotor outcome measures to determine AMN phenotypic subtypes, and correlate them with spinal cord DTI, QMT and mffe indices to define their neuroanatomic substrates. Quantitative sensorimotor measures will be obtained in a total of 80 individuals with AMN to determine the presence of distinct phenotypic subtypes through machine learning cluster analysis. The relationship of phenotypic subtype clusters to MRI abnormalities in the dorsal columns and lateral corticospinal tracts will then be evaluated. Aim 2A. Determine if longitudinal sensorimotor outcome measures and longitudinal spinal cord DTI, QMT, and mffe indices change over 2 years. Published data shows the feasibility of measuring AMN progression using quantitative sensorimotor assessments. Aim 2A will validate this work in a prospective study and concurrently evaluate if advanced MRI indices change within 2 years, using AI-based tools and high-resolution mffe sequences to quantify spinal cord white matter microstructural changes. Aim 2B. Use longitudinal sensorimotor outcome measures to determine disease trajectory clusters over 2 years and determine the relationship of these trajectories with changes in spinal cord DTI, QMT and mffe indices. This aim will evaluate if 2-year trajectory clustering of quantitative sensorimotor assessments can identify fast VS. Slow progressor clusters, as indicated in the preliminary data, then determine if the clusters are related to MRI indices of spinal cord pathology. The candidate requires additional training in MRI acquisition and analysis, advanced machine learning, and biostatistics to complete these aims. This project will provide critically needed information on phenotypes and pathological structure-function relationships in AMN. The knowledge gaps addressed may inform disease prognostication and conduct of trials. This approach can also be useful across other leukodystrophies with modest sample size and high-dimensional data acquisition.