Diffusion basis spectrum imaging provides insights into MS pathology

Abstract

ObjectiveTo use diffusion basis spectrum imaging (DBSI) to assess how damage to normal-appearing white matter (NAWM) in the corpus callosum (CC) influences neurologic impairment in people with MS (pwMS).MethodsUsing standard MRI, the primary pathologies in MS of axonal injury/loss, demyelination, and inflammation are not differentiated well. DBSI has been shown in animal models, phantoms, and in biopsied and autopsied human CNS tissues to distinguish these pathologies. Fifty-five pwMS (22 relapsing-remitting, 17 primary progressive, and 16 secondary progressive) and 13 healthy subjects underwent DBSI analyses of NAWM of the CC, the main WM tract connecting the cerebral hemispheres. Tract-based spatial statistics were used to minimize misalignment. Results were correlated with scores from a battery of clinical tests focused on deficits typical of MS.ResultsNormal-appearing CC in pwMS showed reduced fiber fraction and increased nonrestricted isotropic fraction, with the most extensive abnormalities in secondary progressive MS (SPMS). Reduced DBSI-derived fiber fraction and increased DBSI-derived nonrestricted isotropic fraction of the CC correlated with worse cognitive scores in pwMS. Increased nonrestricted isotropic fraction in the body of the CC correlated with impaired hand function in the SPMS cohort.ConclusionsDBSI fiber fraction and nonrestricted isotropic fraction were the most useful markers of injury in the NAWM CC. These 2 DBSI measures reflect axon loss in animal models. Because of its ability to reveal axonal loss, as well as demyelination, DBSI may be a useful outcome measure for trials of CNS reparative treatments.

Diffusion Basis Spectrum Imaging Measures Anti-Inflammatory and Neuroprotective Effects of Fingolimod on Murine Optic Neuritis

“…Multiple sclerosis (MS) is an immune-mediated in ammatory demyelinating disease of central nervous system (CNS). Irreversible axonal injury and axon loss are believed to be major causes of permanent neurologic impairments in MS (1,2). Although early intervention with disease-modifying therapy (DMT) has been shown to decrease the relapse rate and slow short-term disability progression in MS patients (3), it is still uncertain whether it is associated with a lower long-term risk of disability (4,5).…”

“…This paradox is due to many factors, including the heterogeneous nature of MS in clinical course and underlying pathologies (15,16). We have developed diffusion basis spectrum imaging (DBSI) and demonstrated its ability to detect and differentiate in ammation, axonal injury, axon loss and demyelination in mice with experimental autoimmune encephalomyelitis (EAE), and autopsied and biopsied specimens from MS patients (2,(17)(18)(19)(20)(21)(22).…”

Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions

“…Our laboratory developed a novel diffusion basis spectrum imaging (DBSI) method, 11,12 and demonstrated its ability to quantitatively characterize the pathologies that underlie MRI lesions in a biopsy of a demyelinating brain lesion and in postmortem MS specimens 13,14 . While DBSI‐derived metrics were correlated with axonal injury/loss, demyelination, and inflammation, 11,15 a comprehensive analysis employing DBSI‐derived metrics to detect and differentiate cMRI‐based MS lesion subtypes have yet to be conducted. Herein, we introduce a novel imaging approach which combines DBSI‐derived structural metrics (as the classifiers) with a deep neural network (DNN) algorithm.…”

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

“…We developed diffusion basis spectrum imaging (DBSI) (11) and demonstrated its ability to quantitatively characterize pathologies in multiple central nervous system diseases, including glioblastoma (12), multiple sclerosis (13)(14)(15), spinal cord injury (16), and epilepsy (17). We modified DBSI to better characterize these pathology-directed structural changes to ultimately incorporate DBSI-derived diffusion metrics with a deep neural network (DNN) algorithm to detect and differentiate various tumor histologic components in pediatric high-grade brain tumors.…”