MP2RAGE Ventricle Distance Profiling Improves Classification of Multiple Sclerosis Subtypes
Poster No:
207
Submission Type:
Abstract Submission
Authors:
Puti Wen1, Osama Abdullah1, Lev Brylev2, Mariia Matrosova3, Vasiliy Bryukhov3, Bas Rokers1, Alessio Fracasso4
Institutions:
1New York University Abu Dhabi, Abu Dhabi, 2Abu Dhabi Stem Cells Center, Abu Dhabi, 3Research Center of Neurology, Moscow, 4The University of Glasgow, Glasgow, Scotland
First Author:
Co-Author(s):
Introduction:
Multiple sclerosis (MS), the most common demyelinating disease of the central nervous system, is characterized by inflammatory attacks on the myelin sheath. Notably, periventricular lesions are a hallmark of MS, and around 82-98% of MS patients exhibit these abnormalities in proximity to the ventricles (Adams et al., 1987; Raz et al., 2014). While conventional assessments rely heavily on lesion counts and volumes-collectively termed "lesion load"-this measure lacks the spatial specificity and prognostic nuance needed to capture disease progression fully. To address this gap, we propose a quantitative MRI approach leveraging MP2RAGE-derived T1 relaxation times to create a ventricle distance profile. MP2RAGE imaging is recognized for its clinical utility in MS for detecting lesions (Beck et al., 2018; Testud et al., 2023), and provides a reliable proxy for myelin content (Mancini et al., 2020). By focusing on the periventricular region, this approach adds spatial context to the evaluation of MS impairment. We anticipate that the ventricle distance profile will yield a more detailed characterization of tissue damage, enhance MS subtypes differentiation, and better inform clinical decision-making.
Methods:
We examined 89 MS patients (50 relapsing-remitting [RRMS], 17 secondary progressive [SPMS], 22 primary progressive[PPMS]) and 42 healthy controls. Lesions were segmented from FLAIR images using the Lesion Segmentation Tool with Automated Lesion Identification (LST-AI). Brain segmentation was performed using FreeSurfer to create ventricle masks. We then generated ventricle distance maps by assigning to each voxel the Euclidean distance to the nearest point on the ventricular surface (Figure 1A). T1 relaxation times were estimated from MP2RAGE using qMRLab.
Results:
We derived average T1 relaxation time as a function of ventricular distance for each MS subtype and controls (Figure 1B). All MS groups showed elevated T1, indicating demyelination with respect to controls. While RRMS and PPMS T1 profiles overlapped, SPMS showed higher values, suggesting more severe demyelination in line with previous evidence (Bramow et al., 2010). Using ventricle distance profiles with SVM (leave-one-out cross-validation), we achieved classification accuracies of 67.9% (RRMS), 89.6% (SPMS), and 70.0% (PPMS) against controls, all exceeding chance (50%). RRMS patients likely transition to SPMS – an event that is both difficult to predict and highly consequential – hence, we next focused on distinguishing these two forms (Figure 1C). The ventricle distance profile separated RRMS from SPMS with 79.4% accuracy, outperforming lesion load (69.3%). A subset of RRMS patients showed near-chance classification, possibly reflecting imminent progression to SP. In this group, fine motor dexterity deficits in the non-dominant hand were more pronounced, as indicated by their longer 9-Hole Peg Test times (25.56 s vs. 21.11 s), significantly different from the regular RRMS patients (p=0.0068) and not distinguishable from the SPMS patients (p=0.1251).
·T1 relaxation time as a function of ventricular distance
Conclusions:
In conclusion, our ventricle distance approach directly builds on a well-understood MRI metric – T1 relaxation times – and relates it to a relevant anatomical landmark, the ventricles. This metric can readily serve as a basis for developing normative datasets. With these reference standards, clinicians could more accurately position a patient's condition within the spectrum of expected outcomes. In particular, such normative frameworks may aid in identifying RRMS patients at heightened risk of progressing to SPMS.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Other - Multiple Sclerosis
1|2Indicates the priority used for review
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Provide references using APA citation style.
Raz, E. (2014). Periventricular lesions help differentiate neuromyelitis optica spectrum disorders from multiple sclerosis. Multiple sclerosis international, 2014(1), 986923.
Beck, E. (2018). Improved visualization of cortical lesions in multiple sclerosis using 7T MP2RAGE. American Journal of Neuroradiology, 39(3), 459-466.
Testud, B. (2023). Contribution of the MP2RAGE 7T sequence in MS lesions of the cervical spinal cord. American Journal of Neuroradiology, 44(9), 1101-1107.
Mancini, M. (2020). An interactive meta-analysis of MRI biomarkers of myelin. elife, 9, e61523.
Bramow, S. (2010). Demyelination versus remyelination in progressive multiple sclerosis. Brain, 133(10), 2983-2998.