Journal of neuroscience methods · 2025
Advanced brain diffusion MRI and image texture measures have the potential to predict multi-domain functional outcomes in multiple sclerosis.
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Oladosu Olayinka, Zhang Yunyan
Аннотация
BACKGROUND: Multiple sclerosis (MS) causes diverse functional impairments that require early accurate characterization, but pertinent methods are scarce. This study aimed to develop new imaging-driven approaches for predicting MS functions.
NEW METHOD: 19 women with MS [10 relapsing-remitting (RRMS) and 9 secondary progressive (SPMS) subtypes] and 19 matched controls were examined including 3 T imaging. Advanced measures of nerve tract integrity were derived using diffusion MRI and anatomical MRI texture analysis with phase congruency, respectively. Imaging analysis focused on three tract regions critical in MS: the corpus callosum, corticospinal tracts, and optic radiations. Top-ranked tract measures sensitive to MS severity were employed to predict physical, neurocognitive, and affective functions facilitated by Ridge regression.
RESULTS: Top predictors included diffusion apparent fiber density and fractional anisotropy, and phase congruency measures across tracts. The predictions were mostly strong for physical functions including Timed 25-Foot walk, Nine-Hole Peg Test, and neurological disability, strong-to-moderate for neurocognitive functions led by the symbol digit modality test, and relatively weak for affective functions. Further, the normal-appearing white matter (NAWM) models were superior or similar to NAWM+Lesion models based on either imaging type, and the best phase congruency models outperformed the best diffusion-based models.
COMPARISON WITH EXISTING METHODS: Few studies attempted to derive novel measures of nerve integrity using clinical MRI, and virtually no study modelled the utility of these measures for predicting multi-domain MS functions.
CONCLUSION: Advanced imaging models could predict MS functions for early intervention, especially phase congruency NAWM models for physical functions.
NEW METHOD: 19 women with MS [10 relapsing-remitting (RRMS) and 9 secondary progressive (SPMS) subtypes] and 19 matched controls were examined including 3 T imaging. Advanced measures of nerve tract integrity were derived using diffusion MRI and anatomical MRI texture analysis with phase congruency, respectively. Imaging analysis focused on three tract regions critical in MS: the corpus callosum, corticospinal tracts, and optic radiations. Top-ranked tract measures sensitive to MS severity were employed to predict physical, neurocognitive, and affective functions facilitated by Ridge regression.
RESULTS: Top predictors included diffusion apparent fiber density and fractional anisotropy, and phase congruency measures across tracts. The predictions were mostly strong for physical functions including Timed 25-Foot walk, Nine-Hole Peg Test, and neurological disability, strong-to-moderate for neurocognitive functions led by the symbol digit modality test, and relatively weak for affective functions. Further, the normal-appearing white matter (NAWM) models were superior or similar to NAWM+Lesion models based on either imaging type, and the best phase congruency models outperformed the best diffusion-based models.
COMPARISON WITH EXISTING METHODS: Few studies attempted to derive novel measures of nerve integrity using clinical MRI, and virtually no study modelled the utility of these measures for predicting multi-domain MS functions.
CONCLUSION: Advanced imaging models could predict MS functions for early intervention, especially phase congruency NAWM models for physical functions.