科研之友
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摘要
Introduction: Quantitative T-2 mapping may provide an objective biomarker for occult nervous tissue pathology in relapsing-remitting multiple sclerosis (RRMS). We applied a novel echo modulation curve (EMC) algorithm to identify T-2 changes in normal-appearing brain regions of subjects with RRMS (N = 27) compared to age-matched controls (N = 38). Methods: The EMC algorithm uses Bloch simulations to model T-2 decay curves in multi-spin-echo MRI sequences, independent of scanner, and scan-settings. T-2 values were extracted from normal-appearing white and gray matter brain regions using both expert manual regions-of-interest and user-independent Free Surfer segmentation. Results: Compared to conventional exponential T-2 modeling, EMC fitting provided more accurate estimations of T-2 with less variance across scans, MRI systems, and healthy individuals. Thalamic T-2 was increased 8.5% in RRMS subjects (p < 0.001) and could be used to discriminate RRMS from healthy controls well (AUC = 0.913). Manual segmentation detected both statistically significant increases (corpus callosum & temporal stem) and decreases (posterior limb internal capsule) in T-2 associated with RRMS diagnosis (all p < 0.05). In healthy controls, we also observed statistically significant T-2 differences for different white and gray matter structures. Conclusions: The EMC algorithm precisely characterizes T-2 values, and is able to detect subtle T-2 changes in normal-appearing brain regions of RRMS patients. These presumably capture both axon and myelin changes from inflammation and neurodegeneration. Further, T-2 variations between different brain regions of healthy controls may correlate with distinct nervous tissue environments that differ from one another at a mesoscopic length-scale.
