Quality Assurance Strategies for Brain State Characterization by MEMRI

Abstract

BackgroundManganese-enhanced magnetic resonance imaging (MEMRI) is a powerful approach for mapping brain-wide neural activity and axonal projections in vivo. Yet standardized computational frameworks for voxel-wise and atlas-based characterization of brain states across large experimental cohorts remain limited. New methodHere, we present methodological advances for preprocessing and statistical analysis of MEMRI datasets to support scalable, reproducible cohort-level analyses. Quality assurance metrics were developed to evaluate images, cohort-level anatomical alignment, and intensity normalization. Using simulated data, we optimized smoothing, effect-size, and cluster-size thresholds to balance sensitivity and specificity in voxel-wise statistical mapping. We developed InVivoSegment software to apply to our new InVivo Atlas for segmentation of MEMRI data and interpretation of brain-wide activity. ResultsQuality assurance analyses established benchmarks for Mn(II)-induced signal- and contrast-to-noise evaluation, precise cohort-level alignment at 100 m isotropic resolution, and robust intensity normalization. Balanced accuracy and Youdens J statistics were calculated from simulated true positive and noise-only intensities, which defined optimal parameters for smoothing kernel, cluster-size and effect-size thresholds during voxel-wise mapping. Segmentation of simulated data demonstrated reliable transformation of voxel-wise results into regional summaries and identified secondary thresholds that minimize noise-driven artifacts. Comparison with existing methodsApproach to optimize correction parameters for statistical mapping using simulated images improves voxel- and segment-wise sensitivity compared to FDR/FWE-based correction procedures. ConclusionsThese methodological advances enable scalable, reproducible, brain-wide quantification of longitudinal changes in MEMRI studies, strengthen mechanistic investigation of brain-state dynamics relevant to human health, and provide broadly applicable tools for neuroimaging analyses beyond MEMRI applications. HighlightsO_LIQuantitative assurance of image quality complements visual assessment for cohort-level batch processing. C_LIO_LIOptimization of parameters using simulated noise-only images with and without investigator-embedded signal for voxel-wise mapping. C_LIO_LIA new software, "InVivoSegment" together with a labeled atlas, automates reliable user-friendly segmentation of voxel-wise data. C_LIO_LIMethodological advances in MEMRI data processing and computational analyses support scalable voxel- and segment-wise quantification of brain-wide neural activity. C_LI