ArticleJournal of neuroscience methods2027
Quality assurance strategies for brain state characterization by MEMRI.
Article in Journal of neuroscience methods, 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundManganese-enhanced MRI (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
methodWe 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' 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 μm isotropic resolution, and robust intensity normalization. Balanced accuracy and Youden's 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 simulations 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 other neuroimaging studies. Software is maintained in publicly accessible GitHub repositories.
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