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Quality Assurance Strategies for Brain State Characterization by MEMRI.

Taylor W Uselman1, Russell E Jacobs2,3, Elaine L Bearer1,3

  • 1University of New Mexico, School of Medicine, Albuquerque, NM.

Biorxiv : the Preprint Server for Biology
|April 27, 2026
PubMed
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This summary is machine-generated.

We developed new computational methods for analyzing Manganese-enhanced MRI (MEMRI) data, enabling scalable and reproducible brain-wide mapping of neural activity in large cohorts. This improves the analysis of brain states for human health research.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Manganese-enhanced magnetic resonance imaging (MEMRI) is crucial for mapping neural activity and axonal projections.
  • Existing computational frameworks for analyzing large MEMRI datasets are limited, hindering cohort-level studies.
  • Standardized, reproducible analysis pipelines are needed for MEMRI data.

Purpose of the Study:

  • To present methodological advances for preprocessing and statistical analysis of MEMRI datasets.
  • To enable scalable, reproducible, cohort-level analyses of brain-wide neural activity.
  • To provide tools for mechanistic investigation of brain-state dynamics.

Main Methods:

  • Developed quality assurance metrics for image evaluation, anatomical alignment, and intensity normalization.
Keywords:
Atlas segmentationBrain-wide mappingIn vivo neuroimagingLongitudinal imagingManganese-enhanced MRI (MEMRI)Optimization of statistical mapping parameters to maximize accuracySimulation of noise-only images with and without investigator-embedded signalsStatistical analysis

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  • Utilized simulated data to optimize smoothing, effect-size, and cluster-size thresholds for voxel-wise mapping.
  • Created InVivoSegment software and InVivo Atlas for MEMRI data segmentation and interpretation.
  • Established benchmarks for signal-to-noise ratio, alignment precision (100 μm), and normalization.

    • Main Results:

    • Optimized statistical mapping parameters using balanced accuracy and Youden J statistics from simulated data.
    • Demonstrated reliable transformation of voxel-wise results to regional summaries using InVivoSegment.
    • Identified thresholds to minimize noise-driven artifacts in segmented MEMRI data.
    • Showcased improved sensitivity in voxel- and segment-wise analyses compared to FDR/FWE methods.

    Conclusions:

    • Methodological advances enable scalable, reproducible quantification of longitudinal changes in MEMRI studies.
    • Strengthened mechanistic investigation of brain-state dynamics relevant to human health.
    • Provided broadly applicable tools for neuroimaging analyses beyond MEMRI.