Diffusion tensor imaging of whole human brains during long-term formaldehyde fixation: Temporal evolution of diffusion parameters, post-mortem conditions, and dependence on tissue structure

Abstract

Purpose: Post-mortem Diffusion Magnetic Resonance Imaging (dMRI) findings on fixation-related changes in diffusion tensor imaging (DTI) parameters remain inconsistent, partly due to lower diffusion weighting and sparse early-fixation sampling. This study investigated how post-mortem tissue condition, fixation progression, and diffusion weighting shape DTI parameters in whole human brains, including whether an ex-vivo-adjusted protocol also suits in-situ and early fixation measurements. Methods: Six neurologically healthy whole human brains (post-mortem interval (PMI) 12 - 24 h) were scanned across tissue conditions, including an in-vivo reference cohort. Five brains were scanned longitudinally through immersion fixation (0 - 150 days), yielding 175 datasets with dense early sampling. Diffusion measurements at b-values 1000 - 4000 s/mm2 assessed the influence of diffusion weighting on DTI parameters. Analyses covered multiple white- and gray-matter regions and within white matter stratification by fiber orientation dispersion ({kappa}). Results: The largest mean diffusivity (MD) shift occurred between in-vivo and in-situ, whereas fractional anisotropy (FA) changed most strongly during early fixation. During immersion fixation, MD showed reproducible monoexponential saturation, while FA was heterogeneous across brains. PMI, {kappa}, and regional anatomy explained substantial FA heterogeneity across specimens. Qualitatively, the fixationrelated changes matched across diffusion weightings, but precision across specimens was highest at b-value 4000 s/mm2. Conclusion: During fixation, MD is a robust temporal marker, whereas FA requires microstructureaware interpretation. Diffusion weighting modulates how clearly fixation effects can be resolved, but the same qualitative fixation dynamics remained detectable at in-vivo-like weighting with reduced precision.