0.8-2

• Add a voxel_psc intensity-normalization mode that uses the existing robust reference-core and eligible-frame policy and applies denominator-guarded baseline-to-100 scaling after spatial processing. Reliable local baselines use ordinary PSC scaling, very low positive baselines use a lower denominator bound, and invalid baselines or those with too few eligible frames use a conservative run-level fallback. The guards do not clip observations or mask voxels; the user's apply_mask decision is preserved, and the multiplier map and guard counts are saved for provenance. Guard counts and percentages within the conservative automask are logged at info level, with complete-grid counts at debug level.
• Add a user-oriented intensity-normalization vignette documenting the target convention, robust reference-core policy, provenance outputs, QA, and limitations.
• Replace automask()'s background-sensitive positive-voxel quantile interpolation with an iterative AFNI-style clip estimator and a smoothly varying local threshold field.
• Match AFNI's automask() peeling more closely with a 17-of-18 NN2 survival rule, layer-aware restoration, and post-peel face-connected reclustering.
• Replace postprocessing's late 4D-median intensity estimate with an automask-based robust reference core selected from the original positive-scale BOLD image; measure and apply the run factor after masking/smoothing but before AROMA, temporal filtering, confound regression, or timepoint removal, and save the core mask and JSON provenance.
• Accept postprocess/intensity_normalize/target as the simplified normalization setting while retaining global_median as a backward-compatible alias.
• Preserve each voxel's pre-AROMA temporal mean during both aggressive and non-aggressive AROMA denoising, retaining the positive baseline intensity used for cross-run scaling.
• Refactor postprocessing to use job arrays and sentinels for cleanup
• Add additional templates to prefetch needed by MRIQC
• Preserve user-specified metadata/sqlite_db values and expose sqlite_db in edit_project().
• Clean postprocessing scratch workspaces and temporary automask files on errors as well as successful exits.
• Use exit-time cleanup for temporary FSL postprocessing files generated during temporal filtering, smoothing, confound regression, and brain-mask computation.
• Add regression tests for editable SQLite database configuration and postprocessing temp-file cleanup after failures.
• Prefetch resolution-1 T1w and brain-mask assets used by fMRIPrep anatomical reports, including when an output space explicitly requests another resolution.