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Updated: Jan 12, 2026

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Multi-cohort, multi-sequence harmonisation for cerebrovascular brain age.

Mathijs B J Dijsselhof1,2, Candace Moore3, Saba Amiri3

  • 1Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands.

Imaging Neuroscience (Cambridge, Mass.)
|October 30, 2025
PubMed
Summary
This summary is machine-generated.

Data harmonization of arterial spin labelling (ASL) perfusion MRI improves cerebrovascular brain age predictions. ASL-specific methods like AutoComBat offer better consistency across studies for brain aging research.

Keywords:
arterial spin labellingbrain agecerebral blood flowcerebrovascular ageingharmonisationmachine learning

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Area of Science:

  • Neuroimaging
  • Radiology
  • Gerontology

Background:

  • Higher brain-predicted age gaps (BAG), derived from anatomical scans, correlate with cognitive decline in older adults.
  • Incorporating arterial spin labelling (ASL) perfusion MRI adds a cerebrovascular component, potentially enhancing BAG predictions and sensitivity to cardiovascular health's impact on brain aging.

Purpose of the Study:

  • To investigate data harmonization methods for improving the generalizability of cerebrovascular brain age estimation using ASL MRI.
  • To compare the performance of various harmonization techniques on multi-cohort, multi-sequence ASL and structural MRI data.

Main Methods:

  • Utilized a multi-study dataset (n=2608) with T1-weighted (T1w), FLAIR, and ASL MRI data from healthy participants and multiple testing cohorts.
  • Extracted features including grey/white matter volumes (T1w), white matter hyperintensity (FLAIR), and ASL cerebral blood flow (CBF) and its spatial coefficient of variation (sCoV).
  • Applied several harmonization methods (NeuroComBat, CovBat, NeuroHarmonize, OPNested ComBat, AutoComBat, RELIEF) to ASL features before training ExtraTrees-based brain age models (ASL-only and T1w+FLAIR+ASL).

Main Results:

  • Harmonization significantly reduced ASL feature differences between cohorts across all methods, particularly with RELIEF.
  • ASL-only models showed improved mean absolute error (MAE) post-harmonization (from 11.1 ± 7.5 to <8.8 ± 6.2 years), with AutoComBat performing nominally best.
  • Multi-modal models (T1w+FLAIR+ASL) showed less improvement from ASL-only harmonization, but ASL-specific methods like AutoComBat demonstrated better performance.

Conclusions:

  • Harmonization of ASL features enhances consistency across studies and improves brain age estimations, especially when using ASL data alone.
  • ASL-specific parameter harmonization methods are recommended, considering acquisition parameters for optimal results in multi-cohort studies.
  • These findings support using ASL-parameter specific harmonization to investigate links between cardiovascular health, brain aging, and cognitive decline.