Summary The blood-brain barrier (BBB) limits entry of blood-derived products, pathogens and cells into the brain that is essential for normal neuronal functioning and information processing. Post-mortem tissue analysis indicates BBB damage in Alzheimer’s disease (AD). The timing of BBB breakdown remains, however, elusive. Using an advanced dynamic contrast-enhanced magnetic resonance imaging protocol with high spatial and temporal resolutions to quantify regional BBB permeability in the living human brain, we show an age-dependent BBB breakdown in the hippocampus, a region critical for learning and memory that is affected early in AD. The BBB breakdown in the hippocampus and its CA1 and dentate gyrus subdivisions worsened with mild cognitive impairment that correlated with injury to BBB-associated pericytes, as shown by the cerebrospinal fluid analysis. Our data suggest that BBB breakdown is an early event in the aging human brain that begins in the hippocampus and may contribute to cognitive impairment.

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Purpose To determine optimal parameters for acquisition and processing of dynamic contrast-enhanced (DCE) MRI to detect small changes in near normal low blood-brain barrier (BBB) permeability. Methods Using a contrast-to-noise ratio metric (K-CNR) for Ktrans precision and accuracy, the effects of kinetic model selection, scan duration, temporal resolution, signal drift and length of baseline on the estimation of low permeability values was evaluated with simulations. Results The Patlak model was shown to give the highest K-CNR at low Ktrans. The Ktrans transition point, above which other models gave superior results, was highly dependent on scan duration and tissue extravascular extracellular volume fraction (ve). The highest K-CNR for low Ktrans was obtained when Patlak model analysis was combined with long scan times (10-30 minutes), modest temporal resolution (<60 seconds/image), and long baseline scans (1-4 minute). Signal drift as low as 3% was shown to affect the accuracy of Ktrans estimation with Patlak analysis. Conclusion DCE acquisition and modeling parameters are interdependent and should be optimized together for the tissue being imaged. Appropriately optimized protocols can detect even the subtlest changes in BBB integrity and may be used to probe the earliest changes in neurodegenerative diseases such as Alzheimer’s disease and Multiple Sclerosis.