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The role of dendritic spines in water exchange measurements with diffusion MRI: Double Diffusion Encoding and free-waveform MRI
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The role of dendritic spines in water exchange measurements with diffusion MRI: Double Diffusion Encoding and free-waveform MRI
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Time-dependent diffusion MRI enables the estimation of water exchange rates in vivo, yet reported values in grey matter remain inconsistent. While most studies attribute these estimates to membrane permeability, non-permeative geometric exchange has also been proposed. The present study investigates the contribution of geometric exchange between dendritic spines and shafts to diffusion MRI-derived exchange estimates. Monte Carlo simulations were performed in synthetic dendrites with varying spine morphology, density, and membrane permeability. Diffusion-weighted signals were generated using multiple protocols - including single diffusion encoding, double diffusion encoding, and free waveforms - and were analysed using four frameworks: the K\"arger model (via kurtosis time-dependence), correlation tensor imaging, Restriction-Exchange, and Multi-Gaussian Exchange with transient kurtosis (tMGE). Dendritic spines were found to impart similar time-dependence signatures on the diffusion-weighted signal as permeative exchange (signal decrease with diffusion time). The effect was modulated by both spine morphology and density. Both the exchange rate and microscopic kurtosis increased with spine density. The tMGE method demonstrated the ability to disentangle geometric from permeative exchange. Non-permeative exchange in dendritic spines has a non-negligible impact on exchange estimates obtained with diffusion MRI and should be considered in future studies. Diffusion MRI exchange estimates may provide a non-invasive proxy for dendritic spine density, with potential applications in studies of neurological disorders.
Forward citations
Cited by 2 Pith papers
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ConCeG grows realistic multi-cellular grey-matter substrates from topological barcodes and constrained network navigation, matching key morphological and packing statistics of real tissue for dMRI Monte Carlo use.
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