Validating the neurite exchange imaging (NEXI) model of gray matter using Monte Carlo simulations in numerical...

NEXI is a gray matter (GM) microstructural model designed to probe brain tissue microstructure in vivo using diffusion MRI. NEXI describes GM as two exchanging Gaussian compartments-neurites, modeled as randomly oriented, infinitely long sticks, and the extracellular space-allowing the estimation of biophysically interpretable parameters related to neurite microstructure and intercompartmental exchange. While modeling cell processes as sticks and each compartment as Gaussian are common assumptions for brain biophysical models of diffusion, neurite structural irregularities and the presence of somas, particularly in GM, may violate them and bias NEXI parameter estimates. Furthermore, the barrier-limited exchange assumed in the Kärger model that underlies NEXI may also be violated in realistic conditions. Therefore, in this work, we evaluate NEXI's accuracy in numerical substrates that contain GM features such as neurite beading, undulation, orientation dispersion, and the presence of somas across a range of membrane permeabilities. Diffusion signals were generated with Monte Carlo simulations of water diffusion and subsequently fitted with NEXI. Overall, NEXI captured trends of exchange time across permeability levels and showed good ability to disentangle exchange effects from other microstructural features. The largest bias in t_ex was introduced by somas, and in D_i by permeability, somas and beading. Otherwise, the structural irregularities evaluated in this study had a relatively modest impact on NEXI estimates. Most bias in NEXI estimates comes from finite SNR. Future work should aim to further improve substrate realism to better represent the complexity of GM morphology.