Inference in imaging-based biophysical modelling provides a principled way of estimating model parameters, but also assessing confidence/uncertainty on results, quantifying noise effects and aiding experimental design. Traditional approaches in neuroimaging can either be very computationally expensive (e.g., Bayesian) or suitable to only certain assumptions (e.g., bootstrapping). We present a simulation-based inference approach to estimate diffusion MRI model parameters and their uncertainty. This novel framework trains a neural network to learn a Bayesian model inversion, allowing inference given unseen data. Results show a high level of agreement with conventional Markov-Chain-Monte-Carlo estimates, while offering 2-3 orders of magnitude speed-ups and inference amortisation.
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