RT Journal Article SR Electronic T1 On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge JF bioRxiv FD Cold Spring Harbor Laboratory SP 2021.03.02.433228 DO 10.1101/2021.03.02.433228 A1 Alberto De Luca A1 Andrada Ianus A1 Alexander Leemans A1 Marco Palombo A1 Noam Shemesh A1 Hui Zhang A1 Daniel C Alexander A1 Markus Nilsson A1 Martijn Froeling A1 Geert-Jan Biessels A1 Mauro Zucchelli A1 Matteo Frigo A1 Enes Albay A1 Sara Sedlar A1 Abib Alimi A1 Samuel Deslauriers-Gauthier A1 Rachid Deriche A1 Rutger Fick A1 Maryam Afzali A1 Tomasz Pieciak A1 Fabian Bogusz A1 Santiago Aja-Fernández A1 Evren Özarslan A1 Derek K Jones A1 Haoze Chen A1 Mingwu Jin A1 Zhijie Zhang A1 Fengxiang Wang A1 Vishwesh Nath A1 Prasanna Parvathaneni A1 Jan Morez A1 Jan Sijbers A1 Ben Jeurissen A1 Shreyas Fadnavis A1 Stefan Endres A1 Ariel Rokem A1 Eleftherios Garyfallidis A1 Irina Sanchez A1 Vesna Prchkovska A1 Paulo Rodrigues A1 Bennet A Landman A1 Kurt G Schilling YR 2021 UL http://biorxiv.org/content/early/2021/03/02/2021.03.02.433228.abstract AB Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.Competing Interest StatementThe authors have declared no competing interest.