TY - JOUR TI - Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers AU - Engemann, Denis A AU - Kozynets, Oleh AU - Sabbagh, David AU - LemaƮtre, Guillaume AU - Varoquaux, Gael AU - Liem, Franziskus AU - Gramfort, Alexandre A2 - Shackman, Alexander A2 - de Lange, Floris P A2 - Tsvetanov, Kamen A2 - Trujillo-Barreto, Nelson VL - 9 PY - 2020 DA - 2020/05/19 SP - e54055 C1 - eLife 2020;9:e54055 DO - 10.7554/eLife.54055 UR - https://doi.org/10.7554/eLife.54055 AB - Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals. KW - biomarker KW - aging KW - magnetic resonance imaging KW - magnetoencephalogrphy KW - oscillations KW - machine learning JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -