O'Reilly, JAZhu, JDSowman, PF2024-10-022024-10-022024-09-11Neural Networks, ISSN: 0893-6080 (Print); 1879-2782 (Online), Elsevier BV, 180, 106731-. doi: 10.1016/j.neunet.2024.1067310893-60801879-2782http://hdl.handle.net/10292/18093Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes. This paper describes current source estimation from simultaneously recorded magneto- and electro-encephalography (MEEG) using a recurrent neural network (RNN) that learns sequential relationships from neural data. The RNN was trained in two phases: (1) pre-training and (2) transfer learning with L1 regularization applied to the source estimation layer. Performance of using scaled labels derived from MEEG, magnetoencephalography (MEG), or electroencephalography (EEG) were compared, as were results from volumetric source space with free dipole orientation and surface source space with fixed dipole orientation. Exact low-resolution electromagnetic tomography (eLORETA) and mixed-norm L1/L2 (MxNE) source estimation methods were also applied to these data for comparison with the RNN method. The RNN approach outperformed other methods in terms of output signal-to-noise ratio, correlation and mean-squared error metrics evaluated against reference event-related field (ERF) and event-related potential (ERP) waveforms. Using MEEG labels with fixed-orientation surface sources produced the most consistent estimates. To estimate sources of ERF and ERP waveforms, the RNN generates temporal dynamics within its internal computational units, driven by sequential structure in neural data used as training labels. It thus provides a data-driven model of computational transformations from psychophysiological events into corresponding event-related neural signals, which is unique among MEEG source reconstruction solutions.© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by nc-nd/4.0/).http://creativecommons.org/licenses/by-nc-nd/4.0/Artificial neural networksBiomedical signal processingComputational neuroscienceDeep learningElectroencephalography (EEG)Magnetoencephalography (MEG)Neural source reconstruction46 Information and Computing Sciences4611 Machine LearningBioengineeringMental HealthBiomedical ImagingMachine Learning and Artificial IntelligenceNeurosciencesArtificial Intelligence & Image Processing4602 Artificial intelligence4611 Machine learning4905 StatisticsJournal ArticleOpenAccess10.1016/j.neunet.2024.106731