A deep learning framework integrating multiscale source-space attention and spatiotemporal feature fusion for magnetic source imaging

Magnetic source imaging (MSI) is a noninvasive neuroimaging technique for reconstructing brain activity from scalp magnetoencephalography (MEG) recordings in vivo. However, due to the limited resolution of MEG from measured sensors, MSI still involves the solution of a highly ill-posed inverse problem. Superior to the traditional localization methods, the deep learning (DL) approaches have been used in the MSI for more accurately mapping the relationships between the sensors and sources. This study aims to develop a DL framework that enhances both spatial and temporal resolution for improved source localization, particularly in complex scenarios involving multiple sources or low signal-to-noise ratios (SNR). In this study, a DL-based MSI localization, multiscale source-space attention-based encoder-decoder network (MSA-EDNet), was proposed to accomplish the accurate localization of brain activities. By implementing the encoding–decoding structure, the spatiotemporal features are extracted by multi-scale and dilated convolutions and the source space dimension is enhanced by the source-space attention mechanism (SSAM) during encoding; the deconvolution with bidirectional long short-term memory (BiLSTM) is employed to recovery the source features for decoding. For performance evaluation, our proposed MSA-EDNet was compared with eight novel methods of MNE, dSPM, sLORETA, eLORETA, LCMV, ConvDip, LSTM, and SSINet on simulated and real MEG datasets, respectively. It was demonstrated that our approach outperformed other MSI algorithms, and achieved the lowest localization error (LE), the highest area under the curve (AUC) of (5.305 ± 4.953 mm), (0.961 ± 0.027) for the single source, and the highest AUCs of (0.947 ± 0.093), (0.901 ± 0.099) for two sources and three sources at the same condition (LNs = 1, SNR = 30 dB) on simulated dataset, respectively. Our proposed approach demonstrates robust capacities for betterment localization of MEG brain activities and shows great potentials for the related fields of brain research.