STARTS: A Self-Adapted Spatio-Temporal Framework for Automatic E/MEG Source Imaging

• Published in: IEEE transactions on medical imaging Vol. 44; no. 3; pp. 1230 - 1242
• Main Authors: Feng, Zhao, Guan, Cuntai, Zheng, Ruifeng, Sun, Yu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2025
• Subjects: Algorithms; Bayesian framework; Brain - diagnostic imaging; Brain - physiology; Brain modeling; Computational modeling; Computer Simulation; Correlation; Data models; E/MEG; Electroencephalography - methods; Epilepsy - diagnostic imaging; Epilepsy - physiopathology; Estimation; Humans; Image Processing, Computer-Assisted - methods; Image reconstruction; Imaging; Magnetoencephalography - methods; Noise; Probabilistic logic; Signal Processing, Computer-Assisted; source imaging; spatio-temporal; Sun
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2024.3483292

To obtain accurate brain source activities, the highly ill-posed source imaging of electro- and magneto-encephalography (E/MEG) requires proficiency in incorporation of biophysiological constraints and signal-processing techniques. Here, we propose a spatio-temporal-constrainted E/MEG source imaging framework-STARTS that can reconstruct the source in a fully automatic way. Specifically, a block-diagonal covariance was adopted to reconstruct the source extents while maintain spatial homogeneity. Temporal basis functions (TBFs) of both sources and noise were estimated and updated in a data-driven fashion to alleviate the influence of noises and further improve source localization accuracy. The performance of the proposed STARTS was quantitatively assessed through a series of simulation experiments, wherein superior results were obtained in comparison with the benchmark ESI algorithms (including LORETA, EBI-Convex, BESTIES & SI-STBF). Additional validations on epileptic and resting-state EEG data further indicate that the STARTS can produce neurophysiologically plausible results. Moreover, a computationally efficient version of STARTS: smooth STARTS was also introduced with an elementary spatial constraint, which exhibited comparable performance and reduced execution cost. In sum, the proposed STARTS, with its advanced spatio-temporal constraints and self-adapted update operation, provides an effective and efficient approach for E/MEG source imaging.