Decoding semantics from dynamic brain activation patterns: From trials to task in EEG/MEG source space

Abstract The temporal dynamics within the semantic brain network and its dependence on stimulus and task parameters are still not well understood. Here, we addressed this by decoding task as well as stimulus information from source-estimated EEG/MEG data. We presented the same visual word stimuli in a lexical decision (LD) and three semantic decision (SD) tasks. The meanings of the presented words varied across five semantic categories. Source space decoding was applied over time in five ROIs in the left hemisphere (Anterior and Posterior Temporal Lobe, Inferior Frontal Gyrus, Primary Visual Areas, and Angular Gyrus) and one in the right hemisphere (Anterior Temporal Lobe). Task decoding produced sustained significant effects in all ROIs from 50-100 ms, both when categorising tasks with different semantic demands (LD-SD) as well as for similar semantic tasks (SD-SD). In contrast, semantic word category could only be decoded in lATL, rATL, PTC and IFG, between 250-500 ms. Furthermore, we compared two approaches to source space decoding: Conventional ROI-by-ROI decoding and combined-ROI decoding with back-projected activation patterns. The former produced more reliable results for word-category decoding while the latter was more informative for task-decoding. This indicates that task effects are distributed across the whole semantic network while stimulus effects are more focal. Our results demonstrate that the semantic network is widely distributed but that bilateral anterior temporal lobes together with control regions are particularly relevant for the processing of semantic information. Significance Statement Most previous decoding analyses of EEG/MEG data have focussed on decoding performance over time in sensor space. Here for the first time we compared two approaches to source space decoding in order to reveal the spatio-temporal dynamics of both task and stimulus features in the semantic brain network. This revealed that even semantic tasks with similar task demands can be decoded across the network from early latencies, despite reliable differences in their evoked responses. Furthermore, stimulus features can be decoded in both tasks but only for a subset of ROIs and following the earliest task effects. These results inform current neuroscientific models of controlled semantic cognition.