Characterizing directional dynamics of semantic prediction based on inter-regional temporal generalization.

The event-related potential/field component N400(m) has been widely used as a neural index for semantic prediction. It has long been hypothesized that feedback information from inferior frontal areas plays a critical role in generating the N400. However, due to limitations in causal connectivity estimation, direct testing of this hypothesis has remained difficult. Here, magnetoencephalography (MEG) data was obtained during a classic N400 paradigm where the semantic predictability of a fixed target noun was manipulated in simple German sentences. To estimate causality, we implemented a novel approach based on machine learning and temporal generalization to estimate the effect of inferior frontal gyrus (IFG) on temporal areas. In this method, a support vector machine (SVM) classifier is trained on each time point of the neural activity in IFG to classify less predicted (LP) and highly predicted (HP) nouns and then tested on all time points of superior/middle temporal sub-regions activity (and vice versa, to establish spatio-temporal evidence for or against causality). The decoding accuracy was significantly above chance level when the classifier was trained on IFG activity and tested on future activity in superior and middle temporal gyrus (STG/MTG). The results present new evidence for a model predictive speech comprehension where predictive IFG activity is fed back to shape subsequent activity in STG/MTG, implying a feedback mechanism in N400 generation. In combination with the also observed strong feedforward effect from left STG/MTG to IFG, our findings provide evidence of dynamic feedback and feedforward influences between IFG and temporal areas during N400 generation.

Decoding perceptual awareness across the brain with a no-report fMRI masking paradigm.

Does perceptual awareness arise within the sensory regions of the brain or within higher-level regions (e.g., the frontal lobe)? To answer this question, researchers traditionally compare neural activity when observers report being aware versus being unaware of a stimulus. However, it is unclear whether the resulting activations are associated with the conscious perception of the stimulus or the post-perceptual processes associated with reporting that stimulus. To address this limitation, we used both report and no-report conditions in a visual masking paradigm while participants were scanned using functional MRI (fMRI). We found that the overall univariate response to visible stimuli in the frontal lobe was robust in the report condition but disappeared in the no-report condition. However, using multivariate patterns, we could still decode in both conditions whether a stimulus reached conscious awareness across the brain, including in the frontal lobe. These results help reconcile key discrepancies in the recent literature and provide a path forward for identifying the neural mechanisms associated with perceptual awareness.

A Temporal Neural Trace of Wavelet Coefficients in Human Object Vision: An MEG Study.

Wavelet transform has been widely used in image and signal processing applications such as denoising and compression. In this study, we explore the relation of the wavelet representation of stimuli with MEG signals acquired from a human object recognition experiment. To investigate the signature of wavelet descriptors in the visual system, we apply five levels of multi-resolution wavelet decomposition to the stimuli presented to participants during MEG recording and extract the approximation and detail sub-bands (horizontal, vertical, diagonal) coefficients in each level of decomposition. Apart from, employing multivariate pattern analysis (MVPA), a linear support vector classifier (SVM) is trained and tested over the time on MEG pattern vectors to decode neural information. Then, we calculate the representational dissimilarity matrix (RDM) on each time point of the MEG data and also on wavelet descriptors using classifier accuracy and one minus Pearson correlation coefficient, respectively. Given the time-courses calculated from performing the Pearson correlation between the wavelet descriptors RDMs and MEG decoding accuracy in each time point, our result shows that the peak latency of the wavelet approximation time courses occurs later for higher level coefficients. Furthermore, studying the neural trace of detail sub-bands indicates that the overall number of statistically significant time points for the horizontal and vertical detail coefficients is noticeably higher than diagonal detail coefficients, confirming the evidence of the oblique effect that the horizontal and vertical lines are more decodable in the human brain.