Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

The roles of gaze and head orientation in face categorization during rapid serial visual presentation

Little is known about how perceived gaze direction and head orientation may influence human categorization of visual stimuli as faces. To address this question, a sequence of unsegmented natural images, each containing a random face or a non-face object, was presented in rapid succession (stimulus duration: 91.7 ms per image) during which human observers were instructed to respond immediately to every face presentation. Faces differed in gaze and head orientation in 7 combinations - full-front views with perceived gaze (1) directed to the observer, (2) averted to the left, or (3) averted to the right, left ¾ side views with (4) direct gaze or (5) averted gaze, and right ¾ side views with (6) direct gaze or (7) averted gaze - were presented randomly throughout the sequence. We found highly accurate and rapid behavioural responses to all kinds of faces. Crucially, both perceived gaze direction and head orientation had comparable, non-interactive effects on response times, where direct gaze was responded faster than averted gaze by 48 ms and full-front view faster than ¾ side view also by 48 ms on average. Presentations of full-front faces with direct gaze led to an additive speed advantage of 96 ms to ¾ faces with averted gaze. The results reveal that the effects of perceived gaze direction and head orientation on the speed of face categorization probably depend on the degree of social relevance of the face to the viewer.

Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains

Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain.

Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain.