Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

A Robust Neural Index of High Face Familiarity.

Humans are remarkably accurate at recognizing familiar faces, whereas their ability to recognize, or even match, unfamiliar faces is much poorer. However, previous research has failed to identify neural correlates of this striking behavioral difference. Here, we found a clear difference in brain potentials elicited by highly familiar faces versus unfamiliar faces. This effect starts 200 ms after stimulus onset and reaches its maximum at 400 to 600 ms. This sustained-familiarity effect was substantially larger than previous candidates for a neural familiarity marker and was detected in almost all participants, representing a reliable index of high familiarity. Whereas its scalp distribution was consistent with a generator in the ventral visual pathway, its modulation by repetition and degree of familiarity suggests an integration of affective and visual information.

Later but not early stages of familiar face recognition depend strongly on attentional resources: Evidence from event-related brain potentials.

In everyday life we usually recognise personally familiar faces efficiently and without apparent effort. This study examined to which extent the neural processes involved in recognising personally familiar faces depend on attentional resources by analysing event-related brain potentials. In two experiments, participants were presented with multiple ambient images of highly personally familiar and unfamiliar faces and pictures of butterflies, with a letter string superimposed on each image. Their task was either to indicate when a butterfly occurred (effectively ignoring the letter strings) or to indicate whether each letter string contained the letter X or N. Attentional resource load was manipulated in the letter task by presenting the target among different distractor letters (high load; Experiment 1) or by using only a single repeated letter in each string (low load; Experiment 2). ERPs revealed more negative amplitudes for familiar relative to unfamiliar faces under both high and low load conditions, both in the N250, reflecting the activation of perceptual face representations, and in the subsequent Sustained Familiarity Effect (SFE). Nonetheless, while the magnitude of the N250 effect was not substantially affected by attentional load, the SFE was still present but reduced in the high relative to the low load experiment. These findings suggest that perceptual face representations are activated independent of the demands of a competing task. However, the subsequent SFE, presumably reflecting more sustained activation needed to access identity-specific knowledge that can guide potential interactions, strongly relies on the availability of attentional resources.