A magnetoencephalography study of first-time mothers listening to infant cries.

Studies using magnetoencephalography (MEG) have identified the orbitofrontal cortex (OFC) to be an important early hub for a "parental instinct" in the brain. This complements the finding from functional magnetic resonance imaging studies linking reward, emotion regulation, empathy, and mentalization networks to the "parental brain." Here, we used MEG in 43 first-time mothers listening to infant and adult cry vocalizations to investigate the link with mother-infant postpartum bonding scores and their level of sleep deprivation (assessed using both actigraphy and sleep logs). When comparing brain responses to infant versus adult cry vocalizations, we found significant differences at around 800-1,000 ms after stimuli onset in the primary auditory cortex, superior temporal gyrus, hippocampal areas, insula, precuneus supramarginal gyrus, postcentral gyrus, and posterior cingulate gyrus. Importantly, mothers with weaker bonding scores showed decreased brain responses to infant cries in the auditory cortex, middle and superior temporal gyrus, OFC, hippocampal areas, supramarginal gyrus, and inferior frontal gyrus at around 100-300 ms after the stimulus onset. In contrast, we did not find correlations with sleep deprivation scores. The significant decreases in brain processing of an infant's distress signals could potentially be a novel signature of weaker infant bonding in new mothers and should be investigated in vulnerable populations.

Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep.

The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.