Lorena Santamaria

The development of functional brain connectivity during early life depends on social experience and is best understood in the context of interactions with a caregiver. It is still an open question to what extent distinct modes of functional connectivity dominate in different stages of human brain development, and whether cognitive tasks can differentially modulate them. Using electroencephalography (EEG), we investigated the development of linear and nonlinear functional brain connectivity in infants and adults while they socially interacted. Using simultaneous EEG recordings in parent-infant dyads performing two different experiments (N = 160; 80 adults and 80 infants), we computed functional connectivity capturing distinct dynamics: a linear measure capturing phase synchronization (weighted phase lag index; WPLI), and a nonlinear measure capturing information sharing (weighted symbolic mutual information; WSMI). In both tasks, adults showed higher WSMI than infants, whereas infants showed higher WPLI than adults. Moreover, infant age predicted only task-related connectivity values computed with the nonlinear measure and not with its linear counterpart, suggesting that the information-theoretic measure was more sensitive to developmental changes in task-relevant neural processing. These findings suggest that over development, a shift in dominance from linear to nonlinear modes of brain communication may be essential for supporting emerging higher cognitive abilities, such as precursors to executive function (here, attention shifting) and social decision-making. Further, this work highlights the importance of using nonlinear measures in addition to traditional linear ones, which collectively permit a more robust capture of maturational changes.

Sleep boosts the integration of memories, and can thus facilitate relational learning. This benefit may be due to memory reactivation during non-REM sleep. We set out to test this by explicitly cueing reactivation using a technique called targeted memory reactivation (TMR), in which sounds are paired with learned material in wake and then softly played during subsequent sleep, triggering reactivation of the associated memories. We specifically tested whether TMR in slow wave sleep leads to enhancements in inferential thinking in a transitive inference task. Because the Up-phase of the slow oscillation is more responsive to cues than the Down-phase, we also asked whether Up-phase stimulation is more beneficial for such integration. Our data show that TMR during the Up-Phase boosts the ability to make inferences, but only for the most distant inferential leaps. Up-phase stimulation was also associated with detectable memory reinstatement, whereas Down-phase stimulation led to below-chance performance the next morning. Detection of memory reinstatement after Up-state stimulation was negatively correlated with performance on the most difficult inferences the next morning. These findings demonstrate that cueing memory reactivation at specific time points in sleep can benefit difficult relational learning problems.

Sleep is a complex physiological process with an important role in memory consolidation characterised by a series of spatiotemporal changes in brain activity and connectivity. Here, we investigate how task-related responses differ between pre-sleep wake, sleep, and post-sleep wake. To this end, we trained participants on a serial reaction time task using both right and left hands using Targeted Memory Reactivation (TMR), in which auditory cues are associated with learned material and then re-presented in subsequent wake or sleep periods in order to elicit memory reactivation. The neural responses just after each cue showed increased theta band connectivity between frontal and other cortical regions, as well as between hemispheres, in slow wave sleep compared to pre- or post-sleep wake. This pattern was consistent across the cues associated with both right- and left-handed movements. We also searched for hand-specific connectivity and found that this could be identified in within-hemisphere connectivity after TMR cues during sleep and post-sleep sessions. The fact that we could identify which hand had been cued during sleep suggests that these connectivity measures could potentially be used to determine how successfully memory is reactivated by our manipulation. Collectively, these findings indicate that TMR modulates the brain cortical networks showing clear differences between wake and sleep connectivity patterns.

Transitive inference is a measure of relational learning that has been shown to improve across sleep. Here, we examine this phenomenon further by studying the impact of encoding strength and joint rank. In experiment 1, participants learned adjacent premise pairs and were then tested on inferential problems derived from those pairs. In line with prior work, we found improved transitive inference performance after retention across a night of sleep compared with wake alone. Experiment 2 extended these findings using a within-subject design and found superior transitive inference performance on a hierarchy, consolidated across 27 h including sleep compared with just 3 h of wake. In both experiments, consolidation-related improvement was enhanced when presleep learning (i.e., encoding strength) was stronger. We also explored the interaction of these effects with the joint rank effect, in which items were scored according to their rank in the hierarchy, with more dominant item pairs having the lowest scores. Interestingly, the consolidation-related benefit was greatest for more dominant inference pairs (i.e., those with low joint rank scores). Overall, our findings provide further support for the improvement of transitive inference across a consolidation period that includes sleep. We additionally show that encoding strength and joint rank strongly modulate this effect.

Neural connectivity analysis is often performed on continuous data that has been discretized into temporal windows of a fixed length. However, the selection of an optimal window length is non-trivial, and depends on the properties of the connectivity metric being used as well as the effects of interest within the data (e.g. developmental or inter-brain effects). A systematic investigation of these factors, and objective criteria for window size selection are currently missing in the literature, particularly in regard to pediatric datasets. Here, we provide a principled examination of the effect of window size on optimization of signal to noise ratio for linear and non-linear EEG connectivity, as applied to infant, adult and dyadic (infant-adult) datasets. We employed a linear weighted phase lag index (wPLI), and a nonlinear weighted symbolic mutual information (wSMI) metric to assess brain connectivity for each dataset. Our results showed a clear polar dissociation between linear and non-linear metrics, as well as between infant and adult datasets in optimal window size. Further, optimal dyadic (infant-adult) window size settings defaulted to one or the partner rather than reflecting an intermediate compromise. Given the specificity of these results (i.e. there was no single window size that was optimal for all contrasts), we conclude that a formal analysis of optimal window size may be useful prior to conducting any new connectivity analysis. Here, we recommend guiding principles, performance metrics and decision criteria for optimal and unbiased window size selection.

Sleep has been shown to boost the integration of memories, and thus to facilitate relational learning. This benefit is thought to rely upon memory reactivation during non-REM sleep. We set out to test this possibility by explicitly cueing such reactivation using a technique called targeted memory reactivation (TMR), in which sounds are paired with learned material in wake and then softly played back to the participant in sleep, triggering reactivation of the associated memories. Specifically, we tested whether TMR during slow wave sleep leads to enhancements in inferential thinking in a transitive inference task. Because the Up-phase of the slow oscillation is more responsive to external cues than the Down-phase, we also asked whether stimulation at this specific phase is more beneficial for such integration. Our data show that Up-phase TMR boosts the ability to make inferences, but only for the most distant inferential leaps. Such stimulation was also associated with detectable memory reinstatement, whereas Down-phase stimulation produced no such trace and led to below-chance performance. These findings demonstrate that cueing memory reactivation at the right time points in sleep can provide a direct benefit to difficult relational learning problems.

Sleep has been shown to boost the integration of memories, and thus to facilitate relational learning. This benefit is thought to rely upon memory reactivation during non-REM sleep. We set out to test this possibility by explicitly cueing such reactivation using a technique called targeted memory reactivation (TMR), in which sounds are paired with learned material in wake and then softly played back to the participant in sleep, triggering reactivation of the associated memories. Specifically, we tested whether TMR during slow wave sleep leads to enhancements in inferential thinking in a transitive inference task. Because the Up-phase of the slow oscillation is more responsive to external cues than the Down-phase, we also asked whether stimulation at this specific phase is more beneficial for such integration. Our data show that Up-phase TMR boosts the ability to make inferences, but only for the most distant inferential leaps. Such stimulation was also associated with detectable memory reinstatement, whereas Down-phase stimulation produced no such trace and led to below-chance performance. These findings demonstrate that cueing memory reactivation at the right time points in sleep can provide a direct benefit to difficult relational learning problems. Significance Statement: Memory reactivation in sleep is thought to be important for integrative thinking. We examined this by explicitly cueing reactivation of a transitive inference task during slow wave sleep using Targeted Memory Reactivation (TMR). Because TMR at different phases of the slow oscillation has different impacts, we cued different hierarchies at Up and Down phases. Up-phase TMR enhanced inferential performance and was associated with classifiable memory reactivation. Conversely, Down-phase TMR lead to a short-term impairment in inferential thinking and no detectable reactivation. These findings provide the first evidence that TMR can boost transitive inference and is thus important for integration and reasoning.

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.

The commercial availability of many real-life smart sensors, wearables and mobile apps provides a valuable information resource about a wide range of human behavioral, physiological and social markers which can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly-sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper we propose a Multi-Domain Digital Neurophenotyping Model which is based on the socio-ecological model of health. The proposed model presents a holistic approach of digital mental health, leveraging on recent neuroscientific advances, and therefore could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.