Singing training predicts increased insula connectivity with speech and respiratory sensorimotor areas at rest

The insula contributes to the detection of salient events during goal-directed behavior and participates in the coordination of motor, multisensory, and cognitive systems. Recent task-fMRI studies with trained singers suggest that singing experience can enhance the access to these resources. However, the long-term effects of vocal training on insula-based networks are still unknown. In this study, we employed resting-state fMRI to assess experience-dependent differences in insula co-activation patterns between conservatory-trained singers and non-singers. Results indicate enhanced bilateral anterior insula connectivity in singers relative to non-singers with constituents of the speech sensorimotor network. Specifically, with the cerebellum (lobule V-VI) and the superior parietal lobes. The reversed comparison showed no effects. The amount of accumulated singing training predicted enhanced bilateral insula co-activation with primary sensorimotor areas representing the diaphragm and the larynx/phonation area-crucial regions for cortico-motor control of complex vocalizations-as well as the bilateral thalamus and the left putamen. Together, these findings highlight the neuroplastic effect of expert singing training on insula-based networks, as evidenced by the association between enhanced insula co-activation profiles in singers and the brain's speech motor system components.

Moderate associations between BDNF Val66Met gene polymorphism, musical expertise, and mismatch negativity

Auditory predictive processing relies on a complex interaction between environmental, neurophysiological, and genetic factors. In this view, the mismatch negativity (MMN) and intensive training on a musical instrument for several years have been used for studying environment-driven neural adaptations in audition. In addition, brain-derived neurotrophic factor (BDNF) has been shown crucial for both the neurogenesis and the later adaptation of the auditory system. The functional single-nucleotide polymorphism (SNP) Val66Met (rs6265) in the BDNF gene can affect BDNF protein levels, which are involved in neurobiological and neurophysiological processes such as neurogenesis and neuronal plasticity. In this study, we hypothesised that genetic variation within the BDNF gene would be associated with different levels of neuroplasticity of the auditory cortex in 74 musically trained participants. To achieve this goal, musicians and non-musicians were recruited and divided in Val/Val and Met- (Val/Met and Met/Met) carriers and their brain activity was measured with magnetoencephalography (MEG) while they listened to a regular auditory sequence eliciting different types of prediction errors. MMN responses indexing those prediction errors were overall enhanced in Val/Val carriers who underwent intensive musical training, compared to Met-carriers and non-musicians with either genotype. Although this study calls for replications with larger samples, our results provide a first glimpse of the possible role of gene-regulated neurotrophic factors in the neural adaptations of automatic predictive processing in the auditory domain after long-term training.

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.

Brain recognition of previously learned versus novel temporal sequences: a differential simultaneous processing

Memory for sequences is a central topic in neuroscience, and decades of studies have investigated the neural mechanisms underlying the coding of a wide array of sequences extended over time. Yet, little is known on the brain mechanisms underlying the recognition of previously memorized versus novel temporal sequences. Moreover, the differential brain processing of single items in an auditory temporal sequence compared to the whole superordinate sequence is not fully understood. In this magnetoencephalography (MEG) study, the items of the temporal sequence were independently linked to local and rapid (2–8 Hz) brain processing, while the whole sequence was associated with concurrent global and slower (0.1–1 Hz) processing involving a widespread network of sequentially active brain regions. Notably, the recognition of previously memorized temporal sequences was associated to stronger activity in the slow brain processing, while the novel sequences required a greater involvement of the faster brain processing. Overall, the results expand on well-known information flow from lower- to higher order brain regions. In fact, they reveal the differential involvement of slow and faster whole brain processing to recognize previously learned versus novel temporal information.

Enhanced mismatch negativity in harmonic compared to inharmonic sounds

Many natural sounds have frequency spectra composed of integer multiples of a fundamental frequency. This property, known as harmonicity, plays an important role in auditory information processing. However, the extent to which harmonicity influences the processing of sound features beyond pitch is still unclear. This is interesting because harmonic sounds have lower information entropy than inharmonic sounds. According to predictive processing accounts of perception, this property could produce more salient neural responses due to the brain's weighting of sensory signals according to their uncertainty. In the present study, we used electroencephalography to investigate brain responses to harmonic and inharmonic sounds commonly occurring in music: Piano tones and hi‐hat cymbal sounds. In a multifeature oddball paradigm, we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants in listeners with and without congenital amusia—an impairment of pitch processing. As hypothesized, we observed larger amplitudes and earlier latencies (for both MMN and P3a) in harmonic compared with inharmonic sounds. These harmonicity effects were modulated by sound feature. Moreover, the difference in P3a latency between harmonic and inharmonic sounds was larger for controls than amusics. We propose an explanation of these results based on predictive coding and discuss the relationship between harmonicity, information entropy, and precision weighting of prediction errors.

Data and model considerations for estimating time-varying functional connectivity in fMRI

•

Time-varying FC models sometimes fail to detect temporal changes in fMRI data.

•

Between-subject and within-session FC variability affect model stasis.

•

The choice of parcellation affects model stasis in real fMRI data.

•

The number of observations and free parameters per state critically affect model stasis.

Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the model's ability to detect temporal changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the model's ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters per state that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model.

Rapid encoding of musical tones discovered in whole-brain connectivity

Information encoding has received a wide neuroscientific attention, but the underlying rapid spatiotemporal brain dynamics remain largely unknown. Here, we investigated the rapid brain mechanisms for encoding of sounds forming a complex temporal sequence. Specifically, we used magnetoencephalography (MEG) to record the brain activity of 68 participants while they listened to a highly structured musical prelude. Functional connectivity analyses performed using phase synchronisation and graph theoretical measures showed a large network of brain areas recruited during encoding of sounds, comprising primary and secondary auditory cortices, frontal operculum, insula, hippocampus and basal ganglia. Moreover, our results highlighted the rapid transition of brain activity from primary auditory cortex to higher order association areas including insula and superior temporal pole within a whole-brain network, occurring during the first 220 ms of the encoding process. Further, we discovered that individual differences along cognitive abilities and musicianship modulated the degree centrality of the brain areas implicated in the encoding process. Indeed, participants with higher musical expertise presented a stronger centrality of superior temporal gyrus and insula, while individuals with high working memory abilities showed a stronger centrality of frontal operculum. In conclusion, our study revealed the rapid unfolding of brain network dynamics responsible for the encoding of sounds and their relationship with individual differences, showing a complex picture which extends beyond the well-known involvement of auditory areas. Indeed, our results expanded our understanding of the general mechanisms underlying auditory pattern encoding in the human brain.

Applying Spike-density component analysis for high-accuracy auditory event-related potentials in children

OBJECTIVE

Overlapping neurophysiological signals are the main obstacle preventing from using cortical auditory event-related potentials (AEPs) in clinical settings. Children AEPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently developed, to isolate with high accuracy the neural components of auditory responses of 8-year-old children.

METHODS

Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard AEP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC).

RESULTS

SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected.

CONCLUSION

Superior accuracy in isolating neural components at the individual level was demonstrated for SCA-HSAC over other SCA approaches even for children AEPs.

SIGNIFICANCE

Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults.

Brain predictive coding processes are associated to COMT gene Val158Met polymorphism

Predicting events in the ever-changing environment is a fundamental survival function intrinsic to the physiology of sensory systems, whose efficiency varies among the population. Even though it is established that a major source of such variations is genetic heritage, there are no studies tracking down auditory predicting processes to genetic mutations. Thus, we examined the neurophysiological responses to deviant stimuli recorded with magnetoencephalography (MEG) in 108 healthy participants carrying different variants of Val158Met single-nucleotide polymorphism (SNP) within the catechol-O-methyltransferase (COMT) gene, responsible for the majority of catecholamines degradation in the prefrontal cortex. Our results showed significant amplitude enhancement of prediction error responses originating from the inferior frontal gyrus, superior and middle temporal cortices in heterozygous genotype carriers (Val/Met) vs homozygous (Val/Val and Met/Met) carriers. Integrating neurophysiology and genetics, this study shows how the neural mechanisms underlying optimal deviant detection vary according to the gene-determined cathecolamine levels in the brain.

Decomposing neural responses to melodic surprise in musicians and non-musicians: Evidence for a hierarchy of predictions in the auditory system

Neural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than probabilistic prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper probabilistic prediction were reflected in later components: The mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning.

Pessimistic outcome expectancy does not explain ambiguity aversion in decision-making under uncertainty

When faced with a decision, most people like to know the odds and prefer to avoid ambiguity. It has been suggested that this aversion to ambiguity is linked to people's assumption of worst possible outcomes. We used two closely linked behavioural tasks in 78 healthy participants to investigate whether such pessimistic prior beliefs can explain ambiguity aversion. In the risk-taking task, participants had to decide whether or not they place a bet, while in the beliefs task, participants were asked what they believed would be the outcome. Unexpectedly, we found that in the beliefs task, participants were not overly pessimistic about the outcome in the ambiguity condition and in fact closer to optimal levels of decision-making than in the risk conditions. While individual differences in pessimism could explain outcome expectancy, they had no effect on ambiguity aversion. Consequently, ambiguity aversion is more likely caused by general caution than by expectation of negative outcomes despite pessimism-dependent subjective weighting of probabilities.

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.

Sleep is composed of a number of different stages, each associated with a different pattern of brain activity. Here, using a data-driven Hidden Markov Model (HMM) of fMRI data, the authors discover a more complex set of neural activity states underlying the conventional stages of non-REM sleep.

Oxytocin improves synchronisation in leader-follower interaction

The neuropeptide oxytocin has been shown to affect social interaction. Meanwhile, the underlying mechanism remains highly debated. Using an interpersonal finger-tapping paradigm, we investigated whether oxytocin affects the ability to synchronise with and adapt to the behaviour of others. Dyads received either oxytocin or a non-active placebo, intranasally. We show that in conditions where one dyad-member was tapping to another unresponsive dyad-member – i.e. one was following another who was leading/self-pacing – dyads given oxytocin were more synchronised than dyads given placebo. However, there was no effect when following a regular metronome or when both tappers were mutually adapting to each other. Furthermore, relative to their self-paced tapping partners, oxytocin followers were less variable than placebo followers. Our data suggests that oxytocin improves synchronisation to an unresponsive partner’s behaviour through a reduction in tapping-variability. Hence, oxytocin may facilitate social interaction by enhancing sensorimotor predictions supporting interpersonal synchronisation. The study thus provides novel perspectives on how neurobiological processes relate to socio-psychological behaviour and contributes to the growing evidence that synchronisation and prediction are central to social cognition.

It don't mean a thing…

Music is experienced and understood on the basis of foreground/background relationships created between actual music and the underlying meter. In contemporary styles of music so-called polyrhythmic, structures hence create tension between a counter pulse and the main pulse. This exerts a marked influence on the listener, particularly when the experience of the original meter is maintained during the counter pulse. We here demonstrate that Brodmann area 47, an area associated with higher processing of language, is activated bilaterally when musicians tap the main pulse in a polymetric context where the music emphasizes a counter meter. This suggests that the processing of metric elements of music relies on brain areas also involved in language comprehension. We propose that BA47 is involved in general neuronal processing of temporal coherence subserving both language and music.