Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding.

Our brain is constantly extracting, predicting, and recognising key spatiotemporal features of the physical world in order to survive. While neural processing of visuospatial patterns has been extensively studied, the hierarchical brain mechanisms underlying conscious recognition of auditory sequences and the associated prediction errors remain elusive. Using magnetoencephalography (MEG), we describe the brain functioning of 83 participants during recognition of previously memorised musical sequences and systematic variations. The results show feedforward connections originating from auditory cortices, and extending to the hippocampus, anterior cingulate gyrus, and medial cingulate gyrus. Simultaneously, we observe backward connections operating in the opposite direction. Throughout the sequences, the hippocampus and cingulate gyrus maintain the same hierarchical level, except for the final tone, where the cingulate gyrus assumes the top position within the hierarchy. The evoked responses of memorised sequences and variations engage the same hierarchical brain network but systematically differ in terms of temporal dynamics, strength, and polarity. Furthermore, induced-response analysis shows that alpha and beta power is stronger for the variations, while gamma power is enhanced for the memorised sequences. This study expands on the predictive coding theory by providing quantitative evidence of hierarchical brain mechanisms during conscious memory and predictive processing of auditory sequences.

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.

Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study.

Brain network analysis represents a powerful technique to gain insights into the connectivity profile characterizing individuals with different levels of fluid intelligence (Gf). Several studies have used diffusion tensor imaging (DTI) and slow-oscillatory resting-state fMRI (rs-fMRI) to examine the anatomical and functional aspects of human brain networks that support intelligence. In this study, we expand this line of research by investigating fast-oscillatory functional networks. We performed graph theory analyses on resting-state magnetoencephalographic (MEG) signal, in addition to structural brain networks from DTI data, comparing degree, modularity and segregation coefficient across the brain of individuals with high versus average Gf scores. Our results show that high Gf individuals have stronger degree and lower segregation coefficient than average Gf participants in a significantly higher number of brain areas with regards to structural connectivity and to the slower frequency bands of functional connectivity. The opposite result was observed for higher-frequency (gamma) functional networks, with higher Gf individuals showing lower degree and higher segregation across the brain. We suggest that gamma oscillations in more intelligent individuals might support higher local processing in segregated subnetworks, while slower frequency bands would allow a more effective information transfer between brain subnetworks, and stronger information integration.

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

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.

Capturing the non-stationarity of whole-brain dynamics underlying human brain states.

Brain dynamics depicts an extremely complex energy landscape that changes over time, and its characterisation is a central unsolved problem in neuroscience. We approximate the non-stationary landscape sustained by the human brain through a novel mathematical formalism that allows us characterise the attractor structure, i.e. the stationary points and their connections. Due to its time-varying nature, the structure of the global attractor and the corresponding number of energy levels changes over time. We apply this formalism to distinguish quantitatively between the different human brain states of wakefulness and different stages of sleep, as a step towards future clinical applications.

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.

How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure.

Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

Brain fingerprints of olfaction: a novel structural method for assessing olfactory cortical networks in health and disease.

Olfactory deficits are a common (often prodromal) symptom of neurodegenerative or psychiatric disorders. As such, olfaction could have great potential as an early biomarker of disease, for example using neuroimaging to investigate the breakdown of structural connectivity profile of the primary olfactory networks. We investigated the suitability for this purpose in two existing neuroimaging maps of olfactory networks. We found problems with both existing neuroimaging maps in terms of their structural connectivity to known secondary olfactory networks. Based on these findings, we were able to merge the existing maps to a new template map of olfactory networks with connections to all key secondary olfactory networks. We introduce a new method that combines diffusion tensor imaging with probabilistic tractography and pattern recognition techniques. This method can obtain comprehensive and reliable fingerprints of the structural connectivity underlying the neural processing of olfactory stimuli in normosmic adults. Combining the novel proposed method for structural fingerprinting with the template map of olfactory networks has great potential to be used for future neuroimaging investigations of olfactory function in disease. With time, the proposed method may even come to serve as structural biomarker for early detection of disease.

Post-traumatic stress influences the brain even in the absence of symptoms: A systematic, quantitative meta-analysis of neuroimaging studies.

Stress affects brain function, and may lead to post-traumatic stress disorder (PTSD). Considerable empirical data for the neurobiology of PTSD has been derived from neuroimaging studies, although findings have proven inconsistent. We used an activation likelihood estimation analysis to explore differences in brain activity between adults with and without PTSD in response to affective stimuli. We separated studies by type of control group: trauma-exposed and trauma-naïve. This revealed distinct patterns of differences in functional activity. Compared to trauma-exposed controls, regions of the basal ganglia were differentially active in PTSD; whereas the comparison with trauma-naïve controls revealed differential involvement in the right anterior insula, precuneus, cingulate and orbitofrontal cortices known to be involved in emotional regulation. Changes in activity in the amygdala and parahippocampal cortex distinguished PTSD from both control groups. Results suggest that trauma has a measurable, enduring effect upon the functional dynamics of the brain, even in individuals who experience trauma but do not develop PTSD. These findings contribute to the understanding of whole-brain network activity following trauma, and its transition to clinical PTSD.

Olfactory screening: validation of Sniffin' Sticks in Denmark.

OBJECTIVES: The Sniffin' Sticks 12-identification test (SIT-12) is the most commonly applied Danish olfaction screening tool; however, it has never been validated in a Danish population. The screening score depends on familiarity with descriptors, which is strongly influenced by linguistic and cultural factors, why validation is mandatory. This study aimed to validate the SIT-12 in a Danish population. DESIGN: Prospective controlled study. SETTING: Otorhinolaryngology department. PARTICIPANTS: The SIT-12 was applied to 100 normosmic, healthy adult Danish participants. MAIN OUTCOME MEASURES: Choice of descriptors was registered, along with nasal endoscopic examination, screening for cognitive impairment, depression and sinonasal symptoms. Descriptors of the original version of SIT-12 were evaluated in 50 participants, and misleading descriptors were identified. Modifications to these descriptors were subsequently validated in a comparable group of 50 participants. RESULTS: Mean odorant identification score in the evaluation group was 11.0 of a possible 12, and 11.6 in the validation group (P < 0.0001). Among all odorant identification errors in the evaluation group, 60% were due to two incorrect descriptors having close resemblance to the correct descriptors, lemon and cinnamon. Two additional descriptors were unfamiliar to more than half the participants. There was a significant difference in the distribution of wrong identification answers between odorants in the evaluation group (P < 0.001), but not in the validation group. CONCLUSIONS: The identified systematically wrong descriptors have been modified and validated in the Danish SIT-12.

Role of white-matter pathways in coordinating alpha oscillations in resting visual cortex.

In the absence of cognitive tasks and external stimuli, strong rhythmic fluctuations with a frequency ≈ 10 Hz emerge from posterior regions of human neocortex. These posterior α-oscillations can be recorded throughout the visual cortex and are particularly strong in the calcarine sulcus, where the primary visual cortex is located. The mechanisms and anatomical pathways through which local \alpha-oscillations are coordinated however, are not fully understood. In this study, we used a combination of magnetoencephalography (MEG), diffusion tensor imaging (DTI), and biophysical modeling to assess the role of white-matter pathways in coordinating cortical α-oscillations. Our findings suggest that primary visual cortex plays a special role in coordinating α-oscillations in higher-order visual regions. Specifically, the amplitudes of α-sources throughout visual cortex could be explained by propagation of α-oscillations from primary visual cortex through white-matter pathways. In particular, α-amplitudes within visual cortex correlated with both the anatomical and functional connection strengths to primary visual cortex. These findings reinforce the notion of posterior α-oscillations as intrinsic oscillations of the visual system. We speculate that they might reflect a default-mode of the visual system during which higher-order visual regions are rhythmically primed for expected visual stimuli by α-oscillations in primary visual cortex.

The human sexual response cycle: brain imaging evidence linking sex to other pleasures.

Sexual behavior is critical to species survival, yet comparatively little is known about the neural mechanisms in the human brain. Here we systematically review the existing human brain imaging literature on sexual behavior and show that the functional neuroanatomy of sexual behavior is comparable to that involved in processing other rewarding stimuli. Sexual behavior clearly follows the established principles and phases for wanting, liking and satiety involved in the pleasure cycle of other rewards. The studies have uncovered the brain networks involved in sexual wanting or motivation/anticipation, as well as sexual liking or arousal/consummation, while there is very little data on sexual satiety or post-orgasmic refractory period. Human sexual behavior also interacts with other pleasures, most notably social interaction and high arousal states. We discuss the changes in the underlying brain networks supporting sexual behavior in the context of the pleasure cycle, the changes to this cycle over the individual's life-time and the interactions between them. Overall, it is clear from the data that the functional neuroanatomy of sex is very similar to that of other pleasures and that it is unlikely that there is anything special about the brain mechanisms and networks underlying sex.

Functional graph alterations in schizophrenia: a result from a global anatomic decoupling?

During rest, the brain exhibits slow hemodynamic fluctuations (<0.1 Hz) that are correlated across spatially segregated brain regions, defining functional networks. Resting-state functional networks of people with schizophrenia were found to have graph properties that differ from those of control subjects. Namely, functional graphs from patients exhibit reduced small-worldness, increased hierarchy, lower clustering, improved efficiency and greater robustness. Notably, most of these parameters correlate with patients' cognitive performance.To test if a brain-wide coupling deficit could be at the origin of such network reorganization, we use a model of resting-state activity where the coupling strength can be manipulated. For a range of coupling values, the simulated functional graphs obtained were characterized using graph theory.For a coupling range, simulated graphs shared properties of healthy resting-state functional graphs. On decreasing the coupling strength, the resultant functional graphs exhibited a topological reorganization, in the same way as described in schizophrenia.This work shows how complex functional graph alterations reported in schizophrenia can be accounted for by a decrease in the structural coupling strength. These results are corroborated by reports of lower white matter density in schizophrenia.

The functional neuroanatomy of the evolving parent-infant relationship.

Infant survival and the development of secure and cooperative relationships are central to the future of the species. In humans, this relies heavily on the evolving early parent-infant social and affective relationship. While much is known about the behavioural and psychological components of this relationship, relatively little is known about the underlying functional neuroanatomy. Affective and social neuroscience has helped to describe the main adult brain networks involved, but has so far engaged very little with developmental findings. In this review, we seek to highlight future avenues for research by providing a coherent framework for describing the parent-infant relationship over the first 18 months. We provide an outline of the evolving nature of the relationship, starting with basic orienting and recognition processes, and culminating in the infant's attainment of higher socio-emotional and cognitive capacities. Key social and affective interactions, such as communication, cooperative play and the establishment of specific attachments propel the development of the parent-infant relationship. We summarise our current knowledge of the developing infant brain in terms of structure and function, and how these relate to the emergent abilities necessary for the formation of a secure and cooperative relationship with parents or other caregivers. Important roles have been found for brain regions including the orbitofrontal, cingulate, and insular cortices in parent-infant interactions, but it has become clear that much more information is needed about the developmental time course and connectivity of these regions.

Abnormal thalamocortical dynamics may be altered by deep brain stimulation: using magnetoencephalography to study phantom limb pain.

Deep brain stimulation (DBS) is used to alleviate chronic pain. Using magnetoencephalography (MEG) to study the mechanisms of DBS for pain is difficult because of the artefact caused by the stimulator. We were able to record activity over the occipital lobe of a patient using DBS for phantom limb pain during presentation of a visual stimulus. This demonstrates that MEG can be used to study patients undergoing DBS provided control stimuli are used to check the reliability of the data. We then asked the patient to rate his pain during and off DBS. Correlations were found between these ratings and power in theta (6-9) and beta bands (12-30). Further, there was a tendency for frequencies under 25 Hz to correlate with each other after a period off stimulation compared with immediately after DBS. The results are interpreted as reflecting abnormal thalamocortical dynamics, previously implicated in painful syndromes.