Active neural coordination of motor behaviors with internal states.

The brain continuously coordinates skeletomuscular movements with internal physiological states like arousal, but how is this coordination achieved? One possibility is that the brain simply reacts to changes in external and/or internal signals. Another possibility is that it is actively coordinating both external and internal activities. We used functional ultrasound imaging to capture a large medial section of the brain, including multiple cortical and subcortical areas, in marmoset monkeys while monitoring their spontaneous movements and cardiac activity. By analyzing the causal ordering of these different time series, we found that information flowing from the brain to movements and heart-rate fluctuations were significantly greater than in the opposite direction. The brain areas involved in this external versus internal coordination were spatially distinct, but also extensively interconnected. Temporally, the brain alternated between network states for this regulation. These findings suggest that the brain's dynamics actively and efficiently coordinate motor behavior with internal physiology.

A mechanism for punctuating equilibria during mammalian vocal development.

Evolution and development are typically characterized as the outcomes of gradual changes, but sometimes (states of equilibrium can be punctuated by sudden change. Here, we studied the early vocal development of three different mammals: common marmoset monkeys, Egyptian fruit bats, and humans. Consistent with the notion of punctuated equilibria, we found that all three species undergo at least one sudden transition in the acoustics of their developing vocalizations. To understand the mechanism, we modeled different developmental landscapes. We found that the transition was best described as a shift in the balance of two vocalization landscapes. We show that the natural dynamics of these two landscapes are consistent with the dynamics of energy expenditure and information transmission. By using them as constraints for each species, we predicted the differences in transition timing from immature to mature vocalizations. Using marmoset monkeys, we were able to manipulate both infant energy expenditure (vocalizing in an environment with lighter air) and information transmission (closed-loop contingent parental vocal playback). These experiments support the importance of energy and information in leading to punctuated equilibrium states of vocal development.

Arousal dynamics drive vocal production in marmoset monkeys.

Vocal production is the result of interacting cognitive and autonomic processes. Despite claims that changes in one interoceptive state (arousal) govern primate vocalizations, we know very little about how it influences their likelihood and timing. In this study we investigated the role of arousal during naturally occurring vocal production in marmoset monkeys. Throughout each session, naturally occurring contact calls are produced more quickly, and with greater probability, during higher levels of arousal, as measured by heart rate. On average, we observed a steady increase in heart rate 23 s before the production of a call. Following call production, there is a sharp and steep cardiac deceleration lasting ∼8 s. The dynamics of cardiac fluctuations around a vocalization cannot be completely predicted by the animal's respiration or movement. Moreover, the timing of vocal production was tightly correlated to the phase of a 0.1-Hz autonomic nervous system rhythm known as the Mayer wave. Finally, a compilation of the state space of arousal dynamics during vocalization illustrated that perturbations to the resting state space increase the likelihood of a call occurring. Together, these data suggest that arousal dynamics are critical for spontaneous primate vocal production, not only as a robust predictor of the likelihood of vocal onset but also as scaffolding on which behavior can unfold.

A comparative study of statistical methods used to identify dependencies between gene expression signals.

One major task in molecular biology is to understand the dependency among genes to model gene regulatory networks. Pearson's correlation is the most common method used to measure dependence between gene expression signals, but it works well only when data are linearly associated. For other types of association, such as non-linear or non-functional relationships, methods based on the concepts of rank correlation and information theory-based measures are more adequate than the Pearson's correlation, but are less used in applications, most probably because of a lack of clear guidelines for their use. This work seeks to summarize the main methods (Pearson's, Spearman's and Kendall's correlations; distance correlation; Hoeffding's D: measure; Heller-Heller-Gorfine measure; mutual information and maximal information coefficient) used to identify dependency between random variables, especially gene expression data, and also to evaluate the strengths and limitations of each method. Systematic Monte Carlo simulation analyses ranging from sample size, local dependence and linear/non-linear and also non-functional relationships are shown. Moreover, comparisons in actual gene expression data are carried out. Finally, we provide a suggestive list of methods that can be used for each type of data set.

Cooperative vocal control in marmoset monkeys via vocal feedback.

Humans adjust speech amplitude as a function of distance from a listener; we do so in a manner that would compensate for such distance. This ability is presumed to be the product of high-level sociocognitive skills. Nonhuman primates are thought to lack such socially related flexibility in vocal production. Using predictions from a simple arousal-based model whereby vocal feedback from a conspecific modulates the drive to produce a vocalization, we tested whether another primate exhibits this type of cooperative vocal control. We conducted a playback experiment with marmoset monkeys and simulated "far-away" and "nearby" conspecifics using contact calls that differed in sound intensity. We found that marmoset monkeys increased the amplitude of their contact calls and produced such calls with shorter response latencies toward more distant conspecifics. The same was not true in response to changing levels of background noise. To account for how simulated conspecific distance can change both the amplitude and timing of vocal responses, we developed a model that incorporates dynamic interactions between the auditory system and limbic "drive" systems. Overall, our data show that, like humans, marmoset monkeys cooperatively control the acoustics of their vocalizations according to changes in listener distance, increasing the likelihood that a conspecific will hear their call. However, we propose that such cooperative vocal control is a system property that does not necessitate any particularly advanced sociocognitive skill. At least in marmosets, this vocal control can be parsimoniously explained by the regulation of arousal states across two interacting individuals via vocal feedback.

Facial expressions and the evolution of the speech rhythm.

In primates, different vocalizations are produced, at least in part, by making different facial expressions. Not surprisingly, humans, apes, and monkeys all recognize the correspondence between vocalizations and the facial postures associated with them. However, one major dissimilarity between monkey vocalizations and human speech is that, in the latter, the acoustic output and associated movements of the mouth are both rhythmic (in the 3- to 8-Hz range) and tightly correlated, whereas monkey vocalizations have a similar acoustic rhythmicity but lack the concommitant rhythmic facial motion. This raises the question of how we evolved from a presumptive ancestral acoustic-only vocal rhythm to the one that is audiovisual with improved perceptual sensitivity. According to one hypothesis, this bisensory speech rhythm evolved through the rhythmic facial expressions of ancestral primates. If this hypothesis has any validity, we expect that the extant nonhuman primates produce at least some facial expressions with a speech-like rhythm in the 3- to 8-Hz frequency range. Lip smacking, an affiliative signal observed in many genera of primates, satisfies this criterion. We review a series of studies using developmental, x-ray cineradiographic, EMG, and perceptual approaches with macaque monkeys producing lip smacks to further investigate this hypothesis. We then explore its putative neural basis and remark on important differences between lip smacking and speech production. Overall, the data support the hypothesis that lip smacking may have been an ancestral expression that was linked to vocal output to produce the original rhythmic audiovisual speech-like utterances in the human lineage.

A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data.

Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting 'significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically tests whether two or more populations are equally clustered. The proposed method allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest.

Vocal communication is multi-sensorimotor coordination within and between individuals.

Speech is an exquisitely coordinated interaction among effectors both within and between individuals. No account of human communication evolution that ignores its foundational multisensory characteristics and cooperative nature will be satisfactory. Here, we describe two additional capacities - rhythmic audiovisual speech and cooperative communication - and suggest that they may utilize the very same or similar circuits as those proposed for vocal learning.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks.

BACKGROUND: Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered. NEW METHOD: We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task. RESULTS: We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored. COMPARISON WITH EXISTING METHODS: Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data. CONCLUSIONS: Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.

Measuring network's entropy in ADHD: a new approach to investigate neuropsychiatric disorders.

The application of graph analysis methods to the topological organization of brain connectivity has been a useful tool in the characterization of brain related disorders. However, the availability of tools, which enable researchers to investigate functional brain networks, is still a major challenge. Most of the studies evaluating brain images are based on centrality and segregation measurements of complex networks. In this study, we applied the concept of graph spectral entropy (GSE) to quantify the complexity in the organization of brain networks. In addition, to enhance interpretability, we also combined graph spectral clustering to investigate the topological organization of sub-network's modules. We illustrate the usefulness of the proposed approach by comparing brain networks between attention deficit hyperactivity disorder (ADHD) patients and the brain networks of typical developing (TD) controls. The main findings highlighted that GSE involving sub-networks comprising the areas mostly bilateral pre and post central cortex, superior temporal gyrus, and inferior frontal gyri were statistically different (p-value=0.002) between ADHD patients and TD controls. In the same conditions, the other conventional graph descriptors (betweenness centrality, clustering coefficient, and shortest path length) commonly used to identify connectivity abnormalities did not show statistical significant difference. We conclude that analysis of topological organization of brain sub-networks based on GSE can identify networks between brain regions previously unobserved to be in association with ADHD.

Active Sampling in Primate Vocal Interactions.

Active sensing is a behavioral strategy for exploring the environment. In this study, we show that contact vocal behaviors can be an active sensing mechanism that uses sampling to gain information about the social environment, in particular, the vocal behavior of others. With a focus on the realtime vocal interactions of marmoset monkeys, we contrast active sampling to a vocal accommodation framework in which vocalizations are adjusted simply to maximize responses. We conducted simulations of a vocal accommodation and an active sampling policy and compared them with real vocal exchange data. Our findings support active sampling as the best model for marmoset monkey vocal exchanges. In some cases, the active sampling model was even able to predict the distribution of vocal durations for individuals. These results suggest a new function for primate vocal interactions in which they are used by animals to seek information from social environments.

Prenatal development of neonatal vocalizations.

Human and non-human primates produce rhythmical sounds as soon as they are born. These early vocalizations are important for soliciting the attention of caregivers. How they develop remains a mystery. The orofacial movements necessary for producing these vocalizations have distinct spatiotemporal signatures. Therefore, their development could potentially be tracked over the course of prenatal life. We densely and longitudinally sampled fetal head and orofacial movements in marmoset monkeys using ultrasound imaging. We show that orofacial movements necessary for producing rhythmical vocalizations differentiate from a larger movement pattern that includes the entire head. We also show that signature features of marmoset infant contact calls emerge prenatally as a distinct pattern of orofacial movements. Our results establish that aspects of the sensorimotor development necessary for vocalizing occur prenatally, even before the production of sound.

Much like human babies, newborn monkeys cry and coo to get their caregiver's attention. They all produce these sounds in the same way. They push air from the lungs to vibrate the vocal cords, and adjust the movement of their jaws, lips, tongue and other muscles to create different kinds of sounds. Ultrasounds show that human fetuses begin making crying-like mouth movements during the last trimester of pregnancy. Yet the prenatal development of this crucial skill remains unclear, as most studies of early primate vocalization take place after birth. To explore this question, Narayanan et al. focused on a small species of monkeys known as marmosets. Regular ultrasounds were performed on four pregnant marmosets, starting on the first day the fetuses' faces became visible and ending the day before delivery. The developing marmosets acquired the ability to independently move their mouth from their head over time, a skill crucial for feeding and vocalizing. By the end of pregnancy, a subset of fetal mouth movements were nearly identical to those produced when baby marmosets call for their caregivers after birth. Human ultrasound studies are needed to confirm whether vocal development follows a similar trajectory in our species.This is likely given the developmental similarities between both species. If so, work in marmosets could be helpful to understand how conditions such as cerebral palsy interfere with this process, and to potentially develop early interventions.

Hyperdopaminergia and NMDA receptor hypofunction disrupt neural phase signaling.

Neural phase signaling has gained attention as a putative coding mechanism through which the brain binds the activity of neurons across distributed brain areas to generate thoughts, percepts, and behaviors. Neural phase signaling has been shown to play a role in various cognitive processes, and it has been suggested that altered phase signaling may play a role in mediating the cognitive deficits observed across neuropsychiatric illness. Here, we investigated neural phase signaling in two mouse models of cognitive dysfunction: mice with genetically induced hyperdopaminergia [dopamine transporter knock-out (DAT-KO) mice] and mice with genetically induced NMDA receptor hypofunction [NMDA receptor subunit-1 knockdown (NR1-KD) mice]. Cognitive function in these mice was assessed using a radial-arm maze task, and local field potentials were recorded from dorsal hippocampus and prefrontal cortex as DAT-KO mice, NR1-KD mice, and their littermate controls engaged in behavioral exploration. Our results demonstrate that both DAT-KO and NR1-KD mice display deficits in spatial cognitive performance. Moreover, we show that persistent hyperdopaminergia alters interstructural phase signaling, whereas NMDA receptor hypofunction alters interstructural and intrastructural phase signaling. These results demonstrate that dopamine and NMDA receptor dependent glutamate signaling play a critical role in coordinating neural phase signaling, and encourage further studies to investigate the role that deficits in phase signaling play in mediating cognitive dysfunction.

Information theoretic interpretation of frequency domain connectivity measures.

In order to provide adequate multivariate measures of information flow between neural structures, modified expressions of partial directed coherence (PDC) and directed transfer function (DTF), two popular multivariate connectivity measures employed in neuroscience, are introduced and their formal relationship to mutual information rates are proved.

Domestication Phenotype Linked to Vocal Behavior in Marmoset Monkeys.

The domestication syndrome refers to a set of traits that are the by-products of artificial selection for increased tolerance toward humans [1-3]. One hypothesis is that some species, like humans and bonobos, "self-domesticated" and have been under selection for that same suite of domesticated phenotypes [4-8]. However, the evidence for this has been largely circumstantial. Here, we provide evidence that, in marmoset monkeys, the size of a domestication phenotype-a white facial fur patch-is linked to their degree of affiliative vocal responding. During development, the amount of parental vocal feedback experienced influences the rate of growth of this facial white patch, and this suggests a mechanistic link between the two phenotypes, possibly via neural crest cells. Our study provides evidence for links between vocal behavior and the development of morphological phenotypes associated with domestication in a nonhuman primate.

Author Correction: Vocal state change through laryngeal development.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Frequency domain connectivity identification: an application of partial directed coherence in fMRI.

Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity.

Vocal Learning: Shaping by Social Reinforcement.

Animals modulate their behavior by interacting with others. Nevertheless, popular theories of vocal learning frequently overlook the role of ongoing social interactions. New research suggests that a social feedback loop between young male zebra finches and adult females guides the process of song learning.

Vocal and locomotor coordination develops in association with the autonomic nervous system.

In adult animals, movement and vocalizations are coordinated, sometimes facilitating, and at other times inhibiting, each other. What is missing is how these different domains of motor control become coordinated over the course of development. We investigated how postural-locomotor behaviors may influence vocal development, and the role played by physiological arousal during their interactions. Using infant marmoset monkeys, we densely sampled vocal, postural and locomotor behaviors and estimated arousal fluctuations from electrocardiographic measures of heart rate. We found that vocalizations matured sooner than postural and locomotor skills, and that vocal-locomotor coordination improved with age and during elevated arousal levels. These results suggest that postural-locomotor maturity is not required for vocal development to occur, and that infants gradually improve coordination between vocalizations and body movement through a process that may be facilitated by arousal level changes.

Vocal state change through laryngeal development.

Across vertebrates, progressive changes in vocal behavior during postnatal development are typically attributed solely to developing neural circuits. How the changing body influences vocal development remains unknown. Here we show that state changes in the contact vocalizations of infant marmoset monkeys, which transition from noisy, low frequency cries to tonal, higher pitched vocalizations in adults, are caused partially by laryngeal development. Combining analyses of natural vocalizations, motorized excised larynx experiments, tensile material tests and high-speed imaging, we show that vocal state transition occurs via a sound source switch from vocal folds to apical vocal membranes, producing louder vocalizations with higher efficiency. We show with an empirically based model of descending motor control how neural circuits could interact with changing laryngeal dynamics, leading to adaptive vocal development. Our results emphasize the importance of embodied approaches to vocal development, where exploiting biomechanical consequences of changing material properties can simplify motor control, reducing the computational load on the developing brain.