Infants Have an Innate Perception of Musical Patterns

Post by Leanna Kalinowski

The takeaway

Infants can perceive recurring patterns in music (called “meter”), regardless of whether the patterns are marked by obvious tones, in a similar manner to adults.

What's the science?

Across different cultures, music has several universal features that often cause humans to bob their heads or tap their feet in time with it. Within a given song, there is a small set of recurring patterns and accents, called “meter”, that allows humans to synchronize their perception of music and move along to it together. Sometimes, meter is made obvious by including tones that indicate its pulse, but other times, meter is much more implicit and marked by periods of silence. While we know that the adult brain can perceive both obvious and more implicit meter, less is known about how early this perceptual ability develops in infants. Recently in Developmental Science, Lenc and colleagues recorded brain activity in infants that were exposed to rhythms that are known to induce the perception of meter in adults.

How did they do it?

The researchers recruited 20 infants between five and six months old to participate in this study, during which they were exposed to two rhythms: a “strongly-periodic” rhythm and a “weakly-periodic” rhythm. The two rhythms typically produce the same perceived meter in adults, but the tones in the strongly-periodic rhythm matched the pulse of the typically perceived meter, while the tones in the weakly-periodic rhythm did not. The infants were played each rhythm twice -- once with a high-pitched tone, and once with a low-pitched tone – for a total of four sound clips. Brain activity while listening to each sound clip was measured using electroencephalography (EEG).

What did they find?

The researchers found that listening to both rhythms induced brain activity in infants that matched the frequency of the meter in a similar pattern to what is seen in adults. This brain activity was present not only for the rhythm with beats that matched the meter (i.e., strongly periodic) but also for the rhythm that did not have beats that matched the meter (i.e., weakly periodic). They also found that meter-related brain activity of both rhythm types was enhanced when the rhythm was produced by bass sounds (i.e., low-pitched tones) as compared to high-pitched tones. Together, these results suggest that high-level neural processes that facilitate music perception are already present soon after birth.

What's the impact?

Results from this study show that infants are able to perceive musical patterns shortly after birth – even when these patterns are not marked by obvious tones – well before infants even develop the motor ability to bob along to the music. These findings may help pave the way for developing age-appropriate interventions for developmental disorders that rely on auditory stimulation and rhythm perception.  

Access the original scientific publication here.

Both Cardiovascular and Behavioral Threat Responses Contribute to Defensive States

Post by Lincoln Tracy

The takeaway

A new framework developed in mice involving microstates, macrostates, specific behaviors, and heart rate dynamics has proven beneficial in learning more about the complex neural states and associated systemic functions in response to an external threat.

What's the science?

The typical defensive reaction to an external threat, a key part of fear or anxiety, involves multiple behavioral and physiological responses that are controlled by our neural circuitry. There have been attempts to propose a unified and species-preserved concept describing defensive responses, or ‘states’, but such attempts have largely focused on behavioral mechanisms and ignored autonomic responses. This week in Nature Neuroscience, Signoret-Genest and colleagues provide a novel framework for characterizing integrated cardiovascular and behavioral defensive states using behavioral, heart rate, and thermal imaging data from freely moving mice across several experimental paradigms. They show how this allows linking of specific defensive states to key brain circuitry such as the periaqueductal gray (PAG).

How did they do it?

The authors implanted electrocardiogram electrodes in mice, which allowed them to record changes in heart rate while the mice were exposed to a series of environments that varied in threat intensity and acuteness, each eliciting a different emotional state. The environments included low-threat situations (i.e., their home cage) to high-threat, fear and anxiety-evoking experiments such as a conditioned fear (or ‘flight’) paradigm, the open field test, and the elevated plus maze. The authors were interested in comparing the association between immobility, or ‘freezing’ behaviors, and decreases in heart rate when the mice were exposed to different threat environments. Finally, they used optogenetics to manipulate the activity of glutamatergic neurons as well as subtypes of this population (vesicular glutamate transporter 2 [Vglut2] and Chx10) in the PAG in an attempt to identify the specific neural circuits involved in the control of these defensive states.

What did they find?

First, the authors identified threat ‘microstates’, involving immobility and bradycardia (a decrease in heart rate) during the conditioned flight paradigm. They noticed that the immobility-associated bradycardia increased as the conditioning session went on, and hypothesized there were other underlying processes interfering with the heart rate changes at both a global level and in the defensive microstates. They called the dynamic changes operating at an extended timescale ‘macrostates’. This suggests that rather than simply following changes in behavioral activity (rearing or immobility), threat-induced changes in heart rate reflect integrated defensive microstates involving both behavioral and autonomic components. The authors also found the changes in heart rate were strongly influenced by the pre-existing state of the animal. Furthermore, they identified that the integrated defensive response was context-dependent, with higher contextual threat levels resulting in more constrained heart rate changes. Finally, they found stimulating Vglut2+ neurons evoked intensity-depended behavioral and cardiovascular responses (low intensity stimulation led to immobility and bradycardia, while high intensity stimulation led to a mixture of flight responses and immobility accompanied by bradycardia) but stimulating Chx10+ neurons led to robust immobility and bradycardia. This suggests Chx10+ neurons in the midbrain periaqueductal gray mediate a particular defensive microstate associated with both immobility and bradycardia.

What's the impact?

The findings from this study act as a starting point for a more complete understanding of the neuronal mechanisms underlying emotions such as fear and anxiety. The novel framework teases the possibility of returning to a translational research pathway (from mice to humans) and the potential ability to explore maladapted fear and anxiety responses across species.

“Sleep Fingerprints” Can Identify Signatures of Psychiatric Disease

Post by Anastasia Sares

The takeaway

Researchers have developed a way to concisely quantify the activity of tens of thousands of waveforms occurring in a person’s brain during sleep, uncovering stable profiles of different people. This will help us understand individual differences in sleep, from the normal to the pathological: as a first step, it has revealed new insights into the brains of people with schizophrenia.

What's the science?

Our brains don’t turn off when we go to sleep. They remain hard at work, forming memories, dreaming, and recuperating from the day. We know this, in part, because we can observe them through a technique called polysomnography, where we record the electrical activity of the sleeping brain. The signal we get from polysomnography is composed of a symphony of overlapping brain frequencies, as well as short bursts of oscillatory activity called transient oscillations. Among transient oscillations, sleep scientists are especially interested in sleep spindles, which are involved in memory consolidation and are altered in disorders such as Alzheimer’s, autism, and schizophrenia. However, the current methods for defining sleep spindles are based on methods from the early 1900s and are therefore biased toward the waveforms people could see easily by eye in paper tape traces of brain activity. 

This month in the journal Sleep, Stokes and colleagues developed a new approach, which, rather than just looking at traditional spindles, identifies tens of thousands of spindle-like waveforms at all times and frequencies during sleep. They summarized these waveforms in a sleep “fingerprint” that can be used to identify signatures of neurological health and disease.

How did they do it?

The authors analyzed data from both healthy, neurotypical people and people with schizophrenia who had had recordings taken of their brain waves in the lab while they slept. They transformed the signal from each person into a time-frequency plot, with high frequencies (fast oscillations) near the top and low frequencies (slow oscillations) near the bottom, and time going from left to right. They then applied an image processing technique called the watershed algorithm to identify regions with spindle-like brain waveforms. Finally, they created visual summaries showing how these tens of thousands of waveforms continuously evolve across the night. This is more effective than dividing activity into the traditional “sleep stages” because it retains more information.

What did they find?

The team generated these visualizations for each participant, showing how brain activity changed as a person went deeper and deeper into sleep, and also how brain activity related to the slowest sleep oscillations, like tiny waves superimposed on a rising and falling tide. Surprisingly, they found a variety of activity patterns in healthy, neurotypical participants, yet each individual had the same pattern from night to night. Therefore, these representations acted like a fingerprint: different across individuals but consistent over time. They then demonstrated the clinical usefulness of this method by comparing the control and schizophrenia cohorts, which revealed two new classes of spindle-like events that traditional methods had been unable to capture.

What's the impact?

The sleep fingerprints developed by this team offer a highly informative way to summarize a large amount of data: electrical signals gathered over the course of an entire night. What’s more, these fingerprints are different between people but stable within the same person—meaning we may be able to use them to inform clinical approaches for psychiatric conditions, sleep disorders, and more. The researchers have released an open-source toolbox to allow other scientists to use this powerful technique.