Research Spotlights February 2020

Research Spotlights February 2020

Activating endocannabinoid receptors can disrupt anxiety-causing connections in the brain

Stress exposure is a major risk factor for the development and exacerbation of mental illnesses such as depression, post-traumatic stress disorder, and anxiety disorders. Researchers supported by grants from the NIMH, NIDA, NINDS, and the Brain & Behavior Research Foundation recently published research that elucidates the role of endogenous cannabinoid (eCB) signaling in brain regions associated with the stress-response, the amygdala and the dorsomedial prefrontal cortex (dmPFC). Functional coupling between the amygdala and the dmPFC has been implicated in the generation of negative affective states; however, the mechanisms by which stress increases amygdala-dmPFC synaptic strength and generates anxiety-like behaviors are not well understood. 2-arachidonoylglycerol (2-AG)-mediated eCB signaling activates presynaptic cannabinoid receptor type 1 (CB1) receptors to reduce the likelihood of neurotransmitter release. Recent studies have implicated 2-AG signaling as a critical stress modulatory system and is involved in a connection between the amygdala and dmPFC. For example, 2-AG deficiency is associated with increased anxiety, impaired fear extinction, and increased susceptibility to stress-induced anxiety, while increasing 2-AG signaling promotes stress resilience and prevents stress-induced anxiety.

In this study, researchers used male and female mice for electrophysiological and behavioral experiments. Behavioral experiments included anxiety/stress inducing paradigms (elevated-zero maze, foot-shock stress, elevated-plus maze). Neurological regions assessed were the mouse basolateral amygdala (BLA)-prelimbic prefrontal cortex (plPFC), analogous to the human brain areas of the amygdala and dmPFC, respectively. To further examine the role of 2-AG-CB1, the researchers inserted a virus into the mice to selectively delete the CB1 receptor from BLA neurons projecting to the plPFC that compromised the signaling of 2-AG.

The researchers found that during stress, the break between the amygdala and frontal cortex temporarily disappeared, while the connection between these areas was strengthened and triggered a spike in anxiety-related behaviors. This strengthened the connection between the amygdala and plPFC, exacerbating anxiety-related behaviors even in animals that had not been exposed to stress paradigms. Under normal conditions, 2-AG maintains a break in a connection between the two regions. These data suggest that the enhancing 2-AG-CB1 signaling, may be a potential therapeutic approach for the treatment of stress-induced psychiatric disorders. Additionally, 2-AG and tetrahydrocannabinol (THC), the main psychoactive component of cannabis, both target the same receptor. While the paper did not directly look at the effect of cannabis on the brain, these findings may explain, at a mechanistic level, why people turn to the drug when stressed.

Marcus DJ, Bedse G, Gaulden AD, Ryan JD, Kondev V, Winters ND, Rosas-Vidal LE, Altemus M, Mackie K, Lee FS, Delpire E, Patel, S. 2019. Endocannabinoid Signaling Collapse Mediates Stress-Induced Amygdalo-Cortical Strengthening, Neuron

During play, infant and adult brains synchronize

The dynamics of the social environment are considered crucial to a child’s development of communication skills. Researchers funded by the NICHD, Princeton University, and the Eric and Wendy Schmidt Transformative Technology Award conducted a study of how the brains of infants and adults interact during natural play. Previous studies have used methods involving scanning adults' brains with functional magnetic resonance imaging (fMRI), in separate sessions, while the adults lay down and watched movies or listened to stories. However, in order to study real-time communication, a child-friendly method is needed for recording brain activity simultaneously from both the infant and adult brains. In order to do this the researchers developed a new dual-brain neuroimaging system that uses functional near-infrared spectroscopy (fNIRS), a noninvasive method of recording oxygenation in the blood as a proxy for neural activity.

Using this naturalistic approach, the researchers studied real-time interactions between infants and adults in order to understand how the brains of infants and adults are coupled in real-time, both to each other and to natural social behaviors. The adult and infant dyads’ behavior and brain activity were simultaneously and continuously measured using fNIRS while the they played with toys, sang songs and read a book. The fNIRS caps collected data from 57 channels of the brain known to be involved in prediction, language processing and understanding the perspectives of others. The same adult interacted with all the infants and toddlers who participated in the study (N = 18; 9–15 months of age). In one paradigm the adult experimenter spent five minutes interacting directly with a child while they sat on their parent's lap. In the other paradigm, the experimenter turned to the side and told a story to another adult while the child played with their parent.

The results showed that during face-to-face coupled interactions, infant and adult brains are dynamically linked to important social cues (gaze, smiling, joint attention, and speech prosody). The infants' brains were synchronized with the adult's brain in areas known to be involved in high-level understanding of the world. The strongest coupling occurred in the prefrontal cortex, which is involved in learning, planning, and executive functioning and was previously thought to be underdeveloped during infancy. Overall, the prefrontal cortex activity in infants’ brains slightly preceded similar activity in the adult’s brain, even without overt behavioral cues from the infants, indicating the adult was sensitive to subtle cues from the infants (likely through both explicit and implicit processes), which then modified the adult’s brain responses and behaviors and improving alignment with the infants. When the adult and infant were turned away from each other and engaging with other people, the coupling between the dyad disappeared.

These findings support transactional development models, which emphasize the role of the children in shaping their own input, as well as the role of adults’ input, and expand on these frameworks and demonstrate a new application of dynamic-systems theory by showing how adults’ and infants’ brains reflect each other during natural, social interactions that are mediated by sensitivity to each other’s behaviors. This novel two-brain approach to the study of social interaction could advance the understanding how coupling with caregivers breaks down in atypical development, such as in children diagnosed with autism. Additionally, these findings have implications for educators and improvements in teaching approaches to accommodate children's diverse brains.

Piazza EA, Hasenfratz L, Hasson U, Lew-Williams C. 2019. Infant and Adult Brains Are Coupled to the Dynamics of Natural Communication. Psychol Sci. DOI: 10.1177/0956797619878698.

Activity tracker data may help improve flu-like outbreak predictions

Worldwide, influenza results in up to 650,000 deaths each year. Researchers supported by grants from the NCATS are trying to improve methods for real-time surveillance and reporting of influenza infection trends using activity trackers. Traditional influenza surveillance reporting is often not in “real-time” and delayed by approximately 1–3 weeks, or more, and often revised months later, which may allow outbreaks to go unnoticed and spread. Previous studies have attempted to use data on internet search terms and social media to provide real-time influenza surveillance, a method also known as nowcasting. However, these methods have their limitations and challenges such as overestimating rates during epidemic periods since it is difficult to distinguish between a person’s activity due to their illness and those related to increased media attention or heightened awareness during the influenza season. In this study, the researchers leveraged physiological changes due to infection, such as an elevated resting heart rate and changes in sleep to evaluate if wearable sensors to determine if these data could be used to identify population trends of seasonal respiratory infections, such as influenza.

For this study, the researchers obtained de-identified sensor data from activity tracker users (n = 200,000) over a 2-year period from the top five states with the most activity tracker users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Users were included if they were born between 1930 and 2004, height was more than 1 m, and weighed more than 20 kg. Data were excluded for days where the resting heart rate was missing, wear time was missing, and wear time was less than 1000 min per day. Weeks were identified from the sensor data in which activity tracker users had alterations in physiological data (increased resting heart rates, increased sleep levels) and then compared with weekly estimates of influenza-like illness rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC). Influenza-like infection cases were modelled for each state with a negative binomial model. Weekly changes in influenza-like infection rates were evaluated by linear regression using change in proportion of elevated activity tracker data. Pearson correlation was used to compare predicted versus CDC reported influenza-like infection rates.

The results of this study indicated that activity tracker data significantly improved influenza-like infection rate predictions by 6.3 – 32.9% in all five states. Additionally, the weekly changes in the proportion of activity tracker users with data indicating alterations in physiological data (resting heart rate, sleep duration, etc.) were usually associated with weekly changes in influenza-like infection rates. These associations remained consistent when correcting for first-order autocorrelation in time-matched or 1-week-lagged models.

Capturing physiological and behavioral data from a growing number of wearable devices from users globally could improve timeliness and precision of public health responses to prevent further transmission of influenza-like cases during outbreaks. This study does have some limitations, including not having activity data, which may have improved the predictive ability of the models by allowing for the control for seasonal activity changes, and the low accuracy of sleep measured collected. In the future, advances in wearables that include continuous sensors for temperature, blood pressure, pulse oximetry, ECG, or cough recognition are likely to further improve influenza surveillance at the populations and individual-level.

Radin JM, Wineinger NE, Topol EJ, Steinhubl SR, 2020. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet. DOI:

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