Neuron to Neuron Information Transfer is Critical for Emotion Recognition and Social Cognition

Post by Soumilee Chaudhuri

The takeaway

Information transfer from the medial prefrontal cortex (mPFC) to the retrosplenial cortex (RSC) of the brain is crucial for emotion recognition - the ability to recognize and respond to the emotional states of others. This study found that inhibiting the mPFC-to-RSC brain pathway in mice affects their ability to recognize emotional states like stress and relief in their peers, shedding light on the neural mechanisms behind social cognition.

What's the science?

Emotion recognition is essential for appropriate social interactions, enabling individuals to respond to the emotional states of others. Recent research has revealed that recognizing emotions in others involves complex communication between different parts of the brain, but understanding the exact brain pathways involved has been challenging. This study in Nature Neuroscience by Dautan et. al., delves into the mPFC-to-RSC pathway, focusing specifically on the role of somatostatin (SOM) neurons that project from the mPFC to the RSC. SOM neurons are known for their inhibitory role in the brain - they produce Gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter. It is also known that these SOM neurons help filter and process socially derived information, enabling accurate emotion recognition. In this study, researchers used optogenetics to manipulate the mPFC-to-RSC SOM neurons and observe their impact on the behavior of the mice.

How did they do it?

In this study, researchers utilized a combination of behavioral tests, optogenetics, and calcium imaging to investigate how information transfer between the mPFC and RSC affects emotion recognition in mice. The focus was on the interaction between specific neuron types, including pyramidal neurons in the RSC and SOM neurons in the mPFC. They specifically targeted genetically modified SOM neurons that expressed light-sensitive proteins. Light was used to either activate or inhibit these neurons and behavioral tests in mice assessed their emotions during these periods of activation or inhibition. Behavioral tests in mice included a series of assessments wherein mice had to recognize emotional states (stress or relief) in other mice. By stimulating or inhibiting the SOM neurons in the mPFC, researchers could observe changes in the mice's ability to recognize these emotional cues and record their responses.

What did they find?

The researchers found that inhibiting the mPFC-to-RSC pathway impaired the mice's ability to recognize emotions in their peers while stimulating it enhanced their emotional recognition capabilities. The results indicated that SOM neurons in the mPFC regulate the activity of RSC pyramidal neurons, influencing how mice process and respond to social and emotional stimuli. When the mPFC-to-RSC SOM neurons were inhibited, the activity of RSC pyramidal neurons increased, indicating that the SOM neurons help regulate the signal-to-noise ratio within the RSC. This regulation of mPFC SOM neurons to RSC pyramidal neurons was vital for processing and interpreting social and emotional stimuli accurately and it was shown that about 10% of mPFC SOM neurons project to RSC and thus modulate the activity of RSC pyramidal neurons. Interestingly, these results in mice were similar to what other scientists have observed in recent functional magnetic resonance imaging(fMRI) studies in humans.

What's the impact?

Understanding the mPFC-to-RSC pathway's role in emotion recognition has significant implications for studying social cognitive disorders like autism and schizophrenia, where emotion recognition is often impaired. This research offers avenues for therapeutic strategies targeting specific brain pathways to improve social functioning. Additionally, it provides a deeper understanding of the neural mechanisms underlying social behavior and emotional processing, which could inform future studies in both animals and humans. 

Access the original scientific publication here.

Modeling the Dose-Dependent Effects of Ketamine

Post by Lani Cupo

The takeaway

Ketamine produces sedation and disassociation at low doses and anesthesia at high doses accompanied by specific patterns of brain activity characteristic of each state. Disinhibition of neural circuits leading to a global increase in excitation may underlie both low and high-dose states.

What's the science?

The dose-dependent effects of ketamine are well known, with low doses producing psychoactive effects and high doses producing anesthesia. Likewise, it is known that ketamine administration produces patterns of brain activity consistent with gamma oscillations (associated with cognitive function) at low doses, but these are interrupted by slow-delta oscillations (associated with deep sleep) at higher doses. Nevertheless, it’s still an open question how cellular processes relate to the emergence of these patterns of brain activity. This week in PNAS, Adam and colleagues present a biophysical model to simulate cellular changes and observe the effect on brain oscillatory behavior, finding that interactions between inhibitory and excitatory neurotransmitters play a role in the distinctive patterns of brain oscillations observed following ketamine exposure.

How did they do it?

First, the authors acquired electroencephalogram (EEG) data from a human volunteer and a nonhuman primate who were administered ketamine at doses sufficiently high to induce anesthesia. Then, they created a biophysical model (a simulation of biological processes) representing interactions between excitatory pyramidal neurons and inhibitory interneurons. The model focused on the activity of NMDA receptors (a major receptor of interest for ketamine), allowing NMDA receptors to change state (“open” ones can become “closed”) based on other activity in the system. Specifically, in biology, ketamine is known to block the excitatory NMDA receptors, which is interesting given the fact that low levels of ketamine create an excitatory state. It is thought that this is because ketamine blocks inhibitory neurons from firing, leading to an overall excitatory state. The authors tested this hypothesis in their biophysical model. Then, they examined what changes in the activity of neurons could explain gamma oscillations seen following ketamine exposure. Next, they examine why slow-wave delta oscillations emerge when ketamine is “increased” in the model. 

What did they find?

First, the authors found characteristic patterns of EEG activity: at low levels of ketamine, gamma oscillations, representing cortical activity, were evident, whereas at higher levels of ketamine exposure, gamma oscillations were interrupted by delta waves, characteristic of sleep.

Then, using the biophysical model, the authors found evidence for the cellular mechanisms contributing to the gamma oscillations. They found evidence that ketamine blocked NMDA receptors on inhibitory interneurons, contributing to an overall excitatory state. Specifically, some neurons have a subthreshold excitatory state, meaning at baseline they are close to firing, but not quite over the threshold that makes them fire. Blocking these neurons’ NMDA receptors with ketamine can shut them down. When these neurons release inhibitory neurotransmitters, shutting them down leads to a downstream increase in excitatory neurotransmitter release, or a global increase in excitation referred to as disinhibition, because the excitatory neurons are no longer inhibited.

With their biophysical model, the authors next observed that this global excitation gave rise to gamma patterns of brain activity. This behavior is dependent on inhibitory GABA-ergic neurons, some of which are not blocked by ketamine, which can contribute to individual neurons firing at a gamma timescale. These individual neurons are synchronized across the brain, giving rise to global gamma wave activity.

In their model, the authors also find that higher doses of ketamine can induce “down-states” associated with slow-wave delta oscillations. Neurons with background excitatory states shut down under increased ketamine administration while other neurons have a reduced timescale of firing, contributing to the slower delta waves.

What's the impact?

The authors demonstrate that ketamine can produce characteristic brain waves in a biophysical model by blocking NMDA receptors. Their findings increase our understanding of the cellular mechanisms contributing to global brain activity.

 Access the original scientific publication here.

Identifying an fMRI Biomarker for Cognitive Decline in Alzheimer’s Disease

Post by Kelly Kadlec

The takeaway

Two fMRI-based metrics previously used to evaluate cognitive decline with age may also be useful for assessing both the risk and severity of Alzheimer’s disease. These scores can help distinguish between rates of memory decline in healthy individuals and those with varying levels of risk for developing Alzheimer’s.

What's the science?

A formal diagnosis of Alzheimer’s disease (AD) is often preceded by progressive stages of cognitive decline. At each of these stages, patients are at varying risk for advancing to AD, but assessing the risk of an individual is difficult due to a high degree of heterogeneity in neurocognitive aging. Previously, functional magnetic resonance imaging (fMRI) contrast maps for novelty and memory tasks have yielded two corresponding single-value scores that have been proposed as biomarkers of neurocognitive aging. This week in Brain, Soch and colleagues compare these fMRI-based scores in healthy individuals, individuals with AD, and individuals in different risk categories for developing AD, to assess their ability to distinguish between clinical and healthy rates of cognitive decline.

How did they do it?

This study comprised five groups of individuals: healthy controls with no family history of AD, healthy individuals with a first-degree relative with AD, patients with AD, and patients in one of two symptom-based risk states for AD: mild cognitive impairment (MCI) or subjective cognitive decline (SCD), where MCI is considered the more severe.

The authors collected fMRI data from the participants during image-based novelty and memory tasks. They used the resulting contrast maps to calculate two scores: Functional Activity Deviation during Encoding (FADE) and Similarity of Activations during Memory Encoding (SAME). Additionally, psychometric and genetic testing was done for each participant, and a subset had amyloid positivity testing. The authors hypothesized that increasing FADE scores and decreasing SAME scores would be associated with worse AD severity and higher risk for AD.

What did they find?

The authors found that memory and novelty-based FADE and SAME scores could be used to distinguish between the different participant groups and also correlated with known risk factors and cognitive assessments.

The authors reported that increasing risk for AD corresponded to larger deviations in FADE and SAME scores (i.e. more atypical fMRI results). Only memory-based FADE and SAME scores differentiated between the two more severe clinical groups (AD and MCI) and all other participant groups, and only novelty-based scores distinguished between AD and MCI patients.

The authors confirmed that FADE and SAME scores for memory and novelty tasks corresponded to other currently used psychometric tests of cognitive decline and AD severity. The authors also found that within the AD-related participants, higher FADE and lower SAME scores corresponded to the presence of an AD genotype. In addition, the authors found that especially novelty-based scores were sensitive to amyloid positivity.

To demonstrate the potential clinical value of these fMRI biomarkers, the authors used FADE and SAME scores to predict diagnostic groups for the participants and classified each group with above-chance accuracy. They also used these scores to predict the presence of an AD genotype in participants with AD relatives.

What's the impact?

Proper assessment of AD risk and severity is challenging, and this study proposes two promising neural biomarkers. These fMRI-based scores distinguished between differing stages of the disease and predicted other proposed risk factors for AD. This knowledge is critical for choosing the correct treatment routes and improving diagnosis accuracy earlier in the development of AD. Further, a longitudinal study is needed to determine how predictive they are of future outcomes (i.e. MCI progressing to AD). 

Access the original scientific publication here.