Serotonin modulates an inhibitory input to the central amygdala from the ventral periaqueductal gray

Expression of GAD2 in excitatory neurons projecting from the ventrolateral periaqueductal gray to the locus coeruleus

The ventrolateral periaqueductal gray (vlPAG) functionally projects to diverse brain regions, including the locus coeruleus (LC). Excitatory projections from the vlPAG to the LC are well described, while few studies have indicated the possibility of inhibitory projections. Here, we quantified the relative proportion of excitatory and inhibitory vlPAG-LC projections in male and female mice, and found an unexpected overlapping population of neurons expressing both GAD2 and VGLUT2. Combined in vitro optogenetic stimulation and electrophysiology of LC neurons revealed that vlPAG neurons expressing channelrhodopsin-2 under the GAD2 promoter release both GABA and glutamate. Subsequent experiments identified a population of GAD2+/VGLUT2+ vlPAG neurons exclusively releasing glutamate onto LC neurons. Altogether, we demonstrate that ∼25% of vlPAG-LC projections are inhibitory, and that there is a significant GAD2 expressing population of glutamatergic projections. Our findings have broad implications for the utility of GAD2-Cre lines within midbrain and brainstem regions, and especially within the PAG.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

The gut metabolite indole-3-propionic acid activates ERK1 to restore social function and hippocampal inhibitory synaptic transmission in a 16p11.2 microdeletion mouse model

BACKGROUND

Microdeletion of the human chromosomal region 16p11.2 (16p11.2+ / - ) is a prevalent genetic factor associated with autism spectrum disorder (ASD) and other neurodevelopmental disorders. However its pathogenic mechanism remains unclear, and effective treatments for 16p11.2+ / -  syndrome are lacking. Emerging evidence suggests that the gut microbiota and its metabolites are inextricably linked to host behavior through the gut-brain axis and are therefore implicated in ASD development. Despite this, the functional roles of microbial metabolites in the context of 16p11.2+ / -  are yet to be elucidated. This study aims to investigate the therapeutic potential of indole-3-propionic acid (IPA), a gut microbiota metabolite, in addressing behavioral and neural deficits associated with 16p11.2+ / - , as well as the underlying molecular mechanisms.

RESULTS

Mice with the 16p11.2+ / -  showed dysbiosis of the gut microbiota and a significant decrease in IPA levels in feces and blood circulation. Further, these mice exhibited significant social and cognitive memory impairments, along with hyperactivation of hippocampal dentate gyrus neurons and reduced inhibitory synaptic transmission in this region. However, oral administration of IPA effectively mitigated the histological and electrophysiological alterations, thereby ameliorating the social and cognitive deficits of the mice. Remarkably, IPA treatment significantly increased the phosphorylation level of ERK1, a protein encoded by the Mapk3 gene in the 16p11.2 region, without affecting the transcription and translation of the Mapk3 gene.

CONCLUSIONS

Our study reveals that 16p11.2+ / -  leads to a decline in gut metabolite IPA levels; however, IPA supplementation notably reverses the behavioral and neural phenotypes of 16p11.2+ / -  mice. These findings provide new insights into the critical role of gut microbial metabolites in ASD pathogenesis and present a promising treatment strategy for social and cognitive memory deficit disorders, such as 16p11.2 microdeletion syndrome. Video Abstract.

The role of serotonin in depression-A historical roundup and future directions

Depression is one of the most common psychiatric disorders worldwide, affecting approximately 280 million people, with probably much higher unrecorded cases. Depression is associated with symptoms such as anhedonia, feelings of hopelessness, sleep disturbances, and even suicidal thoughts. Tragically, more than 700 000 people commit suicide each year. Although depression has been studied for many decades, the exact mechanisms that lead to depression are still unknown, and available treatments only help a fraction of patients. In the late 1960s, the serotonin hypothesis was published, suggesting that serotonin is the key player in depressive disorders. However, this hypothesis is being increasingly doubted as there is evidence for the influence of other neurotransmitters, such as noradrenaline, glutamate, and dopamine, as well as larger systemic causes such as altered activity in the limbic network or inflammatory processes. In this narrative review, we aim to contribute to the ongoing debate on the involvement of serotonin in depression. We will review the evolution of antidepressant treatments, systemic research on depression over the years, and future research applications that will help to bridge the gap between systemic research and neurotransmitter dynamics using biosensors. These new tools in combination with systemic applications, will in the future provide a deeper understanding of the serotonergic dynamics in depression.

The role of pain modulation pathway and related brain regions in pain

Pain is a multifaceted process that encompasses unpleasant sensory and emotional experiences. The essence of the pain process is aversion, or perceived negative emotion. Central sensitization plays a significant role in initiating and perpetuating of chronic pain. Melzack proposed the concept of the "pain matrix", in which brain regions associated with pain form an interconnected network, rather than being controlled by a singular brain region. This review aims to investigate distinct brain regions involved in pain and their interconnections. In addition, it also sheds light on the reciprocal connectivity between the ascending and descending pathways that participate in pain modulation. We review the involvement of various brain areas during pain and focus on understanding the connections among them, which can contribute to a better understanding of pain mechanisms and provide opportunities for further research on therapies for improved pain management.

Subcortical contributions to salience network functioning during negative emotional processing

Core regions of the salience network (SN), including the anterior insula (aINS) and dorsal anterior cingulate cortex (dACC), coordinate rapid adaptive changes in attentional and autonomic processes in response to negative emotional events. In doing so, the SN incorporates bottom-up signals from subcortical brain regions, such as the amygdala and periaqueductal gray (PAG). However, the precise influence of these subcortical regions is not well understood. Using ultra-high field 7-Tesla functional magnetic resonance imaging, this study investigated the bottom-up interactions of the amygdala and PAG with the SN during negative emotional salience processing. Thirty-seven healthy participants completed an emotional oddball paradigm designed to elicit a salient negative emotional response via the presentation of random, task-irrelevant negative emotional images. Negative emotional processing was associated with prominent activation in the SN, spanning the amygdala, PAG, aINS, and dACC. Consistent with previous research, analysis using dynamic causal modelling revealed an excitatory influence from the amygdala to the aINS, dACC, and PAG. In contrast, the PAG showed an inhibitory influence on amygdala, aINS and dACC activity. Our findings suggest that the amygdala may amplify the processing of negative emotional stimuli in the SN to enable upstream access to attentional resources. In comparison, the inhibitory influence of the PAG possibly reflects its involvement in modulating sympathetic-parasympathetic autonomic arousal mediated by the SN. This PAG-mediated effect may be driven by amygdala input and facilitate bottom-up processing of negative emotional stimuli. Overall, our results show that the amygdala and PAG modulate divergent functions of the SN during negative emotional processing.

Using deep learning to study emotional behavior in rodent models

Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained.