Executive function (EF) consists of explicit emotion regulation (EER) and cognitive control (CC). Childhood EER in particular predicts mental and physical health in adulthood. Identifying factors affecting EER development has implications for lifelong physical and mental health. Gut microbiota (GM) has attracted attention as a potential biomarker for risk of physical and mental problems in adulthood. Furthermore, GM is related to brain function/structure, which plays a crucial role in emotional processing. However, little is known about how GM compositions are associated with the development of emotion regulation in early childhood. Therefore, in this study, we examined 257 children aged 3–4 to investigate links between GM and risk to EF. EF was measured using the Mother-Reported Behavior Rating Inventory of Executive Function–Preschool version. GM composition (alpha/beta diversity and genus abundance) was evaluated using 16S rRNA gene sequencing and compared between EF-risk and non-risk groups. Our results show that children with EER-risk (an index of inhibitory self-control) had a higher abundance of the genera Actinomyces and Sutterella. Although we have not established a direct link between GM and CC risk, our findings indicate that GM of preschoolers is closely associated with emotional processing and that EERrisk children have more inflammation-related bacteria.

Temperament is a key predictor of human mental health, and cognitive and emotional development. While human fear behavior is reportedly associated with gut microbiome in infancy, infant gut microbiota changes dramatically during the first five years, when the diversity and composition of gut microbiome are established. This period is crucial for developing human executive functioning, including emotion regulation. Therefore, this study investigated the relationship between temperament and gut microbiota in 284 preschool children aged 3–4 years. Child temperament was assessed by maternal reports of the Children’s Behavior Questionnaire. Gut microbiota (alpha/beta diversity and genera abundance) was evaluated using 16S rRNA sequencing of stool samples. A high abundance of anti-inflammatory bacteria (e.g., Faecalibacterium) and low abundance of inflammatory bacteria (e.g., Eggerthella, Flavonifractor) were associated with higher positive emotionality and reward-seeking (i.e., Surgency/Extraversion, β =0.15, p = 0.013), and lower negative emotionality and behavioral inhibition (i.e., Negative Affectivity, β = -0.17, p = 0.004). Additionally, gut microbiota diversity was associated with a more active approach and exploration (i.e., Impulsivity, a specific aspect of Surgency/Extraversion, β = 0.16, p = 0.008). This study provides insight into the biological mechanisms of temperament and takes important steps toward identifying predictive markers of psychological/emotional risk.

Purpose: Interoception is the sensing and interpretation of internal bodily signals and may be a key process linking exercise-induced bodily changes to mood. This study examined whether acute moderate-intensity treadmill running changes interoception and mood states and whether running-related changes in interoception statistically account for mood improvement. Methods: Twenty-seven healthy young adults completed a within-subject crossover experiment consisting of 15 min of moderate-intensity treadmill running at 50% estimated V̇O _₂peak and seated rest on separate days. Before and after each condition, cardiac interoceptive accuracy was assessed using heartbeat counting and heartbeat tapping tasks, present-moment interoceptive sensibility using a state-adapted Body Perception Questionnaire (BPQ), and mood using the Profile of Mood States. Running-related changes were calculated as ΔRUN − ΔCTL. Results: Compared with seated rest, running reduced total mood disturbance (TMD) and tension–anxiety (TA) and increased vigor–activity (VA). Running did not significantly change task-based indices of cardiac interoceptive accuracy but increased BPQ scores. Greater running-related increases in BPQ scores were associated with greater reductions in TMD ( r = −.61, p < .001), TA ( r = −.45, p = .018), and depression–dejection ( r = −.49, p = .009). Greater increases in heartbeat tapping task d′ were associated with greater increases in VA ( r = .56, p = .003). Mediation analysis indicated that BPQ changes were statistically consistent with an indirect pathway linking running to reduced TMD, with weaker exploratory evidence for TA. Conclusions: A single bout of moderate-intensity running improved mood and increased present-moment interoceptive sensibility, but not task-based cardiac interoceptive accuracy. Enhanced subjective awareness of bodily sensations may be one proximal process related to the mood benefits of acute running.

Measuring human brain activity in the real world has long remained a major challenge in conventional neuroscience. Pupil dynamics offer a promising non-invasive proxy for arousal-related multidimensional brain states. However, interpreting pupil data collected outside the laboratory is notoriously difficult due to complex, multilayered factors, including lighting conditions, arousal states, cognitive control as well as the influence of the viscoelastic properties of the iris, that can confound results. Here, we propose an AI-driven analytical strategy as a potential solution to this interpretational bottleneck. We discuss an approach that combines deep learning with mechanical physics equations to predict and model human pupil dynamics from multimodal contexts, such as first-person vision video and physiological and physical data as well as the application of translational data from non-human studies. This AI strategy, combined with consideration of the viscoelastic properties of the iris, may enable the extraction of internal-state (i.e., neurological) factors by detangling complex real-world pupil data. It may, in addition, lead to the development of virtual predictions of internal states even in the absence of pupil data. While there are challenges to realizing this vision, this approach has great potential to contribute to NeuroAI as it could open a promising new door to natural neuroscience in fields such as sports science, clinical medicine, and human-computer interaction.