Sympathovagal quotient and resting-state functional connectivity of control networks are related to gut Ruminococcaceae abundance in healthy men

INTRODUCTION

Heart rate variability (HRV), brain resting-state functional connectivity (rsFC), and gut microbiota (GM) are three recognized indicators of health status, whose relationship has not been characterized. We aimed to identify the GM genera and families related to HRV and rsFC, the interaction effect of HRV and rsFC on GM taxa abundance, and the mediation effect of diet on these relationships.

METHODS

Eighty-eight healthy, young Colombian men were included in this cross-sectional study. HRV metrics were extracted from 24-hour Holter monitoring data and the resting functional connectivity strength (FCS) of 15 networks were derived from functional magnetic resonance imaging. Gut microbiota composition was assessed using the sequences of the V3-V4 regions of the 16 S rRNA gene, and diet was evaluated using a food frequency questionnaire. Multivariate linear regression analyses were performed to evaluate the correlations between the independent variables (HRV metrics and FCS) and the dependent variables (GM taxa abundance or alpha diversity indexes). Mediation analyses were used to test the role of diet in the relationship between HRV and GM.

RESULTS

The sympathovagal quotient (SQ) and the FCS of control networks were positively correlated with the abundance of the gut Ruminococcaceae family and an unclassified Ruminococcaceae genus (Ruminococcaceae_unc). Additionally, the interaction between the FCS of the control network and SQ reduced the individual main effects on the Ruminococcaceae_unc abundance. Finally, reduced habitual fiber intake partially mediated the relationship between SQ and this genus.

CONCLUSION

Two indicators of self-regulation, HRV and the rsFC of control networks, are related to the abundance of gut microbiota taxa in healthy men. However, only HRV is related to habitual dietary intake; thus, HRV could serve as a marker of food choice and GM composition in the future.

Binge drinking is associated with altered resting state functional connectivity of reward-salience and top down control networks

Binge drinking is characterized by bouts of high-intensity alcohol intake and is associated with an array of health-related harms. Even though the transition from occasional impulsive to addictive alcohol use is not well understood, neurobiological models of addiction suggest that repeated cycles of intoxication and withdrawal contribute to the development of addiction in part through dysregulation of neurofunctional networks. Research on the neural sequelae associated with binge drinking is scant but resting state functional connectivity (RSFC) studies of alcohol use disorders (AUD) indicate that the development and maintenance of long-term excessive drinking may be mediated by network-level disruptions. The present study examined RSFC in young adult binge (BD) and light (LD) drinkers with seeds representing the networks subserving reward (the nucleus accumbens and caudate nucleus), salience (anterior cingulate cortex, ACC), and executive control (inferior frontal cortex, IFC). BDs exhibited enhanced connectivity between the striatal reward areas and the orbitofrontal cortex and the ACC, which is consistent with AUD studies and may be indicative of alcohol-motivated appetitive behaviors. Conversely, BDs demonstrated lower connectivity between the IFC and hippocampus which was associated with higher craving. This may indicate impaired ability to suppress unwanted thoughts and a failure to employ memory of the harmful consequences of heavy drinking in prospective plans and intentions. The observed greater connectivity of the reward/salience network and the lower prefrontal-hippocampal connectivity were associated with hazardous drinking levels indicating that dysregulation of neurofunctional networks may underlie binge drinking patterns.

Determining the control networks regulating stem cell lineages in colonic crypts

The question of stem cell control is at the center of our understanding of tissue functioning, both in healthy and cancerous conditions. It is well accepted that cellular fate decisions (such as divisions, differentiation, apoptosis) are orchestrated by a network of regulatory signals emitted by different cell populations in the lineage and the surrounding tissue. The exact regulatory network that governs stem cell lineages in a given tissue is usually unknown. Here we propose an algorithm to identify a set of candidate control networks that are compatible with (a) measured means and variances of cell populations in different compartments, (b) qualitative information on cell population dynamics, such as the existence of local controls and oscillatory reaction of the system to population size perturbations, and (c) statistics of correlations between cell numbers in different compartments. Using the example of human colon crypts, where lineages are comprised of stem cells, transit amplifying cells, and differentiated cells, we start with a theoretically known set of 32 smallest control networks compatible with tissue stability. Utilizing near-equilibrium stochastic calculus of stem cells developed earlier, we apply a series of tests, where we compare the networks' expected behavior with the observations. This allows us to exclude most of the networks, until only three, very similar, candidate networks remain, which are most compatible with the measurements. This work demonstrates how theoretical analysis of control networks combined with only static biological data can shed light onto the inner workings of stem cell lineages, in the absence of direct experimental assessment of regulatory signaling mechanisms. The resulting candidate networks are dominated by negative control loops and possess the following properties: (1) stem cell division decisions are negatively controlled by the stem cell population, (2) stem cell differentiation decisions are negatively controlled by the transit amplifying cell population.

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition.

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Presents the first network-based treatment of dynamic connectivity in autism

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Analyzes whole-brain functional organization at multiple levels simultaneously

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Examines motifs in a large subject group reflecting autism's clinical diversity

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Utilizes a high-order model to delineate a more complete set of brain networks

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Uncovers significant coupling differences among control networks in autism