Beyond Statistical Significance: Implications of Network Structure on Neuronal Activity

It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having revealed, within their own experimental domain, a “surprising” anomaly, possibly indicative of a hitherto hidden fragment of the underlying “ground-truth”. What is often neglected, though, is the actual importance of these experimental outcomes for understanding the system under investigation. We illustrate this point by giving practical and intuitive examples from the field of systems neuroscience. Specifically, we use the notion of embeddedness to quantify the impact of a neuron's activity on its downstream neurons in the network. We show that the network response strongly depends on the embeddedness of stimulated neurons and that embeddedness is a key determinant of the importance of neuronal activity on local and downstream processing. We extrapolate these results to other fields in which networks are used as a theoretical framework

How Structure Determines Correlations in Neuronal Networks

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks

Rationale and design of the participant, investigator, observer, and data-analyst-blinded randomized AGENDA trial on associations between gene-polymorphisms, endophenotypes for depression and antidepressive intervention: the effect of escitalopram versus placebo on the combined dexamethasone-corticotrophine releasing hormone test and other potential endophenotypes in healthy first-degree relatives of persons with depression

<p>Abstract</p> <p>Background</p> <p>Endophenotypes are heritable markers, which are more prevalent in patients and their healthy relatives than in the general population. Recent studies point at disturbed regulation of the hypothalamic-pituitary-adrenocortical axis as a possible endophenotype for depression. We hypothesize that potential endophenotypes for depression may be affected by selective serotonin re-uptake inhibitor antidepressants in healthy first-degree relatives of depressed patients. The primary outcome measure is the change in plasma cortisol in the dexamethasone-corticotrophin releasing hormone test from baseline to the end of intervention.</p> <p>Methods</p> <p>The AGENDA trial is designed as a participant, investigator, observer, and data-analyst-blinded randomized trial. Participants are 80 healthy first-degree relatives of patients with depression. Participants are randomized to escitalopram 10 mg per day versus placebo for four weeks. Randomization is stratified by gender and age. The primary outcome measure is the change in plasma cortisol in the dexamethasone-corticotrophin releasing hormone test at entry before intervention to after four weeks of intervention. With the inclusion of 80 participants, a 60% power is obtained to detect a clinically relevant difference in the primary outcome between the intervention and the placebo group. Secondary outcome measures are changes from baseline to four weeks in scores of: 1) cognition and 2) neuroticism. Tertiary outcomes measures are changes from baseline to four weeks in scores of: 1) depression and anxiety symptoms; 2) subjective evaluations of depressive symptoms, perceived stress, quality of life, aggression, sleep, and pain; and 3) salivary cortisol at eight different timepoints during an ordinary day. Assessments are undertaken by assessors blinded to the randomization group.</p> <p>Trial registration</p> <p>Local Ethics Committee: H-KF 307413</p> <p>Danish Medicines Agency: 2612-3162.</p> <p>EudraCT: 2006-001750-28.</p> <p>Danish Data Agency: 2006-41-6737.</p> <p>ClinicalTrials.gov: NCT 00386841</p

The solute carrier SLC7A1 may act as a protein transporter at the blood-brain barrier

Despite extensive research, targeted delivery of substances to the brain still poses a great challenge due to the selectivity of the blood-brain barrier (BBB). Most molecules require either carrier- or receptor-mediated transport systems to reach the central nervous system (CNS). These transport systems form attractive routes for the delivery of therapeutics into the CNS, yet the number of known brain endothelium-enriched receptors allowing the transport of large molecules into the brain is scarce. Therefore, to identify novel BBB targets, we combined transcriptomic analysis of human and murine brain endothelium and performed a complex screening of BBB-enriched genes according to established selection criteria. As a result, we propose the high-affinity cationic amino acid transporter 1 (SLC7A1) as a novel candidate for transport of large molecules across the BBB. Using RNA sequencing and in situ hybridization assays, we demonstrated elevated SLC7A1 gene expression in both human and mouse brain endothelium. Moreover, we confirmed SLC7A1 protein expression in brain vasculature of both young and aged mice. To assess the potential of SLC7A1 as a transporter for larger proteins, we performed internalization and transcytosis studies using a radiolabelled or fluorophore-labelled anti-SLC7A1 antibody. Our results showed that SLC7A1 internalised a SLC7A1-specific antibody in human colorectal carcinoma (HCT116) cells. Moreover, transcytosis studies in both immortalised human brain endothelial (hCMEC/D3) cells and primary mouse brain endothelial cells clearly demonstrated that SLC7A1 effectively transported the SLC7A1-specific antibody from luminal to abluminal side. Therefore, here in this study, we present for the first time the SLC7A1 as a novel candidate for transport of larger molecules across the BBB

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

The Limits to Moral Erosion in Markets: Social Norms and the Replacement Excuse

This paper studies the impact of a key feature of competitive markets on moral behavior: the possibility that a competitor will step in and conclude the deal if a conscientious market actor forgoes a profitable business opportunity for ethical reasons. We study experimentally whether people employ the argument "if I don’t do it, someone else will" to justify taking a narrowly self-interested action. Our data reveal a clear pattern. Subjects do not employ the "replacement excuse" if a social norm exists that classifies the selfish action as immoral. But if no social norm exists, subjects are more inclined to take a selfish action in situations where another subject can otherwise take it. By demonstrating the importance of social norms of moral behavior for limiting the power of the replacement excuse, our paper informs the long-standing debate on the effect of markets on morals