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ABSTRACT The nucleus accumbens (NAcc) and ventral pallidum (VP) are key nodes in the mesolimbic reward pathway that facilitate stimulus salience, including the regulation of social motivation and attachment. Primate species display variation in social behaviors, including different levels of impulsivity, bonding, and aggression. Previous research has implicated neuromodulation of the reward pathway in the differential expression of various social behaviors, suggesting that differences in neurotransmitter innervation may play a role in species‐specific patterns. To explore this, we examined serotonergic innervation in the NAcc and VP among primates. We used stereology to quantify serotonin transporter‐immunoreactive (SERT‐ir) axon length density in the NAcc and VP of 13 primate species, including humans, great apes, and cercopithecid and platyrrhine monkeys. Our data show that serotonergic innervation density within both the NAcc and VP is highly conserved among species. This finding contrasts with our previous findings of higher levels of SERT‐ir axons in the dorsal striatum of humans and great apes relative to monkeys, a human‐specific increase in dopaminergic innervation within the NAcc and VP, and a human‐specific increase of neuropeptide Y in the NAcc, highlighting the mosaic nature of innervation patterns among species. 

The nucleus accumbens (NAc) is central to motivation and action, exhibiting one of the highest densities of neuropeptide Y (NPY) in the brain. Within the NAc, NPY plays a role in reward and is involved in emotional behavior and in increasing alcohol and drug addiction and fat intake. Here, we examined NPY innervation and neurons of the NAc in humans and other anthropoid primates in order to determine whether there are differences among these various species that would correspond to behavioral or life history variables. We quantified NPY-immunoreactive axons and neurons in the NAc of 13 primate species, including humans, great apes, and monkeys. Our data show that the human brain is unique among primates in having denser NPY innervation within the NAc, as measured by axon length density to neuron density, even after accounting for brain size. Combined with our previous finding of increased dopaminergic innervation in the same region, our results suggest that the neurochemical profile of the human NAc appears to have rendered our species uniquely susceptible to neurophysiological conditions such as addiction. The increase in NPY specific to the NAc may represent an adaptation that favors fat intake and contributes to an increased vulnerability to eating disorders, obesity, as well as alcohol and drug dependence. Along with our findings for dopamine, these deeply rooted structural attributes of the human brain are likely to have emerged early in the human clade, laying the groundwork for later brain expansion and the development of cognitive and behavioral specializations. 

The questions we ask and the way in which we ask them can make all the difference in how successful we are in meetings, in collaborations, and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? In this paper, we develop a theory for asking great questions that elicit information useful for accomplishing the tasks of a collaborative project and also strengthen the statistician-domain expert relationship. We deconstruct asking great questions into three parts: the question, the answer, and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question, follow the question with behaviors and actions consistent with the prefaced words including actions such as listening, paraphrasing, and summarizing; and model a collaborative relationship via the asking of a great question. We provide practical guidelines for learning these skills so that statisticians can improve their statistical collaboration skills and thus increase their impact to help address societal challenges. 

Our ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives. 

The questions we ask and how we ask them will make a difference in how successful we are in meetings, in collaborations and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? Great questions elicit information useful for accomplishing the tasks of a project and strengthen the statistician–domain expert relationship. Great questions have three parts: the question, the answer and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question; follow the question with behaviours and actions consistent with the prefaced words including actions such as listening, paraphrasing and summarizing; and model a collaborative relationship via the asking of a great question. We describe the methods and results of a study that shows how questions can be assessed, that statisticians can learn to ask great questions and that those who have learned this skill consider it to be valuable for their careers. We provide practical guidelines for learning how to ask great questions so that statisticians can improve their collaboration skills and thus increase their impact to help address societal challenges.