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Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs—operationalizing effort—distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one’s own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

In disciplines outside of communication, papers with women as first and last (i.e., senior) authors attract fewer citations than papers with men in those positions. Using data from 14 communication journals from 1995 to 2018, we find that reference lists include more papers with men as first and last author, and fewer papers with women as first and last author, than would be expected if gender were unrelated to referencing. This imbalance is driven largely by the citation practices of men and is slowly decreasing over time. The structure of men’s co-authorship networks partly accounts for the observed over-citation of men by other men. We discuss ways researchers might approach gendered citations in their work.

The open-ended and internally driven nature of curiosity makes characterizing the information seeking that accompanies it a daunting endeavour. We use a historico-philosophical taxonomy of information seeking coupled with a knowledge network building framework to capture styles of information-seeking in 149 participants as they explore Wikipedia for over 5 hours spanning 21 days. We create knowledge networks in which nodes represent distinct concepts and edges represent the similarity between concepts. We quantify the tightness of knowledge networks using graph theoretical indices and use a generative model of network growth to explore mechanisms underlying information-seeking. Deprivation curiosity (the tendency to seek information that eliminates knowledge gaps) is associated with the creation of relatively tight networks and a relatively greater tendency to return to previously visited concepts. With this framework in hand, future research can readily quantify the information seeking associated with curiosity.

Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. The structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state-transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in fMRI data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions - especially those to high information content states - are less costly than expected from random network null models, thereby indicating the brain's marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations., 16 pages, 4 figures + supplement (5 pages, 5 figures)

Mindfulness is characterized by attentiveness to the present experience with nonjudgmental awareness and acceptance. Practicing mindfulness alters brain function to support the executive regulation of thoughts, feelings, and behavior. While early stages of practice are thought to require greater "neural effort" for later efficiency, current evidence relies on circular definitions of effort based on functional activity magnitude. Here we used network control theory as a model of how external control inputs, which operationalize effort, can distribute changes in neural activity across the macro-scale structural brain network. Further, we inferred the intrinsic timescale of activity to operationalize present-centered activity as shorter momentary timescales that discontinue the past and update the present. To explain effects of mindful regulation on alcohol consumption, we applied these methods to a randomized controlled intervention study with resting-state and task fMRI data. The task primed participants to either mindfully respond or naturally react to alcohol cues. Mobile text interventions and measurements of alcohol consumption were administered using ecological momentary assessments during the subsequent 4 weeks. We hypothesized that neural states of mindfulness require greater effort to enact and sustain. This effort may support deautomatized habitual natural reactions, discontinued processing, and updated present-centered neural dynamics. We found that mindful regulation of alcohol cues, compared to the natural reactions of the benchmark group, involved more effortful control of neural dynamics across cognitive control and attention networks. This effort persisted in the natural reactions of the mindful group compared to the benchmark group. Using resting-state fMRI, we found that more effortful neural states tended to occur over shorter timescales than less effortful states. Our findings provide an explanation for how neural dynamics with altered effort and stability, such as mindful states, tend to center the present experience.

Philosophers of science have long questioned how collective scientific knowledge grows. Although disparate answers have been posited, empirical validation has been challenging due to limitations in collecting and systematizing large historical records. Here, we introduce new methods to analyze scientific knowledge formulated as a growing network of articles on Wikipedia and their hyperlinks. We demonstrate that in Wikipedia, concept networks in subdisciplines of science do not grow by expanding from their central core to reach an ancillary periphery. Instead, science concept networks in Wikipedia grow by creating and filling knowledge gaps. Notably, the process of gap formation and closure may be valued by the scientific community, as evidenced by the fact that it produces discoveries that are more frequently awarded Nobel prizes than other processes. To determine whether and how the gap process is interrupted by paradigm shifts, we operationalize a paradigm as a particular subdivision of scientific concepts into network modules. Hence, paradigm shifts are reconfigurations of those modules. The approach allows us to identify a temporal signature in structural stability across scientific subjects in Wikipedia. In a network formulation of scientific discovery, our findings suggest that data-driven conditions underlying scientific breakthroughs depend as much on exploring uncharted gaps as on exploiting existing disciplines and support policies that encourage new interdisciplinary research.

Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information. Despite its importance, curiosity has been challenging to computationally model because the practice of curiosity often flourishes without specific goals, external reward, or immediate feedback. Here, we show how network science, statistical physics, and philosophy can be integrated into an approach that coheres with and expands the psychological taxonomies of specific-diversive and perceptual-epistemic curiosity. Using this interdisciplinary approach, we distill functional modes of curious information seeking as searching movements in information space. The kinesthetic model of curiosity offers a vibrant counterpart to the deliberative predictions of model-based reinforcement learning. In doing so, this model unearths new computational opportunities for identifying what makes curiosity curious.

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy – the control energy required to maintain brain activation states – during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings reveal differences in emotion-processing circuitry associated with genetic vulnerability to schizophrenia.

Most theories of curiosity emphasize the acquisition of information. Such conceptualizations focus on the actions of the knower in seeking units of knowledge. Each unit is valued as an unknown and appropriated in becoming known. Yet, recent advances across a range of disciplines from philosophy to cognitive science suggest that it may be time to complement the acquisitional theory of curiosity with a connectional theory of curiosity. This alternative perspective focuses on the actions of the knower in seeking relations among informational units, laying down lines of intersection, and thereby building a scaffold or network of knowledge. Intuitively, curiosity becomes edgework. In this chapter, we dwell on the notion of edgework, wrestle with its relation to prior accounts, and exercise its unique features to craft alternative reasons for curiosity's value to humanity. To begin, we engage in a philosophical discussion of the evidence for connectional curiosity across the last two millennia in the Western intellectual tradition. We then move to a contemporary operationalization of connectional curiosity in the mathematical language of network science. To make our discussion more concrete, we walk through a case study of humans browsing Wikipedia. The groundwork laid, we turn to the practical question of how (if at all) the paradigm of curiosity as edgework manifests in the contemporary lives of humans today. Does such a conceptualization help us to better understand the relations between curiosity and mental health? Might the edgework paradigm explain the drive to build specific structures of knowledge? Would the account help us to encode, test, and validate existing theories of curiosity, or propose new ones? Could it clarify why and how our culture values curiosity, in its multiple manifestations, plethora of practices, and kindred kinds in many bodies? In considering interdisciplinary answers to these questions, we find that the notion of edgework offers a fresh, flexible, and explanatory account of curiosity. More broadly, it uncovers new opportunities to use the lens of science to examine, probe, and interrogate this important dimension of the human experience.

Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. Here we assess the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years. We find that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing. We show that this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies. To further explain our findings, we examine co-authorship networks and find that while the network has become markedly more integrated in general, the current degree of segregation by race/ethnicity is greater now than it has been in the past. Citing further from oneself on the network is associated with greater balance, but White authors’ preferential citation of White authors remains even at high levels of network exploration. We also quantify the effects of intersecting identities, determining the relative costs of gender and race/ethnicity, and their combination in women of color. Our findings represent a call to scientists and journal editors of all disciplines to consider the ethics of citation practices, and actions to be taken in support of an equitable future.

Pervasive gender imbalances exist in the field of communication. In disciplines outside of communication, papers with women as first and last (i.e., senior) authors attract fewer citations than papers with men in those positions. This disparity is partially explained by men’s co-authorship networks. The extent to which co-authorship explains the over-citation of men in communication has not yet been quantified. Using data from 14 communication journals from 1995 to 2018, we find that reference lists include more papers with men as first and last author and fewer papers with women as first and last author than would be expected if gender were unrelated to referencing. This imbalance is driven largely by the citation practices of men and is slowly decreasing over time. The structure of men’s co-authorship networks partly accounts for the observed over-citation of men by other men. We discuss ways researchers might approach gendered citations in their work.

Intellectual disability, in its more common mild form and its more severe form, is the consequence of highly interactive genetic and environmental causes. It is an important area for psychiatrists, as comorbidity with psychiatric disorders is frequent. This chapter presents the causes and clinical features of the best characterized syndromes associated with intellectual disability. Common environmental causes, such as fetal alcohol or lead exposure, and common genetic causes, such as trisomy 21 (Down’s syndrome), are briefly summarized. Classic syndromes, such as tuberous sclerosis, are also presented, though new research identifying many syndromes makes some of these classifications less useful. Prenatal and newborn screenings are fast-developing areas.

Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work., 23 pages, 4 figures

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior—beyond the conventional network efficiency metric—for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.Author SummaryMacroscale communication between interconnected brain regions underpins most aspects of brain function and incurs substantial metabolic cost. Understanding efficient and behaviorally meaningful information transmission dependent on structural connectivity has remained challenging. We validate a model of communication dynamics atop the macroscale human structural connectome, finding that structural networks support dynamics that strike a balance between information transmission fidelity and lossy compression. Notably, this balance is predictive of behavior and explanatory of biology. In addition to challenging and reformulating the currently held view that communication occurs by routing dynamics along metabolically efficient direct anatomical pathways, our results suggest that connectome architecture and behavioral demands yield communication dynamics that accord to neurobiological and information theoretical principles of efficient coding and lossy compression.

Language is hierarchically organized: words are built into phrases, sentences, and paragraphs to represent complex ideas. Here we ask whether the organization of language in written text displays the fractal hierarchical architecture common in systems optimized for efficient information transmission. We test the hypothesis that the expositional structure of scientific research articles displays Rentian scaling, and that the exponent of the scaling law changes as the article's information transmission capacity changes. Using 32 scientific manuscripts - each containing between three and 26 iterations of revision - we construct semantic networks in which nodes represented unique words in each manuscript, and edges connect nodes if two words appeared within the same 5-word window. We show that these semantic networks display clear Rentian scaling, and that the Rent exponent varies over the publication life cycle, from the first draft to the final revision. Furthermore, we observe that manuscripts fell into three clusters in terms of how the scaling exponents changed across drafts: exponents rising over time, falling over time, and remaining relatively stable over time. This change in exponent reflects the evolution in semantic network structure over the manuscript revision process, highlighting a balance between network complexity, which increases the exponent, and network efficiency, which decreases the exponent. Lastly, the final value of the Rent exponent is negatively correlated with the number of authors. Taken together, our results suggest that semantic networks reflecting the structure of exposition in scientific research articles display striking hierarchical architecture that arbitrates tradeoffs between competing constraints on network organization, and that this arbitration is navigated differently depending on the social environment characteristic of the collaboration.

The information gained when practicing curiosity promotes well-being over extended timescales. The open-ended and internally driven nature of curiosity, however, makes characterizing the diverse styles of information seeking that accompany it a daunting endeavor. A recently developed historicophilosophical taxonomy of curious practice distinguishes between the collection of disparate, loosely connected pieces of information and the seeking of related, tightly connected pieces of information. With this taxonomy, we use a novel knowledge network building framework of curiosity to capture styles of curious information seeking in 149 participants as they explore Wikipedia for over 5 hours spanning 21 days. We create knowledge networks in which nodes consist of distinct concepts (unique Wikipedia pages) and edges represent the similarity between the content of Wikipedia pages. We quantify the tightness of each participants' knowledge networks using graph theoretical indices and use a generative model of network growth to explore mechanisms underlying the observed information seeking. We find that participants create knowledge networks with small-world and modular structure. Deprivation sensitivity, the tendency to seek information that eliminates knowledge gaps, is associated with the creation of relatively tight networks and a relatively greater tendency to return to previously-visited concepts. We further show that there is substantial within-person variability in knowledge network building over time and that building looser networks than usual is linked with higher than usual sensation seeking. With this framework in hand, future research can quantify the information collected during curious practice and examine its association with well-being.

We report two cases of paternally inherited 15q13.3 duplications in carriers diagnosed with childhood-onset schizophrenia (COS), a rare neurodevelopmental disorder of proposed polygenic origin with onset in children before age 13. This study documents that the 15q13.3 deletion and duplication exhibit pathogenicity for COS, with both copy number variants (CNVs) sharing a disrupted CHRNA7 gene. CHRNA7 encodes the neuronal alpha7 nicotinic acetylcholine receptor (α7nAChR) and is a candidate gene that has been suggested as a pathophysiological process mediating adult-onset schizophrenia (AOS) and other neurodevelopmental disorders. These results support the incomplete penetrance and variable expressivity of this CNV and represent the first report of 15q13.3 duplication carriers exhibiting COS. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics published by Wiley Periodicals, Inc.

Risk abounds as individuals engage in activities involving the sharing of sensitive information through a third-party cloud storage tool. In this research, we investigate the storage and sharing of very sensitive information (an individual’s tax filing information) through a specific third-party technology provider, Google Drive. Within the specifics of such a potentially risky act we argue that information assurance mechanisms implemented by the cloud storage service provider reduce risk perceptions of individuals even when very sensitive information is being shared. Previous positive experience with a cloud service is likely to mitigate concerns on information sharing on the cloud. To elaborate this proposed relationship a research model of information assurance is proposed and tested in the context of tax filing sharing intention.