Identification and characterisation of functional Kir6.1-containing ATP-sensitive potassium channels in the cardiac ventricular sarcolemmal membrane.

BACKGROUND AND PURPOSE: The canonical Kir6.2/SUR2A ventricular KATP channel is highly ATP-sensitive and remains closed under normal physiological conditions. These channels activate only when prolonged metabolic compromise causes significant ATP depletion and then shortens the action potential to reduce contractile activity. Pharmacological activation of KATP channels is cardioprotective, but physiologically, it is difficult to understand how these channels protect the heart if they only open under extreme metabolic stress. The presence of a second KATP channel population could help explain this. Here, we characterise the biophysical and pharmacological behaviours of a constitutively active Kir6.1-containing KATP channel in ventricular cardiomyocytes. EXPERIMENTAL APPROACH: Patch-clamp recordings from rat ventricular myocytes in combination with well-defined pharmacological modulators was used to characterise these newly identified K+ channels. Action potential recording, calcium (Fluo-4) fluorescence measurements and video edge detection of contractile function were used to assess functional consequences of channel modulation. KEY RESULTS: Our data show a ventricular K+ conductance whose biophysical characteristics and response to pharmacological modulation were consistent with Kir6.1-containing channels. These Kir6.1-containing channels lack the ATP-sensitivity of the canonical channels and are constitutively active. CONCLUSION AND IMPLICATIONS: We conclude there are two functionally distinct populations of ventricular KATP channels: constitutively active Kir6.1-containing channels that play an important role in fine-tuning the action potential and Kir6.2/SUR2A channels that activate with prolonged ischaemia to impart late-stage protection against catastrophic ATP depletion. Further research is required to determine whether Kir6.1 is an overlooked target in Comprehensive in vitro Proarrhythmia Assay (CiPA) cardiac safety screens.

A growth chart of brain function from infancy to adolescence based on EEG.

BACKGROUND: In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS: We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FINDINGS: The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028). INTERPRETATION: A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FUNDING: This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.

Early EEG-burst sharpness and 2-year disability in extremely preterm infants.

BACKGROUND: Automated computational measures of EEG have the potential for large-scale application. We hypothesised that a predefined measure of early EEG-burst shape (increased burst sharpness) could predict neurodevelopmental impairment (NDI) and mental developmental index (MDI) at 2 years of age over-and-above that of brain ultrasound. METHODS: We carried out a secondary analysis of data from extremely preterm infants collected for an RCT (SafeBoosC-II). Two hours of single-channel cross-brain EEG was used to analyse burst sharpness with an automated algorithm. The co-primary outcomes were moderate-or-severe NDI and MDI. Complete data were available from 58 infants. A predefined statistical analysis was adjusted for GA, sex and no, mild-moderate, and severe brain injury as detected by cranial ultrasound. RESULTS: Nine infants had moderate-or-severe NDI and the mean MDI was 87 ± 17.3 SD. The typical burst sharpness was low (negative values) and varied relatively little (mean -0.81 ± 0.11 SD), but the odds ratio for NDI was increased by 3.8 (p = 0.008) and the MDI was reduced by -3.2 points (p = 0.14) per 0.1 burst sharpness units increase (+1 SD) in the adjusted analysis. CONCLUSION: This study confirms the association between EEG-burst measures in preterm infants and neurodevelopment in childhood. Importantly, this was by a priori defined analysis. IMPACT: A fully automated, computational measure of EEG in the first week of life was predictive of neurodevelopmental impairment at 2 years of age. This confirms many previous studies using expert reading of EEG. Only single-channel EEG data were used, adding to the applicability. EEG was recorded by several different devices thus this measure appears to be robust to differences in electrodes, amplifiers and filters. The likelihood ratio of a positive EEG test, however, was only about 2, suggesting little immediate clinical value.

The Dual Nature of Social Media: Examining the Direction of Causal Flow Between Fear of Missing Out and Social Media Use.

The dual (bidirectional) nature of social media suggests that fear-of-missing-out (FOMO) leads to greater social media use (SMU). In turn, higher levels of SMU lead to heightened FOMO. Ironically, individuals use social media to assuage their FOMO but end up with higher levels of FOMO after being exposed to a wide variety of social opportunities, where they may not have been included. The present research examines the hypothesized bidirectional causal flow between FOMO and SMU. Extant research involving FOMO has been largely correlational. In Study 1, FOMO was manipulated and found to increase reported levels of SMU. Study 2 manipulated SMU, which led to higher levels of FOMO. It appears that, regarding FOMO, social media does exhibit a dual (bidirectional) nature.

Optimization of time series features to estimate brain age in children from electroencephalography.

Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance-Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.

Mechanisms underlying pathological cortical bursts during metabolic depletion.

Cortical activity depends upon a continuous supply of oxygen and other metabolic resources. Perinatal disruption of oxygen availability is a common clinical scenario in neonatal intensive care units, and a leading cause of lifelong disability. Pathological patterns of brain activity including burst suppression and seizures are a hallmark of the recovery period, yet the mechanisms by which these patterns arise remain poorly understood. Here, we use computational modeling of coupled metabolic-neuronal activity to explore the mechanisms by which oxygen depletion generates pathological brain activity. We find that restricting oxygen supply drives transitions from normal activity to several pathological activity patterns (isoelectric, burst suppression, and seizures), depending on the potassium supply. Trajectories through parameter space track key features of clinical electrophysiology recordings and reveal how infants with good recovery outcomes track toward normal parameter values, whereas the parameter values for infants with poor outcomes dwell around the pathological values. These findings open avenues for studying and monitoring the metabolically challenged infant brain, and deepen our understanding of the link between neuronal and metabolic activity.

Mitigation of Cardiovascular Disease and Toxicity through NRF2 Signalling.

Cardiovascular toxicity and diseases are phenomena that have a vastly detrimental impact on morbidity and mortality. The pathophysiology driving the development of these conditions is multifactorial but commonly includes the perturbance of reactive oxygen species (ROS) signalling, iron homeostasis and mitochondrial bioenergetics. The transcription factor nuclear factor erythroid 2 (NFE2)-related factor 2 (NRF2), a master regulator of cytoprotective responses, drives the expression of genes that provide resistance to oxidative, electrophilic and xenobiotic stresses. Recent research has suggested that stimulation of the NRF2 signalling pathway can alleviate cardiotoxicity and hallmarks of cardiovascular disease progression. However, dysregulation of NRF2 dynamic responses can be severely impacted by ageing processes and off-target toxicity from clinical medicines including anthracycline chemotherapeutics, rendering cells of the cardiovascular system susceptible to toxicity and subsequent tissue dysfunction. This review addresses the current understanding of NRF2 mechanisms under homeostatic and cardiovascular pathophysiological conditions within the context of wider implications for this diverse transcription factor.

Instagram and TikTok Flow States and Their Association with Psychological Well-Being.

Despite their growing popularity, little research has focused on the association between Instagram and TikTok use and psychological well-being. Informed by Uses and Gratifications Theory, the present study investigates the flow states experienced when using each social media platform and whether these flow states differentially impact user well-being. A flow state is achieved when people are so engrossed in an activity that little else seems to matter to them and they will often continue the activity despite its negative consequences. Based upon a survey of adult Instagram (n = 195, Mage = 38) and TikTok users (n = 225, Mage = 37), the present study identified four unique clusters of users for both platforms based upon the levels of five flow dimensions: focused attention, curiosity, enjoyment, telepresence, and time distortion. Results found that TikTok users reported higher levels of overall flow than Instagram users. TikTok users also reported higher levels of the flow dimensions "enjoyment" and "time distortion." The flow dimension of "telepresence" (immersion in a world created by the social media application) for both TikTok and Instagram users was associated with higher levels of depression and anxiety. These social media may provide an escape from everyday worries for users, although a suboptimal coping strategy. Whether Instagram and TikTok use are but "improved means to an unimproved end" is an important question that requires additional research.

Mechanisms of imbalanced frontostriatal functional connectivity in obsessive-compulsive disorder.

The diagnosis of obsessive-compulsive disorder (OCD) has been linked with changes in frontostriatal resting-state connectivity. However, replication of prior findings is lacking, and the mechanistic understanding of these effects is incomplete. To confirm and advance knowledge on changes in frontostriatal functional connectivity in OCD, participants with OCD and matched healthy controls underwent resting-state functional, structural and diffusion neuroimaging. Functional connectivity changes in frontostriatal systems were here replicated in individuals with OCD (n = 52) compared with controls (n = 45). OCD participants showed greater functional connectivity (t = 4.3, PFWE = 0.01) between the nucleus accumbens (NAcc) and the orbitofrontal cortex (OFC) but lower functional connectivity between the dorsal putamen and lateral prefrontal cortex (t = 3.8, PFWE = 0.04) relative to controls. Computational modelling suggests that NAcc-OFC connectivity changes reflect an increased influence of NAcc over OFC activity and reduced OFC influence over NAcc activity (posterior probability, Pp > 0.66). Conversely, dorsal putamen showed reduced modulation over lateral prefrontal cortex activity (Pp > 0.90). These functional deregulations emerged on top of a generally intact anatomical substrate. We provide out-of-sample replication of opposite changes in ventro-anterior and dorso-posterior frontostriatal connectivity in OCD and advance the understanding of the neural underpinnings of these functional perturbations. These findings inform the development of targeted therapies normalizing frontostriatal dynamics in OCD.

Bedside tracking of functional autonomic age in preterm infants.

BACKGROUND: Preterm birth predisposes infants to adverse outcomes that, without early intervention, impacts their long-term health. To assist bedside monitoring, we developed a tool to track the autonomic maturation of the preterm by assessing heart rate variability (HRV) changes during intensive care. METHODS: Electrocardiogram (ECG) recordings were longitudinally recorded in 67 infants (26-38 weeks postmenstrual age (PMA)). Supervised machine learning was used to generate a functional autonomic age (FAA), by combining 50 computed HRV features from successive 5-minute ECG epochs (median of 23 epochs per infant). Performance of the FAA was assessed by correlation to PMA, clinical outcomes and the infant's functional brain age (FBA), an index of maturation derived from the electroencephalogram. RESULTS: The FAA was strongly correlated to PMA (r = 0.86, 95% CI: 0.83-0.93) with a mean absolute error (MAE) of 1.66 weeks and also accurately estimated FBA (MAE = 1.58 weeks, n = 54 infants). The relationship between PMA and FAA was not confounded by neurodevelopmental outcome (p = 0.18, n = 45), sex (p = 0.88, n = 56), patent ductus arteriosus (p = 0.08, n = 56), IVH (p = 0.63, n = 56) or body weight at birth (p = 0.95, n = 56). CONCLUSIONS: The FAA, an index derived from the ubiquitous ECG signal, offers direct avenues towards estimating autonomic maturation at the bedside during intensive care monitoring. IMPACT: The development of a tool to track functional autonomic age in preterm infants based on heart rate variability features in the electrocardiogram provides a rapid and specialized view of autonomic maturation at the bedside. Functional autonomic age is linked closely to postmenstrual age and central nervous system function response, as determined by its relationship to functional brain age from the electroencephalogram. Tracking functional autonomic age during neonatal intensive care unit monitoring offers a unique insight into cardiovascular health in infants born extremely preterm and their maturational trajectories to term age.

Evolutionary shaping of human brain dynamics.

The human brain is distinct from those of other species in terms of size, organization, and connectivity. How do structural evolutionary differences drive patterns of neural activity enabling brain function? Here, we combine brain imaging and biophysical modeling to show that the anatomical wiring of the human brain distinctly shapes neural dynamics. This shaping is characterized by a narrower distribution of dynamic ranges across brain regions compared with that of chimpanzees, our closest living primate relatives. We find that such a narrow dynamic range distribution supports faster integration between regions, particularly in transmodal systems. Conversely, a broad dynamic range distribution as seen in chimpanzees facilitates brain processes relying more on neural interactions within specialized local brain systems. These findings suggest that human brain dynamics have evolved to foster rapid associative processes in service of complex cognitive functions and behavior.

Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Risk of sustained SARS-CoV-2 transmission in Queensland, Australia.

We used an agent-based model Covasim to assess the risk of sustained community transmission of SARSCoV-2/COVID-19 in Queensland (Australia) in the presence of high-transmission variants of the virus. The model was calibrated using the demographics, policies, and interventions implemented in the state. Then, using the calibrated model, we simulated possible epidemic trajectories that could eventuate due to leakage of infected cases with high-transmission variants, during a period without recorded cases of locally acquired infections, known in Australian settings as "zero community transmission". We also examined how the threat of new variants reduces given a range of vaccination levels. Specifically, the model calibration covered the first-wave period from early March 2020 to May 2020. Predicted epidemic trajectories were simulated from early February 2021 to late March 2021. Our simulations showed that one infected agent with the ancestral (A.2.2) variant has a 14% chance of crossing a threshold of sustained community transmission (SCT) (i.e., > 5 infections per day, more than 3 days in a row), assuming no change in the prevailing preventative and counteracting policies. However, one agent carrying the alpha (B.1.1.7) variant has a 43% chance of crossing the same threshold; a threefold increase with respect to the ancestral strain; while, one agent carrying the delta (B.1.617.2) variant has a 60% chance of the same threshold, a fourfold increase with respect to the ancestral strain. The delta variant is 50% more likely to trigger SCT than the alpha variant. Doubling the average number of daily tests from ∼ 6,000 to 12,000 results in a decrease of this SCT probability from 43 to 33% for the alpha variant. However, if the delta variant is circulating we would need an average of 100,000 daily tests to achieve a similar decrease in SCT risk. Further, achieving a full-vaccination coverage of 70% of the adult population, with a vaccine with 70% effectiveness against infection, would decrease the probability of SCT from a single seed of alpha from 43 to 20%, on par with the ancestral strain in a naive population. In contrast, for the same vaccine coverage and same effectiveness, the probability of SCT from a single seed of delta would decrease from 62 to 48%, a risk slightly above the alpha variant in a naive population. Our results demonstrate that the introduction of even a small number of people infected with high-transmission variants dramatically increases the probability of sustained community transmission in Queensland. Until very high vaccine coverage is achieved, a swift implementation of policies and interventions, together with high quarantine adherence rates, will be required to minimise the probability of sustained community transmission.

Automated detection of artefacts in neonatal EEG with residual neural networks.

BACKGROUND AND OBJECTIVE: To develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals. METHODS: As part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Performance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm. RESULTS: The Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact. CONCLUSION: Artefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms.

The Pandemic within a Pandemic: Testing a Sequential Mediation Model to Better Understand Racial/Ethnic Disparities in COVID-19 Preventive Behavior.

Recent Centers for Disease Control and Prevention (CDC) data reveal that COVID-19 hospitalization and mortality rates are higher for certain racial/ethnic groups. Labeled as the "pandemic within a pandemic", African Americans and Hispanics are bearing more of the brunt of the disease compared to Caucasians. Testing a new sequential mediation model on a sample of 483 US African American, Caucasian, and Hispanic adults, the present study investigates the role of fear of COVID-19, information receptivity, perceived knowledge, and self-efficacy to explain disparities in preventive behaviors. Study contributions include the specification of a new predictive model that improves upon the long-used Health Belief Model (HBM). The Sequential Mediation Model appears to have greater explanatory capacity than the HBM. Study results also provide important insights into racial/ethnic differences in health-seeking behavior related to the coronavirus. Findings show that African Americans reported higher levels of preventive behaviors and self-efficacy than Caucasians. It is possible that SES, rather than race per se, is more important in explaining differences in COVID-19 preventive behaviors. Certain "cues to action" (precipitating factors) also help explain this somewhat surprising result. Additionally, significant differences were found across the three racial/ethnic groups for all the new model's variables except perceived knowledge. The new model was supported across all three racial/ethnic groups with notable differences across each group. Given the severity of implications surrounding the COVID-19 pandemic (physical, mental, and economic), it is critical that an improved understanding of what drives individual health-seeking behavior be achieved. Study limitations and future research suggestions are discussed.

Improving Predictions of COVID-19 Preventive Behavior: Development of a Sequential Mediation Model.

BACKGROUND: Since the beginning of the COVID-19 pandemic, social distancing, self-quarantining, wearing masks, and washing hands have become part of the new norm for many, but not all. It appears that such preventive measures are critical to "flattening the curve" of the spread of COVID-19. The public's adoption of such behaviors is an essential component in the battle against what has been referred to as the "invisible enemy." OBJECTIVE: The primary objective of this study was to develop a model for predicting COVID-19 preventive behaviors among US college students. The Health Belief Model has a long history of use and empirical support in predicting preventive health behaviors, but it is not without its purported shortcomings. This study identifies a more optimal and defensible combination of variables to explain preventive behaviors among college students. This segment of the US population is critical in helping slow the spread of COVID-19 because of the relative reluctance of college students to perform the needed behaviors given they do not feel susceptible to or fearful of COVID-19. METHODS: For this study, 415 US college students were surveyed via Qualtrics and asked to answer questions regarding their fear of COVID-19, information receptivity (seeking relevant information), perceived knowledge of the disease, self-efficacy, and performance of preventive behaviors. The PROCESS macro (Model 6) was used to test our conceptual model, including predictions involving sequential mediation. RESULTS: Sequential mediation results show that fear of COVID-19 leads individuals to seek out information regarding the disease, which increases their perceived knowledge and fosters self-efficacy; this is key to driving preventive behaviors. CONCLUSIONS: Self-imposed preventive measures can drastically impact the rate of infection among populations. Based on this study's newly created sequential mediation model, communication strategies for encouraging COVID-19 preventive behaviors are offered. It is clear that college students, and very possibly adults of all ages, must have a healthy fear of COVID-19 to set in motion a process where concerned individuals seek out COVID-19-related information, increasing their store of knowledge concerning the disease, their self-efficacy, and ultimately their likelihood of performing the needed preventive behaviors.

Smartphone Use during the COVID-19 Pandemic: Social Versus Physical Distancing.

The COVID-19 pandemic continues to wreak havoc across the globe. According to the Centers for Disease Control and Prevention, limiting face-to-face interaction is the best strategy for reducing the spread of COVID-19. We investigate the impact of social distancing on social connection and well-being, while also considering the moderating influence of smartphone use. In a survey of 400 students, the study presented herein finds that smartphone use attenuates the negative impact of social distancing on social connection and well-being. Contrary to popular sentiments regarding the influence of smartphone use on well-being, increased smartphone use during the pandemic may foster social connection and well-being. Overall, the research presented provides evidence that the perceived loss of social connection with others is not a de facto outcome of social distancing. The study's findings have important implications for public policymakers, government officials, and others, including consumer researchers. These implications include stressing the important role technology can play in staying socially connected during the current pandemic and the importance of reframing "social distancing" as "physical distancing with social connectedness".

Stochastic synchronization of dynamics on the human connectome.

Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network's topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome's subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.

ADHD symptoms map onto noise-driven structure-function decoupling between hub and peripheral brain regions.

Adults with childhood-onset attention-deficit hyperactivity disorder (ADHD) show altered whole-brain connectivity. However, the relationship between structural and functional brain abnormalities, the implications for the development of life-long debilitating symptoms, and the underlying mechanisms remain uncharted. We recruited a unique sample of 80 medication-naive adults with a clinical diagnosis of childhood-onset ADHD without psychiatric comorbidities, and 123 age-, sex-, and intelligence-matched healthy controls. Structural and functional connectivity matrices were derived from diffusion spectrum imaging and multi-echo resting-state functional MRI data. Hub, feeder, and local connections were defined using diffusion data. Individual-level measures of structural connectivity and structure-function coupling were used to contrast groups and link behavior to brain abnormalities. Computational modeling was used to test possible neural mechanisms underpinning observed group differences in the structure-function coupling. Structural connectivity did not significantly differ between groups but, relative to controls, ADHD showed a reduction in structure-function coupling in feeder connections linking hubs with peripheral regions. This abnormality involved connections linking fronto-parietal control systems with sensory networks. Crucially, lower structure-function coupling was associated with higher ADHD symptoms. Results from our computational model further suggest that the observed structure-function decoupling in ADHD is driven by heterogeneity in neural noise variability across brain regions. By highlighting a neural cause of a clinically meaningful breakdown in the structure-function relationship, our work provides novel information on the nature of chronic ADHD. The current results encourage future work assessing the genetic and neurobiological underpinnings of neural noise in ADHD, particularly in brain regions encompassed by fronto-parietal systems.

Developing and Testing a Scale Designed to Measure Perceived Phubbing.

Phubbing (phone snubbing) has become a commonplace behavior. The more we are phubbed the more likely we are to phub others. The extraordinary attention-grabbing ability of the smartphone would only be an interesting story if not for its impact on social media use and, ultimately, stress and depression. In Study 1 (n = 258, Mage = 20), we develop a parsimonious and valid measure of phubbing. Extant "phubbing" measures all lack important qualities needed to be able to assess phubbing with a brief and valid scale that can be replicated and used in a variety of research settings. In Study 2 (n = 157, Mage = 39), we test and extend the David and Roberts (2017) phubbing model, while further validating our perceived phubbing measure. We use Social Exchange Theory and Kardefelt-Winther's (2014) model of compensatory internet use as theoretical support for our expected findings. Results find that phubbed individuals experience a sense of social exclusion that, paradoxically, is associated with an increased use of social media. This increased use of social media is associated with higher reported levels of anxiety and depression. Future research directions and study limitations are discussed.