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Convergent research identifies a general factor (“P factor”) that confers transdiagnostic risk for psychopathology. Large-scale networks are key organizational units of the human brain. However, studies of altered network connectivity patterns associated with the P factor are limited, especially in early adolescence when most mental disorders are first emerging. We studied 11,875 9- and 10-year olds from the Adolescent Brain and Cognitive Development (ABCD) study, of whom 6593 had high-quality resting-state scans. Network contingency analysis was used to identify altered interconnections associated with the P factor among 16 large-scale networks. These connectivity changes were then further characterized with quadrant analysis that quantified the directionality of P factor effects in relation to neurotypical patterns of positive versus negative connectivity across connections. The results showed that the P factor was associated with altered connectivity across 28 network cells (i.e., sets of connections linking pairs of networks); pPERMUTATION values pPERMUTATION

RationaleSubstance use peaks during the developmental period known as emerging adulthood (ages 18–25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology.ObjectivesWe test whether lower efficiency of evidence accumulation (EEA), a computationally-characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults.Methods and ResultsIn an fMRI substudy within a sociobehavioral longitudinal study (n=106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18–21 are both prospectively related to greater substance use during ages 22–26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neuroimaging correlates contain unique predictive information about substance use involvement.ConclusionsThese findings highlight EEA as a computationally-characterized neurocognitive risk factor for substance use during a critical developmental period, with clear links to both neuroimaging measures and well-established formal theories of brain function.

Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test–retest reliability. We hypothesize that time‐varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data‐driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole‐brain functional activation, rather than a fixed‐length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block‐design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject‐specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole‐brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time‐varying FC in rest., We present a new approach for assessing dynamic functional connectivity that leverages instantaneous changes in functional activations to detect transition points between brain states and derives an informed segmentation of the fMRI time series. Our approach accurately recovers known state transitions in the context of a structured working memory task, outperforming the popular sliding window approach. In resting data, our method identifies five dynamic states that are highly test‐retest reliable, exhibit complex transition dynamics, and are correlated with multiple behavioral phenotypes.

Importance: Childhood Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms are linked to many negative outcomes and widely believed to result from disrupted neurocognitive development. However, evidence for the clinical value of neurocognitive assessments in this context has been mixed and there have been no large-scale efforts to quantify the potential of neurocognitive abilities, along with data from other domains (e.g., child and family characteristics, demographics), for use in generalizable machine learning models that predict individual youths’ ADHD symptoms in independent data.Objective: To develop and test, in a large and diverse sample of youth, cross-validated machine learning models for predicting youths’ ADHD symptoms using demographics, child and family characteristics, and neurocognitive assessments. Design, Setting, and Participants: Data were from participants of the Adolescent Brain Cognitive Development Study (ABCD), a collaborative consortium that recruited a diverse sample of youth (ages 9-10 at baseline) across 21 sites in the United States. Participants with complete parent and teacher reports of ADHD symptoms at the 1-year follow-up (n = 5,900) were included.Exposures: Predictor variables were from the ABCD baseline timepoint and included basic demographic and biometric measures, geocoded community and neighborhood data, youths’ self-report about child and family characteristics, and neurocognitive tests.Main Outcome: Models predicted a latent cross-rater ADHD symptoms factor derived from a bifactor model of parent and teacher ratings at the 1-year follow-up.Results: Predictive models explained about 20% of the variance in ADHD symptoms for ABCD Study sites that were left out of the model-fitting process, with high generalizability across sites and only trivial loss of predictive power when applied to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, screen time, parental monitoring, school involvement, and socioeconomic disadvantage. A sparse model including only seven neurocognitive measures, male sex, screen time, and two impulsivity scales displayed comparable performance to larger models.Conclusions and Relevance: This work quantifies the informational value of neurocognitive abilities and other child characteristics for predicting individual children’s ADHD symptoms in unseen data and provides a foundational method for the prediction of ADHD symptoms in ABCD and across other research and clinical contexts.

RATIONALE: Substance use peaks during the developmental period known as emerging adulthood (ages 18–25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here, we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. OBJECTIVES: We test whether lower efficiency of evidence accumulation (EEA), a computationally characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults. METHODS AND RESULTS: In an fMRI substudy within a sociobehavioral longitudinal study (n = 106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18–21 are both prospectively related to greater substance use during ages 22–26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neuroimaging correlates contain unique predictive information about substance use involvement. CONCLUSIONS: These findings highlight EEA as a computationally characterized neurocognitive risk factor for substance use during a critical developmental period, with clear links to both neuroimaging measures and well-established formal theories of brain function.

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.

Philosophers, psychologists, and economists have reached the consensus that one can use two different kinds of regulation to achieve self-control. Synchronic regulation uses willpower to resist current temptation. Diachronic regulation implements a plan to avoid future temptation. Yet this consensus may rest on contaminated intuitions. Specifically, agents typically use willpower (synchronic regulation) to achieve their plans to avoid temptation (diachronic regulation). So even if cases of diachronic regulation seem to involve self-control, this may be because they are contaminated by synchronic regulation. We therefore developed a novel multifactorial method to disentangle synchronic and diachronic regulation. Using this method, we find that ordinary usage assumes that only synchronic––not diachronic––regulation counts as self-control. We find this pattern across four experiments involving different kinds of temptation, as well as a paradigmatic case of diachronic regulation based on the classic story of Odysseus and the Sirens. Our final experiment finds that self-control in a diachronic case depends on whether the agent uses synchronic regulation at two moments: when she (1) initiates and (2) follows-through on a plan to resist temptation. Taken together, our results strongly suggest that synchronic regulation is the sole difference maker in the folk concept of self-control.

Questions about measurement of individual differences in implicit attitudes, which have been the focus so far in this exchange, should be distinguished from more general questions about whether implicit attitudes exist and operate in our minds. Theorists frequently move too quickly from pessimistic results regarding the first set of questions to pessimistic conclusions about the second. That is, they take evidence that indirect measures such as the implicit association test (IAT) disappoint as individual difference measures and use it to (mistakenly) suggest that people do not in fact have implicit attitudes directed at stigmatized groups. In this commentary, I dissect this mistake in detail, drawing key lessons from a parallel debate that has unfolded in cognitive science about "conflict tasks" such as the Stroop task. I argue that the evidence overall supports a nuanced conclusion: Indirect measures such as the IAT measure individual differences in implicit attitudes poorly, but they-via distinct lines of evidence-still support the view that implicit attitudes exist. This article is categorized under: PsychologyTheory and Methods.

Youth self-reports are a mainstay of delinquency assessment; however, making valid inferences about delinquency using these assessments requires equivalent measurement across groups of theoretical interest. We examined whether a brief 10-item delinquency measure exhibited measurement invariance across non-Hispanic White ( n = 6,064) and Black ( n = 1,666) youth (ages 10–11 years old) in the Adolescent Brain Cognitive Developmentsm Study (ABCD Study®). We detected differential item functioning (DIF) in two items. Black youth were more likely to report being arrested or picked up by police than White youth with the same score on the latent delinquency trait. Although multiple covariates (income, urgency, and callous–unemotional traits) reduced mean-level difference in overall delinquency, they were generally unrelated to the DIF in the Arrest item. However, the DIF in the Arrest item was reduced in size and no longer significant after adjusting for neighborhood safety. Results illustrate the importance of considering measurement invariance when using self-reported delinquency scores to draw inferences about group differences, and the utility of measurement invariance analyses for helping to identify mechanisms that contribute to group differences generally.

BackgroundStructural models of psychopathology consistently identify internalizing (INT) and externalizing (EXT) specific factors as well as a superordinate factor that captures their shared variance, the p factor. Questions remain, however, about the meaning of these data-driven dimensions and the interpretability and distinguishability of the larger nomological networks in which they are embedded.MethodsThe sample consisted of 10 645 youth aged 9–10 years participating in the multisite Adolescent Brain and Cognitive Development (ABCD) Study. p, INT, and EXT were modeled using the parent-rated Child Behavior Checklist (CBCL). Patterns of associations were examined with variables drawn from diverse domains including demographics, psychopathology, temperament, family history of substance use and psychopathology, school and family environment, and cognitive ability, using instruments based on youth-, parent-, and teacher-report, and behavioral task performance.Resultsp exhibited a broad pattern of statistically significant associations with risk variables across all domains assessed, including temperament, neurocognition, and social adversity. The specific factors exhibited more domain-specific patterns of associations, with INT exhibiting greater fear/distress and EXT exhibiting greater impulsivity.ConclusionsIn this largest study of hierarchical models of psychopathology to date, we found that p, INT, and EXT exhibit well-differentiated nomological networks that are interpretable in terms of neurocognition, impulsivity, fear/distress, and social adversity. These networks were, in contrast, obscured when relying on the a priori Internalizing and Externalizing dimensions of the CBCL scales. Our findings add to the evidence for the validity of p, INT, and EXT as theoretically and empirically meaningful broad psychopathology liabilities.

Recent studies found low test–retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test–retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the “good” range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test–retest reliability by making greater use of predictive models.

Some theorists say that addiction involves loss of control over using drugs, while others say control is preserved. In this chapter, I disagree with both sides, not so much in substance, but rather in epistemic tenor. Both sides, I argue, run well ahead of what the evidence allows. I frame the discussion in terms of a key division in human motivational architecture: We not only have desires, we also have powerful capacities to exercise top-down regulation over these desires. I review a number of influential theories of addiction, both ones that favour loss of control and ones that deny it, and I find that they all have a massive gap: they lack an adequate explanation for when and how top-down regulation over inappropriate desires succeeds and fails. Without this critical piece, we simply cannot have much confidence in these views.

General cognitive ability (GCA) refers to a trait‐like ability that contributes to performance across diverse cognitive tasks. Identifying brain‐based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole‐brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N‐back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in 10‐fold cross‐validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain‐based prediction of GCA., We investigated prediction of general cognitive ability (GCA) based on fMRI task activation patterns with 15 task contrasts in the Human Connectome Project dataset. The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in ten10‐fold cross‐validation analysis. Additionally, we found that task contrasts that produce greater fronto‐parietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in GCA prediction.

According to a standard picture of agency, a person’s actions always reflect what they most desire, and many theorists extend this model to mental illness. In this chapter, I pin down exactly where this “volitional” view goes wrong. The key is to recognize that human motivational architecture involves a regulatory control structure: we have both spontaneous states (e.g., automatically-elicited thoughts and action tendencies, etc.) as well as regulatory mechanisms that allow us to suppress or modulate these spontaneous states. Our regulatory abilities, however, are bounded. Mental illnesses, I argue, arise precisely where these bounds are reached, thus allowing inappropriate spontaneous states to regularly manifest in thought and action. I conclude that the volitional view of mental illness is wrong: when a person with mental illness reaches the limits of control, what they do often does not reflect what they most prefer.

General cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η2). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p −16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.

Philosophers, psychologists, and economists have reached the consensus that one can use two different kinds of regulation to achieve self-control. Synchronic regulation uses willpower to resist current temptation. Diachronic regulation implements a plan to avoid future temptation. Yet this consensus may rest on contaminated intuitions. Specifically, agents typically use willpower (synchronic regulation) to achieve their plans to avoid temptation (diachronic regulation). So even if cases of diachronic regulation seem to involve self-control, this may be because they are contaminated by synchronic regulation. We therefore developed a novel multifactorial method to disentangle synchronic and diachronic regulation. Using this method, we find that ordinary usage assumes that only synchronic--not diachronic--regulation counts as self-control. We find this pattern across four experiments involving different kinds of temptation, as well as a paradigmatic case of diachronic regulation based on the classic story of Odysseus and the Sirens. Our final experiment finds that self-control in a diachronic case depends on whether the agent uses synchronic regulation at two moments: when she (1) initiates and (2) follows-through on a plan to resist temptation. Taken together, our results strongly suggest that synchronic regulation is the sole difference maker in the folk concept of self-control.

Impaired control in addiction involves a characteristic but obscure kind of partial control. Certain aspects of control over drug use are clearly reduced, reflected in difficulty cutting back and relapse. However, other aspects of control are clearly preserved, as reflected in substantial sensitivity to situational incentives—for example, the ability to defer use when needed. This juxtaposition is puzzling, and a clear mechanistically precise understanding of impaired control has yet to emerge. In this article, a Distortion model of impaired control is put forward. The key insight of the model is that the puzzling pattern of partial control seen in addiction can be understood in terms of unreliable control. The model posits large populations of distorted automatic thoughts (e.g., about drugs, one’s self, one’s circumstances, and one’s abilities to cope), coupled with unreliable control over these distorted thoughts. These distorted thoughts, typically gradually and cumulatively, lead to illusion-like misvaluation of costs and benefits of drug use, in turn eventually leading to decisions to use. The model captures an elusive middle ground in addiction in which substantially preserved control over drug use for briefer intervals coexists with difficulty maintaining sobriety over the long-term. Moreover, the model explains a range of clinical findings in addiction that are not easily accommodated on leading alternative views.

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.

We investigate implicit principles that inform folk moral judgments about mood enhancement. We presented a series of vignettes involving mood enhancement to lay participants via Amazon’s Mechanical Turk platform. We hypothesized that in making intuitive moral determinations about the appropriateness of enhancement, participants would rely primarily on set points (specifically, how far away from “normal” a character’s mood is prior to or after intervention). Contrary to our hypotheses, set point was not strongly predictive of enhancement judgments. Instead, participants’ perception of the mood enhancement’s effect on the long-term well-being of the character in the vignette was robustly correlated with participants’ approval ratings for the enhancement. Long-term well-being predicted enhancement judgments even after controlling for perceived medical necessity (whether the character is perceived to have a medical need for mood improvement), indicating that long-term well-being does not simply serve as a proxy for medical judgments being applied to enhancement. This work highlights the centrality of well-being in folk judgments about enhancement and generates new considerations for adjudicating among competing views in the enhancement debate.

Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2013 9-10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. For General Ability scores in particular, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These results survived several tests for robustness including replication in split-half analysis and in a low head motion subsample. We additionally found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting differences in General Ability across participants. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network interconnections as a major locus of individual differences in cognitive functioning in early adolescence.

Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, climate science, economics, and more. In domains where networks are discovered on multiple time series, the most common approach is to compute measures of association or similarity between all pairs of time series. The nodes in the resultant network correspond to time series, which are linked by edges weighted according to the association scores of their endpoints. Finally, the fully connected network is thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic (or higher) time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to circumvent the inefficient and wasteful intermediary step of building a fully connected graph before network sparsification, we propose a fast network discovery approach based on probabilistic hashing. Our methods emphasize consecutiveness, or the intuition that time series following similar fluctuations in longer time-consecutive intervals are more similar overall. Evaluation on real data shows that our method can build graphs nearly 15 times faster than baselines (when the baselines do not run out of memory), while achieving accuracy comparable to, or better than, baselines in task-based evaluation. Furthermore, our proposals are general, modular, and may be applied to a variety of sequence similarity search tasks.

Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the past 2 decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., inhibition, updating) through a combination of experimental manipulation and factor analysis. However, the putative constructs obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of this approach is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation, a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that efficiency of evidence accumulation is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which this insight can advance clinical neuroscience research. We propose that recognition of efficiency of evidence accumulation as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.

Structural models of psychopathology consistently identify internalizing (INT) and externalizing (EXT) specific factors as well as a superordinate factor that captures their shared variance, the p factor. Questions remain, however, about the meaning of these data-driven dimensions and the interpretability and distinguishability of the larger nomological networks in which they are embedded.The sample consisted of 10 645 youth aged 9-10 years participating in the multisite Adolescent Brain and Cognitive Development (ABCD) Study. p, INT, and EXT were modeled using the parent-rated Child Behavior Checklist (CBCL). Patterns of associations were examined with variables drawn from diverse domains including demographics, psychopathology, temperament, family history of substance use and psychopathology, school and family environment, and cognitive ability, using instruments based on youth-, parent-, and teacher-report, and behavioral task performance.p exhibited a broad pattern of statistically significant associations with risk variables across all domains assessed, including temperament, neurocognition, and social adversity. The specific factors exhibited more domain-specific patterns of associations, with INT exhibiting greater fear/distress and EXT exhibiting greater impulsivity.In this largest study of hierarchical models of psychopathology to date, we found that p, INT, and EXT exhibit well-differentiated nomological networks that are interpretable in terms of neurocognition, impulsivity, fear/distress, and social adversity. These networks were, in contrast, obscured when relying on the a priori Internalizing and Externalizing dimensions of the CBCL scales. Our findings add to the evidence for the validity of p, INT, and EXT as theoretically and empirically meaningful broad psychopathology liabilities.

Social dysfunction is an intractable problem in a wide spectrum of psychiatric illnesses, undermining patients’ capacities for employment, independent living, and maintaining meaningful relationships. Identifying common markers of social impairment across disorders and understanding their mechanisms are prerequisites to developing targeted neurobiological treatments that can be applied productively across diagnoses and illness stages to improve functional outcome. This project focuses on eye gaze perception, the ability to accurately and efficiently discriminate others’ gaze direction, as a potential biomarker of social functioning that cuts across psychiatric diagnoses. This premise builds on both the monkey and human literatures showing gaze perception as a basic building block supporting higher-level social communication and social development, and reports of abnormal gaze perception in multiple psychiatric conditions accompanied by prominent social dysfunction (e.g., psychosis-spectrum disorders, autism-spectrum disorders, social phobia). A large sample (n = 225) of adolescent and young adult (age 14–30) psychiatric patients (regardless of diagnosis) with various degrees of impaired social functioning, and demographically-matched healthy controls (n = 75) will be recruited for this study. Participant’s psychiatric phenotypes, cognition, social cognition, and community functioning will be dimensionally characterized. Eye gaze perception will be assessed using a psychophysical task, and two metrics (precision, self-referential bias) that respectively tap into gaze perception disturbances at the visual perceptual and interpretation levels, independent of general deficits, will be derived using hierarchical Bayesian modeling. A subset of the participants (150 psychiatric patients, 75 controls) will additionally undergo multimodal fMRI to determine the functional and structural brain network features of altered gaze perception. The specific aims of this project are three-fold: (1) Determine the generality of gaze perception disturbances in psychiatric patients with prominent social dysfunction; (2) Map behavioral indices of gaze perception disturbances to dimensions of psychiatric phenotypes and core functional domains; and (3) Identify the neural correlates of altered gaze perception in psychiatric patients with social dysfunction. Successfully completing these specific aims will identify the specific basic deficits, clinical profile, and underlying neural circuits associated with social dysfunction that can be used to guide targeted, personalized treatments, thus advancing NIMH’s Strategic Objective 1 (describe neural circuits associated with mental illnesses and map the connectomes for mental illnesses) and Objective 3 (develop new treatments based on discoveries in neuroscience and behavioral science).

Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test-retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here we introduce a straightforward data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states, and provides strong evidence for the existence of time-varying FC in rest.

Top-down control of responses is a key construct in cognitive science that is thought to be critical for self-control. It is typically measured by subtracting performance in experimental conditions in which top-down control is theoretically present against performance in matched conditions in which it is assumed to be absent. Recently, however, subtraction-based metrics of top-down control have been criticized for having low test-retest reliability, weak intercorrelations, and little relation to self-report measures of self-control. Concurrently, there is growing evidence that task-general cognitive efficiency, indexed by the drift rate parameter of the diffusion model ( Ratcliff, 1978 ), constitutes a cohesive, reliable individual difference dimension relevant to self-control. However, no previous studies have directly compared latent factors for top-down control (derived from subtraction metrics) with factors for task-general efficiency “head-to-head” in the same sample in terms of their cohesiveness, temporal stability, and relation to self-control. In this re-analysis of a large open data set ( Eisenberg et al., 2019 ; N = 522), we find that top-down control metrics fail to form cohesive latent factors, that the resulting factors have poor temporal stability, and that they exhibit tenuous connections to questionnaire measures of self-control. In contrast, cognitive efficiency measures—drawn from conditions of the same tasks that both are, and are not, assumed to demand top-down control—form a robust, temporally stable factor that correlates with questionnaire measures of self-control. These findings suggest that task-general efficiency is a central individual difference dimension relevant to self-control. Moreover, they go beyond recent measurement-based critiques of top-down control metrics, and instead suggest problems with key theoretical assumptions that have long guided this research paradigm.

The development of objective brain-based measures of individual differences in psychological traits is a longstanding goal of clinical neuroscience. Here we show that reliable objective markers of children’s neurocognitive abilities can be built from measures of brain connectivity. The sample consists of 5,937 9- and 10-year-olds in the Adolescent Brain Cognitive Development multi-site study with high-quality functional connectomes that capture brain-wide connectivity. Using multivariate methods, we built predictive neuromarkers for a general factor of neurocognitive ability as well as for a number of specific cognitive abilities (e.g., spatial reasoning, working memory). Neuromarkers for the general neurocognitive factor successfully predicted scores for held-out participants at 19 out of 19 held-out sites, explaining over 14% of the variance in their scores. Neuromarkers for specific neurocognitive abilities also exhibited statistically reliable generalization to new participants. This study provides the strongest evidence to date that objective quantification of psychological traits is possible with functional neuroimaging.

BACKGROUNDConvergent research identifies a general factor (“P factor”) that confers transdiagnostic risk for psychopathology. However, brain functional connectivity patterns that underpin the P factor remain poorly understood, especially at the transition to adolescence when many serious mental disorders have their onset.OBJECTIVEIdentify a distributed connectome-wide neurosignature of the P factor and assess the generalizability of this neurosignature in held out samples.DESIGN, SETTING, AND PARTICIPANTSThis study used data from the full baseline wave of the Adolescent Brain and Cognitive Development (ABCD) national consortium study, a prospective, population-based study of 11,875 9- and 10-year olds. Data for this study were collected from September 1, 2016 to November 15, 2018 at 21 research sites across the United States.MAIN OUTCOMES AND MEASURESWe produced whole brain functional connectomes for 5,880 youth with high quality resting state scans. We then constructed a low rank basis set of 250 components that captures interindividual connectomic differences. Multi-level regression modeling was used to link these components to the P factor, and leave-one-site-out cross-validation was used to assess generalizability of P factor neurosignatures to held out subjects across 19 ABCD sites.RESULTSThe set of 250 connectomic components was highly statistically significantly related to the P factor, over and above nuisance covariates alone (ANOVA nested model comparison, incremental R-squared 6.05%, χ2(250) =412.1,p−10). In addition, two individual connectomic components were statistically significantly related to the P factor after Bonferroni correction for multiple comparisons (t(5511)= 4.8,p−06; t(5121)= 3.9, p−05). Functional connections linking control networks and default mode network were prominent in the P factor neurosignature. In leave-one-site-out cross-validation, the P factor neurosignature generalized to held out subjects (average correlation between actual and predicted P factor scores across 19 held out sites=0.13;pPERMUTATIONCONCLUSIONS AND RELEVANCEThe general factor of psychopathology is associated with connectomic alterations involving control networks and default mode network. Brain imaging combined with network neuroscience can identify distributed and generalizable signatures of transdiagnostic risk for psychopathology during emerging adolescence.

Introduction: Structural models of psychopathology consistently identify internalizing (INT) and externalizing (EXT) specific factors as well as a superordinate factor that captures their shared variance, the P factor. Questions remain, however, about meaning of these data-driven dimensions and the interpretability and distinguishability of the larger nomological networks in which they are embedded. Methods: The sample consisted of 11,875 youth aged 9 to 10 years participating in the multisite Adolescent Brain and Cognitive Development (ABCD) Study. P, INT, and EXT were modeled using the parent-rated Child Behavior Checklist (CBCL). Patterns of associations were examined with variables drawn from diverse domains including: demographics, psychopathology, temperament, family history of substance use and psychopathology, school and family environment, and cognitive ability, using instruments based on youth-, parent-, and teacher-report and behavioral task performance.Results: P exhibited a broad pattern of statistically significant associations with risk variables across all domains assessed, including temperament, neurocognition, and social adversity. The specific factors exhibited more domain-specific patterns of associations, with INT exhibiting greater fear/distress and EXT exhibiting greater impulsivity. Conclusions: In this largest study of hierarchical models of psychopathology to date, we found that P, INT, and EXT exhibit well differentiated nomological networks that are interpretable in terms of neurocognition, impulsivity, fear/distress, and social adversity. These networks were, in contrast, obscured when relying on the a priori internalizing and externalizing dimensions of the CBCL scales. Our findings add to the evidence for the validity of P, INT, and EXT as theoretically and empirically meaningful broad psychopathology liabilities.

Background: Many contemporary structural models of psychopathology include a single general factor of psychopathology (GFP) or “p factor”, to account for covariation across a wide range of psychiatric symptoms. The Adolescent Brain Cognitive Development (ABCD) Study is a large longitudinal study of youth that provides a rich opportunity to study the development of the GFP. However, one challenge for the field concerns the variety of approaches for modeling the GFP that have emerged, raising questions about how different modeling choices impact estimated GFP scores. Method: We used the ABCD baseline assessment (ages 9-10 years-old; N = 11,875) of the parent-rated Child Behavior Checklist (CBCL) to examine the implications of different strategies for modeling the GFP. As a preliminary step we assessed the psychometric properties of the CBCL. Next, we considered modeling the GFP using items versus scales; using a priori CBCL scales versus data-driven dimensions; and using bifactor, higher-order, or simple confirmatory factor analytic models. Results: CBCL scales were unidimensional and psychometrically robust. Children’s rank-ordering on the GFP was stable across models (mean r = .92), and the different GFP scores had similar associations with other problem behavior measures and general cognitive ability. Similar results were observed for internalizing and externalizing subfactors across GFP models. Conclusions: The parent-rated CBCL is a sound basis for GFP construction, with children’s GFP, internalizing, and externalizing scores largely robust to model choice in the ABCD study.

There is substantial interest in identifying biobehavioral dimensions of individual variation that cut across heterogenous disorder categories, and computational models can play a major role in advancing this goal. In this report, we focused on efficiency of evidence accumulation (EEA), a computationally characterized variable derived from sequential sampling models of choice tasks. We created an EEA factor from three behavioral tasks in the UCLA Phenomics dataset (n = 272), which includes healthy participants (n = 130) as well-participants with schizophrenia (n = 50), bipolar disorder (n = 49), and attention-deficit/hyperactivity disorder (n = 43). We found that the EEA factor was significantly reduced in all three disorders, and that it correlated with an overall severity score for psychopathology as well as self-report measures of impulsivity. Although EEA was significantly correlated with general intelligence, it remained associated with psychopathology and symptom scales even after controlling for intelligence scores. Taken together, these findings suggest EEA is a promising computationally-characterized dimension of neurocognitive variation, with diminished EEA conferring transdiagnostic vulnerability to psychopathology.

Neuropeptide Y (NPY) produces anxiolytic effects in rodent models, and naturally occurring low NPY expression in humans has been associated with negative emotional phenotypes. Studies in rodent models have also demonstrated that NPY elicits reward behaviors through its action in the nucleus accumbens (NAc), but the impact of NPY on the human NAc is largely unexplored. We recruited 222 healthy young adults of either sex and genetically selected 53 of these subjects at the extremes of NPY expression (Low-NPY and High-NPY) to participate in functional magnetic resonance imaging. Responses of the NAc and surrounding ventral striatum were quantified during a monetary incentive delay task in which stimuli varied by salience (high versus low) and valence (win versus loss). We found that bilateral NAc responses to high-salience versus low-salience stimuli were greater for Low-NPY subjects relative to High-NPY subjects, regardless of stimulus valence. To our knowledge, these results provide the first evidence in humans linking NPY with salience sensitivity of the NAc, raising the possibility that individual differences in NPY expression moderate the risk for disorders of mesoaccumbal function such as addictions and mood disorders. Additionally, we found that head motion was greater among High-NPY subjects, consistent with previous reports linking NPY with hyperactivity. Future studies in animal models are warranted to elucidate the neural mechanisms through which NPY influences NAc function and related behaviors.

A key question about the spontaneous stream of thought (SST), often called the stream of consciousness, concerns its serial structure: How are thoughts in an extended sequence related to each other? In this study, we used a verbalized thought protocol to investigate “clump-and-jump” structure in SST—clusters of related thoughts about a topic followed by a jump to a new topic, in a repeating pattern. Several lines of evidence convergently supported the presence of clump-and-jump structure: high interrater agreement in identifying jumps, corroboration of rater-assigned jumps by automated text analytic methods, identification of clumps and jumps by a data-driven algorithm, and the inferred presence of clumps and jumps in unverbalized SST. We also found evidence that jumps involve a discontinuous shift in which a new clump is only modestly related to the previous one. These results illuminate serial structure in SST and invite research into the processes that generate the clump-and-jump pattern.

Purpose We combined a large longitudinal neuroimaging dataset that includes children who do and do not stutter and a whole-brain network analysis in order to examine the intra- and inter-network connectivity changes associated with stuttering. Additionally, we asked whether whole brain connectivity patterns observed at the initial year of scanning could predict persistent stuttering in later years. Methods A total of 224 high-quality resting state fMRI scans collected from 84 children (42 stuttering, 42 controls) were entered into an independent component analysis (ICA), yielding a number of distinct network connectivity maps (“components”) as well as expression scores for each component that quantified the degree to which it is expressed for each child. These expression scores were compared between stuttering and control groups’ first scans. In a second analysis, we examined whether the components that were most predictive of stuttering status also predicted persistence in stuttering. Results Stuttering status, as well as stuttering persistence, were associated with aberrant network connectivity involving the default mode network and its connectivity with attention, somatomotor, and frontoparietal networks. The results suggest developmental alterations in the balance of integration and segregation of large-scale neural networks that support proficient task performance including fluent speech motor control. Conclusions This study supports the view that stuttering is a complex neurodevelopmental disorder and provides comprehensive brain network maps that substantiate past theories emphasizing the importance of considering situational, emotional, attentional and linguistic factors in explaining the basis for stuttering onset, persistence, and recovery.