Investigating robust associations between functional connectivity based on graph theory and general intelligence.

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

Principles and procedures for revising the hierarchical taxonomy of psychopathology.

Quantitative, empirical approaches to establishing the structure of psychopathology hold promise to improve on traditional psychiatric classification systems. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a framework that summarizes the substantial and growing body of quantitative evidence on the structure of psychopathology. To achieve its aims, HiTOP must incorporate emerging research in a systematic, ongoing fashion. In this article, we describe the historical context and grounding of the principles and procedures for revising the HiTOP framework. Informed by strengths and shortcomings of previous classification systems, the proposed revisions protocol is a formalized system focused around three pillars: (a) prioritizing systematic evaluation of quantitative evidence by a set of transparent criteria and processes, (b) balancing stability with flexibility, and (c) promoting inclusion over gatekeeping in all aspects of the process. We detail how the revisions protocol will be applied in practice, including the scientific and administrative aspects of the process. Additionally, we describe areas of the HiTOP structure that will be a focus of early revisions and outline challenges for the revisions protocol moving forward. The proposed revisions protocol is designed to ensure that the HiTOP framework reflects the current state of scientific knowledge on the structure of psychopathology and fulfils its potential to advance clinical research and practice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The effect of using group-averaged or individualized brain parcellations when investigating connectome dysfunction in psychosis.

Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or underestimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than did group-based parcellations. At the level of individual connections, case-control FC differences were widespread, but the group-based parcellation identified approximately 7.7% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis may be driven by the parcellation method, rather than by a pathophysiological process related to psychosis.

Genetic and neural bases of the neuroticism general factor.

We applied structural equation modeling to conduct a genome-wide association study (GWAS) of the general factor measured by a neuroticism questionnaire administered to ∼380,000 participants in the UK Biobank. We categorized significant genetic variants as acting either through the neuroticism general factor, through other factors measured by the questionnaire, or through paths independent of any factor. Regardless of this categorization, however, significant variants tended to show concordant associations with all items. Bioinformatic analysis showed that the variants associated with the neuroticism general factor disproportionately lie near or within genes expressed in the brain. Enriched gene sets pointed to an underlying biological basis associated with brain development, synaptic function, and behaviors in mice indicative of fear and anxiety. Psychologists have long asked whether psychometric common factors are merely a convenient summary of correlated variables or reflect coherent causal entities with a partial biological basis, and our results provide some support for the latter interpretation. Further research is needed to determine the extent to which causes resembling common factors operate alongside other mechanisms to generate the correlational structure of personality.

Working memory and attention in choice.

We study the role of attention and working memory in choices where options are presented sequentially rather than simultaneously. We build a model where a costly attention effort is chosen, which can vary over time. Evidence is accumulated proportionally to this effort and the utility of the reward. Crucially, the evidence accumulated decays over time. Optimal attention allocation maximizes expected utility from final choice; the optimal solution takes the decay into account, so attention is preferentially devoted to later times; but convexity of the flow attention cost prevents it from being concentrated near the end. We test this model with a choice experiment where participants observe sequentially two options. In our data the option presented first is, everything else being equal, significantly less likely to be chosen. This recency effect has a natural explanation with appropriate parameter values in our model of leaky evidence accumulation, where the decline is stronger for the option observed first. Analysis of choice, response time and brain imaging data provide support for the model. Working memory plays an essential role. The recency bias is stronger for participants with weaker performance in working memory tasks. Also activity in parietal areas, coding the stored value in working, declines over time as predicted.

Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology.

Our capacity to measure diverse aspects of human biology has developed rapidly in the past decades, but the rate at which these techniques have generated insights into the biological correlates of psychopathology has lagged far behind. The slow progress is partly due to the poor sensitivity, specificity and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes and inadequate statistical power. A commonly proposed solution is to focus on large, consortia-sized samples. Yet it is abundantly clear that increasing sample sizes will have a limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. Here, we discuss challenges, outline several ways forward and provide worked examples to demonstrate key problems and potential solutions. A precision phenotyping approach can enhance the discovery and replicability of associations between biology and psychopathology.

A cybernetic perspective on the nature of psychopathology: Transcending conceptions of mental illness as statistical deviance and brain disease.

Explicitly or implicitly, psychopathology is often defined in terms of statistical deviance, requiring that an affected individual be sufficiently distant from the norm in some dimension of psychological or neural function. In recent decades, the dominant paradigm in psychiatric research has focused primarily on deviance in neural function, treating psychopathology as disease of the brain. We argue that these conceptualizations are misguided. We recently proposed a novel theory of psychopathology, based in cybernetics and drawing additionally from neuroscience, psychometrics, and personality theory (DeYoung & Krueger, 2018a). In this theory, deviations from the norm in psychological and neural functioning serve as important risk factors for psychopathology but are not in themselves necessary or sufficient to identify psychopathology, which requires the presence of cybernetic dysfunction. Psychopathology is defined as persistent failure to move toward one's goals, due to failure to generate effective new goals, interpretations, or strategies when existing ones prove unsuccessful. We argue that adopting a cybernetic theory to replace conceptualizations of psychopathology as statistical deviance or brain disease would facilitate improvements in measurement, diagnosis, prevention, and treatment of psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Conscientiousness associated with efficiency of the salience/ventral attention network: Replication in three samples using individualized parcellation.

Conscientiousness, and related constructs impulsivity and self-control, have been related to structural and functional properties of regions in the prefrontal cortex (PFC) and anterior insula. Network-based conceptions of brain function suggest that these regions belong to a single large-scale network, labeled the salience/ventral attention network (SVAN). The current study tested associations between conscientiousness and resting-state functional connectivity in this network using two community samples (N's = 244 and 239) and data from the Human Connectome Project (N = 1000). Individualized parcellation was used to improve functional localization accuracy and facilitate replication. Functional connectivity was measured using an index of network efficiency, a graph theoretical measure quantifying the capacity for parallel information transfer within a network. Efficiency of a set of parcels in the SVAN was significantly associated with conscientiousness in all samples. Findings are consistent with a theory of conscientiousness as a function of variation in neural networks underlying effective prioritization of goals.

The Construct Validity of Intellect and Openness as Distinct Aspects of Personality through Differential Associations with Reaction Time.

The construct validity of group factor models of personality, which are typically derived from factor analysis of questionnaire items, relies on the ability of each factor to predict meaningful and differentiated real-world outcomes. In a sample of 481 participants, we used the Big Five Aspect Scales (BFAS) personality questionnaire, two laboratory-measured reaction time (RT) tasks, and a short-form test of cognitive ability (ICAR-16) to test the hypothesis that the Intellect and Openness aspects of Big Five Openness to Experience differentially correlate with reaction time moments. We found that higher scores on the Intellect aspect significantly correlate with faster and less variable response times, while no such association is observed for the Openness aspect. Further, we found that this advantage lies solely in the decisional, but not perceptual, stage of information processing; no other Big Five aspect showed a similar pattern of results. In sum, these findings represent the largest and most comprehensive study to date on personality factors and reaction time, and the first to demonstrate a mechanistic validation of BFAS Intellect through a differential pattern of associations with RT and Big Five personality aspects.

Robust associations between white matter microstructure and general intelligence.

Few tract-based spatial statistics (TBSS) studies have investigated the relations between intelligence and white matter microstructure in healthy (young) adults, and those have yielded mixed observations, yet white matter is fundamental for efficient and accurate information transfer throughout the human brain. We used a multicenter approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. TBSS indicated 188 voxels exhibited significant positive associations between g factor scores and fractional anisotropy (FA) in all 4 data sets. Replicable voxels formed 3 clusters, located around the left-hemispheric forceps minor, superior longitudinal fasciculus, and cingulum-cingulate gyrus with extensions into their surrounding areas (anterior thalamic radiation, inferior fronto-occipital fasciculus). Our results suggested that individual differences in general intelligence are robustly associated with white matter FA in specific fiber bundles distributed across the brain, consistent with the Parieto-Frontal Integration Theory of intelligence. Three possible reasons higher FA values might create links with higher g are faster information processing due to greater myelination, more direct information processing due to parallel, homogenous fiber orientation distributions, or more parallel information processing due to greater axon density.

Four types of change and self-other agreement on change in personality traits during college years: A multi-informant longitudinal study.

Research in personality trait change has largely relied on mean-level and rank-order change across the lifespan. The current research expanded the literature in several ways: analyzing four types of change and correlated change patterns, obtaining multi-informant reports, including lower-order personality traits, and collecting multiple assessments during a short yet important time for college-attending emerging adults (baseline N = 259, Mage  = 18.79). There was little evidence for mean-level change, yet participants showed significant individual differences such that rank-ordering and ipsative profiles were much more dynamic than mean score patterns. Informant-reports from close others demonstrated largely similar patterns: little to no mean-level change, significant increase in rank-ordering, and about half of participants reporting configural change mostly in elevation and scatter rather than in profile shapes. Interestingly, there was no correlated change between self and other-reports. This indicated that close others do not share individuals' perception of their own personality trait change, at least not in the demographic group studied. By examining individual-level, sample-level, and multi-informant perspectives, our thorough investigation provided useful benchmarks for future research to examine the source of variability in change trajectories.

A cybernetic theory of autism: Autism as a consequence of low trait plasticity.

OBJECTIVE: According to Cybernetic Big Five Theory (CB5T), personality traits reflect variation in the parameters of evolved cybernetic mechanisms, and extreme manifestations of these traits correspond to a risk for psychopathology because they threaten the organism's ability to pursue its goals effectively. Our theory of autism as a consequence of low Plasticity extends CB5T to provide a cybernetic account of the origin of autistic traits. The theory argues that, because all psychological competencies are initially developed through exploration, typical development requires sensitivity to the incentive reward value of the unknown (i.e., the unpredicted). According to CB5T, motivation to explore the unknown is the core function underlying the metatrait Plasticity, the shared variance of Extraversion and Openness/Intellect. This theory makes predictions regarding the downstream developmental consequences of early low Plasticity, and each prediction maps well onto autistic symptomatology. METHOD: We surveyed 387 people. Measures included the Autism Quotient (AQ) scale and International Personality Item Pool items that are indicators of Plasticity and Stability. RESULTS: The association between AQ and Plasticity was ß = -.64. CONCLUSION: A strong negative correlation between Plasticity and AQ suggests ASD may be closely linked to a low sensitivity to the incentive reward value of the unknown.

Value Fulfillment from a Cybernetic Perspective: A New Psychological Theory of Well-Being.

Value Fulfillment Theory (VFT) is a philosophical theory of well-being. Cybernetic Big Five Theory (CB5T) is a psychological theory of personality. Both start with a conception of the person as a goal-seeking (or value-pursuing) organism, and both take goals and the psychological integration of goals to be key to well-being. By joining VFT and CB5T, we produce a cybernetic value fulfillment theory in which we argue that well-being is best conceived as the fulfillment of psychologically integrated values. Well-being is the effective pursuit of a set of nonconflicting values that are emotionally, motivationally, and cognitively suitable to the person. The primary difference in our theory from other psychological theories of well-being is that it does not provide a list of intrinsic goods, instead emphasizing that each person may have their own list of intrinsic goods. We discuss the implications of our theory for measuring, researching, and improving well-being.

New approaches to deep phenotyping in addictions.

OBJECTIVE: The causes of substance use disorders (SUDs) are largely unknown and the effectiveness of their treatments is limited. One crucial impediment to research and treatment progress surrounds how SUDs are classified and diagnosed. Given the substantial heterogeneity among individuals diagnosed with a given SUD (e.g., alcohol use disorder [AUD]), identifying novel research and treatment targets and developing new study designs is daunting. METHOD: In this article, we review and integrate two recently developed frameworks, the National Institute on Drug Abuse's Phenotyping Assessment Battery (NIDA PhAB) and the Hierarchical Taxonomy of Psychopathology (HiTOP), that hope to accelerate progress in understanding the causes and consequences of psychopathology by means of deep phenotyping, or finer-grained analysis of phenotypes. RESULTS AND CONCLUSIONS: NIDA PhAB focuses on addiction-related processes across multiple units of analysis, whereas HiTOP focuses on clinical phenotypes and covers a broader range of psychopathology. We highlight that NIDA PhAB and HiTOP together provide deep and broad characterizations of people diagnosed with SUDs and complement each other in their efforts to address widely known limitations of traditional classification systems and their diagnostic categories. Next, we show how NIDA PhAB and HiTOP can be integrated to facilitate optimal rich phenotyping of addiction-related phenomena. Finally, we argue that such deep phenotyping promises to advance our understanding of the neurobiology of SUD and addiction, which will guide the development of personalized medicine and interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personality Neuroscience: An Emerging Field with Bright Prospects.

Personality neuroscience is the study of persistent psychological individual differences, typically in the general population, using neuroscientific methods. It has the potential to shed light on the neurobiological mechanisms underlying individual differences and their manifestation in ongoing behavior and experience. The field was inaugurated many decades ago, yet has only really gained momentum in the last two, as suitable technologies have become widely available. Personality neuroscience employs a broad range of methods, including molecular genetics, pharmacological assays or manipulations, electroencephalography, and various neuroimaging modalities, such as magnetic resonance imaging and positron emission tomography. Although exciting progress is being made in this young field, much remains unknown. In this brief review, we discuss discoveries that have been made, methodological challenges and advances, and important questions that remain to be answered. We also discuss best practices for personality neuroscience research and promising future directions for the field.

How Robust Is the p Factor? Using Multitrait-Multimethod Modeling to Inform the Meaning of General Factors of Youth Psychopathology.

We used multitrait-multimethod (MTMM) modeling to examine general factors of psychopathology in three samples of youth (Ns = 2119, 303, 592) for whom three informants reported on the youth's psychopathology (e.g., child, parent, teacher). Empirical support for the p-factor diminished in multi-informant models compared with mono-informant models: the correlation between externalizing and internalizing factors decreased and the general factor in bifactor models essentially reflected externalizing. Widely used MTMM-informed approaches for modeling multi-informant data cannot distinguish between competing interpretations of the patterns of effects we observed, including that the p-factor reflects, in part, evaluative consistency bias or that psychopathology manifests differently across contexts (e.g., home vs. school). Ultimately, support for the p-factor may be stronger in mono-informant designs, although it is does not entirely vanish in multi-informant models. Instead, the general factor of psychopathology in any given mono-informant model likely reflects a complex mix of variances, some substantive and some methodological.

Obsessive compulsive symptom dimensions are linked to altered white-matter microstructure in a community sample of youth.

Obsessive-compulsive symptoms (OCS) are common in school-aged children and predict the development of obsessive compulsive disorder (OCD). White-matter abnormalities have been described in OCD, but the white matter correlates of OCS in the developing brain are unclear. Some correlates of OCS (or a diagnosis of OCD) may reflect correlates of a transdiagnostic or even general psychopathology factor. We examined these questions in a large sample of typically developing youth (N = 1208), using a hierarchical analysis of fixel-based white matter measures in relation to OCS and general psychopathology. General psychopathology was associated with abnormalities in the posterior corpus callosum and forceps major in an age-dependent manner, suggesting altered maturation (specifically, hypermaturation in younger subjects). A unidimensional measure of OCS did not associate with any white-matter abnormalities, but analysis of separate OCS dimensions (derived from factor analysis within this sample) revealed the 'Bad Thoughts' dimension to associate with white-matter abnormalities in dorsal parietal white-matter and descending corticospinal tracts, and the 'Symmetry' dimension to associate with abnormalities in the anterior corpus callosum. Repetition/checking and Symmetry OCS were additionally associated with posterior abnormalities overlapping with the correlates of general psychopathology. Contamination symptoms had no white-matter correlates. Secondary analysis of fractional anisotropy (FA) revealed distinct white-matter abnormalities, suggesting that fixel-based and FA analyses identify distinct features of white matter relevant to psychopathology. These findings suggest that OCS dimensions correlate with dissociable abnormalities in white matter, implicating separable networks. Future studies should examine these white-matter signatures in a longitudinal framework.

The Hierarchical Taxonomy of Psychopathology (HiTOP) in psychiatric practice and research.

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

Answering questions about the Hierarchical Taxonomy of Psychopathology (HiTOP): Analogies to whales and sharks miss the boat.

This commentary discusses questions and misconceptions about HiTOP raised by Haeffel et al. (2021). We explain what the system classifies and why it is descriptive and atheoretical, highlighting benefits and limitations of this approach. We clarify why the system is organized according to patterns of covariation or comorbidity among signs and symptoms of psychopathology, and we discuss how it is designed to be falsifiable and revised in a manner that is responsive to data. We refer to the body of evidence for HiTOP's external validity and for its scientific and clinical utility. We further describe how the system is currently used in clinics. In sum, many of Haeffel et al.'s concerns about HiTOP are unwarranted, and for those concerns that reflect real current limitations of HiTOP, our consortium is working to address them, with the aim of creating a nosology that is comprehensive and useful to both scientists and clinicians.

The distinction between symptoms and traits in the Hierarchical Taxonomy of Psychopathology (HiTOP).

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirically and quantitatively derived dimensional classification system designed to describe the features of psychopathology and, ultimately, to replace categorical nosologies. Among the constructs that HiTOP organizes are "symptom components" and "maladaptive traits," but past HiTOP publications have not fully explicated the distinction between symptoms and traits. We propose working definitions of symptoms and traits and explore challenges, exceptions, and remaining questions. Specifically, we propose that the only systematic difference between symptoms and traits in HiTOP is one of time frame. Maladaptive traits are dispositional constructs that describe persistent tendencies to manifest features of psychopathology, whereas symptoms are features of psychopathology as they are manifest during any specific time period (from moments to days to months). This has the consequence that almost every HiTOP dimension, at any level of the hierarchy, can be assessed as either a trait or a symptom dimension, by adjusting the framing of the assessment. We discuss the implications of these definitions for causal models of the relations between symptoms and traits and for distinctions between psychopathology, normal personality variation, and dysfunction.