Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

2019 ◽  
Author(s):  
Scott D. Blain ◽  
Rachael Grazioplene ◽  
Yizhou Ma ◽  
Colin G. DeYoung
Keyword(s):  
Functional Connectivity ◽  
Structural Equation ◽  
Psychotic Disorders ◽  
Neural Model ◽  
Equation Modeling ◽  
Control Networks ◽  
Psychosis Proneness ◽  
Human Connectome Project ◽  
Intrinsic Connectivity Networks ◽  
Personality Psychopathology

Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance, were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.

scholarly journals Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

2019 ◽  
Vol 46 (3) ◽  
pp. 540-551
Author(s):  
Scott D Blain ◽  
Rachael G Grazioplene ◽  
Yizhou Ma ◽  
Colin G DeYoung
Keyword(s):  
Functional Connectivity ◽  
Structural Equation ◽  
Psychotic Disorders ◽  
Neural Model ◽  
Equation Modeling ◽  
Control Networks ◽  
Psychosis Proneness ◽  
Human Connectome Project ◽  
Intrinsic Connectivity Networks ◽  
Personality Psychopathology

Abstract Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests that psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality–psychopathology continuum.

scholarly journals Structural equation modeling of the associations between amygdala activation, personality, and internalizing, externalizing symptoms of psychopathology

2020 ◽  
Vol 3 ◽  
Author(s):  
Craig S. Neumann
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Externalizing Symptoms ◽  
Self Report ◽  
Equation Modeling ◽  
Affective Neuroscience ◽  
Large Sample ◽  
Amygdala Activation ◽  
Human Connectome Project ◽  
Personality Psychopathology

Abstract There is an expanding literature on the theoretical and empirical connections between personality and psychopathology, and their shared neurobiological correlates. Recent cybernetic theories of personality and psychopathology, as well as affective neuroscience theory, provide grounding for understanding neurobiological–personality–psychopathology (NPP) associations. With the emergence of large sample datasets (e.g., Human Connectome Project) advanced quantitative modeling can be used to rigorously test dynamic statistical representations of NPP connections. Also, research suggests that these connections are influenced by sex, and large samples provide the opportunity to examine how NPP associations might be moderated by sex. The current study used a large sample from the Duke Neurogenetics Study (DNS) to examine how amygdala activation to facial expressions was linked with self-report of personality traits and clinical interviews of internalizing and externalizing symptoms of psychopathology. Structural equation modeling results revealed direct associations of amygdala activation with personality trait expression, as well as indirect associations (though personality) with symptoms of psychopathology. Moreover, the NPP links were moderated by sex. The current results are in line with research that identifies a broader role played by the amygdala in personality and provide potential insights for continued research in personality neuroscience and recent theories on the neurobiology of personality.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

scholarly journals An Investigation of Descending Pain Modulation in Women With Provoked Vestibulodynia (PVD): Alterations of Spinal Cord and Brainstem Connectivity

2021 ◽  
Vol 2 ◽  
Author(s):  
Lindsey R. Yessick ◽  
Caroline F. Pukall ◽  
Gabriela Ioachim ◽  
Susan M. Chamberlain ◽  
Patrick W. Stroman
Keyword(s):  
Spinal Cord ◽  
Functional Connectivity ◽  
Structural Equation ◽  
Brain Regions ◽  
Pain Modulation ◽  
Equation Modeling ◽  
Control Group ◽  
Pain Processing ◽  
Provoked Vestibulodynia ◽  
Fmri Session

The most common subtype of vulvodynia (idiopathic chronic vulvar pain) is provoked vestibulodynia (PVD). Previous imaging studies have shown that women with vulvodynia exhibit increased neural activity in pain-related brain regions (e.g., the secondary somatosensory cortex, insula, dorsal midcingulate, posterior cingulate, and thalamus). However, despite the recognized role of the spinal cord/brainstem in pain modulation, no previous neuroimaging studies of vulvodynia have examined the spinal cord/brainstem. Sixteen women with PVD and sixteen matched Control women underwent a spinal cord/brainstem functional magnetic resonance imaging (fMRI) session consisting of five runs with no painful thermal stimuli (No Pain), interleaved randomly with five runs with calibrated, moderately painful heat stimulation (Pain). Functional connectivity was also assessed in periods before, during, and after, pain stimulation to investigate dynamic variations in pain processing throughout the stimulation paradigm. Functional connectivity in the brainstem and spinal cord for each group was examined using structural equation modeling (SEM) for both Pain and No Pain conditions. Significant connectivity differences during stimulation were identified between PVD and Control groups within pain modulatory regions. Comparisons of Pain and No Pain conditions identified a larger number of connections in the Control group than in the PVD group, both before and during stimulation. The results suggest that women with PVD exhibit altered pain processing and indicate an insufficient response of the pain modulation system. This study is the first to examine the spinal cord/brainstem functional connectivity in women with PVD, and it demonstrates altered connectivity related to pain modulation in the spinal cord/brainstem.

Inter-Individual Differences in Striatal Connectivity Is Related to Executive Function Through Fronto-Parietal Connectivity

2019 ◽  
Author(s):  
Fahimeh Darki ◽  
Bruno Sauce ◽  
Torkel Klingberg ◽  
Keyword(s):  
Executive Function ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Structural Equation ◽  
Dorsal Striatum ◽  
Childhood Development ◽  
Equation Modeling ◽  
Resting State Functional Connectivity ◽  
Memory Inhibition

Abstract The striatum has long been associated with cognitive functions, but the mechanisms behind this are still unclear. Here we tested a new hypothesis that the striatum contributes to executive function (EF) by strengthening cortico-cortical connections. Striatal connectivity was evaluated by measuring the resting-state functional connectivity between ventral and dorsal striatum in 570 individuals, aged 3–20 years. Using structural equation modeling, we found that inter-individual differences in striatal connectivity had an indirect effect (via fronto-parietal functional connectivity) and a direct effect on a compound EF measure of working memory, inhibition, and set-shifting/flexibility. The effect of fronto-parietal connectivity on cognition did not depend on age: the influence was as strong in older as younger children. In contrast, striatal connectivity was closely related to changes in cognitive ability during childhood development, suggesting a specific role of the striatum in cognitive plasticity. These results support a new principle for striatal functioning, according to which striatum promotes cognitive development by strengthening of cortico-cortical connectivity.

The Dimensional Assessment of Personality Psychopathology Basic Questionnaire: Shortened Versions Item Analysis

2014 ◽  
Vol 17 ◽  
Author(s):  
Anton Aluja ◽  
Àngel Blanch ◽  
Eduardo Blanco ◽  
Maite Martí-Guiu ◽  
Ferran Balada
Keyword(s):  
Structural Equation ◽  
Short Form ◽  
Item Analysis ◽  
Population Sample ◽  
Reduced Form ◽  
Equation Modeling ◽  
Psychological Assessments ◽  
Personality Psychopathology ◽  
Dimensional Assessment ◽  
R Factors

AbstractThis study has been designed to evaluate and replicate the psychometric properties of the Dimensional Assessment of Personality Psychopathology-Basic Questionnaire (DAPP-BQ) and the DAPP-BQ short form (DAPP-SF) in a large Spanish general population sample. Additionally, we have generated a reduced form called DAPP-90, using a strategy based on a structural equation modeling (SEM) methodology in two independent samples, a calibration and a validation sample. The DAPP-90 scales obtained a more satisfactory fit on SEM adjustment values (average: TLI > .97 and RMSEA < .04) respect to full DAPP-BQ and the 136-item version. According to the factorial congruency coefficients, the DAPP-90 obtains a similar structure to the DAPP-BQ and the DAPP-SF. The DAPP-90 internal consistency is acceptable, with a Cronbach’s alpha mean of .75. We did not find any differences in the pattern of relations between the two DAPP-BQ shortened versions and the SCL-90-R factors. The new 90-items version is especially useful when it is difficult to use the long version for diverse reasons, such as the assessment of patients in hospital consultation or in brief psychological assessments.

scholarly journals Using Structural Equation Modeling to Assess Functional Connectivity in the Brain

2014 ◽  
Vol 74 (5) ◽  
pp. 733-758 ◽  
Author(s):  
Georgios Sideridis ◽  
Panagiotis Simos ◽  
Andrew Papanicolaou ◽  
Jack Fletcher
Keyword(s):  
Structural Equation Modeling ◽  
Functional Connectivity ◽  
Structural Equation ◽  
Equation Modeling ◽  
The Brain

Impact of life events on symptoms in psychotic disorders: 10-year longitudinal study following first admission

2020 ◽  
Author(s):  
Kayla Donaldson ◽  
Katherine Jonas ◽  
Yuan Tian ◽  
Emmett M. Larsen ◽  
Daniel Klein ◽  
...  
Keyword(s):  
Life Events ◽  
Structural Equation ◽  
Negative Symptoms ◽  
Psychotic Disorders ◽  
Equation Modeling ◽  
Negative Events ◽  
First Onset ◽  
Specific Symptoms ◽  
The Impact ◽  
First Admission

Life events (LEs) are a risk factor for first onset and relapse of psychotic disorders. However, the impact of LEs on specific symptoms—namely psychosis, disorganization, negative symptoms, depression, and mania—remains unclear. Moreover, the differential effects of negative versus positive LEs is poorly understood. The present study utilizes an epidemiologic cohort of patients (N=428) ascertained at first-admission for psychosis and followed for a decade thereafter. Symptoms were assessed at 6-, 24-, 48- and 120-month follow-ups. We examined symptom change within-person and found that negative events in the previous 6 months predicted an increase in psychotic (β= .07), disorganized (β= .07), manic (β= .08), and depressive symptoms (β= .06), and a decrease in negative symptoms (β= -.08). Conversely, positive LEs predicted fewer psychotic (β= -.04), disorganized (β= -.04), and negative (β= -.13) symptoms, and were unrelated to mood symptoms. Structural equation modeling confirmed that negative LEs predicted change in all symptoms, while positive LEs predicted change only in negative symptoms. In contrast, symptoms rarely predicted future LEs. These findings confirm that LEs have an effect on symptoms, and thus contribute to the burden of psychotic disorders. That LEs increase positive symptoms and decrease negative symptoms suggests at least two different mechanisms underlying the relationship between LEs and symptoms. Our findings underscore the need for close symptom monitoring in months following negative LEs, as symptoms may worsen during that time.

scholarly journals Cooperative contributions of structural and functional connectivity to successful memory in aging

2019 ◽  
Vol 3 (1) ◽  
pp. 173-194 ◽  
Author(s):  
Simon W. Davis ◽  
Amanda Szymanski ◽  
Homa Boms ◽  
Thomas Fink ◽  
Roberto Cabeza
Keyword(s):  
Older Adults ◽  
Functional Connectivity ◽  
Structural Equation ◽  
Healthy Aging ◽  
Memory Performance ◽  
Structural Connectivity ◽  
Equation Modeling ◽  
Cognitive Changes ◽  
Age Related ◽  
The Relationship

Understanding the precise relation between functional connectivity and structural (white matter) connectivity and how these relationships account for cognitive changes in older adults are major challenges for neuroscience. We investigate these issues using an approach in which structural equation modeling (SEM) is employed to integrate functional and structural connectivity data from younger and older adults ( n = 62), analyzed with a common framework based on regions connected by canonical tract groups (CTGs). CTGs (e.g., uncinate fasciculus) serve as a common currency between functional and structural connectivity matrices, and ensure equivalent sparsity in connectome information. We used this approach to investigate the neural mechanisms supporting memory for items and memory for associations, and how they are affected by healthy aging. We found that different structural and functional CTGs made independent contributions to source and item memory performance, suggesting that both forms of connectivity underlie age-related differences in specific forms of memory. Furthermore, the relationship between functional and structural connectivity was best explained by a general relationship between latent constructs—a relationship absent in any specific CTG group. These results provide insights into the relationship between structural and functional connectivity patterns, and elucidate their relative contribution to age-related differences in source memory performance.

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