scholarly journals Functional Connectome of the Five-Factor Model of Personality

2018 ◽  
Vol 1 ◽  
Author(s):  
Nicola Toschi ◽  
Roberta Riccelli ◽  
Iole Indovina ◽  
Antonio Terracciano ◽  
Luca Passamonti
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Factor Model ◽  
Brain Connectivity ◽  
Five Factor Model ◽  
Mechanistic Explanation ◽  
Data Sets ◽  
Personality Differences ◽  
Human Connectome Project ◽  
Maximal Information Coefficient

Abstract A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analysing big data-sets with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and nonlinear statistical dependencies across the graph “nodes”, which were defined as distinct large-scale brain circuits identified via independent component analysis. Multivariate regression models and “train/test” approaches were used to examine the associations between FFM traits and connectomic indices as well as to assess the generalizability of the main findings, while accounting for age and sex variability. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This offers a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.

scholarly journals Time-resolved connectome of the five-factor model of personality

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
L. Passamonti ◽  
R. Riccelli ◽  
I. Indovina ◽  
A. Duggento ◽  
A. Terracciano ◽  
...  
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Factor Model ◽  
Five Factor Model ◽  
Future Research ◽  
Time Resolved ◽  
Personality Differences ◽  
Network Time ◽  
Large Scale Networks ◽  
The Individual

Abstract The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant ‘connectome’ is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the ‘communication’ across large-scale networks offers a mechanistic description of the capacity of conscientious people to ‘protect’ non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

scholarly journals An fMRI investigation of the relations between Extraversion, internalizing psychopathology, and neural activation following reward receipt in the Human Connectome Project sample

2020 ◽  
Vol 3 ◽  
Author(s):  
Courtland S. Hyatt ◽  
Emily S. Hallowell ◽  
Max M. Owens ◽  
Brandon M. Weiss ◽  
Lawrence H. Sweet ◽  
...  
Keyword(s):  
Personality Traits ◽  
Personality Trait ◽  
Factor Model ◽  
Five Factor Model ◽  
Effect Sizes ◽  
Levels Of Analysis ◽  
Future Research ◽  
Neural Activation ◽  
Internalizing Psychopathology ◽  
Human Connectome Project

Abstract Quantitative models of psychopathology (i.e., HiTOP) propose that personality and psychopathology are intertwined, such that the various processes that characterize personality traits may be useful in describing and predicting manifestations of psychopathology. In the current study, we used data from the Human Connectome Project (N = 1050) to investigate neural activation following receipt of a reward during an fMRI task as one shared mechanism that may be related to the personality trait Extraversion (specifically its sub-component Agentic Extraversion) and internalizing psychopathology. We also conducted exploratory analyses on the links between neural activation following reward receipt and the other Five-Factor Model personality traits, as well as separate analyses by gender. No significant relations (p < .005) were observed between any personality trait or index of psychopathology and neural activation following reward receipt, and most effect sizes were null to very small in nature (i.e., r < |.05|). We conclude by discussing the appropriate interpretation of these null findings, and provide suggestions for future research that spans psychological and neurobiological levels of analysis.

scholarly journals Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3156
Author(s):  
Lili Zhou ◽  
Runzhe Geng
Keyword(s):  
Spatial Distribution ◽  
Large Scale ◽  
Factor Model ◽  
Five Factor Model ◽  
Pollution Source ◽  
Nonpoint Source ◽  
Vegetation Coverage ◽  
Buffer Strip ◽  
Rural Living ◽  
Agricultural Nonpoint Source

The transport of agricultural nonpoint source (NPS) pollutants in water pathways is affected by various factors such as precipitation, terrain, soil erosion, surface and subsurface flows, soil texture, land management, and vegetation coverage. In this study, based on the transmission mechanism of NPS pollutants, we constructed a five-factor model for predicting the path-through rate of NPS pollutants. The five indices of the hydrological processes, namely the precipitation index (α), terrain index (β), runoff index (TI), subsurface runoff index (LI), and buffer strip retention index (RI), are integrated with the pollution source data, including the rural living, livestock and farmland data, obtained from the national pollution source census. The proposed model was applied to the headwater of the Miyun Reservoir watershed for identifying the areas with high path-through rates of agricultural NPS pollutants. The results demonstrated the following. (1) The simulation accuracy of the model is acceptable in mesoscale watersheds. The total nitrogen (TN) and total phosphorus (TP) agriculture loads were determined as 705.11 t and 3.16 t in 2014, with the relative errors of the simulations being 19.62% and 24.45%, respectively. (2) From the spatial distribution of the agricultural NPS, the TN and TP resource loads were mainly distributed among the upstream of Dage and downstream of Taishitun, as well as the towns of Bakshiying and Gaoling. The major source of TN was found to be farmland, accounting for 47.6%, followed by livestock, accounting for 37.4%. However, the path-through rates of TP were different from those of TN; rural living was the main TP source (65%). (3) The path-through rates of agricultural NPS were the highest for the towns of Wudaoying, Dage, Tuchengzi, Anchungoumen, and Huodoushan, where the path-through rate of TN ranged from 0.17 to 0.26. As for TP, it was highest in Wudaoying, Kulongshan, Dage, and Tuchengzi, with values ranging from 0.012 to 0.019. (4) A comprehensive analysis of the distribution of the NPS pollution load and the path-through rate revealed the towns of Dage, Wudaoying, and Tuchengzi as the critical source areas of agricultural NPS pollutants. Therefore, these towns should be seriously considered for effective watershed management. In addition, compared with field monitoring, the export coefficient model, and the physical-based model, the proposed five-factor model, which is based on the path-through rate and the mechanism of agricultural NPS pollutant transfer, cannot only obtain the spatial distribution characteristics of the path-through rate on a field scale but also be applicable to large-scale watersheds for estimating the path-through rates of NPS pollutants.

scholarly journals Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

2020 ◽  
Author(s):  
Alba Xifra-Porxas ◽  
Michalis Kassinopoulos ◽  
Georgios D. Mitsis
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Large Data ◽  
Head Motion ◽  
Time Varying ◽  
Resting State Networks ◽  
Human Connectome Project ◽  
Noninvasive Biomarker ◽  
Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

scholarly journals Cortical morphometry of the five-factor model of personality: findings from the Human Connectome Project full sample

2019 ◽  
Vol 14 (4) ◽  
pp. 381-395 ◽  
Author(s):  
Max M Owens ◽  
Courtland S Hyatt ◽  
Joshua C Gray ◽  
Nathan T Carter ◽  
James MacKillop ◽  
...  
Keyword(s):  
Factor Model ◽  
Five Factor Model ◽  
Full Sample ◽  
Human Connectome Project ◽  
Cortical Morphometry

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Alessio Boschi ◽  
Martina Brofiga ◽  
Paolo Massobrio
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Neuronal Networks ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Synthetic Data ◽  
Connectivity Matrix ◽  
Causal Connection ◽  
Topological Properties ◽  
Functional Connections

The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the “noisy” connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.

scholarly journals The development of the Compulsive Sexual Behavior Disorder Scale (CSBD-19): An ICD-11 based screening measure across three languages

2020 ◽  
Vol 9 (2) ◽  
pp. 247-258 ◽  
Author(s):  
Beáta Bőthe ◽  
Marc N. Potenza ◽  
Mark D. Griffiths ◽  
Shane W. Kraus ◽  
Verena Klein ◽  
...  
Keyword(s):  
Sexual Behavior ◽  
High Risk ◽  
Measurement Invariance ◽  
Large Scale ◽  
Factor Model ◽  
Five Factor Model ◽  
Behavior Disorder ◽  
The United States ◽  
Compulsive Sexual Behavior ◽  
Negative Consequences

AbstractBackgroundCompulsive Sexual Behavior Disorder (CSBD) is included in the eleventh edition of The International Classification of Diseases (ICD-11) as an impulse-control disorder.AimsThe aim of the present work was to develop a scale (Compulsive Sexual Behavior Disorder Scale–CSBD-19) that can reliably and validly assess CSBD based on ICD-11 diagnostic guidelines.MethodFour independent samples of 9,325 individuals completed self-reported measures from three countries (the United States, Hungary, and Germany). The psychometric properties of the CSBD-19 were examined in terms of factor structure, reliability, measurement invariance, and theoretically relevant correlates. A potential threshold was determined to identify individuals with an elevated risk of CSBD.ResultsThe five-factor model of the CSBD-19 (i.e., control, salience, relapse, dissatisfaction, and negative consequences) had an excellent fit to the data and demonstrated appropriate associations with the correlates. Measurement invariance suggested that the CSBD-19 functions similarly across languages. Men had higher means than women. A score of 50 points was found as an optimal threshold to identify individuals at high-risk of CSBD.ConclusionsThe CSBD-19 is a short, valid, and reliable measure of potential CSBD based on ICD-11 diagnostic guidelines. Its use in large-scale, cross-cultural studies may promote the identification and understanding of individuals with a high risk of CSBD.

scholarly journals The heterogeneous functional architecture of the posteromedial cortex is associated with selective functional connectivity differences in Alzheimer’s disease

2019 ◽  
Author(s):  
Wasim Khan ◽  
Ali Amad ◽  
Vincent Giampietro ◽  
Emilio Werden ◽  
Sara De Simoni ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Disease Progression ◽  
Large Scale ◽  
Posterior Cingulate Cortex ◽  
Subjective Memory ◽  
Functional Architecture ◽  
Human Connectome Project ◽  
Data Driven Approach

AbstractThe posteromedial cortex (PMC) is a key region involved in the development and progression of Alzheimer’s disease (AD). Previous studies have demonstrated a heterogenous functional architecture of the region, with different subdivisions reflecting distinct connectivity profiles. However, little is understood about PMC functional connectivity and its differential vulnerability to AD pathogenesis. Using a data-driven approach, we applied a constrained independent component analysis (ICA) on healthy adults from the Human Connectome Project (HCP) to characterise the distinct functional subdivisions and unique functional-anatomic connectivity patterns of the PMC. These connectivity profiles were subsequently quantified in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, to examine functional connectivity differences in (1) AD patients and cognitively normal (CN) participants and (2) the entire AD pathological spectrum, ranging from CN participants and participants with subjective memory complaints (SMC), through to those with mild cognitive impairment (MCI), and finally, patients diagnosed with AD. Our findings revealed decreased functional connectivity in the anterior precuneus, dorsal posterior cingulate cortex, and the central precuneus in AD patients compared to CN participants. Functional abnormalities in these subdivisions were also related to high amyloid burden and lower hippocampal volumes. Across the entire AD spectrum, functional connectivity of the central precuneus was associated with disease progression and specific deficits in memory and executive function. These findings provide new evidence showing that specific vulnerabilities in PMC functional connectivity are associated with large-scale network disruptions in AD and that these patterns may be useful for elucidating potential biomarkers for measuring disease progression in future work.

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