scholarly journals Robust prediction of individual personality from brain functional connectome

2020 ◽  
Vol 15 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Huanhuan Cai ◽  
Jiajia Zhu ◽  
Yongqiang Yu
Keyword(s):  
Personality Traits ◽  
Large Scale ◽  
Five Factor Model ◽  
Brain Regions ◽  
Individual Variability ◽  
Personality Factors ◽  
Imaging Features ◽  
Imaging Data ◽  
Functional Connectome ◽  
Human Connectome Project

Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.

scholarly journals No evidence for morphometric associations of the amygdala and hippocampus with the five-factor model personality traits

2018 ◽  
Author(s):  
Joshua Gray ◽  
Max Michael Owens ◽  
Courtland Hyatt ◽  
Josh Miller
Keyword(s):  
Personality Traits ◽  
Gray Matter ◽  
Factor Model ◽  
Five Factor Model ◽  
Gray Matter Volume ◽  
Brain Regions ◽  
Matter Volume ◽  
Human Connectome Project ◽  
Hippocampus Segmentation ◽  
Ffm Personality Traits

Despite the important functional role of the amygdala and hippocampus in socioemotional functioning, there have been limited adequately powered studies testing how the structure of these regions relates to putatively relevant personality traits such as neuroticism. Additionally, recent advances in MRI analysis methods provide unprecedented accuracy in measuring these structures and enable segmentation into their substructures. Using the new FreeSurfer amygdala and hippocampus segmentation pipelines with the full Human Connectome Project sample (N = 1105), the current study investigated whether the morphometry of these structures is associated with the five-factor model (FFM) personality traits in a sample of relatively healthy young adults. Drawing from prior findings, the following hypotheses were tested: 1) amygdala and hippocampus gray matter volume would be associated with neuroticism, 2) CA2/3 and dentate gyrus would account for the relationship of the hippocampus with neuroticism, and 3) amygdala gray matter volume would be inversely associated with extraversion. Exploratory analyses were conducted investigating potential associations between all of the FFM traits and the structure of the hippocampus and amygdala and their subregions. Despite some previous positive findings of whole amygdala and hippocampus with personality traits and related psychopathology (e.g., depression), the current results indicated no relationships between the any of the brain regions and the FFM personality traits. Given the large sample and utilization of sophisticated analytic methodology, the current study suggests no association of amygdala and hippocampus morphometry with major domains of personality.

The Relationship Between Narcissism and Personality Traits of the Five-Factor-Model in Adolescents and Young Adults

2018 ◽  
Vol 46 (6) ◽  
pp. 516-522 ◽  
Author(s):  
Marc Allroggen ◽  
Peter Rehmann ◽  
Eva Schürch ◽  
Carolyn C. Morf ◽  
Michael Kölch
Keyword(s):  
Young Adults ◽  
Personality Traits ◽  
Five Factor Model ◽  
Self Report ◽  
Adolescents And Young Adults ◽  
Personality Factors ◽  
Big Five Personality ◽  
Grandiose Narcissism ◽  
Vulnerable Narcissism ◽  
The Relationship

Abstract.Narcissism is seen as a multidimensional construct that consists of two manifestations: grandiose and vulnerable narcissism. In order to define these two manifestations, their relationship to personality factors has increasingly become of interest. However, so far no studies have considered the relationship between different phenotypes of narcissism and personality factors in adolescents. Method: In a cross-sectional study, we examine a group of adolescents (n = 98; average age 16.77 years; 23.5 % female) with regard to the relationship between Big Five personality factors and pathological narcissism using self-report instruments. This group is compared to a group of young adults (n = 38; average age 19.69 years; 25.6 % female). Results: Grandiose narcissism is primarily related to low Agreeableness and Extraversion, vulnerable narcissism to Neuroticism. We do not find differences between adolescents and young adults concerning the relationship between grandiose and vulnerable narcissism and personality traits. Discussion: Vulnerable and grandiose narcissism can be well differentiated in adolescents, and the pattern does not show substantial differences compared to young adults.

Individual Uniqueness in the Neonatal Functional Connectome

2021 ◽  
Author(s):  
Qiushi Wang ◽  
Yuehua Xu ◽  
Tengda Zhao ◽  
Zhilei Xu ◽  
Yong He ◽  
...  
Keyword(s):  
Individual Differences ◽  
Individual Identification ◽  
Imaging Data ◽  
Functional Connectome ◽  
Middle Range ◽  
Human Connectome Project ◽  
The Individual ◽  
And Behavior ◽  
Identification Analysis ◽  
The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

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 Factors underlying cognitive decline in old age and Alzheimer’s disease: the role of the hippocampus

2017 ◽  
Vol 28 (7) ◽  
pp. 705-714 ◽  
Author(s):  
Wafa Jaroudi ◽  
Julia Garami ◽  
Sandra Garrido ◽  
Michael Hornberger ◽  
Szabolcs Keri ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Personality Traits ◽  
Old Age ◽  
Brain Regions ◽  
Personality Factors ◽  
Associated Symptoms ◽  
Lifestyle Patterns

AbstractThere are many factors that strongly influence the aetiology, development, and progression of cognitive decline in old age, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). These factors include not only different personality traits and moods but also lifestyle patterns (e.g. exercise and diet) and awareness levels that lead to cognitive decline in old age. In this review, we discuss how personality traits, mood states, and lifestyle impact brain and behaviour in older adults. Specifically, our review shows that these lifestyle and personality factors affect several brain regions, including the hippocampus, a region key for memory that is affected by cognitive decline in old age as well as AD. Accordingly, appropriate recommendations are presented in this review to assist individuals in decreasing chances of MCI, dementia, AD, and associated symptoms.

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 Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

2021 ◽  
Vol 7 (29) ◽  
pp. eabf2513
Author(s):  
Luke J. Hearne ◽  
Ravi D. Mill ◽  
Brian P. Keane ◽  
Grega Repovš ◽  
Alan Anticevic ◽  
...  
Keyword(s):  
Cognitive Dysfunction ◽  
Large Scale ◽  
Brain Activity ◽  
Memory Task ◽  
Clinical Intervention ◽  
Brain Regions ◽  
Imaging Data ◽  
Flow Mapping ◽  
Flow Models ◽  
Evoked Activity

Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.

scholarly journals Emotional states as distinct configurations of functional brain networks

2021 ◽  
Author(s):  
Rotem Dan ◽  
Marta Weinstock ◽  
Gadi Goelman
Keyword(s):  
Large Scale ◽  
Emotional Experience ◽  
Brain Networks ◽  
Emotional Valence ◽  
Individual Variability ◽  
Modular Organization ◽  
Emotional States ◽  
Functional Brain ◽  
Functional Connectome ◽  
Functional Brain Networks

The conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain's network structure during emotional experiences. Here, we investigated the brain's functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain's functional connectome. Next, for each state, we defined a modular organization of the brain and quantified its segregation and integration. Our results show that emotional states increase the similarity between and within individuals in the brain's functional connectome. Second, in the brain's modular organization, sadness, relative to amusement, was associated with higher integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Modular metrics of brain segregation and integration were further associated with the reported emotional valence. Last, in both the functional connectome and emotional report, a higher similarity was found among women. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional brain networks and that the topological structure carries information about the subjective emotional experience.

scholarly journals The HCP 7T Retinotopy Dataset: Description and pRF Analysis

2018 ◽  
Author(s):  
Noah C. Benson ◽  
Keith W. Jamison ◽  
Michael J. Arcaro ◽  
An Vu ◽  
Matthew F. Glasser ◽  
...  
Keyword(s):  
Visual Processing ◽  
Large Scale ◽  
Individual Variability ◽  
7 Tesla ◽  
Individual Subject ◽  
Retinotopic Mapping ◽  
Human Connectome Project ◽  
The Brain ◽  
Model Based Analysis

AbstractAbout a quarter of human cerebral cortex is dedicated mainly to visual processing. The large-scale organization of visual cortex can be measured with functional magnetic resonance imaging (fMRI) while subjects view spatially modulated visual stimuli, also known as ‘retinotopic mapping’. One of the datasets collected by the Human Connectome Project (HCP) involved ultra-high-field (7 Tesla) fMRI retinotopic mapping in 181 healthy young adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we describe the experimental paradigm and the results of model-based analysis of the fMRI data. These results provide estimates of population receptive field position and size. Our analyses include both results from individual subjects as well as results obtained by averaging fMRI time-series across subjects at each cortical and subcortical location and then fitting models. Both the group-average and individual-subject results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal cortex as well as subcortical areas. The group-average results agree well with previously published parcellations of visual areas. In addition, split-half analyses show strong within-subject reliability, further demonstrating the high quality of the data. We make publicly available the analysis results for individual subjects and the group average, as well as associated stimuli and analysis code. These resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical organization and the properties of high-resolution fMRI. In addition, they provide a set of observations that can be compared with other HCP measures acquired in these same participants.

scholarly journals Brain hubs defined in the group do not overlap with regions of high inter-individual variability

2021 ◽  
Author(s):  
Derek Martin Smith ◽  
Brian T Kraus ◽  
Ally Dworetsky ◽  
Evan M Gordon ◽  
Caterina Gratton
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Critical Role ◽  
Brain Regions ◽  
Individual Variability ◽  
Spatial Proximity ◽  
Functional Magnetic Resonance ◽  
Multiple Networks ◽  
Human Connectome Project ◽  
The Brain

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability, to better understand if hubs are (a) relatively conserved across people, (b) locations with malleable connectivity, leading individuals to show variable hub profiles, or (c) artifacts arising from cross-person variation. To answer this question, we compared the locations of hubs and regions of strong idiosyncratic functional connectivity ("variants") in both the Midnight Scan Club and Human Connectome Project datasets. Group hubs defined based on the participation coefficient did not overlap strongly with variants. These hubs have relatively strong similarity across participants and consistent cross-network profiles. Consistency across participants was further improved when participation coefficient hubs were allowed to shift slightly in local position. Thus, our results demonstrate that group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density, which are based on spatial proximity and show higher correspondence to locations of individual variability.

Sign in / Sign up

Export Citation Format

Share Document