Behavioral–genetic associations in the Human Connectome Project

2020 ◽  
Vol 238 (11) ◽  
pp. 2445-2456
Author(s):  
Peka Christova ◽  
Jasmine Joseph ◽  
Apostolos P. Georgopoulos
Keyword(s):  
Genetic Associations ◽  
Behavioral Genetic ◽  
Human Connectome Project

Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results

2019 ◽  
Vol 35 (19) ◽  
pp. 3771-3778 ◽  
Author(s):  
Zhaowen Liu ◽  
Edmund T Rolls ◽  
Zhi Liu ◽  
Kai Zhang ◽  
Ming Yang ◽  
...  
Keyword(s):  
Psychiatric Patients ◽  
Brain Regions ◽  
Brain Atlas ◽  
Supplementary Information ◽  
Genetic Associations ◽  
Matlab Toolbox ◽  
Similarity Network ◽  
Biological Meaning ◽  
Human Connectome Project ◽  
Highly Correlated

Abstract Motivation Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. Results We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. Availability and implementation BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. Supplementary information Supplementary data are available at Bioinformatics online.

Phenotypic and Genetic Associations Between the Big Five and Trait Emotional Intelligence

2008 ◽  
Vol 11 (5) ◽  
pp. 524-530 ◽  
Author(s):  
Philip A. Vernon ◽  
Vanessa C. Villani ◽  
Julie Aitken Schermer ◽  
K. V. Petrides
Keyword(s):  
Emotional Intelligence ◽  
Environmental Factors ◽  
Big Five ◽  
Genetic Model ◽  
Personality Factors ◽  
Big Five Personality ◽  
Trait Emotional Intelligence ◽  
Genetic Associations ◽  
Behavioral Genetic ◽  
Phenotypic Correlations

AbstractThis study reports the first behavioral genetic investigation of the extent to which genetic and/or environmental factors contribute to the relationship between the Big Five personality factors and trait emotional intelligence. 213 pairs of adult monozygotic twins and 103 pairs of same-sex dizygotic twins completed the NEO-PI-R and the Trait Emotional Intelligence Questionnaire (TEIQue). Replicating previous non-twin studies, many significant phenotypic correlations were found between the Big Five factors — especially Neuroticism, Extraversion, and Conscientiousness — and the facets, factors, and global scores derived from the TEIQue. Bivariate behavioral genetic model-fitting analyses revealed that these phenotypic correlations were primarily attributable to correlated genetic factors and secondarily to correlated non-shared environmental factors. The results support the feasibility of incorporating EI as a trait within existing personality taxonomies.

scholarly journals The functional and genetic associations of neuroimaging data: a toolbox

2017 ◽  
Author(s):  
Zhaowen Liu ◽  
Edmund T. Rolls ◽  
Jie Zhang ◽  
Ming Yang ◽  
Jingnan Du ◽  
...  
Keyword(s):  
Permutation Test ◽  
Statistical Tests ◽  
Brain Regions ◽  
Brain Atlas ◽  
Genetic Associations ◽  
Generate Functional ◽  
Exact Test ◽  
Human Connectome Project ◽  
Neuroimaging Data ◽  
Highly Correlated

AbstractAdvances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and to identify the roots of psychiatric diseases. However, the results generated by most neuroimaging studies, i.e., activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a Brain Annotation Toolbox (BAT), a toolbox that helps to generate functional and genetic annotations for neuroimaging results. The toolbox can take data from brain regions identified with an atlas, or from brain regions identified as activated in tasks, or from functional connectivity links or networks of links. Then, the voxel-level functional description from the Neurosynth database and the gene expression profile from the Allen Brain Atlas are used to generate functional and genetic knowledge for such region-level data. Parametric (Fisher’s exact test) or non-parametric (permutation test) statistical tests are adopted to identify significantly related functional descriptors and genes for the neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and gene co-expression networks are highly correlated for many major brain atlases. One application of BAT is to help provide functional and genetic annotations for the newly discovered regions with unknown functions, e.g., the 97 new regions identified in the Human Connectome Project. Importantly too, this toolbox can help understand differences between patients with psychiatric disorders and controls, and this is demonstrated using data for schizophrenia and autism, for which the functional and genetic annotations for the neuroimaging data differences between patients and controls are consistent with each other and help with the interpretation of the differences.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Erweiterung der Library of Genetic Associations – Update 2009

2009 ◽  
Vol 47 (01) ◽  
Author(s):  
M Krupp ◽  
T Maass ◽  
S Buchkremer ◽  
A Weinmann ◽  
F Thieringer ◽  
...  
Keyword(s):  
Genetic Associations

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.

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.

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