scholarly journals A regression framework for brain network distance metrics

2021 ◽  
pp. 1-38
Author(s):  
Chal E. Tomlison ◽  
Paul J. Laurienti ◽  
Robert G. Lyday ◽  
Sean L. Simpson
Keyword(s):  
Fluid Intelligence ◽  
Distance Matrix ◽  
Generalized Least Squares ◽  
Brain Network ◽  
Categorical Variables ◽  
Phenotypic Traits ◽  
Permutation Testing ◽  
F Test ◽  
Human Connectome Project ◽  
Regression Framework

Abstract Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Feasible Generalized Least Squares (FGLS), and Permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing Multivariate Distance Matrix Regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

scholarly journals A Regression Framework for Brain Network Distance Metrics

2021 ◽  
Author(s):  
Chal E Tomlinson ◽  
Paul J Laurienti ◽  
Robert G Lyday ◽  
Sean L Simpson
Keyword(s):  
Mixed Model ◽  
Fluid Intelligence ◽  
Generalized Least Squares ◽  
Brain Network ◽  
Categorical Variables ◽  
Phenotypic Traits ◽  
Permutation Testing ◽  
F Test ◽  
Human Connectome Project ◽  
Regression Framework

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Generalized Least Squares (GLS), Mixed Model, and Permutation. We also propose a novel modification of the standard GLS approach for estimation and inference. We assess all approaches for estimation and inference via simulation studies, and illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals The Neural Architecture of General Knowledge

2019 ◽  
Vol 33 (5) ◽  
pp. 589-605 ◽  
Author(s):  
Erhan Genç ◽  
Christoph Fraenz ◽  
Caroline Schlüter ◽  
Patrick Friedrich ◽  
Manuel C. Voelkle ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Fluid Intelligence ◽  
Network Connectivity ◽  
Brain Network ◽  
Information Storage ◽  
General Knowledge ◽  
Resonance Imaging ◽  
Crystallized Intelligence ◽  
Brain Network Connectivity

Cognitive performance varies widely between individuals and is highly influenced by structural and functional properties of the brain. In the past, neuroscientific research was principally concerned with fluid intelligence, while neglecting its equally important counterpart crystallized intelligence. Crystallized intelligence is defined as the depth and breadth of knowledge and skills that are valued by one's culture. The accumulation of crystallized intelligence is guided by information storage capacities and is likely to be reflected in an individual's level of general knowledge. In spite of the significant role general knowledge plays for everyday life, its neural foundation largely remains unknown. In a large sample of 324 healthy individuals, we used standard magnetic resonance imaging along with functional magnetic resonance imaging and diffusion tensor imaging to examine different estimates of brain volume and brain network connectivity and assessed their predictive power with regard to both general knowledge and fluid intelligence. Our results demonstrate that an individual's level of general knowledge is associated with structural brain network connectivity beyond any confounding effects exerted by age or sex. Moreover, we found fluid intelligence to be best predicted by cortex volume in male subjects and functional network connectivity in female subjects. Combined, these findings potentially indicate different neural architectures for information storage and information processing. © 2019 European Association of Personality Psychology

scholarly journals Are global and specific interindividual differences in cortical thickness associated with facets of cognitive abilities, including face cognition?

2019 ◽  
Vol 6 (7) ◽  
pp. 180857 ◽  
Author(s):  
Kristina Meyer ◽  
Benjamín Garzón ◽  
Martin Lövdén ◽  
Andrea Hildebrandt
Keyword(s):  
Cortical Thickness ◽  
Cognitive Abilities ◽  
Structural Equation ◽  
Brain Network ◽  
Morphological Properties ◽  
Interindividual Differences ◽  
Measurement Models ◽  
The Core ◽  
Human Connectome Project ◽  
Small Effect Size

Face cognition (FC) is a specific ability that cannot be fully explained by general cognitive functions. Cortical thickness (CT) is a neural correlate of performance and learning. In this registered report, we used data from the Human Connectome Project (HCP) to investigate the relationship between CT in the core brain network of FC and performance on a psychometric task battery, including tasks with facial content. Using structural equation modelling (SEM), we tested the existence of face-specific interindividual differences at behavioural and neural levels. The measurement models include general and face-specific factors of performance and CT. There was no face-specificity in CT in functionally localized areas. In post hoc analyses, we compared the preregistered, small regions of interest (ROIs) to larger, non-individualized ROIs and identified a face-specific CT factor when large ROIs were considered. We show that this was probably due to low reliability of CT in the functional localization (intra-class correlation coefficients (ICC) between 0.72 and 0.85). Furthermore, general cognitive ability, but not face-specific performance, could be predicted by latent factors of CT with a small effect size. In conclusion, for the core brain network of FC, we provide exploratory evidence (in need of cross-validation) that areas of the cortex sharing a functional purpose did also share morphological properties as measured by CT.

scholarly journals Permutation tests for heterogeneity comparisons in presence of categorical variables with application to university evaluation

2007 ◽  
Vol 4 (1) ◽  
Author(s):  
Rosa Arboretti Giancristofaro ◽  
Stefano Bonnini
Keyword(s):  
Social Sciences ◽  
Categorical Variables ◽  
Real Problem ◽  
Permutation Testing ◽  
Order Restrictions ◽  
Job Opportunities ◽  
Ordered Categorical Variables ◽  
University Evaluation ◽  
Phd Programs ◽  
Better Than

In social sciences researchers often meet the problem of determining if the distribution of a categorical variable is more concentrated in population X1 than in population X2. For example the effectiveness of two different PhD programs can be evaluated in terms of the heterogeneity of the set of job opportunities. The job opportunities are nominal categorical variables and populations X1 and X2 include all PhD holders for program 1 and program 2. We may define that a PhD program is "better than another" if it is able to offer a larger variety of job opportunities. Several other examples can be mentioned to highlight the importance of heterogeneity comparison problems in social sciences; moreover this problem occurs also very often in genetics, biology, medical studies and other sciences. The nonparametric solution of this problem has similarities to that of permutation testing for stochastic dominance on ordered categorical variables, i.e. testing under order restrictions. If ordering of probability parameters in H0 is unknown and it has to be estimated by sampling data, only approximate nonparametric solutions are possible within the permutation approach. Main properties of test solutions and some Monte Carlo simulations in order to evaluate the tests' behaviour under H0 and H1, will be presented. A real problem concerned with University evaluation is also discussed.

scholarly journals A distributed brain network predicts general intelligence from resting-state human neuroimaging data

2018 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Resting State ◽  
Brain Volume ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Reliable Estimate ◽  
General Intelligence ◽  
Total Brain Volume ◽  
General Ability ◽  
Human Connectome Project

AbstractIndividual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of longterm life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

scholarly journals Phenotype Discovery from Population Brain Imaging

2020 ◽  
Author(s):  
Weikang Gong ◽  
Christian F. Beckmann ◽  
Stephen M. Smith
Keyword(s):  
Fluid Intelligence ◽  
Disease Diagnosis ◽  
Population Variability ◽  
Non Invasive ◽  
Human Connectome Project ◽  
Analysis Strategies ◽  
Neuroimaging Data ◽  
Using Data ◽  
First Time ◽  
The Brain

Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.

scholarly journals Structural disconnections explain brain network dysfunction after stroke

2019 ◽  
Author(s):  
Joseph C. Griffis ◽  
Nicholas V. Metcalf ◽  
Maurizio Corbetta ◽  
Gordon L. Shulman
Keyword(s):  
System Integration ◽  
Brain Network ◽  
Structural Basis ◽  
Multivariate Models ◽  
Stroke Patients ◽  
Behavioral Deficits ◽  
Damage Models ◽  
Human Connectome Project ◽  
Low Dimensional ◽  
Critical Regions

SummaryFunctional connectivity (FC) studies have identified physiological signatures of stroke that correlate with behavior. Using structural and functional MRI data from 114 stroke patients, 24 matched controls, and the Human Connectome Project, we tested the hypothesis that structural disconnection, not damage to critical regions, underlies FC disruptions. Disconnection severity outperformed damage to putative FC connector nodes for explaining reductions in system modularity, and multivariate models based on disconnection outperformed damage models for explaining FC disruptions within and between systems. Across patients, disconnection and FC patterns exhibited a low-dimensional covariance dominated by a single axis linking interhemispheric disconnections to reductions in FC measures of interhemispheric system integration, ipsilesional system segregation, and system modularity, and that correlated with multiple behavioral deficits. These findings clarify the structural basis of FC disruptions in stroke patients and demonstrate a low-dimensional link between perturbations of the structural connectome, disruptions of the functional connectome, and behavioral deficits.

scholarly journals A Longitudinal Model for Functional Connectivity Networks Using Resting-State fMRI

2017 ◽  
Author(s):  
Brian Hart ◽  
Ivor Cribben ◽  
Mark Fiecas ◽  
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Resting State Fmri ◽  
Testing Procedure ◽  
Fmri Data ◽  
Series Data ◽  
Group Differences ◽  
Permutation Testing ◽  
Novel Method

AbstractMany neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC network model using a variance components approach. First, for all subjects’ visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the FC network, and 3) the FC network’s longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in baseline FC and change in FC over time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer’s Disease Neuroimaging Initiative database.

Sign in / Sign up

Export Citation Format

Share Document