A two-part mixed-effects modeling framework for analyzing whole-brain network data

Simpson, Sean L.; Laurienti, Paul J.

doi:10.1016/j.neuroimage.2015.03.021

Cited by 42 publications

(65 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…local clustering and global efficiency) over all age categories, in contrast with previous literature showing alterations in both structural (Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2010;Montembeault et al, 2012;Otte et al, 2015;Wu et al, 2012;Zhu et al, 2012) and functional brain networks across life-span (Achard and Bullmore, 2007;Betzel et al, 2014;Meier et al, 2012;Meunier et al, 2009;Nathan Spreng and Schacter, 2012;Simpson and Laurienti, 2015;Smit et al, 2016;Wang et al, 2012). Furthermore, our findings are also in contrast with significant differences found in a previous exponential random graph modeling study in functional networks , and a recently developed similar approach (also discussed below) which revealed differences in functional networks across the lifespan, such as older adults having stronger connections between highly clustered nodes, or less assortativity in visual and multisensory regions (Simpson and Laurienti, 2015). We speculate that functional networks might be more prone to changes across the lifespan, while structural networks remain quite stable, when taking into account density differences and mutual dependencies of network properties.…”

Section: Discussioncontrasting

confidence: 82%

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

Sinke

Dijkhuizen

Caimo³

et al. 2016

NeuroImage

View full text Add to dashboard Cite

a b s t r a c t a r t i c l e i n f oDescriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an alternative framework based on a Bayesian generative modeling approach, i.e. Bayesian exponential random graph modeling (ERGM), which explains an observed network by the joint contribution of local network structures or features (for which we chose neurobiologically meaningful constructs such as connectedness, local clustering or global efficiency). We aimed to identify how these local network structures (or features) are evolving across the life-span, and how sensitive these features are to random and targeted lesions. To that aim we applied Bayesian exponential random graph modeling on structural networks derived from whole-brain diffusion tensor imaging-based tractography of 382 healthy adult subjects (age range: 20.2-86.2 years), with and without lesion simulations. Networks were successfully generated from four local network structures that resulted in excellent goodness-of-fit, i.e. measures of connectedness, local clustering, global efficiency and intrahemispheric connectivity. We found that local structures (i.e. connectedness, local clustering and global efficiency), which give rise to the global network topology, were stable even after lesion simulations across the lifespan, in contrast to overall descriptive network changes -e.g. lower network density and higher clustering -during aging, and despite clear effects of hub damage on network topologies. Our study demonstrates the potential of Bayesian generative modeling to characterize the underlying network structures that drive the brain's global network topology at different developmental stages and/or under pathological conditions.

show abstract

Section: Discussioncontrasting

confidence: 82%

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

Sinke

Dijkhuizen

Caimo³

et al. 2016

NeuroImage

View full text Add to dashboard Cite

show abstract

“…WFU_MMNET models the connectivity patterns using a two-part mixed-effects modeling framework developed by Simpson and Laurienti (Simpson and Laurienti 2015). The relationship between both the probability (presence/absence) and strength of a connection, as the outcome (dependent) variables, and different sets of covariates including dyadic or overall network features, covariates of interest and demographics, as independent variables, is quantified through this framework.…”

Section: Methodsmentioning

confidence: 99%

“…Simpson and Laurienti (Simpson and Laurienti 2015) developed a multivariate mixed effects modeling framework in response to such needs. This framework allows comparing whole-brain functional connectivity patterns between groups, quantifying the relationship between covariates and connectivity patterns while reducing spurious correlations, predicting phenotype from network structure, and simulating brain networks.…”

Section: Introductionmentioning

confidence: 99%

A MATLAB toolbox for multivariate analysis of brain networks

Bahrami

Laurienti

Simpson

2018

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.

show abstract

“…We used a two-part, mixed-effects modeling framework [40] to statistically compare the global and local network properties between Latino immigrant workers employed on farms to those in working in other industries with low likelihood of pesticide exposure. The modeling framework allowed comparing network properties between the two groups through the inclusion of interaction covariates.…”

Section: Methodsmentioning

confidence: 99%

“…This study used brain network analysis of rs-fMRI data and a mixed-effects modeling framework [40] to compare brain network connectivity patterns between Latino immigrant workers engaged in farm work to those not engaged in farm work. The network analysis was used to characterize global as well as local brain connectivity patterns.…”

Section: Introductionmentioning

confidence: 99%

The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers

et al. 2017

Self Cite

View full text Add to dashboard Cite

Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and strength, brain networks in farmworkers had more clustered and modular structures when compared to non-farmworkers. Our findings suggest increased functional specificity and decreased functional integration in farmworkers when compared to non-farmworkers. Cholinesterase activity was associated with population differences in community structure and the strength of brain network functional connections. Urinary cotinine, a marker of nicotine exposure, was associated with the differences in network community structure. Brain network differences between farmworkers and non-farmworkers, as well as pesticide and nicotine exposure effects on brain functional connections in this study, may illuminate underlying mechanisms that cause neurological implications in later life.

show abstract

A two-part mixed-effects modeling framework for analyzing whole-brain network data

Cited by 42 publications

References 56 publications

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

A MATLAB toolbox for multivariate analysis of brain networks

The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers

Contact Info

Product

Resources

About