Comparison of statistical tests for group differences in brain functional networks

Kim, Junghi; Wozniak, Jeffrey R.; Mueller, Bryon A.; Shen, Xiaotong; Pan, Wei

doi:10.1016/j.neuroimage.2014.07.031

Cited by 51 publications

(48 citation statements)

References 51 publications

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“…The hypothesis testing approach (Zalesky et al, 2010; Ginestet et al, 2014; Kim and Pan, 2015) provides test statistics that are applicable to graphs or graph components like nodes and edges. A recent comparison of different statistical tests for group differences in functional connectivity was presented in Kim et al (2014). Finally, the machine learning approach is based on the application of classifiers to the brain graphs.…”

Section: Methodsmentioning

confidence: 99%

Differential Effects of Brain Disorders on Structural and Functional Connectivity

et al. 2017

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Different measures of brain connectivity can be defined based on neuroimaging read-outs, including structural and functional connectivity. Neurological and psychiatric conditions are often associated with abnormal connectivity, but comparing the effects of the disease on different types of connectivity remains a challenge. In this paper, we address the problem of quantifying the relative effects of brain disease on structural and functional connectivity at a group level. Within the framework of a graph representation of connectivity, we introduce a kernel two-sample test as an effective method to assess the difference between the patients and control group. Moreover, we propose a common representation space for structural and functional connectivity networks, and a novel test statistics to quantitatively assess differential effects of the disease on different types of connectivity. We apply this approach to a dataset from BTBR mice, a murine model of Agenesis of the Corpus Callosum (ACC), a congenital disorder characterized by the absence of the main bundle of fibers connecting the two hemispheres. We used normo-callosal mice (B6) as a comparator. The application of the proposed methods to this data-set shows that the two types of connectivity can be successfully used to discriminate between BTBR and B6, meaning that both types of connectivity are affected by ACC. However, our novel test statistics shows that structural connectivity is significantly more affected than functional connectivity, consistent with the idea that functional connectivity has a robust topology that can tolerate substantial alterations in its structural connectivity substrate.

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Section: Methodsmentioning

confidence: 99%

Differential Effects of Brain Disorders on Structural and Functional Connectivity

et al. 2017

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“…Many functional connectivity studies have used Pearson's (full or marginal) correlation between two nodes' BOLD time-course signals (Azari et al, 1992;Horwitz et al, 1987;Kim et al, 2014;Stam et al, 2007;Supekar et al, 2008), which is easy to calculate based on a sample covariance matrix. However, a drawback of using correlations is that it may not be able to distinguish whether the functional connection between two nodes is direct or not.…”

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confidence: 99%

Testing Group Differences in Brain Functional Connectivity: Using Correlations or Partial Correlations?

et al. 2015

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Resting-state functional magnetic resonance imaging allows one to study brain functional connectivity, partly motivated by evidence that patients with complex disorders, such as Alzheimer's disease, may have altered functional brain connectivity patterns as compared with healthy subjects. A functional connectivity network describes statistical associations of the neural activities among distinct and distant brain regions. Recently, there is a major interest in group-level functional network analysis; however, there is a relative lack of studies on statistical inference, such as significance testing for group comparisons. In particular, it is still debatable which statistic should be used to measure pairwise associations as the connectivity weights. Many functional connectivity studies have used either (full or marginal) correlations or partial correlations for pairwise associations. This article investigates the performance of using either correlations or partial correlations for testing group differences in brain connectivity, and how sparsity levels and topological structures of the connectivity would influence statistical power to detect group differences. Our results suggest that, in general, testing group differences in networks deviates from estimating networks. For example, high regularization in both covariance matrices and precision matrices may lead to higher statistical power; in particular, optimally selected regularization (e.g., by cross-validation or even at the true sparsity level) on the precision matrices with small estimation errors may have low power. Most importantly, and perhaps surprisingly, using either correlations or partial correlations may give very different testing results, depending on which of the covariance matrices and the precision matrices are sparse. Specifically, if the precision matrices are sparse, presumably and arguably a reasonable assumption, then using correlations often yields much higher powered and more stable testing results than using partial correlations; the conclusion is reversed if the covariance matrices, not the precision matrices, are sparse. These results may have useful implications to future studies on testing functional connectivity differences.

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“…To do this, we replace

U

in Equation with

{boldU}^{γ}

to obtain the Seq‐SPU test:

T (d_{γ}, γ) = {({bold-italicd}_{γ}^{T} U^{γ})}^{2} / 13pt(d_{γ}^{T} V d_{γ} 13pt)

where

{bold-italicd}_{γ}

is allowed to vary for different

γ

's. By taking the square root of

T ({bold-italicd}_{γ}, γ)

, it is easy to see that this test is equivalent to the weighted version of the SPU test (Kim, Wozniak, Mueller, Shen, & Pan, ) with weights equal to

{bold-italicd}_{γ} {(d_{γ}^{T} boldV d_{γ})}^{- 1 ∕ 2}

. Notice that iSeq‐aSum is equivalent to Seq‐SPU(1).…”

Section: Methodsmentioning

confidence: 99%

A linear mixed model framework for gene‐based gene–environment interaction tests in twin studies

Coombes

Basu

2018

Genetic Epidemiology

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Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model. We demonstrate theoretically that the tests developed by modeling correlated environments separately are valid and present a computationally fast alternative to detect G×E interaction in families. For either strategy, we propose treating the genetic main effects as a random effect to reduce the number of main-effect parameters and thus improve the power to detect interactions. Additionally, we propose a generalization of a test of interaction that adaptively sums the interactions using a sequential algorithm. This generalized set of tests, referred to as the sequential algorithm for the sum of powered score (Seq-SPU) family of tests, can be expressed as a weighted version of the SPU. We find that the adaptive version of our test, Seq-aSPU, can outperform aSPU in cases where the interactions effects are in opposite directions. We applied these methods to the Minnesota Center for Twin and Family Research data set and found one significant gene in interaction with four psychosocial environmental factors affecting the alcohol consumption among the twins.

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Comparison of statistical tests for group differences in brain functional networks

Cited by 51 publications

References 51 publications

Differential Effects of Brain Disorders on Structural and Functional Connectivity

Differential Effects of Brain Disorders on Structural and Functional Connectivity

Testing Group Differences in Brain Functional Connectivity: Using Correlations or Partial Correlations?

A linear mixed model framework for gene‐based gene–environment interaction tests in twin studies

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