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Highly adaptive tests for group differences in brain functional connectivity.

Kim J, Pan W, Alzheimer's Disease Neuroimaging Initiati - Neuroimage Clin (2015)

Bottom Line: The proposed tests combine statistical evidence against a hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth.These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios.The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

View Article: PubMed Central - PubMed

Affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.

ABSTRACT
Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

No MeSH data available.


Related in: MedlinePlus

p-Values for testing brain network differences between the AD group and CN group in the ADNI data. The left column (a) shows the p-values using correlations. The middle column (b) shows the p-values with using partial correlations. The last column (c) taSPU and taNBS shows the p-values by combining two inferences evaluated with correlations or partial correlations.
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f0005: p-Values for testing brain network differences between the AD group and CN group in the ADNI data. The left column (a) shows the p-values using correlations. The middle column (b) shows the p-values with using partial correlations. The last column (c) taSPU and taNBS shows the p-values by combining two inferences evaluated with correlations or partial correlations.

Mentions: We applied the proposed methods to test network differences between 30 Alzheimer's disease (AD) patients and 38 cognitively normal (CN) controls after adjusting for covariates age, gender and education. Both correlations and partial correlations were considered for k = 116 × (116 − 1)/2 = 6670 edges. Demographic information of the participants is reported in Table 1. There was no significant difference in any covariate between AD and CN. 15 different regularization values were applied to generate various connection densities. Fig. 1 illustrates how the p-values changed with the group-level connection density. The p-values are color-coded by statistical tests and sorted by plotting symbols for each parameter. The top row of Fig. 1 illustrates the p-values of SPU tests (only the results of a subset of the γ values are reported), and in the bottom row, the p-values of NBS tests are reported. The left column (a) shows the p-values when using correlations, while the middle column (b) shows the p-values with the use of partial correlations; the last column (c) gives the p-values for the triply adaptive tests, taSPU and taNBS. With no or only moderate regularization, neither SPU nor NBS could find any significant group differences. As regularization increased, the p-values tended to decrease when using correlations (a). Using partial correlations (b), the p-values were unstable when small regularization was imposed. The p-value of taSPU was 0.02, more significant than that of taNBS, 0.06.


Highly adaptive tests for group differences in brain functional connectivity.

Kim J, Pan W, Alzheimer's Disease Neuroimaging Initiati - Neuroimage Clin (2015)

p-Values for testing brain network differences between the AD group and CN group in the ADNI data. The left column (a) shows the p-values using correlations. The middle column (b) shows the p-values with using partial correlations. The last column (c) taSPU and taNBS shows the p-values by combining two inferences evaluated with correlations or partial correlations.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644249&req=5

f0005: p-Values for testing brain network differences between the AD group and CN group in the ADNI data. The left column (a) shows the p-values using correlations. The middle column (b) shows the p-values with using partial correlations. The last column (c) taSPU and taNBS shows the p-values by combining two inferences evaluated with correlations or partial correlations.
Mentions: We applied the proposed methods to test network differences between 30 Alzheimer's disease (AD) patients and 38 cognitively normal (CN) controls after adjusting for covariates age, gender and education. Both correlations and partial correlations were considered for k = 116 × (116 − 1)/2 = 6670 edges. Demographic information of the participants is reported in Table 1. There was no significant difference in any covariate between AD and CN. 15 different regularization values were applied to generate various connection densities. Fig. 1 illustrates how the p-values changed with the group-level connection density. The p-values are color-coded by statistical tests and sorted by plotting symbols for each parameter. The top row of Fig. 1 illustrates the p-values of SPU tests (only the results of a subset of the γ values are reported), and in the bottom row, the p-values of NBS tests are reported. The left column (a) shows the p-values when using correlations, while the middle column (b) shows the p-values with the use of partial correlations; the last column (c) gives the p-values for the triply adaptive tests, taSPU and taNBS. With no or only moderate regularization, neither SPU nor NBS could find any significant group differences. As regularization increased, the p-values tended to decrease when using correlations (a). Using partial correlations (b), the p-values were unstable when small regularization was imposed. The p-value of taSPU was 0.02, more significant than that of taNBS, 0.06.

Bottom Line: The proposed tests combine statistical evidence against a hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth.These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios.The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

View Article: PubMed Central - PubMed

Affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.

ABSTRACT
Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

No MeSH data available.


Related in: MedlinePlus