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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

Simulation set-up 2: power for testing network differences with true sparse covariance matrices; the left panel (a) shows the power using correlations; the middle panel (b) for the power with using partial correlations; in the last panel (c), is the power of taSPU and taNBS.
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f0035: Simulation set-up 2: power for testing network differences with true sparse covariance matrices; the left panel (a) shows the power using correlations; the middle panel (b) for the power with using partial correlations; in the last panel (c), is the power of taSPU and taNBS.

Mentions: Fig. 7 describes power for testing network differences when true covariance matrices are sparse. The proposed adaptive tests were successful in combining multiple testing results over parameters c, γ (or t) and Ω. The patterns of the relative power between using correlations and partial correlations were switched compared to Fig. 4, Fig. 5, Fig. 6, presumably because the true covariance matrices, not true precision matrices, were sparse, highlighting the necessity of data-adaptively choosing an association measure.


Highly adaptive tests for group differences in brain functional connectivity.

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

Simulation set-up 2: power for testing network differences with true sparse covariance matrices; the left panel (a) shows the power using correlations; the middle panel (b) for the power with using partial correlations; in the last panel (c), is the power of taSPU and taNBS.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0035: Simulation set-up 2: power for testing network differences with true sparse covariance matrices; the left panel (a) shows the power using correlations; the middle panel (b) for the power with using partial correlations; in the last panel (c), is the power of taSPU and taNBS.
Mentions: Fig. 7 describes power for testing network differences when true covariance matrices are sparse. The proposed adaptive tests were successful in combining multiple testing results over parameters c, γ (or t) and Ω. The patterns of the relative power between using correlations and partial correlations were switched compared to Fig. 4, Fig. 5, Fig. 6, presumably because the true covariance matrices, not true precision matrices, were sparse, highlighting the necessity of data-adaptively choosing an association measure.

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