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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Estimating and testing variance components in a multi-level GLM.

Martin A Lindquist1, Julie Spicer, Iris Asllani

  • 1Department of Statistics, Columbia University, New York, NY 10027, USA. martin@stat.columbia.edu

Neuroimage
|August 13, 2011
PubMed
Summary
This summary is machine-generated.

Analyzing functional magnetic resonance imaging (fMRI) data, this study introduces variance component testing to identify significant inter-individual differences in brain activation. The iterative generalized least squares (IGLS) method offers a balanced approach for detecting variability in brain regions like the VMPFC.

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Statistical Analysis

Background:

  • Standard functional magnetic resonance imaging (fMRI) analysis focuses on population-level activation, often using the general linear model (GLM).
  • Significant information regarding individual variability in brain responses is often overlooked by traditional methods.
  • Assessing inter-individual differences in effect magnitude (variance of contrast of parameter estimates - COPEs) offers a complementary analytical approach.

Purpose of the Study:

  • To evaluate methods for estimating variance components in multi-subject fMRI data.
  • To introduce and validate a novel iterative generalized least squares (IGLS) method for variance estimation.
  • To demonstrate the utility of inter-individual variance testing in identifying brain regions with significant variability.

Main Methods:

  • Comparison of variance component estimation methods: ordinary least squares (OLS), linear mixed effects in R (LMER), and iterative generalized least squares (IGLS) with its restricted maximum likelihood variant (RIGLS).
  • Application of the IGLS method to a perfusion fMRI study (N=18) investigating social evaluative threat.
  • Functional connectivity analysis using inter-individual variance to define seed regions.

Main Results:

  • All tested methods provided reasonable estimates of within- and between-subject variance components.
  • The IGLS method demonstrated a favorable balance between sensitivity and control of false positives.
  • Significant inter-individual differences were identified in key brain regions including the ventromedial prefrontal cortex (VMPFC), amygdala, hippocampus, medial temporal lobes, insula, and brainstem.
  • Functional connectivity analysis revealed predicted inverse coupling between VMPFC and the midbrain periaqueductal gray when high inter-individual variance defined the seed.

Conclusions:

  • Testing for variance components is a valuable method for detecting significant inter-individual differences in fMRI data.
  • The IGLS approach offers a robust and sensitive method for estimating variance components in neuroimaging studies.
  • Identifying regions with significant inter-individual variability facilitates subsequent analyses aimed at explaining these differences.