4,358 research outputs found
Modeling and inference of multisubject fMRI data
Functional magnetic resonance imaging (fMRI) is a
rapidly growing technique for studying the brain in
action. Since its creation [1], [2], cognitive scientists
have been using fMRI to understand how we remember,
manipulate, and act on information in our environment.
Working with magnetic resonance physicists, statisticians, and
engineers, these scientists are pushing the frontiers of knowledge
of how the human brain works.
The design and analysis of single-subject fMRI studies
has been well described. For example, [3], chapters 10
and 11 of [4], and chapters 11 and 14 of [5] all give accessible
overviews of fMRI methods for one subject. In contrast,
while the appropriate manner to analyze a group of
subjects has been the topic of several recent papers, we do
not feel it has been covered well in introductory texts and
review papers. Therefore, in this article, we bring together
old and new work on so-called group modeling of fMRI
data using a consistent notation to make the methods more
accessible and comparable
Can parametric statistical methods be trusted for fMRI based group studies?
The most widely used task fMRI analyses use parametric methods that depend on
a variety of assumptions. While individual aspects of these fMRI models have
been evaluated, they have not been evaluated in a comprehensive manner with
empirical data. In this work, a total of 2 million random task fMRI group
analyses have been performed using resting state fMRI data, to compute
empirical familywise error rates for the software packages SPM, FSL and AFNI,
as well as a standard non-parametric permutation method. While there is some
variation, for a nominal familywise error rate of 5% the parametric statistical
methods are shown to be conservative for voxel-wise inference and invalid for
cluster-wise inference; in particular, cluster size inference with a cluster
defining threshold of p = 0.01 generates familywise error rates up to 60%. We
conduct a number of follow up analyses and investigations that suggest the
cause of the invalid cluster inferences is spatial auto correlation functions
that do not follow the assumed Gaussian shape. By comparison, the
non-parametric permutation test, which is based on a small number of
assumptions, is found to produce valid results for voxel as well as cluster
wise inference. Using real task data, we compare the results between one
parametric method and the permutation test, and find stark differences in the
conclusions drawn between the two using cluster inference. These findings speak
to the need of validating the statistical methods being used in the
neuroimaging field
Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests
One-sided t-tests are commonly used in the neuroimaging field, but two-sided
tests should be the default unless a researcher has a strong reason for using a
one-sided test. Here we extend our previous work on cluster false positive
rates, which used one-sided tests, to two-sided tests. Briefly, we found that
parametric methods perform worse for two-sided t-tests, and that non-parametric
methods perform equally well for one-sided and two-sided tests
Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates
Methodological research rarely generates a broad interest, yet our work on
the validity of cluster inference methods for functional magnetic resonance
imaging (fMRI) created intense discussion on both the minutia of our approach
and its implications for the discipline. In the present work, we take on
various critiques of our work and further explore the limitations of our
original work. We address issues about the particular event-related designs we
used, considering multiple event types and randomisation of events between
subjects. We consider the lack of validity found with one-sample permutation
(sign flipping) tests, investigating a number of approaches to improve the
false positive control of this widely used procedure. We found that the
combination of a two-sided test and cleaning the data using ICA FIX resulted in
nominal false positive rates for all datasets, meaning that data cleaning is
not only important for resting state fMRI, but also for task fMRI. Finally, we
discuss the implications of our work on the fMRI literature as a whole,
estimating that at least 10% of the fMRI studies have used the most problematic
cluster inference method (P = 0.01 cluster defining threshold), and how
individual studies can be interpreted in light of our findings. These
additional results underscore our original conclusions, on the importance of
data sharing and thorough evaluation of statistical methods on realistic null
data
Striking First: Preemption and Prevention in International Conflict
Even before the United States and its al- lies embarked on war in Iraq in 2003, the question of whether it is acceptable to strike enemies without clear provo- cation was an increasingly vexing one to policy makers, academics, and legal ex- perts. “Preemptive war” (attacking an enemy who is clearly about to strike you first) has always been an acceptable response to a dire and clear threat. But “preventive war” (striking a potential enemy while circumstances are favor- able to the attacker, or striking in early anticipation of a possible, or even only theoretical, threat) has traditionally been regarded in the international community as not only unwise but immoral
An online peer-based spiritual mentoring program for field missionaries
https://place.asburyseminary.edu/ecommonsatsdissertations/1398/thumbnail.jp
Permutation Inference for Canonical Correlation Analysis
Canonical correlation analysis (CCA) has become a key tool for population
neuroimaging, allowing investigation of associations between many imaging and
non-imaging measurements. As other variables are often a source of variability
not of direct interest, previous work has used CCA on residuals from a model
that removes these effects, then proceeded directly to permutation inference.
We show that such a simple permutation test leads to inflated error rates. The
reason is that residualisation introduces dependencies among the observations
that violate the exchangeability assumption. Even in the absence of nuisance
variables, however, a simple permutation test for CCA also leads to excess
error rates for all canonical correlations other than the first. The reason is
that a simple permutation scheme does not ignore the variability already
explained by previous canonical variables. Here we propose solutions for both
problems: in the case of nuisance variables, we show that transforming the
residuals to a lower dimensional basis where exchangeability holds results in a
valid permutation test; for more general cases, with or without nuisance
variables, we propose estimating the canonical correlations in a stepwise
manner, removing at each iteration the variance already explained, while
dealing with different number of variables in both sides. We also discuss how
to address the multiplicity of tests, proposing an admissible test that is not
conservative, and provide a complete algorithm for permutation inference for
CCA.Comment: 49 pages, 2 figures, 10 tables, 3 algorithms, 119 reference
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