[HTML][HTML] Statistical agnostic mapping: A framework in neuroimaging based on concentration inequalities
In the 1970s a novel branch of statistics emerged focusing its effort on the selection of a
function for the pattern recognition problem that would fulfill a relationship between the …
function for the pattern recognition problem that would fulfill a relationship between the …
[HTML][HTML] Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models
The potential of normative modeling to make individualized predictions from neuroimaging
data has enabled inferences that go beyond the case-control approach. However, site …
data has enabled inferences that go beyond the case-control approach. However, site …
[HTML][HTML] Fighting or embracing multiplicity in neuroimaging? neighborhood leverage versus global calibration
Neuroimaging faces the daunting challenge of multiple testing–an instance of multiplicity–
that is associated with two other issues to some extent: low inference efficiency and poor …
that is associated with two other issues to some extent: low inference efficiency and poor …
Mitigating site effects in covariance for machine learning in neuroimaging data
To acquire larger samples for answering complex questions in neuroscience, researchers
have increasingly turned to multi‐site neuroimaging studies. However, these studies are …
have increasingly turned to multi‐site neuroimaging studies. However, these studies are …
Sources of information waste in neuroimaging: mishandling structures, thinking dichotomously, and over-reducing data
Neuroimaging relies on separate statistical inferences at tens of thousands of spatial
locations. Such massively univariate analysis typically requires an adjustment for multiple …
locations. Such massively univariate analysis typically requires an adjustment for multiple …
[HTML][HTML] Spatial confidence sets for raw effect size images
The mass-univariate approach for functional magnetic resonance imaging (fMRI) analysis
remains a widely used statistical tool within neuroimaging. However, this method suffers …
remains a widely used statistical tool within neuroimaging. However, this method suffers …
The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods
Standard neuroimaging data analysis based on traditional principles of experimental
design, modelling, and statistical inference is increasingly complemented by novel analysis …
design, modelling, and statistical inference is increasingly complemented by novel analysis …
MIDAS: Regionally linear multivariate discriminative statistical mapping
Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general
linear model, are commonly used to test hypotheses about regionally specific effects in …
linear model, are commonly used to test hypotheses about regionally specific effects in …
Biased accuracy in multisite machine-learning studies due to incomplete removal of the effects of the site
A Solanes, P Palau, L Fortea, R Salvador… - Psychiatry Research …, 2021 - Elsevier
Brain MRI researchers conducting multisite studies, such as within the ENIGMA Consortium,
are very aware of the importance of controlling the effects of the site (EoS) in the statistical …
are very aware of the importance of controlling the effects of the site (EoS) in the statistical …
Applications of multivariate modeling to neuroimaging group analysis: a comprehensive alternative to univariate general linear model
All neuroimaging packages can handle group analysis with t-tests or general linear
modeling (GLM). However, they are quite hamstrung when there are multiple within-subject …
modeling (GLM). However, they are quite hamstrung when there are multiple within-subject …