Site differences, or systematic differences in feature distributions across multiple data-
acquisition sites, are a known source of heterogeneity that may adversely affect large-scale …

The desire to train complex machine learning algorithms and to increase the statistical
power in association studies drives neuroimaging research to use ever-larger datasets. The …

To acquire larger samples for answering complex questions in neuroscience, researchers
have increasingly turned to multi‐site neuroimaging studies. However, these studies are …

When fields lack consensus standards and ground truths for their analytic methods,
reproducibility can be more of an ideal than a reality. Such has been the case for functional …

The accumulation of multisite large-sample MRI datasets collected during large brain
research projects in the last decade has provided critical resources for understanding the …

The potential of normative modeling to make individualized predictions from neuroimaging
data has enabled inferences that go beyond the case-control approach. However, site …

A concern for researchers planning multisite studies is that scanner and T1-weighted
sequence-related biases on regional volumes could overshadow true effects, especially for …

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the
training, validation, and testing of advanced deep learning (DL)-based automated tools …

Technological and data sharing advances have led to a proliferation of high-resolution
structural and functional maps of the brain. Modern neuroimaging research increasingly …

This is an empirical study to investigate the impact of scanner effects when using machine
learning on multi-site neuroimaging data. We utilize structural T1-weighted brain MRI …