Batch effects introduce significant variability into high-dimensional data, complicating
accurate analysis and leading to potentially misleading conclusions if not adequately …

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-
site variabilities, while preserving effects of biological variables (or covariates). The popular …

Small sample sizes are common in many disciplines, which necessitates pooling roughly
similar datasets across multiple sites/institutions to study weak but relevant associations …

Multicenter and multi-scanner imaging studies may be necessary to ensure sufficiently large
sample sizes for develo** accurate predictive models. However, multicenter studies …

Batch effects, undesirable sources of variability across multiple experiments, present
significant challenges for scientific and clinical discoveries. Batch effects can (i) produce …

Pooling multi-site datasets is the dominant trend to expand sample sizes in neuroimaging
field, thereby enhancing statistical power and reproducibility of research findings …

Batch effects, undesirable sources of variance across multiple experiments, present a
substantial hurdle for scientific and clinical discoveries. Specifically, the presence of batch …