The classical approach for testing statistical images using spatial extent inference (SEI)
thresholds the statistical image based on the p‐value. This approach has an unfortunate …

Spatial extent inference (SEI) is widely used across neuroimaging modalities to adjust for
multiple comparisons when studying brain-phenotype associations that inform our …

Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous
correction methods exist, mainly applied and evaluated on neuroimaging and synthetic …

Resistance of high grade tumors to treatment involves cancer stem cell features,
deregulated cell division, acceleration of genomic errors, and emergence of cellular variants …

Objective To explore concomitant neuroimaging and genetic alterations in patients with
schizophrenia with or without auditory verbal hallucinations (AVHs), and to discuss the use …

Random field theory (RFT) provided a theoretical foundation for cluster-extent-based
thresholding, the most widely used method for multiple comparison correction of statistical …

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of
damaged tissue, yet many diseases are characterized by diffuse or non-regional …

Human brain tumors present significant challenges due to their heterogeneous nature,
known as intra-tumoral heterogeneity (ITH), which evolves over space and time, leading to …

In neuroimaging, the multiplicity issue may sneak into data analysis through several
channels, affecting expected false positive rates (FPRs; type I errors) in diverse ways. One …

Spatial extent inference (SEI) is widely used across neuroimaging modalities to study brain-
phenotype associations that inform our understanding of disease. Recent studies have …