Amanda F. Mejia

The paper identifies inflated effect sizes in functional neuroimaging studies and proposes a correction method to improve study planning and detection of brain effects.

The paper demonstrates that edge time series, a method for tracking connectivity changes, are equivalent to interaction terms in a linear model and show connectivity has unique explanatory power beyond brain activity.

The paper compares group and personalized brain functional connectivity approaches for depressive constructs, finding intersection estimates more valid and predictive than group or personalized estimates alone.

The paper evaluates how different censoring strategies affect cortical parcellations and TMS targets in rs-fMRI, finding that lenient censoring yields better results than strict censoring.

This study uses a Bayesian framework on fMRI data to estimate individual functional connectivity patterns in neonates, revealing age-related changes and individual variability.

The paper argues against hiding subthreshold results in neuroimaging studies and proposes a 'highlighting' approach to improve reproducibility and understanding.

The paper compares inference levels in fMRI, finding that network and whole-brain analyses have higher power than cluster-level analyses for detecting widespread effects.

This study examines psilocybin's effects on thalamic functional organization and connectivity using a novel ICA approach, revealing spatially constrained changes in intrathalamic nuclei and thalamocortical connections.