Crystal Cooper

TL;DR: It is shown that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses, and is useful for combining imaging data with the goal of studying life‐span trajectories in the brain.

TL;DR: The study is designed to standardize assessment of biomarkers across multiple sites as well as institute replicable quality control methods, and to use advanced data analytic methods to integrate these markers.

TL;DR: A latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) was designed and applied to data from the largest imaging-coupled, placebo-controlled antidepressant study and found that the efficacy of an antidepressant is predicted from an EEG signature.

TL;DR: To determine the effect of visual inspection on sample size required for studies of MRI‐derived cortical thickness, the number of subjects required to show group differences was calculated and significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution.

TL;DR: Increased pretreatment rACC theta activity represents a nonspecific prognostic marker of treatment outcome, and predicted greater depressive symptom improvement, even when controlling for clinical and demographic variables previously linked with treatment outcome.