Saloni Mehta

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

This paper introduces a feedback approach to connectome-based predictive modeling to identify which brain systems standardized tests and clinical symptoms reflect, enabling better-targeted test development.

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

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

Flexible brain state engagement predicts cognitive control transdiagnostically

This study shows that flexible brain state engagement predicts cognitive control across multiple psychiatric disorders using machine learning and three transdiagnostic datasets.

Transdiagnostic connectome-based predictive modeling reveals where circuits related to self-reported clinical symptoms impinge upon brain networks supporting cognition

The paper uses connectome-based predictive modeling to show that circuits related to clinical symptoms and cognitive deficits are localized in specific brain networks.

Fetal network controllability co-develops with synaptic development and synchronizes with maternal network controllability during pregnancy

The paper uses network control theory to show fetal brain controllability follows a U-shaped trajectory during pregnancy, co-developing with synaptic functions and synchronizing with maternal controllability changes.

Trends in Self-citation Rates in High-impact Neurology, Neuroscience, and Psychiatry Journals

This study analyzes self-citation rates across 100,347 articles from 63 neuroscience journals between 2000-2020, revealing trends in self-citation by author position, country, gender, and field.

Brain age prediction and deviations from normative trajectories in the neonatal connectome

This study predicts brain age and deviations from normative trajectories in neonatal connectomes using structural and functional data from infants.

Rescuing missing data in connectome-based predictive modeling

This paper improves brain-behavior prediction by imputing missing data in connectome-based models, increasing sample size and explained variance.

(Un)common space in infant neuroimaging studies: a systematic review of infant templates

This study reviews infant neuroimaging studies from 2000 to 2020 and finds that single subject space is most commonly used, with off-the-shelf templates becoming more prevalent over time.