Trends in Childhood Behavioral, Mental, and Developmental Problems (2019-2022) Using the National Survey of Children's Health.

Background: This study examines nationwide prevalence of US childhood behavioral, mental, and developmental problems in the 6-11 age group category between 2019 and 2022, and several key metrics related to caregiver social determinants of health.

Methods: We used NSCH data for 6-11-year-old children for the years 2019-2022. Summary statistics for the selected sample were generated and binary logistic regressions were conducted for each severity level binary variable for each study year with caregiver mental or emotional health as a covariate.

Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning.

Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly in non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults.

Impact of electrode selection on modeling tDCS in the aging brain.

Background: Person-specific computational models can estimate transcranial direct current stimulation (tDCS) current dose delivered to the brain and predict treatment response. Artificially created electrode models derived from virtual 10-20 EEG measurements are typically included in these models as current injection and removal sites. The present study directly compares current flow models generated via artificially placed electrodes ("artificial" electrode models) against those generated using real electrodes acquired from structural MRI scans ("real" electrode models) of older adults.

Individualized tDCS modeling predicts functional connectivity changes within the working memory network in older adults.

Background: Working memory decline has been associated with normal aging. The frontal brain structure responsible for this decline is primarily located in the prefrontal cortex (PFC). Our previous neuroimaging study demonstrated a significant change in functional connectivity between the left dorsolateral PFC (DLPFC) and left ventrolateral PFC (VLPFC) when applying 2 mA tDCS in MRI scanner during an N-Back task.