Validation of the Subaxial Cervical Spine Injury Classification score in children: a single-institution experience at a level 1 pediatric trauma center.

Objective: The Subaxial Cervical Spine Injury Classification (SLIC) score has not been previously validated for a pediatric population. The authors compared the SLIC treatment recommendations for pediatric subaxial cervical spine trauma with real-world pediatric spine surgery practice.

Methods: A retrospective cohort study at a pediatric level 1 trauma center was conducted in patients < 18 years of age evaluated for trauma from 2012 to 2021.

Assessing survival in non-small cell lung cancer brain metastases after stereotactic radiosurgery: before and after the start of the targetable mutation era.

Purpose: Targeted treatment options for non-small cell lung cancer (NSCLC) brain metastases (BMs) may be combined with stereotactic radiosurgery (SRS) to optimize survival. We assessed patient outcomes after SRS for NSCLC BMs, identifying survival trajectories associated with targetable mutations.

Methods: In this retrospective time-dependent analysis, we analyzed median overall survival of patients who received ≥ 1 SRS courses for BM from NSCLC from 2001 to 2021.

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.

Objective: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving the diagnostic accuracy of machine learning classification models. We explored the potential of EEG-based FC during resting state and emotional processing, for diagnosing MDD using machine learning approach.

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets.

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as from resting state functional connectivity.