Aging-Dependent Genetic Effects Associated to ADHD Predict Longitudinal Changes of Ventricular Volumes in Adulthood

Third Ventricle Volume Predicts Functional Outcome in Chronic Subdural Hematoma

OBJECTIVES

There is a lack of evidence demonstrating the utility of computed tomography (CT) to predict chronic subdural hematoma (CSDH) clinical outcomes. We aim to analyze the role of tomographic volumetric analysis in patients with CSDH.

METHODS

We performed a retrospective study of patients undergoing burr-hole craniostomy (BHC) for CSDH over five years at a tertiary care center. Degree of midline shift, radiographic density, subdural hematoma volume, acute blood volume, and third ventricle (3VV) and fourth ventricle (4VV) volume were estimated using semiautomatic segmentation of preoperative CT. Postoperative functional outcome was measured by two endpoints: National Institute of Health Stroke Scale (NIHSS) at discharge and short-term modified Rankin Scale (mRS) at 6-week follow-up. Univariate and multivariate analyses were performed using nonparametric tests. Discriminative capacity and optimal thresholds of independent variables were calculated by means of receiving-operative curves (ROC).

RESULTS

A total of 79 patients were included for analysis with a median age of 78.5 years. Greater preoperative 3VV independently correlated with poor discharge NIHSS (p = .01) and short-term mRS (p = .03). A cutoff value of 0.545 mL demonstrated the highest sensitivity (77.1%) and specificity (88.8%) with an odds ratio for an mRS functional dependence of 9.29 (p = .001).

CONCLUSIONS

Greater preoperative tomographic 3VV independently prognosticates poor discharge NIHSS and 6-week mRS. A threshold 3VV of 0.545 mL can be used to identify patients at higher risk of being dependent at first protocolized follow-up.

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.