Prospective memory after moderate-to-severe traumatic brain injury: a multinomial modeling approach.

PROSPECTIVE MEMORY FOLLOWING MODERATE TO SEVERE TRAUMATIC BRAIN INJURY: A FORMAL MULTINOMIAL MODELING APPROACH By SHITAL PRABODH PAVAWALLA A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Psychology

Effect of Mild Brain Traumatic Injury on Intelligence and memory Function in Motorcycle Riders

Introduction: The most common causes of traumatic brain injury are vehicle crashes, including motorcycles, which lead to long-term disabilities. The purpose of this study was to investigate the effect of mild brain trauma on intelligence and memory function in motorcycle riders suffering from mild tumor injury.   Materials & Methods: In this prospective cohort study, intelligence and memory fu...

Cognitive Patterns of Coma Patients Induced by Severe Traumatic Brain Injury in Working Memory

Background and Aim: Coma caused by severe traumatic brain injury, leads to cognitive function disorder, the most frequently affects being on the working memory. Because of the importance and direct impact of working memory on the quality of human life, especially in military skills that require active memory and high attentional power; the present study aimed to investigate the cognitive patter...

Therapeutic effects of ellagic acid on memory, hippocampus electrophysiology deficits, and elevated TNF-α level in brain due to experimental traumatic brain injury

Objective(s): Cognitive defects such as learning and memory impairment are amongst the most repetitious sequelae after sever and moderate traumatic brain injury (TBI). It was suggested that ellagic acid (EA), an innate phenol product, display neuroprotective properties against oxidative and inflammatory damages after brain injury. The object of the current study was therapeutic properties of EA...

Prediction of mental disorders after Mild Traumatic Brain Injury: principle component Approach

Introduction: In Processes Modeling, when there is relatively a high correlation between covariates, multicollinearity is created, and it leads to reduction in model's efficiency. In this study, by using principle component analysis, modification of the effect of multicolinearity in Artificial Neural Network (ANN) and Logistic Regression (LR) has been studied. Also, the effect of multicolineari...