Tower of London test: a comparison between conventional statistic approach and modelling based on artificial neural network in differentiating fronto-temporal dementia from Alzheimer's disease

The early differentiation of Alzheimer's disease (AD) from frontotemporal dementia (FTD) may be difficult. The Tower of London (ToL), thought to assess executive functions such as planning and visuo-spatial working memory, could help in this purpose. Twentytwo Dementia Centers consecutively recruited patients with early FTD or AD. ToL performances of these groups were analyzed using both the conventional statistical approaches and the Artificial Neural Networks (ANNs) modelling. Ninety-four non aphasic FTD and 160 AD patients were recruited. ToL Accuracy Score (AS) significantly (p < 0.05) differentiated FTD from AD patients. However, the discriminant validity of AS checked by ROC curve analysis, yielded no significant results in terms of sensitivity and specificity (AUC 0.63). The performances of the 12 Success Subscores (SS) together with age, gender and schooling years were entered into advanced ANNs developed by Semeion Institute. The best ANNs were selected and submitted to ROC curves. The non-linear model was able to discriminate FTD from AD with an average AUC for 7 independent trials of 0.82. The use of hidden information contained in the different items of ToL and the non linear processing of the data through ANNs allows a high discrimination between FTD and AD in individual patients.     

Item analysis and differential item functioning of a brief conduct problem screen

Research has shown that boys display higher levels of childhood conduct problems than girls, and Black children display higher levels than White children, but few studies have tested for scalar equivalence of conduct problems across gender and race. The authors conducted a 2-parameter item response theory (IRT) model to examine item characteristics of the Authority Acceptance scale from the Teacher Observation of Classroom Adaptation-Revised (AA-TOCA-R; L. Larsson-Werthamer, S. G. Kellam, & L. Wheeler, 1991) in 8,820 kindergarten children and estimated the degree of differential item functioning (DIF) by gender and race/urban status. The mean level of latent conduct problems was best represented by behaviors such as being stubborn, breaking rules, and being disobedient, whereas breaking things and taking others' property best represented the construct at one standard deviation above the mean. DIF by gender was detected, such that at equivalent levels of latent conduct problems, males received more endorsements of overt behaviors from teachers, whereas females received more endorsements of nonphysical behaviors. Moreover, overt behaviors were better discriminators of latent conduct problems for males, and nonphysical behaviors were better discriminators of latent conduct problems for females. Differences across race/urban status were not found to be conceptually meaningful. The authors' analyses also suggest that the item scaling of the AA-TOCA-R may be best represented by 5e categories instead of 6. These findings provide support for the use of IRT modeling to examine item characteristics of conduct problem scales and DIF to test for scalar equivalence across diverse subpopulations.     

Double Jeopardy: Child and School Characteristics That Predict Aggressive-Disruptive Behavior in First Grade

High rates of aggressive-disruptive behavior exhibited by children during their initial years of elementary school increase their risk for significant behavioral adjustment problems with teachers and peers. The purpose of the present study was to examine the unique and combined contributions of child vulnerabilities and school context to the development of aggressive-disruptive student behavior during first grade. Parent ratings and child interviews assessed three child characteristics associated with risk for the development of aggressive behavior problems in elementary school (aggressive-disruptive behaviors at home, attention problems, and social cognitions) in a sample of 755 first-grade children in four demographically diverse American communities. Two school characteristics associated with student aggressive-disruptive behavior problems (low-quality classroom context, school poverty levels) were also assessed. Linear and multilevel analyses showed that both child and school characteristics made independent and cumulative contributions to the development of student aggressive-disruptive behavior at school. Although rates of student aggressive-disruptive behavior varied by gender and race, the predictive model generalized across all groups of children in the study.