Biomedical subjects

Booil Jo

Publications and source records attributed to Booil Jo.

5 recordsLinked to original sources

PubMed2005

Sex differences in brain activation elicited by humor.

With recent investigation beginning to reveal the cortical and subcortical neuroanatomical correlates of humor appreciation, the present event-related functional MRI (fMRI) study was designed to elucidate sex-specific recruitment of these humor related networks. Twenty healthy subjects (10 females) underwent fMRI scanning while subjectively rating 70 verbal and nonverbal achromatic cartoons as funny or unfunny. Data were analyzed by comparing blood oxygenation-level-dependent signal activation during funny and unfunny stimuli. Males and females share an extensive humor-response strategy as indicated by recruitment of similar brain regions: both activate the temporal-occipital junction and temporal pole, structures implicated in semantic knowledge and juxtaposition, and the inferior frontal gyrus, likely to be involved in language processing. Females, however, activate the left prefrontal cortex more than males, suggesting a greater degree of executive processing and language-based decoding. Females also exhibit greater activation of mesolimbic regions, including the nucleus accumbens, implying greater reward network response and possibly less reward expectation. These results indicate sex-specific differences in neural response to humor with implications for sex-based disparities in the integration of cognition and emotion.

Adult↗

PubMed2005

COMT genotype predicts longitudinal cognitive decline and psychosis in 22q11.2 deletion syndrome.

Although schizophrenia is strongly hereditary, there are limited data regarding biological risk factors and pathophysiological processes. In this longitudinal study of adolescents with 22q11.2 deletion syndrome, we identified the catechol-O-methyltransferase low-activity allele (COMT(L)) as a risk factor for decline in prefrontal cortical volume and cognition, as well as for the consequent development of psychotic symptoms during adolescence. The 22q11.2 deletion syndrome is a promising model for identifying biomarkers related to the development of schizophrenia.

Adolescent↗

PubMed2002

Model misspecification sensitivity analysis in estimating causal effects of interventions with non-compliance.

Randomized trials often face complications in assessing the effect of treatment because of study participants' non-compliance. If compliance type is observed in both the treatment and control conditions, the causal effect of treatment can be estimated for a targeted subpopulation of interest based on compliance type. However, in practice, compliance type is not observed completely. Given this missing compliance information, the complier average causal effect (CACE) estimation approach provides a way to estimate differential effects of treatments by imposing the exclusion restriction for non-compliers. Under the exclusion restriction, the CACE approach estimates the effect of treatment assignment for compliers, but disallows the effect of treatment assignment for non-compliers. The exclusion restriction plays a key role in separating outcome distributions based on compliance type. However, the CACE estimate can be substantially biased if the assumption is violated. This study examines the bias mechanism in the estimation of CACE when the assumption of the exclusion restriction is violated. How covariate information affects the sensitivity of the CACE estimate to violation of the exclusion restriction assumption is also examined.

Algorithms↗

PubMed2002

Statistical power in randomized intervention studies with noncompliance.

This study examined various factors that affect statistical power in randomized intervention studies with noncompliance. On the basis of Monte Carlo simulations, this study demonstrates how statistical power changes depending on compliance rate, study design, outcome distributions, and covariate information. It also examines how these factors influence power in different methods of estimating intervention effects. Intent-to-treat analysis and complier average causal effect estimation are compared as 2 alternative ways of estimating intervention effects under noncompliance. The results of this investigation provide practical implications in designing and evaluating intervention studies taking into account noncompliance.

Humans↗

PubMed2002

General growth mixture modeling for randomized preventive interventions.

This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finite-mixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as non-normative growth. The analysis identifies subgroup membership and allows theory-based modeling of intervention effects in the different subgroups. An example is presented concerning a randomized intervention in Baltimore public schools aimed at reducing aggressive classroom behavior, where only students who were initially more aggressive showed benefits from the intervention.

Journal Article↗