Hierarchical linear modeling (HLM) of longitudinal brain structural and cognitive changes in alcohol-dependent individuals during sobriety
Drug and Alcohol Dependence Article in Press 17 July 2007




Hierarchical linear modeling (HLM) can reveal complex relationships between longitudinal outcome measures and their covariates under proper consideration of potentially unequal error variances.

We demonstrate the application of HLM to the study of magnetic resonance imaging (MRI)-derived brain volume changes and cognitive changes in abstinent alcohol-dependent individuals as a function of smoking status, smoking severity, and drinking quantities.

Different hierarchical linear models with unique model structures are presented and discussed. The results show that smaller brain volumes at baseline predict faster brain volume gains, which were also related to greater smoking and drinking severities.

Over 7 months of abstinence from alcohol, sALC compared to nsALC showed less improvements in visuospatial learning and memory despite larger brain volume gains and ventricular shrinkage.

Different and unique hierarchical linear models allow assessments of the complex relationships among outcome measures of longitudinal data sets. These HLM applications suggest that chronic cigarette smoking modulates the temporal dynamics of brain structural and cognitive changes in alcoholics during prolonged sobriety.

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