Assessing change with the Automated Neuropsychological Assessment Metrics (ANAM): issues and challenges

Accurately documenting cognitive change is important, as neuropsychologists are routinely asked to determine cognitive change following disease progression or medical intervention. Computerized testing batteries, such as the Automated Neuropsychological Assessment Metrics (ANAM), are good tools for assessing change, because they allow for randomization of stimuli, creating near limitless alternate forms and reducing practice effects. The question remains, however, as to how best to determine reliable change in performance using ANAM. The current study compared the use of Reliable Change Index (RCI) and regression based methods (REG) calculated from 28 individuals with migraine. These methods then were applied to an independent sample of 25 individuals with migraine assessed with ANAM at baseline, headache, and following pharmacologic treatment. Traditional repeated measures analyses revealed declines in cognitive efficiency following migraine onset on two of four ANAM tasks and significant improvement on all ANAM tasks following treatment. Rates of deterioration and improvement did not significantly differ between RCI and REG methods, although were slightly different across the ANAM tasks used in this study. A combined ANAM score categorized the most individuals as demonstrating cognitive change, revealing that 60% of subjects declined in performance following headache and 84% improved following migraine treatment.