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  • Title: Using multivariate data reduction to predict postsurgery memory decline in patients with mesial temporal lobe epilepsy.
    Author: St-Laurent M, McCormick C, Cohn M, Mišić B, Giannoylis I, McAndrews MP.
    Journal: Epilepsy Behav; 2014 Feb; 31():220-7. PubMed ID: 24210456.
    Abstract:
    Predicting postsurgery memory decline is crucial to clinical decision-making for individuals with mesial temporal lobe epilepsy (mTLE) who are candidates for temporal lobe excisions. Extensive neuropsychological testing is critical to assess risk, but the numerous test scores it produces can make deriving a formal prediction of cognitive change quite complex. In order to benefit from the information contained in comprehensive memory assessment, we used principal component analysis (PCA) to simplify neuropsychological test scores (presurgical and pre- to postsurgical change) obtained from a cohort of 56 patients with mTLE into a few easily interpretable latent components. We next performed discriminant analyses using presurgery latent components to categorize seizure laterality and then regression analyses to assess how well presurgery latent components could predict postsurgery memory decline. Finally, we validated the predictive power of these regression models in an independent sample of 18 patients with mTLE. Principal component analysis identified three significant latent components that reflected IQ, verbal memory, and visuospatial memory, respectively. Together, the presurgery verbal and visuospatial memory components classified 80% of patients with mTLE correctly according to their seizure laterality. Furthermore, the presurgery verbal memory component predicted postsurgery verbal memory decline, while the presurgery visuospatial memory component predicted visuospatial memory decline. These regression models also predicted postsurgery memory decline successfully in the independent cohort of patients with mTLE. Our results demonstrate the value of data reduction techniques in identifying cognitive metrics that can characterize laterality of damage and risk of postoperative decline.
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