Name as Many AEDs as You Can in One Minute: Antiepileptic Drugs and the Disruption of Verbal Fluency Networks

Commentary

Effect of Topiramate and Zonisamide on fMRI Cognitive Networks.

Wandschneider B, Burdett J, Townsend L, Hill A, Thompson PJ, Duncan JS, Koepp MJ. 2017;88:1165–1171.

OBJECTIVE: To investigate the effects of topiramate (TPM), zonisamide (ZNS), and levetiracetam (LEV) on cognitive network activations in patients with focal epilepsy using an fMRI language task. METHODS: In a retrospective, cross-sectional study, we identified patients from our clinical database of verbal fluency fMRI studies who were treated with either TPM (n = 32) or ZNS (n = 51). We matched 62 patients for clinical measures who took LEV but not TPM or ZNS. We entered antiepileptic comedications as nuisance variables and compared out-of-scanner psychometric measures for verbal fluency and working memory between groups. RESULTS: Out-of-scanner psychometric data showed overall poorer performance for TPM compared to ZNS and LEV and poorer working memory performance in ZNS-treated patients compared to LEV-treated patients. We found common fMRI effects in patients taking ZNS and TPM, with decreased activations in cognitive frontal and parietal lobe networks compared to those taking LEV. Impaired deactivation was seen only with TPM. CONCLUSIONS: Our findings suggest that TPM and ZNS are associated with similar dysfunctions of frontal and parietal cognitive networks, which are associated with impaired performance. TPM is also associated with impaired attenuation of language-associated deactivation. These studies imply medication-specific effects on the functional neuroanatomy of language and working memory networks. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in patients with focal epilepsy, TPM and ZNS compared to LEV lead to disruption of language and working memory networks.

Patients with medically refractory epilepsy, who by definition have unsuccessfully tried at least two antiepileptic drugs (AEDs), and often many more, start to anticipate the key questions to ask their physicians when a new AED is being considered. For most such patients, a question about potential cognitive adverse effects is near the top of the list. While individual responses to drugs are variable, practicing neurologists usually believe they have a sense of which AEDs are most likely to cause cognitive problems, and multiple studies have borne out clear differences: Among broad-spectrum AEDs, for example, topiramate (TPM) and, to a lesser extent, zonisamide (ZNS) are more likely to have negative cognitive effects than levetiracetam (LEV) or lamotrigine (1, 2). Two questions arise naturally from such findings: What is it about these AEDs that increases this risk? Is there a way to better predict the risk for any individual patient?

These are complicated issues that do not currently have easy answers, but the article by Wandschneider et al. is the kind of building block that could contribute to our ability to address these concerns in the future.

The authors retrospectively looked at 145 patients from their institution who had undergone fMRI studies as part of a presurgical evaluation and were taking either TPM, ZNS, or LEV (as part of a polytherapy regimen in most cases). They compared out-of-scanner psychometric results on several measures of cognitive performance (digit span, letter fluency, categorical fluency, and naming) among the groups of patients taking these different AEDs. They also compared patterns of blood oxygenation level-dependent fMRI activation in response to a covert verbal fluency task.

In brief, they found that patients on TPM performed more poorly than those on ZNS or LEV on three of the four cognitive measures, and patients on ZNS performed more poorly than those on LEV on the digit span measure. In addition, patients on either TPM or ZNS showed reduced functional activation in the frontal and parietal brain regions thought to be involved in language processes and working memory compared with patients on LEV, while patients on TPM also showed reduced deactivation in certain nodes of the default mode network.

The neuropsychological findings are largely confirmatory of what has been shown before (1–4), and similar language-based fMRI results in patients on TPM have also been demonstrated previously (5–7). But in the current report the investigators studied a larger sample size with rigorous analytical methods and used medically refractory patients on LEV as a control group, thus trying to mitigate the effect of uncontrolled epilepsy itself. The study has a number of limitations, as the authors acknowledge; among other things, most patients were on multiple AEDs, not just the one of interest, AED serum levels were not available for analysis, and the effects of epilepsy severity and/or seizure frequency were not addressed.

At this point it is worth considering what kinds of evidence we would need in the future to be able to answer the two questions posed originally.

1. What Is It About These AEDs That Increases the Risk of Cognitive Adverse Effects?

Certainly, fMRI studies provide suggestive evidence linking changes in brain activation to poor performance on cognitive tasks. But often the out-of-scanner measures are not identical to the fMRI paradigms, and even when they are, causality can never be assumed from an imaging association. In addition, interpretation of fMRI results can be particularly problematic when patients perform poorly on the task being imaged, since impaired task execution could result in altered neural activation rather than the other way around (8). In this regard, somewhat nonintuitively, differences between groups in their resting-state activation, or in their activation patterns when performing tasks in which they are not impaired, may actually be more illuminating. It would take a closer linkage between our understanding of molecular mechanisms of AED action and their effects on larger functional brain networks for us to feel more confident saying that we know why particular AEDs are more or less likely to lead to cognitive side effects.

2. Is There a Way to Better Predict the Risk for Any Individual Patient?

Here we encounter the well-established problem that even when there is a statistically significant difference in some metric between groups of patients, the actual ranges of results in the groups often overlap enough that the predictive power of knowing a single individual's result is low. That would likely be the case in attempting to use fMRI activation patterns as a predictor of cognitive adverse effect risks in patients with epilepsy, for example. There is already variability in clinically normal activation patterns with various language tasks in healthy individuals (9). And it is not clear in real clinical life whether imaging would identify the risk of cognitive adverse effects in a practically actionable time frame, that is, before the actual effect had already been definitively experienced by the patient anyway. Ultimately, it may be that predictive pharmacogenomic variables could play a role in answering this question more fully.

For now, at least, what we can say from results like those of Wandschneider and colleagues is that certain AEDs are more likely to be associated with poor performance on cognitive tasks, and that patients on these medications show distinctive alterations in brain network function when attempting to perform these tasks in the scanner. That is already a big step forward in our understanding of this area compared with what we knew when these drugs were first introduced, and we can look forward to even more refined information in the years to come.