Levetiracetam and pregabalin for antiepileptic monotherapy in patients with primary brain tumors. A phase II randomized study.

BACKGROUND: In patients with brain tumors, the choice of antiepileptic medication is guided by tolerability and pharmacokinetic interactions. This study investigated the effectiveness of levetiracetam (LEV) and pregabalin (PGB), 2 non-enzyme-inducing agents, in this setting. METHODS: In this pragmatic, randomized, unblinded phase II trial (NCT00629889), patients with primary brain tumors and epilepsy were titrated to a monotherapy of LEV or PGB. Efficacy and tolerability were assessed using structured questionnaires. The primary composite endpoint was the need to discontinue the study drug, add-on of a further antiepileptic treatment, or occurrence of at least 2 seizures with impaired consciousness during 1 year follow-up. RESULTS: Over 40 months, 25 patients were randomized to LEV, and 27 to PGB. Most were middle-aged men, with a high-grade tumor and at least one generalized convulsion. Mean daily doses were 1125 mg (LEV) and 294 mg (PGB). Retention rates were 59% in the LEV group, and 41% in the PGB group. The composite endpoint was reached in 9 LEV and 12 PGB patients-need to discontinue: side effects, 6 LEV, 3 PGB; lack of efficacy, 1 and 2; impaired oral administration, 0 and 2; add-on of another agent: 1 LEV, 4 PGB; and seizures impairing consciousness: 1 in each. Seven LEV and 5 PGB subjects died of tumor progression. CONCLUSIONS: This study shows that LEV and PGB represent valuable monotherapy options in this setting, with very good antiepileptic efficacy and an acceptable tolerability profile, and provides important data for the design of a phase III trial.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.