Intracerebral delivery of antiseizure medications by microinvasive neural implants.

Focal epilepsy is a difficult disease to treat as two-thirds of patients will not respond to oral antiseizure medications (ASMs) or have severe off-target effects that lead to drug discontinuation. Current non-pharmaceutical treatment methods (resection or ablation) are underutilized due to the associated morbidities, invasive nature, and inaccessibility of seizure foci. Less invasive non-ablative modalities may potentially offer an alternative. Targeting the seizure focus in this way may avoid unassociated critical brain structures to preserve function and alleviate seizure burden. Here we report use of an implantable, miniaturized neural drug delivery system [Microinvasive neural implant infusion platform (MINI)] to administer antiseizure medications (ASMs) directly to the seizure focus in a mouse model of temporal lobe epilepsy. We examined the effect local delivery of phenobarbital (PB) and valproate (VPA) had on focal seizures, as well as adverse effects, and compared this to systemic delivery. We show that local delivery of PB and VPA using our chronic implants significantly reduced focal seizures at all doses given. Furthermore, we show that local delivery of these compounds resulted in no adverse effects to motor function, whereas systemic delivery resulted in significant motor impairment. The results of this study demonstrate the potential of ASM micro dosing to the epileptic focus as a treatment option for people with drug resistant epilepsy. This technology could also be applied to a variety of disease states, enabling a deeper understanding of focal drug delivery in the treatment of neurological disorders.

An electroencephalographic signature predicts antidepressant response in major depression.

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.