Distributed networks underlying depression in epilepsy: a computational circuit-based approach to biomarker development

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PROJECT SUMMARY/ABSTRACT Adult patients with epilepsy have an increased prevalence of major depression and other psychiatric co- morbidities. Depression in epilepsy is associated with worse outcome and quality of life. However, it continues to be underdiagnosed and untreated and further attention to this comorbidity is critical. My career goal is to become an academic neuroscientist and clinician focused on understanding the neural networks underlying co- morbid mood and anxiety spectrum disorders in patients with epilepsy. Specific brain circuits may underlie depression and be commonly affected by different precipitants (i.e. stress, inflammation, epilepsy). In this proposal, our model is that a set of neural features across these brain circuits will be shared across many patients with co-morbid depression. Evidence for a strong relationship between epilepsy and depression includes the presence of depression symptoms before, during, after, and in between seizures, evidence of cases of concurrent onset of depression and epilepsy, an increased incidence of interictal depression when limbic structures are involved in seizure occurrence, and evidence that depression scores may be lower after surgical resection for medication refractory epilepsy. Intracranial electroencephalography (iEEG) captured during the pre-surgical recording period offers a particularly promising method to study depression networks in adult epilepsy, offering both high temporal resolution and spatial precision. Despite the enormous potential of iEEG, there are no studies to date that examine the neurophysiological signatures of network dysfunction in mood and anxiety disorders in patients with epilepsy. Such studies are critical in order to better understand the etiology of co-morbid depression and could lead to novel personalized therapies. In our pilot work, we identify a set of power spectral measures within a corticolimbic circuit that appear to be linked to depression and are, therefore, a potential biomarker of co-morbid depression. We also found evidence that supports the basis for testing whether neural features will predict treatment outcome. This proposal builds on these preliminary findings to validate our model and test the hypothesis that a set of neural features is shared across some subjects with MDD in epilepsy and is detectable with machine-learning techniques applied to interictal iEEG recordings. Aim 1 demonstrates the relationship between resting state neural circuit abnormality and depression. Aim 2 tests whether removing the dysfunctional region of the circuit improves depression and whether the presurgical resting state iEEG predicts that improvement. To address these research goals, I will need more rigorous training in computational neuroscience for complex datasets, advanced signal processing, and biostatistics. My training plan and carefully selected mentoring and advisory team across fields of psychiatry, neurosurgery, neurology and statistics will allow me to obtain the necessary experiences to become a fully independent investigator who brings the tools of computational approaches to the service of mental health research and novel personalized treatment paradigms in epilepsy.