2019 — 2020 |
Scangos, Katherine W |
K23Activity Code Description: To provide support for the career development of investigators who have made a commitment of focus their research endeavors on patient-oriented research. This mechanism provides support for a 3 year minimum up to 5 year period of supervised study and research for clinically trained professionals who have the potential to develop into productive, clinical investigators. |
Distributed Networks Underlying Depression in Epilepsy: a Computational Circuit-Based Approach to Biomarker Development @ University of California, San Francisco
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.
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0.939 |
2021 |
Scangos, Katherine W |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Deciphering Principles of Network Dynamics Underlying Depression Symptom Severity From Multi-Day Intracranial Recordings in Patients With Major Depression @ University of California, San Francisco
PROJECT SUMMARY/ABSTRACT Major depressive disorder (MDD) is common and causes significant disability world-wide. While typically responsive to medications and therapy, there remain a subset of patients who are treatment resistant. Novel approaches are critical to treat these patients. MDD is likely caused by dysfunction in distributed neural networks, a perspective consistent with the etiological and diagnostic heterogeneity of this disorder. While imaging and electroencephalography (EEG) have helped identify MDD circuitry, no consensus has been reached on the identification of diagnostic biomarkers. Furthermore, the dynamics of MDD circuitry in relation to symptom severity is unknown. Characterization of circuit signatures that define MDD symptom severity states and the extent to which these circuits are modifiable using electrical stimulation are critical for therapeutic advancement. Intracranial EEG (iEEG) offers a high spatial and temporal resolution method to study depression networks. For the first time, we have an unparalleled opportunity to study such circuits in MDD patients participating in a clinical trial of personalized responsive neurostimulation for treatment resistant depression (PRESIDIO). In stage 1 of this trial, participants are implanted with 160 electrodes from 10 sub-chronic intracranial leads across 10 brain sites for 10 days. The goal of this parent study stage is to optimize brain-site targeting for deep brain stimulation. In this proposal, we will leverage the opportunity to study MDD circuit principles from cortical and deep brain structures over a multi-day time period. In an ancillary study to this parent clinical trial, we propose a set of experiments that establish basic principles of network dynamics underlying MDD from direct neural recordings. This proposal is organized around the principal concept that brain circuit dysfunction is reflected in abnormal signatures of functional connectivity and rhythmic local-field activity. This concept is supported by our pilot work where we found evidence of distinct MDD networks characterized by functional connectivity and spectral activity. Furthermore, in the first parent trial participant we successfully mapped MDD circuits at the individual level and found that gamma power in the amygdala could successfully decode mood state (AUC = 86%). This proposal builds on these preliminary findings in two aims. In Aim 1, we will characterize state-dependent functional connectivity and spectral activity in relation to symptom severity. In Aim 2, we will examine the manner and time course in which targeted electrical stimulation acutely modifies circuits. Together, this research will yield the first characterization of connectivity and activity dynamics in MDD over a multi-day period from direct neural recordings. This rare insight into MDD circuity provided by this novel dataset establishes proof-of-concept principles for biomarker development and therapeutic target selection that could critically advance personalized MDD treatments.
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0.939 |