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| ID | Type | Description | Link |
|---|---|---|---|
| R01MH123770 | U.S. NIH Grant/Contract | View source | |
| DP2MH126378 | U.S. NIH Grant/Contract | View source | |
| R61MH135407 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| University of California, San Francisco | OTHER |
| National Institute of Mental Health (NIMH) | NIH |
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Neuropsychiatric disorders are a leading cause of disability worldwide with depressive disorders being one of the most disabling among them. Also, millions of patients do not respond to current medications or psychotherapy, which makes it critical to find an alternative therapy. Applying electrical stimulation at various brain targets has shown promise but there is a critical need to improve efficacy.
Given inter- and intra-subject variabilities in neuropsychiatric disorders, this study aims to enable personalizing the stimulation therapy via i) tracking a patient's own symptoms based on their neural activity, and ii) a model of how their neural activity responds to stimulation therapy. The study will develop the modeling elements needed to realize a model-based personalized system for electrical brain stimulation to achieve this aim.
The study will provide proof-of-concept demonstration in epilepsy patients who already have intracranial electroencephalography (iEEG) electrodes implanted for their standard clinical monitoring unrelated to this study, and who consent to being part of the study.
The investigators will conduct the study for each subject during their stay in the epilepsy monitoring unit (EMU), which is dictated purely based on their standard clinical needs unrelated to this study. iEEG will be recorded from each patient throughout their stay in the EMU, during which the self-reports from them will be also intermittently collected using validated questionnaires that relate to depression symptoms.
The investigators will build subject-specific decoders that can track these depression symptoms from iEEG activity. The investigators will also apply electrical stimulation to learn a subject-specific input-output model that predicts the iEEG response to ongoing stimulation.
Successful completion of this study will help enable precisely-tailored deep brain stimulation therapies across diverse conditions and have a broad public health impact.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| model-based electrical brain stimulation | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| model-based electrical brain stimulation | Other | Electrical pulse train stimulation delivered to medication refractory epilepsy patients with electrodes already implanted based on clinical criteria for standard monitoring unrelated to this study. The delivery of the electrical brain stimulation can be guided by neural biomarkers of symptom levels computed from ongoing neural activity and by input-output models of neural response to stimulation therapy. The parameters of electrical stimulation will be constrained to be within clinically safe ranges. |
| Measure | Description | Time Frame |
|---|---|---|
| Decoded depression symptom ratings based on neural activity | A personalized decoder is trained for each patient using the recorded neural activity and self-reports. Then this decoder is used to estimate the biomarker purely from neural activity; that is, based on neural activity, it will return the estimation of depression symptom ratings (HAMD-6 or VAS self-reports) | 5-10 days |
| Measure | Description | Time Frame |
|---|---|---|
| Hamilton Depression Rating (HAMD-6) self-reports | Hamilton Depression Rating (HAMD-6) is a widely used questionnaire that measures depressive state severity and intervention response. It can range from 0 to 22, with 22 corresponding to the worst depression symptom. Self-reports are obtained intermittently from the patient. | 5-10 days |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Maryam M Shanechi, PhD | Contact | 213-740-1377 | shanechi@usc.edu | |
| HyeongChan Jo, PhD | Contact | hj_380@usc.edu |
| Name | Affiliation | Role |
|---|---|---|
| Maryam M Shanechi, PhD | University of Southern California | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Southern California | Recruiting | Los Angeles | California | 90089 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 23993280 | Background | Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, Charlson FJ, Norman RE, Flaxman AD, Johns N, Burstein R, Murray CJ, Vos T. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet. 2013 Nov 9;382(9904):1575-86. doi: 10.1016/S0140-6736(13)61611-6. Epub 2013 Aug 29. | |
| 17074942 | Background | Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, Niederehe G, Thase ME, Lavori PW, Lebowitz BD, McGrath PJ, Rosenbaum JF, Sackeim HA, Kupfer DJ, Luther J, Fava M. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006 Nov;163(11):1905-17. doi: 10.1176/ajp.2006.163.11.1905. |
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Upon completion of the study and after publication, fully de-identified IPD can be made available for research purposes.
The sharing will happen upon completion of the study and after publication.
Data will be accessed through data publishing platforms such as the NIH/NIMH Data Archive (NDA).
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| ID | Term |
|---|---|
| D003863 | Depression |
| ID | Term |
|---|---|
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |
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In each patient, the investigators will test the decoders of the symptom level and the input-output models of the neural response to stimulation therapy.
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|
| Visual Analog Scale (VAS) self-reports |
Visual Analog Scale (VAS) is a fast self-report validated against the Hamilton scale. It indicates the symptom level along a continuous line from none to worst symptom level. Self-reports are obtained intermittently from the patient. |
| 5-10 days |
| University of California, San Francisco | Recruiting | San Francisco | California | 94143 | United States |
|
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