Medication Refractory Epilepsy Patients With Electrodes Already Implanted Based on Clinical Criteria for Standard Monitoring
Conditions
Keywords
deep brain stimulation, model-based stimulation, neural biomarkers of depression, neurotechnology, neuropsychiatric disorders, depression, brain-machine interface
Brief summary
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.
Detailed description
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.
Interventions
OTHERmodel-based electrical brain stimulation
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.
Sponsors
University of California, San Francisco
National Institute of Mental Health (NIMH)
University of Southern California
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
BASIC_SCIENCE
Masking
NONE
Intervention model description
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patients being evaluated for surgical treatment of medication refractory epilepsy and brain tumors will be studied. ONLY patients with electrodes implanted based on clinical criteria to locate their seizure focus will be studied. Most patients are healthy adults, outside of their epilepsy and/or brain tumor. * Subjects \>= 18 are only included in this study. * All patients with the above conditions and with implanted electrode arrays who are willing to participate and able to cooperate and follow research instructions will be recruited. However, analysis of research recording data will focus on those subjects with an IQ \>= 80, with no impairments of reading, naming, or articulation (to minimize confounds such as abnormal language processing that may affect their self-reporting with the questionnaire), and with no cerebral pathology affecting the cortical regions from which recordings are made.
Exclusion criteria
* Subjects \< 18 years old will be excluded from this study due to the high concordance of developmental disorders (cognitive and language-related) in pediatric epilepsies. * There will be no involvement of special classes of subjects, such as fetuses, neonates, pregnant women, children, prisoners, institutionalized individuals, or others who may be considered vulnerable populations. * Patients who are unable to give informed consent due to a brain disorder will be excluded from the study, as it is very likely that they would be unable to carry out the tasks demanded by the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Decoded depression symptom ratings based on neural activity | 5-10 days | 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) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Hamilton Depression Rating (HAMD-6) self-reports | 5-10 days | 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. |
| Visual Analog Scale (VAS) self-reports | 5-10 days | 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. |
Countries
United States
Contacts
Primary ContactMaryam M Shanechi, PhD
Backup ContactHyeongChan Jo, PhD
Outcome results
None listed