Assessing an electroencephalography (EEG) biomarker of response to transcranial magnetic stimulation for major depression
评估重度抑郁症对经颅磁刺激反应的脑电图 (EEG) 生物标志物
基本信息
- 批准号:9933192
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-10-01 至 2024-09-30
- 项目状态:已结题
- 来源:
- 关键词:Antidepressive AgentsAntidepressive AgentsAreaAreaBiological MarkersBiological MarkersBrainBrainBrain regionBrain regionClinicalClinicalData SetDeep Brain StimulationDeep Brain StimulationDevicesDevicesDisease remissionDisease remissionElectroencephalographyElectroencephalographyElectromagneticsElectromagneticsEnrollmentEnrollmentFutureFutureGoalsGoalsGrantGrantInfrastructureInfrastructureInterventionInterventionLeftLeftMachine LearningMachine LearningMajor Depressive DisorderMajor Depressive DisorderMeasuresMeasuresMental DepressionMental DepressionMethodsMethodsPatientsPatientsPatternPatternPharmaceutical PreparationsPharmaceutical PreparationsPost-Traumatic Stress DisordersPost-Traumatic Stress DisordersPrefrontal CortexPrefrontal CortexPsychotherapyPsychotherapyROC CurveROC CurveResearchResearchResearch InfrastructureResearch InfrastructureResearch PersonnelResearch PersonnelRestRestSamplingSamplingSeveritiesSeveritiesSiteSiteTestingTestingTimeTimeTranscranial magnetic stimulationTranscranial magnetic stimulationVeteransVeteransWorkWorkbasebasebrain electrical activitybrain electrical activitycostcostdisabilitydisabilityeffective therapyeffective therapyevidence baseevidence baseexpectationexpectationfunctional restorationfunctional restorationinfrastructure developmentinfrastructure developmentinventory of depressive symptomatologyinventory of depressive symptomatologylarge datasetsmood regulationmood regulationmortalitymortalitynetwork dysfunctionnetwork dysfunctionneural circuitneural circuitneural networkneural networknovelnovelnovel strategiesnovel strategiespersonalized medicinepersonalized medicinepotential biomarkerpotential biomarkerpredicting responsepredicting responsepredictive markerpredictive markerprogramsprogramsresponseresponseresponse biomarkerresponse biomarkertherapy developmenttherapy developmenttreatment planningtreatment planningtreatment responsetreatment responsetreatment-resistant depressiontreatment-resistant depression
项目摘要
Major Depressive Disorder (MDD) is highly prevalent among Veterans and associated with significant cost,
disability and mortality. Although evidence-based medications and psychotherapies are available to treat MDD,
full and sustained remission is uncommon. Transcranial magnetic stimulation (TMS) is an FDA-cleared
intervention that offers a novel strategy for treating patients with treatment-resistant depression (TRD). TMS is
available within the VA through the VA Clinical TMS Pilot Program. TMS is based in a neural circuit paradigm
of MDD positing that stimulation at a key node (e.g., dorsolateral prefrontal cortex) can restore function within a
network of brain regions involved in mood regulation. Although a substantial number of TRD patients respond
well to TMS, many do not. This suggests some patients have a form of neural network dysfunction that is more
amenable to TMS. Resting electroencephalography (EEG) provides a safe, convenient and reliable way to
measure focal brain electrical activity and neural network function. Prior studies have identified EEG markers
associated with response to antidepressant medications, but limited research has been conducted to identify
EEG markers predictive of an antidepressant response with TMS. This is a critical gap, since TMS likely
operates via direct modulation of neural network activity, such that baseline differences in neural
network function should help identify which patients are more or less likely to respond to TMS.
Investigators in our group have identified putative predictive EEG-based biomarkers for response to TMS for
TRD. One of these (differential patterns of gamma oscillations) may specifically predict response to 10 Hz TMS
applied to the left dorsolateral prefrontal cortex, the type of TMS received by >80% of Veterans receiving TMS
in the TMS Pilot Program. Another (changes in theta cordance early in the course of treatment) may predict
eventual response to TMS; this potential biomarker was identified by Dr. Andrew Leuchter (a consultant on this
grant) and has been shown to also predict response to antidepressant medications and deep brain stimulation
for TRD. By adding baseline (pretreatment) and weekly resting EEG assessment to the VA National
Clinical TMS Pilot Program, the goals of this study are to: (1) test a potential response biomarker
measured at baseline in a large sample of Veterans receiving TMS to treat depression (N=400); (2)
assess whether a second putative biomarker (early changes in theta cordance during treatment)
predict eventual response to TMS; (3) leverage this large sample to identify other potential biomarkers
(such as markers of early versus late response to TMS, markers of response to other TMS parameters
(e.g., 5 Hz, 1 Hz or theta burst TMS), and markers of change with treatment that may speak to
mechanism); and (3) create an infrastructure to rapidly identify and test additional EEG-based
biomarkers of treatment response in patients with depression and other psychiatric conditions
relevant to the VA, such as PTSD and/or TBI. The infrastructure created will initially consist of the five sites
that form the core of this study group, then expand to include a total of 10 sites over the course of the project. It
is hoped that this infrastructure will continue to expand over time to include as many of the sites in the TMS
Pilot Program as possible. This study, and the infrastructure created, will be of high value to Veterans by taking
an important step towards a personalized medicine approach to the use of TMS for TRD.
重度抑郁症(MDD)在退伍军人中非常普遍,并且与巨大的成本相关,
项目成果
期刊论文数量(0)
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Amit Etkin其他文献
Amit Etkin的其他文献
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{{ truncateString('Amit Etkin', 18)}}的其他基金
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
验证基于机器学习的脑电图治疗抑郁症生物标志物
- 批准号:
10009501 - 财政年份:2020
- 资助金额:
-- - 项目类别:
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
验证基于机器学习的脑电图治疗抑郁症生物标志物
- 批准号:
10116492 - 财政年份:2020
- 资助金额:
-- - 项目类别:
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
验证基于机器学习的脑电图治疗抑郁症生物标志物
- 批准号:
10366060 - 财政年份:2020
- 资助金额:
-- - 项目类别:
A "Circuits-First" Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
- 批准号:
10214488 - 财政年份:2019
- 资助金额:
-- - 项目类别:
A "Circuits-First" Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
- 批准号:
10000142 - 财政年份:2019
- 资助金额:
-- - 项目类别:
A "Circuits-First" Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
- 批准号:
10019435 - 财政年份:2019
- 资助金额:
-- - 项目类别:
A Circuit Approach to Mechanisms and Predictors of Topiramate Response
托吡酯反应机制和预测因子的电路方法
- 批准号:
10473684 - 财政年份:2018
- 资助金额:
-- - 项目类别:
A Circuit Approach to Mechanisms and Predictors of Topiramate Response
托吡酯反应机制和预测因子的电路方法
- 批准号:
10237286 - 财政年份:2018
- 资助金额:
-- - 项目类别:
A “Circuits-First” Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
- 批准号:
9552929 - 财政年份:2017
- 资助金额:
-- - 项目类别:
A “Circuits-First” Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
- 批准号:
9339858 - 财政年份:2017
- 资助金额:
-- - 项目类别:














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