Joint structural-and-functional MRI analysis for predicting electroconvulsive therapy response in major depressive disorder
Joint structural-and-functional MRI analysis for predicting electroconvulsive therapy response in major depressive disorder
批准号:
10225993
负责人:
Lipeng Ning
金额:
$18.42万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-01 至 2024-08-31
关键词:
Adverse effectsAdvisory CommitteesAlgorithmsAnatomyAnteriorAxonBiologicalBiological FactorsBrainBrain regionClinicalCognitiveCorpus CallosumDataData SetDecision MakingDependenceDevelopmentDiffusionDiffusion Magnetic Resonance ImagingDimensionsDiscriminant AnalysisElectroconvulsive TherapyEtiologyFiberForcepFoundationsFrequenciesFunctional Magnetic Resonance ImagingFunctional disorderFutureGrantGuidelinesImageInterventionJointsKnowledgeMagnetic Resonance ImagingMajor Depressive DisorderMeasuresMental DepressionMentored Research Scientist Development AwardMentorsMethodsNeuroanatomyPathologicPathologic ProcessesPathway interactionsPatientsPatternPharmacotherapyPrediction of Response to TherapyProceduresProcessPropertyReportingResearchResearch Scientist AwardRestSeveritiesStructureSubgroupSystemTechniquesTrainingTraining ProgramsWorkbasebrain abnormalitiescomputer frameworkcomputerized toolsdensityelectric fieldexperienceimage guidedimprovedindexingindividual patientinsightlarge datasetslongitudinal datasetmethod developmentmultimodalityneuroimagingneuroregulationnovelnovel therapeuticspatient subsetspersonalized predictionsprognostic toolprospectiveregression algorithmrelating to nervous systemresponders and non-respondersresponsesimulationtooltractographytreatment optimizationtreatment responsetreatment strategytreatment-resistant depressionwhite matter
中文摘要
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英文摘要
Project Summary
Title: Joint structural-and-functional MRI analysis for predicting electroconvulsive therapy response in
major depressive disorder
Electroconvulsive therapy (ECT) is currently the most effective and fast-acting neuromodulation treatments for
major depressive disorder (MDD). Understanding the mechanism of action of ECT-response may be a fruitful
strategy to understand the pathophysiology of MDD. But ECT also frequently provokes cognitive adverse effects
and is often considered as more invasive than pharmacotherapy. Consequently, there is a critical need to
develop reliable prognostic tools for predicting individualized treatment response. The availability of such a
predictor could provide guidelines for the clinician to optimize treatment strategy for individual patient and to
support patient's decision-making process. In Aim 1 of this project, we propose to develop a computational
framework to integrate novel diffusion MRI (dMRI) and resting-state functional MRI (rsfMRI) measures in order
to assess the properties of structural and functional connectivity of brain networks. In Aim 2, we will apply the
joint analysis of dMRI and rsfMRI to two large datasets with a total of 260 MDD patients and 170 controls to
identify depression-related abnormal connections in brain networks. Our approach could provide a novel
probabilistic MDD network that reflects heterogeneous brain abnormalities in different biotypes of MDD. In Aim
3, we will focus our analysis on a subgroup of 120 patients treated by ECT. Specifically, we will use the joint
structural-and-functional analysis to identify ECT-induced changes in brain connections for responders and non-
responders, respectively. Moreover, we will analyze the correlation between changes in brain connections with
changes in clinical MDD severities measures, and also with subject-specific E-field stimulated by ECT. Results
from this project will support future prospective trials aiming to improve therapeutic response using individualized
image-guided ECT in MDD. The training program provided by this K01 Mentored Research Scientist Award
provides the critical knowledge and experience to the PI for him to complete the proposed project. Based his
strong foundation in multimodal MRI analysis techniques, the PI seeks to obtain focused training on
neuroanatomy, clinical aspects of depression and ECT, under the guidance of experienced experts from his
mentoring team and advisory committee. In conclusion, this K01 Award will provide the PI with the training and
research background to conduct independent research that integrates novel MRI techniques and E-field
simulation approaches to develop optimal neuromodulation therapies for individual patients with MDD.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Real-time visualization and precision targeting in transcranial magnetic stimulation
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批准号:10195450
-
项目类别:
-
资助金额:$27.73万
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财政年份:2021
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负责人:Lipeng Ning
-
依托单位:
Real-time visualization and precision targeting in transcranial magnetic stimulation
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批准号:10330032
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项目类别:
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资助金额:$21.57万
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财政年份:2021
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负责人:Lipeng Ning
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依托单位:
Joint structural-and-functional MRI analysis for predicting electroconvulsive therapy response in major depressive disorder
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批准号:10471260
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项目类别:
-
资助金额:$18.42万
-
财政年份:2019
-
负责人:Lipeng Ning
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依托单位:
Multimodal brain-connectivity biomarkers for profiling heterogeneity in early psychosis
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批准号:9789955
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项目类别:
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资助金额:$22.38万
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财政年份:2018
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负责人:Lipeng Ning
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依托单位:
海外基金