Reducing neural perseveration through closed loop real time fMRI neurofeedback to alleviate depressive symptoms
Reducing neural perseveration through closed loop real time fMRI neurofeedback to alleviate depressive symptoms
批准号:
10356604
负责人:
YVETTE I SHELINE
金额:
$82.2万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-12-15 至 2023-11-30
关键词:
AgeAttentionBehavioralBehavioral SciencesBrainClassificationClinicalDepressed moodDoseEyeEye MovementsFaceFeedbackFunctional Magnetic Resonance ImagingGenomicsHospitalsImageIndividualInterventionMajor Depressive DisorderMeasuresMemoryMental DepressionNational Institute of Mental HealthNeurosciencesOutcomeParticipantPatientsPatternPersonsPhasePilot ProjectsProbabilityProceduresRandomizedRandomized Controlled TrialsResearchSeveritiesSourceStimulusTestingTimeTrainingattentional biasbasecloud basedcognitive controldepressive symptomsdesigndisabilityefficacy testingexperiencefollow-upgazeimprovedinterestneurofeedbackneuromechanismrelating to nervous systemresearch facilityresponseruminationselective attentionsuccesstime usetreatment effecttreatment strategy
中文摘要
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英文摘要
ABSTRACT
Current treatment strategies for major depression, the leading cause of disability worldwide, leave more than
half of patients with no meaningful treatment benefit. This R61/R33 proposal was developed in keeping with
NIMH Strategic Objective 3.1 to “Develop new interventions based on discoveries in genomics, neuroscience
and behavioral science.” We will test the efficacy of a new psychotherapeutic strategy, the first real-time fMRI
neurofeedback therapy to use cloud-based pattern classification to decode the patient’s attentional state and
dynamically modulate task stimuli (in a “closed loop”) based on this state, rather than the standard approach of
conveying feedback through a separate gauge that tracks activation in a localized region of interest.
The overall objective of this R61/R33 is to test whether closed-loop real-time fMRI neurofeedback that
specifically targets our hypothesized attentional mechanism of depression (i.e., neural perseveration of negative
states) reduces depression severity. This study will be the first dose-finding test of real-time fMRI effect on
negative attention bias.
During the R61 Phase 60 participants with MDD ages 18-65 years will be randomly assigned to treatment with
active neurofeedback (n=30) vs sham feedback (n=30). We have three aims: 1) Establish neural target
engagement using pre-post change in neural perseveration of negative attentional states in active vs sham
neurofeedback; 2) Determine the lowest “dose” of training necessary to reduce neural perseveration of negative
states; 3) (Exploratory) Establish behavioral target engagement using perseveration on negative images in an
eye gaze task comparing active vs sham neurofeedback.
Go/No-Go Criteria for R61: There will be at least a medium effect size (Cohen’s d ≥ 0.4) in neural perseveration
reduction comparing real to sham NF. Our pilot study (Mennen et al 20) found an effect size of 0.83. Therefore
this criterion is both clinically meaningful and realistic and will demonstrate target engagement.
During the R33 Phase 80 participants with MDD ages 18-65 will be randomly assigned to treatment with active
neurofeedback (n=40) vs sham feedback (n=40). We have three aims: 1) To conduct a randomized controlled
trial (RCT) to compare the effect of real-time neurofeedback vs. sham on depression outcome; 2) To determine
the relationship between the markers of neural perseveration established in the R61 phase and the reduction in
depressive symptoms; and 3) To determine the durability of the treatment effect by comparing MADRS scores
at 3 months followup between those who received real as compared to sham NF.
Impact. This project will establish real-time fMRI neurofeedback as a means of reducing neural perseveration of
negative states as a treatment for MDD. Results from this line of research will inform feedback strategies and
improve understanding of neural mechanisms underlying negative attention and MDD. Our cloud based platform
would be readily scalable and allow dissemination to thousands of hospital and research facilities.
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