Individualized Closed Loop TMS for Working Memory Enhancement
Individualized Closed Loop TMS for Working Memory Enhancement
批准号:
10204952
负责人:
Yong Fan
金额:
$72.36万
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 2024-06-30
关键词:
AgingAttention deficit hyperactivity disorderBase of the BrainBehavioralBrainBrain imagingBudgetsClinicalCodeCommunicationCommunitiesComputer softwareDataData SetDependenceDevice or Instrument DevelopmentDevicesDockingEffectivenessElectric StimulationEnsureEnvironmentEpilepsyFeedbackFrequenciesFunctional ImagingFunctional Magnetic Resonance ImagingImageImaging TechniquesIndividualInvestigationMRI ScansMajor Depressive DisorderMemory impairmentMental HealthMental disordersMethodsMood DisordersMovementNeuroanatomyNeurodegenerative DisordersNeurosciencesNoiseOutcomeParticipantPathway AnalysisPatientsPatternPattern RecognitionPerformancePersonsProtocols documentationReproducibilityResearchRestRunningSchizophreniaScientistSeizuresSeriesShort-Term MemorySignal TransductionSiteSleepSource CodeStressStructureTechniquesTestingTimeTranscranial magnetic stimulationTreatment outcomeVariantawakebasedesignhealth applicationimaging studyimplantable deviceimprovedmultidisciplinarymultimodalityneuropsychiatryneuroregulationnext generationnoninvasive brain stimulationnovelnovel strategiesopen sourceportabilityrecurrent neural networkrepetitive transcranial magnetic stimulationspatiotemporaltool
中文摘要
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英文摘要
ABSTRACT
The proposed project is designed to increase precision and responsiveness in transcranial magnetic
stimulation therapies across the neuropsychiatric spectrum and specifically in working memory deficits which
are common across a variety of neuropsychiatric conditions. Cutting edge functional imaging studies suggest
that using multiple types of imaging datasets yield more reliable estimates of brain network communication.
Our methods yield a combined resting and task fMRI functional network mapping individualized for each
participant that will allow precise identification of brain stimulation targets associated with optimal working
memory performance (Aim 1). To close the loop in designing TMS protocols that respond to an individual
person's brain activation state, we will also develop and test a real-time brain decoder to determine when
optimal working memory states are online (Aim 2). By iteratively testing excitatory neuromodulation
frequencies at this stimulation site and capturing the relative movement of brain states towards or away from
optimal working memory states, we will settle on the optimal frequency for augmenting working memory
performance in each individual (Aim 3). We will validate this approach by administering either the `best' or
`worst' (random assignment to each participant) neuromodulation protocol across several days then testing
working memory performance and brain activation in a final MRI scan session. The multi-modal based TMS
targeting and individualized frequency optimization techniques will be based on our findings and packaged into
a combined software suite in Docker containers made available to the scientific and clinical community at the
conclusion of this project (Aim 4).
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依托单位:
海外基金