Explore in vivo functional connectivity at cellular resolution in drugged brains
Explore in vivo functional connectivity at cellular resolution in drugged brains
批准号:
9404719
负责人:
Su Guo
金额:
$19.81万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-07-01 至 2019-06-30
关键词:
AcuteAnimal ModelBehaviorBehavioralBrainBrain DiseasesCalciumCellsCharacteristicsChronicCommunitiesComputational algorithmComputer AnalysisDataData AnalysesDiagnosisDiseaseDrug abuseDrug effect disorderEventExhibitsExposure toFertilizationFoodFunctional Magnetic Resonance ImagingFunctional disorderFutureGenetic ModelsGenomicsHumanImageImage AnalysisImaging technologyKnowledgeMapsMeasuresMolecularMorphineMotivationMusNeuronsOpiatesPathway AnalysisPharmaceutical PreparationsPreclinical Drug EvaluationPredispositionPublic HealthRelapseReporterResearchResolutionRestSelf AdministrationSubstance of AbuseSystemTechnologyTestingZebrafishaddictiondrug of abusein vivoin vivo imaginginnovationinsightneural circuitneurophysiologypredictive signaturepreferencerelating to nervous systemsmall molecule
中文摘要
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英文摘要
PROJECT SUMMARY
Addiction is a chronic relapsing brain disorder that is characterized by compulsive self-administration of abused
substances despite their negative consequences. Significant progress has been made in elucidating the
molecules and behavioral effects underlying each major drug of abuse. Given that addiction is a disease of
interconnected brain networks, examination of network interactions is critical for understanding addiction-
related dysfunctions. Resting-state functional connectivity (rsFC), which measures correlations among distinct
neurophysiological events in human fMRI studies, has provided insights, but the lack of cellular resolution and
inability to carry out experimental perturbation in humans precludes further cause-effect relationship studies.
At present, network level analyses are scarce in animal models, resulting in a significant knowledge gap
between molecular actions of drugs of abuse and their effects on behavior.
In this exploratory application, we aim to uncover how drugs of abuse alter rsFC at systems levels with
cellular resolution, through employing brain-wide calcium imaging and computational analyses in larval
zebrafish. As a vertebrate genetic model organism, zebrafish shares considerable neuroanatomical and
genomic similarity with humans. Larval zebrafish, with a transparent brain of ~100,000 neurons (as compared
to ~75 million in the mouse, and ~1 billion in the human brain), is particularly suitable for single-cell resolution
analysis of neural circuit activity in vivo. We have established brain-wide calcium imaging and computational
data analysis platforms to characterize rsFC at single-cell resolution in larval zebrafish brain. We wish to to test
whether single or repeated exposure to morphine will significantly alter rsFC.
Impact: If successful, this project will establish a new paradigm to uncover, at systems level and with cellular
resolution, how addictive substances alter brain states. Future efforts can expand to all major substances of
abuse, which will potentially reveal distinct functional connectivity signatures in acutely or chronically drugged
brains. It is also possible to use the platform to examine genetically modified brains and perform small
molecule drug screens. We will make technologies including image acquisition, processing, and computational
algorithms available to the broad research community.
Suitability for CEBRA: (1) This project will develop a new platform to examine the in vivo effects of addictive
substances at systems level and with cellular resolution. Such advances are critical to bridge the gap between
molecules and behavioral effects of drugs of abuse. At the moment, there are scant precedents or preliminary
data regarding this topic, however, if confirmed, it would have a substantial impact on current approaches to
understand drugs of abuse. (2) This project will develop innovative technological platforms for neural activity
imaging, including image acquisition, processing, and computational algorithms for data analyses. This will
have promising applicability to drug abuse research and beyond.
期刊论文(0)
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科研奖励(0)
会议论文
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海外基金