Explainable Machine Learning to Guide Prefrontal Brain Stimulation
可解释的机器学习指导前额脑刺激
基本信息
- 批准号:10367858
- 负责人:
- 金额:$ 83.57万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-07-15 至 2027-05-31
- 项目状态:未结题
- 来源:
- 关键词:AdoptionAnimal BehaviorAnimalsAreaArtificial IntelligenceAssociation LearningBedsBehaviorBiomedical EngineeringBiometryBrainClinicalCognitiveComplexComputer ModelsCuesDataDecision MakingElectrocorticogramEngineeringGenesGoalsGraphInterventionLeadLearningLightLong-Term EffectsMachine LearningMental disordersMethodsModelingNeuronal PlasticityNeuronsNeurosciencesOutcomePatternPerformancePersonalityPrefrontal CortexProcessPropertyProteinsResearch PersonnelResponse to stimulus physiologyRewardsRoleScienceShort-Term MemorySocial BehaviorSoftware ToolsStructureTestingTherapeuticTimeUpdateValidationVirusVisualWritingattentional controlbasecell typecognitive abilitycognitive functioncognitive taskdesignexecutive functionexperimental studyflexibilityfunctional plasticityimprovedin vivoinsightmachine learning modelnervous system disordernetwork modelsneural circuitneuroregulationnonhuman primatenovelopen sourceoptogeneticspublic health relevancerelating to nervous systemresponsestemtoolvisual stimulus
项目摘要
Project Summary
Brain stimulation has shown great therapeutic promise for a wide range of neurological and psychiatric disorders. In
addition to advanced engineering tools, successful implementation of brain stimulation requires a comprehensive un-
derstanding of how this treatment drives changes in network dynamics and connectivity at a large scale and across
multiple brain areas. It also requires the design of controllers that can relate stimulation effects to behavior and function.
To achieve these goals, we will develop novel explainable machine learning models for psychiatric brain stimulation.
To do so, we put forward three overarching goals. First, we aim to learn biologically plausible and flexible functional
connectivity models from electrocorticography (ECoG) data. Then, we plan to develop a computational model based
on a deep graph convolutional net to learn associations between ECoG data and network-scale connectivity. We will
then design a machine learning based guide for psychiatric brain stimulation. Finally, we will use our tools to under-
stand how the network evolves through time. To achieve these goals, the project brings together an interdisciplinary
team of investigators with unique expertise in artificial intelligence and machine learning, computational and theoretical
neuroscience, network science and biostatistics, bioengineering and brain stimulation experiments, and interventional
psychiatric and neural engineering. The team will lead experimental and computational efforts that will produce ad-
vanced explainable machine learning solutions informed by brain stimulation experiments and utilize these tools to
design more efficient and effective brain stimulation therapies.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Zaid Harchaoui其他文献
Zaid Harchaoui的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Zaid Harchaoui', 18)}}的其他基金
Explainable Machine Learning to Guide Prefrontal Brain Stimulation
可解释的机器学习指导前额脑刺激
- 批准号:
10666346 - 财政年份:2022
- 资助金额:
$ 83.57万 - 项目类别:
相似海外基金
Wireless CMOS device for observing real-time brain activity and animal behavior
用于观察实时大脑活动和动物行为的无线 CMOS 设备
- 批准号:
23K06786 - 财政年份:2023
- 资助金额:
$ 83.57万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Landscapes of fear in the Anthropocene: Linking predation risk and human disturbance to animal behavior and ecological outcomes
人类世的恐惧景观:将捕食风险和人类干扰与动物行为和生态结果联系起来
- 批准号:
RGPIN-2022-03096 - 财政年份:2022
- 资助金额:
$ 83.57万 - 项目类别:
Discovery Grants Program - Individual
The role of biological interactions in the evolution of animal behavior
生物相互作用在动物行为进化中的作用
- 批准号:
RGPIN-2019-06689 - 财政年份:2022
- 资助金额:
$ 83.57万 - 项目类别:
Discovery Grants Program - Individual
Development of Semi-Supervised Learning Method using Compressed Video for Real-Time Animal Behavior Analysis
使用压缩视频进行实时动物行为分析的半监督学习方法的开发
- 批准号:
22H03637 - 财政年份:2022
- 资助金额:
$ 83.57万 - 项目类别:
Grant-in-Aid for Scientific Research (B)
Landscapes of fear in the Anthropocene: Linking predation risk and human disturbance to animal behavior and ecological outcomes
人类世的恐惧景观:将捕食风险和人类干扰与动物行为和生态结果联系起来
- 批准号:
DGECR-2022-00323 - 财政年份:2022
- 资助金额:
$ 83.57万 - 项目类别:
Discovery Launch Supplement
Neural and molecular mechanisms of microbe-sensing in the control of animal behavior - Resubmission - 1
微生物传感控制动物行为的神经和分子机制 - 重新提交 - 1
- 批准号:
10315486 - 财政年份:2021
- 资助金额:
$ 83.57万 - 项目类别:
REU Site: Animal Behavior in Context
REU 网站:背景下的动物行为
- 批准号:
2050311 - 财政年份:2021
- 资助金额:
$ 83.57万 - 项目类别:
Standard Grant
Neural and molecular mechanisms of microbe-sensing in the control of animal behavior - Resubmission - 1
微生物传感控制动物行为的神经和分子机制 - 重新提交 - 1
- 批准号:
10412977 - 财政年份:2021
- 资助金额:
$ 83.57万 - 项目类别:
Molecular recording to predict cell fate decisions and animal behavior
分子记录预测细胞命运决定和动物行为
- 批准号:
10260139 - 财政年份:2021
- 资助金额:
$ 83.57万 - 项目类别: