SCH: INT: Collaborative Research: Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy
SCH:INT:合作研究:创伤后癫痫的多模态信号分析和数据融合
基本信息
- 批准号:10093160
- 负责人:
- 金额:$ 24.35万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-05-01 至 2023-02-28
- 项目状态:已结题
- 来源:
- 关键词:AdoptedAlgorithmsAnimal ModelAntiepileptogenicArchitectureBioinformaticsBiological MarkersBiometryBloodBlood specimenBrain imagingCaliforniaChemicalsComplementDataData AnalysesData SetDecision TreesDevelopmentDiffusionDiffusion Magnetic Resonance ImagingDiseaseElectroencephalographyEpilepsyEpileptogenesisFamilyFunctional Magnetic Resonance ImagingGoalsGraphHandHealthHigh Frequency OscillationHippocampus (Brain)HumanImageIndividualInjuryIntuitionInvestigationJointsKnowledgeLeadLearningLengthLimbic SystemLiteratureMachine LearningMagnetic Resonance ImagingMathematicsMedicalMedicineMethodologyMethodsMicroRNAsModelingOnset of illnessOutcomePatientsPerformancePharmaceutical PreparationsPhysiciansPopulationPost-Traumatic EpilepsyPropertyProteinsPsychological TechniquesPsychological TransferRattusRecordsResearchRestScalp structureSeizuresSeriesSignal TransductionStatistical ModelsStructureTechniquesThalamic structureTimeTissuesTraumatic Brain InjuryUniversitiesUpdateValidationVotingWorkanalytical toolanimal databasebiomarker discoverydata acquisitiondata fusiondeep learningdesignfeature extractionhuman dataimaging modalityimprovedinnovationinsightlaboratory experimentlearning strategymultimodal datamultimodalityneural networkneural network classifierneurophysiologynovelpost-traumapredictive modelingpreventrandom forestsupport vector machinetheoriestool
项目摘要
The research objective of this proposal, Multimodal Signal Analysis and Data Fusion for Post-traumatic
Epilepsy Prediction, with Pl Dominique Duncan from the University of Southern California, is to predict the
onset of epileptic seizures following traumatic brain injury (TBI), using innovative analytic tools from machine
learning and applied mathematics to identify features of epileptiform activity, from a multimodal dataset
collected from both an animal model and human patients. The proposed research will accelerate the
discovery of salient and robust features of epileptogenesis following TBI from a rich dataset, collected from
the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), as it is being acquired by
investigating state-of-the-art models, methods, and algorithms from contemporary machine learning theory.
This secondary use of data to support automated discovery of reliable knowledge from aggregated records
of animal model and human patient data will lead to innovative models to predict post-traumatic epilepsy
(PTE). This machine learning based investigation of a rich dataset complements ongoing data acquisition
and classical biostatistics-based analyses ongoing in the study and can lead to rigorous outcomes for the
development of antiepileptogenic therapies, which can prevent this disease. Identifying salient features in
time series and images to help design a predictor of PTE using data from two species and multiple individuals
with heterogeneous TBI conditions presents significant theoretical challenges that need to be tackled. In this
project, it is proposed to adopt transfer learning and domain adaptation perspectives to accomplish these
goals in multimodal biomedical datasets across two populations. Specifically, techniques emerging from d,eep
learning literature will be exploited to augment data, share parameters across model components to reduce
the number of parameters that need to be optimized, and use state-of-the-art architectures to develop models
for feature extraction. These will be compared against established pipelines of hand-crafted feature extraction
in rigorous cross-validation analyses. Developed techniques for transfer learning will be able to extract
features that generalize across animal and human data. Moreover, these theoretical techniques with
associated models and optimization methods will be applicable to other multi-species transfer learning
challenges that may arise in the context of health and medicine. Multimodal feature extraction and
discriminative model learning for disease onset prediction using novel classifiers also offer insights into
biomarker discovery using advanced machine learning techniques through joint multimodal data analysis.
本课题的研究目标为创伤后多模态信号分析与数据融合
项目成果
期刊论文数量(0)
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Dominique Duncan其他文献
Dominique Duncan的其他文献
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{{ truncateString('Dominique Duncan', 18)}}的其他基金
BRAIN Integrated Resource for Human Anatomy and Intracranial Neurophysiology
BRAIN 人体解剖学和颅内神经生理学综合资源
- 批准号:
10505412 - 财政年份:2022
- 资助金额:
$ 24.35万 - 项目类别:
SCH: INT: Collaborative Research: Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy
SCH:INT:合作研究:创伤后癫痫的多模态信号分析和数据融合
- 批准号:
9756832 - 财政年份:2019
- 资助金额:
$ 24.35万 - 项目类别:
SCH: INT: Collaborative Research: Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy
SCH:INT:合作研究:创伤后癫痫的多模态信号分析和数据融合
- 批准号:
9921505 - 财政年份:2019
- 资助金额:
$ 24.35万 - 项目类别:
Data Archive for the Brain Initiative (DABI)
大脑计划数据档案 (DABI)
- 批准号:
10428480 - 财政年份:2018
- 资助金额:
$ 24.35万 - 项目类别:
Data Archive for the Brain Initiative (DABI)
大脑计划数据档案 (DABI)
- 批准号:
10166941 - 财政年份:2018
- 资助金额:
$ 24.35万 - 项目类别:
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