Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
基本信息
- 批准号:10541124
- 负责人:
- 金额:$ 33.18万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-01-01 至 2024-12-31
- 项目状态:已结题
- 来源:
- 关键词:AcuteAddressAdultAgeAnxietyAttentionBrainBrain MappingBrain regionChronicClinicalCognitiveCognitive deficitsDataDatabasesDecision MakingDiagnosisEducationFactor AnalysisFocal Brain InjuriesFoundationsGenderHandednessHumanImpaired cognitionIndividualInternationalIowaIschemic StrokeLanguageLesionLifeLinkLocationLongitudinal cohortMRI ScansMagnetic Resonance ImagingManualsMapsMasksMedicalMemoryMethodsNeuroanatomyNeurologistNeuropsychological TestsNeurosciences ResearchOutcomeOutcome AssessmentOutcome MeasurePatientsPhasePositioning AttributePrognosisProviderRecoveryRegistriesRehabilitation therapyResourcesRiskScanningSiteSourceSpecialistStatistical ModelsStrokeStructureStructure-Activity RelationshipSymptomsTestingTranslatingUniversitiesWorkclinical carecognitive performancecognitive testingcohortcommon symptomconnectomeconnectome datadata integrationdemographicsdesigndisabilitydisabling symptomexperiencehuman dataimprovedinnovationinsightlong term recoveryloved onesmultiple data sourcesneuroimagingoutcome predictionperformance testspersonalized predictionspost strokeprospectivepublic health relevancescreeningstroke cognitive outcomestroke outcomestroke patienttoolvascular risk factor
项目摘要
Abstract
Having a stroke is a frightening experience for both the patient and their loved ones. A major source of anxiety
is not knowing what stroke-related deficits will persist – will my loved one ever be able to talk again? The
likelihood for recovery is estimated by the treating neurologist or rehabilitation specialist based on their
personal experience. There are no tools available that can use information about the location of the stroke,
query it against the outcomes from hundreds or thousands of other individuals, and generate a personalized
quantitative prediction of stroke-related deficits and prognosis for long-term recovery. Here, we propose to
develop such a tool. At the University of Iowa we have one of the most comprehensive lesion registries in the
world of some 3,500 patients with focal acquired brain lesions, neuroimaging, and extensive data on outcomes.
We propose to capitalize on this unique resource by developing a tool to predict cognitive outcomes from
stroke based on lesion location. First, we propose to map brain regions most associated with specific cognitive
deficits across over 1000 patients, including symptoms such as difficulty speaking or problems with attention.
Next, in a prospectively collected cohort with acute ischemic stroke we will attempt to predict cognitive
outcomes by querying lesion location against the aforementioned symptom ‘maps.’ We hypothesize that lesion
location will be a significant predictor of chronic cognitive outcomes. Second, we have recently developed an
innovative strategy that links lesion-associated deficits to specific brain networks. It combines traditional lesion
mapping with human connectome data from healthy adults to infer what networks are disrupted by focal brain
lesions. We will evaluate whether this lesion network mapping approach can be used to compliment the
traditional lesion mapping approach to predict additional variance in cognitive outcomes. Finally, we will use
advanced statistical modeling to evaluate how predictive information from lesion location can be optimally
integrated with demographic information and baseline screening cognitive performance to maximize
predictions of chronic cognitive outcomes in a longitudinal cohort. By addressing these objectives, we will lay
the foundation for developing a clinical tool that can be applied to a clinically-acquired MRI scan to aid in
determining the prognosis of cognitive outcomes, a key factor that will help in the early management,
rehabilitation, and life planning for patients with stroke.
摘要
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)
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Aaron D Boes其他文献
TMS-associated auditory evoked potentials can be effectively masked: Evidence from intracranial EEG
TMS 相关的听觉诱发电位可以被有效掩盖:来自颅内脑电图的证据
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:7.7
- 作者:
Nicholas T. Trapp;Eric W. Tsang;J. Bruss;Simone Russo;P. Gander;Joel I. Berger;K. Nourski;M. Rosanova;Corey Keller;Hiroyuki Oya;M. Howard;Aaron D Boes - 通讯作者:
Aaron D Boes
NEURO-ENHANCEMENT AND THE NOTION OF LEGAL RESPONSIBILITY By GENT CAKAJ
神经增强和法律责任概念作者:GENT CAKAJ
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Iman A. Wahle;Ralph Adolphs;D. Tranel;Aaron D Boes;Frederick Eberhardt - 通讯作者:
Frederick Eberhardt
Aaron D Boes的其他文献
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{{ truncateString('Aaron D Boes', 18)}}的其他基金
Investigating the neural mechanisms of repetitive brain stimulation with invasive and noninvasive electrophysiology in humans
利用侵入性和非侵入性电生理学研究人类重复脑刺激的神经机制
- 批准号:
10735463 - 财政年份:2023
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10216708 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10314064 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10753279 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10475963 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10596755 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10753278 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10218323 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Predicting Cognitive Outcomes from Stroke Based on Lesion Location
根据病变位置预测中风的认知结果
- 批准号:
10600634 - 财政年份:2020
- 资助金额:
$ 33.18万 - 项目类别:
Investigating the Effects of Transcranial Magnetic Stimulation with Intracranial EEG in Humans
研究经颅磁刺激与颅内脑电图对人体的影响
- 批准号:
9805657 - 财政年份:2019
- 资助金额:
$ 33.18万 - 项目类别:
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