Testing the Predictive Power of Structural Neuroimaging in the Estimation of Individuals' Reading and Attentional Abilities
测试结构神经影像在评估个人阅读和注意力能力方面的预测能力
基本信息
- 批准号:9327290
- 负责人:
- 金额:$ 3.58万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-06-01 至 2019-05-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdolescentAlcoholsAnatomyAttentionAttention deficit hyperactivity disorderBase of the BrainBehaviorBehavior assessmentBehavioralBrainClinicalClinical ResearchCognitiveColoradoCommunitiesDataDemographyDevelopmentDiagnosisDimensionsDistantEarly InterventionEnvironmentEquationExcisionFamilyFunctional ImagingFundingGoalsHome environmentHyperactive behaviorImageImpulsivityIndividualIndividual DifferencesInterventionInterviewLaboratoriesLearning DisabilitiesLightMachine LearningMainstreamingMathematicsMeasurementMeasuresMedicalMethodsModalityModelingNeuropsychologyParticipantPatientsPerformancePositioning AttributePreventive InterventionReadingRecording of previous eventsResearchRiskRisk FactorsSamplingScienceSensitivity and SpecificitySiteSpecificityStructureSupervisionSurfaceTechniquesTechnologyTestingValidity and ReliabilityWorkbasebrain behaviordemographicsfallsimprovedinattentioninsightmathematical abilityneuroimagingneurophysiologynovelperformance testsphonological awarenesspredictive modelingprocessing speedpsychologicreading abilityresponseskillssocialtooltractography
项目摘要
PROJECT SUMMARY
Establishing the incremental predictive validity of neuroimaging is a critical prerequisite for this
technology's clinical or educational use outside of medical settings. If specific, well-validated neuroimaging
tools provide little to no information above that of clinical interview or neuropsychological assessment, the use
and funding of those tools should be more critically evaluated against other methods or funding priorities. If
neuroimaging demonstrates unique predictive power for assessment or prediction purposes, however, it may
aid in the identification of concerning developmental trajectories, the provision of early intervention, or the
prediction of individuals' response to specific interventions. Preliminary data from the applicant's laboratory has
demonstrated unique contributions of structural neuroimaging to individual differences in reading and attention
using confirmatory structural equation modeling, but these questions have yet to be addressed using a data-
driven feature-reduction approach that considers numerous types of demographic, behavioral, and brain-
derived measures. This proposal focuses explicitly on structural neuroimaging as it is more easily and more
consistently obtained than functional imaging in clinical and research environments, and recent findings
indicate that it may even be more highly predictive of behavior than functional imaging. In light of these
considerations, this proposal will utilize supervised machine learning to assess the incremental validity of
structural neuroimaging above and beyond that of traditional psychological assessment.
The goals are this project are to (1) develop and evaluate demographic- and behavior-based predictive
models of individuals' reading, inattention, and hyperactivity/impulsivity, (2) replicate and then add
neuroanatomical features to these models in order to test structural neuroimaging's incremental predictive
validity, and (3) test these models' specificity and discriminant validity for measuring the intended constructs.
The long-term goal of this proposal is thus to expand upon the applicant's background in individual difference
analyses by developing skills in machine learning so that the incremental validity of multiple neuroimaging
modalities can eventually be evaluated. The eventual development of a sufficiently validated predictive model
could constitute a behavioral and/or brain-based signature that could serve, along with contextual and
functional considerations, as a quantifiable alternative to clinically-based diagnoses. These methods are but
first steps toward this distant but worthwhile goal.
项目摘要
建立神经影像学的增量预测有效性是一个关键的先决条件
技术在医疗环境之外的临床或教育用途。如果是特异性的,经过充分验证的神经成像
工具提供的信息很少或没有超过临床访谈或神经心理学评估,使用
对这些工具的供资应对照其他方法或供资优先事项进行更严格的评估。如果
神经影像学表现出独特的预测能力,用于评估或预测目的,然而,它可能
帮助确定有关的发展轨迹,提供早期干预,或
预测个人对特定干预措施的反应。申请人实验室的初步数据
证明了结构神经成像对阅读和注意力个体差异的独特贡献
使用验证性结构方程模型,但这些问题尚未得到解决,使用数据-
驱动的特征缩减方法,考虑了多种类型的人口统计学,行为和大脑-
衍生措施。这项建议明确地集中在结构神经成像,因为它更容易,
一致获得比功能成像在临床和研究环境中,最近的发现,
这表明它甚至可能比功能成像更能预测行为。鉴于这些
考虑到这一点,该提案将利用监督机器学习来评估
结构神经成像技术,超越了传统的心理评估。
该项目的目标是(1)开发和评估基于人口统计和行为的预测
个体阅读、注意力不集中和多动/冲动的模型,(2)复制,然后添加
这些模型的神经解剖特征,以测试结构神经成像的增量预测
效度;(3)检验这些模型对测量预期结构的特异性和判别效度。
因此,这项建议的长期目标是扩大申请人的个人差异背景
通过开发机器学习技能进行分析,
最终可以对模式进行评估。最终开发出一个经过充分验证的预测模型
可以构成行为和/或基于大脑的签名,该签名可以与上下文一起沿着,
功能考虑,作为临床诊断的可量化替代方案。这些方法,但
这是迈向这个遥远但有价值的目标的第一步。
项目成果
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