Testing the Predictive Power of Structural Neuroimaging in the Estimation of Individuals' Reading and Attentional Abilities

项目总结 建立神经成像的增量预测有效性是实现这一目标的关键前提 技术在医疗环境之外的临床或教育用途。如果是特定的、经过充分验证的神经成像 与临床访谈或神经心理评估相比，工具提供的信息很少，甚至没有提供任何信息，使用 对这些工具的供资应参照其他方法或供资优先事项进行更严格的评估。如果 神经成像显示出独特的预测能力，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 预测个体对特定干预措施的反应。申请人实验室的初步数据显示 结构神经成像对个体阅读和注意力差异的独特贡献 使用验证性结构方程建模，但这些问题尚未使用数据来解决- 驱动型特征缩减方法，考虑了多种类型的人口统计、行为和大脑- 派生度量。这项建议明确地侧重于结构神经成像，因为它更容易和更多 在临床和研究环境中持续获得比功能成像更好的结果，以及最近的发现 这表明，它甚至可能比功能成像对行为的预测更高。鉴于这些， 考虑因素，该提案将利用有监督的机器学习来评估 结构神经成像是对传统心理评估的超越。 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性 个体阅读、注意力不集中和多动/冲动的模型，(2)复制然后相加 这些模型的神经解剖学特征，以检验结构神经成像的增量预测 效度，以及(3)检验这些模型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者的个人差异背景 通过发展机器学习技能进行分析，从而提高多重神经成像的增量有效性 最终可以对医疗模式进行评估。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，与上下文和 功能性考虑，作为临床诊断的可量化替代。这些方法都是 迈向这个遥远但值得实现的目标的第一步。

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.