Semiparametric Inference for Psychiatric Neuroimaging
Semiparametric Inference for Psychiatric Neuroimaging
批准号:
10225586
负责人:
Simon Neil Vandekar
金额:
$40.98万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-08-01 至 2025-05-31
关键词:
AddressAmericanBehavioralBiologicalBiological MarkersBrainBrain imagingBrain regionCognitiveCommunitiesComputer softwareConfidence IntervalsCross-Sectional StudiesDataData SetDependenceDevelopmentDiagnosisDiagnosticDimensionsFoundationsFunctional Magnetic Resonance ImagingGaussian modelGenotypeGoalsHippocampus (Brain)ImageImage AnalysisIndividual DifferencesInstitutesInterceptJointsLeadLocationMeasurementMeasuresMental disordersMethodsModelingMotionNatureOutcomeParticipantPartner in relationshipPhenotypePopulationProbabilityProceduresPsychiatric HospitalsPsychosesPublic HealthReportingReproducibilityResearchResearch PersonnelSamplingScientistSeriesStructureSymptomsTestingTimeUniversitiesWorkbasebrain behaviorexperimental studygraphical user interfacehigh dimensionalityimprovedinterestmeetingsneuroimagingneuromechanismpsychotic symptomsresponsesemiparametricsimulationsymposiumvector
中文摘要
项目摘要
识别大脑行为关联,以告知个体差异,疾病轨迹,
和神经机制是精神病神经影像学的主要目标之一。大规模多元
神经成像数据的性质,包括大脑结构和功能的空间详细图像,
与高维行为数据相结合,对实现这一目标提出了重大挑战。的
神经影像学研究中出现的复制危机暴露了常用空间范围的局限性
用于分析成像数据的推理(SEI)方法。其中包括对空间的不切实际的假设
成像数据的协方差函数,导致高度膨胀的错误率。该项目将开发一个新的
神经图像的鲁棒半参数推理框架,以满足对鲁棒方法的需求
在实际数据中,将这些方法集成到pbj R包中,并开发图形用户界面
(GUI)让神经成像科学家们也能使用这些方法。我们将使用的方法来研究如何
精神病的多维症状与精神病学中的脑功能和结构有关。
基因型-表型项目(PGPP)收集和范德比尔特大学精神病医院(VUPH),并
研究公共访问内森克莱恩功能连接的横截面和纵向变化
罗克兰研究所样本(NKI-RS)。我们将使用现实的基于引导程序的
神经成像模拟在目标1中,我们将为SEI开发一个多维半参数程序,
将利用计算上有效的参数和非参数引导进行推断。在目标2中,
将框架扩展到重复测量模型(包括纵向数据),这将使科学家
对受试者水平协变量测量值与大脑结构或功能的关联进行鲁棒建模。在Aim中
3、为了解决精神病神经影像学中假设检验替代方法的需求,我们将开发
可用于构建空间置信度的半参数覆盖概率偏移(科普)集
半参数效应量的区间。这些方法将用于神经成像
社区通过pbj R包和GUI,并在神经成像会议上传播。
英文摘要
PROJECT SUMMARY
Identifying brain-behavior associations for the purpose of informing individual differences, illness trajectories,
and neural mechanisms is one of the primary goals of psychiatric neuroimaging. The massively multivariate
nature of neuroimaging data, which consists of spatially detailed images of brain structure and function,
combined with high-dimensional behavioral data pose significant challenges to meeting this goal. The
emerging replication crisis in neuroimaging research has exposed limitations of commonly used spatial extent
inference (SEI) methods for analyzing imaging data. These include unrealistic assumptions about the spatial
covariance function of the imaging data that lead to highly inflated error rates. This project will develop a new
robust semiparametric inference framework for neuroimages to address the need for methods that are robust
in real-world data, integrate these methods into the pbj R package, and develop a graphical user interface
(GUI) to make the methods accessible to neuroimaging scientists. We will use the methods to study how
multidimensional symptoms of psychosis are related to brain function and structure in the Psychiatric
Genotype-Phenotype Project (PGPP) collected and Vanderbilt University Psychiatric Hospital (VUPH) and to
study cross-sectional and longitudinal changes in functional connectivity in the public-access Nathan Kline
Institute Rockland Sample (NKI-RS). We will evaluate the methods using realistic bootstrap-based
neuroimaging simulations. In Aim 1 we will develop a multidimensional semiparametric procedure for SEI that
will leverage computationally efficient parametric and nonparametric bootstraps for inference. In Aim 2 we will
expand the framework to repeated measurement models (including longitudinal data), that will allow scientists
to robustly model associations of subject-level covariate measurements and brain structure or function. In Aim
3, to address the need for alternatives to hypothesis testing in psychiatric neuroimaging, we will develop
semiparametric Coverage Probability Excursion (CoPE) sets that can be used to construct spatial confidence
intervals for semiparametric effect sizes. These methods will be made available to the neuroimaging
community through the pbj R package and GUI, and disseminated at neuroimaging conferences.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Semiparametric Inference for Psychiatric Neuroimaging
-
批准号:10652980
-
项目类别:
-
资助金额:$42.21万
-
财政年份:2020
-
负责人:Simon Neil Vandekar
-
依托单位:
Semiparametric Inference for Psychiatric Neuroimaging
-
批准号:10434748
-
项目类别:
-
资助金额:$40.98万
-
财政年份:2020
-
负责人:Simon Neil Vandekar
-
依托单位:
Semiparametric Inference for Psychiatric Neuroimaging
-
批准号:10031157
-
项目类别:
-
资助金额:$44.61万
-
财政年份:2020
-
负责人:Simon Neil Vandekar
-
依托单位:
海外基金