Developing an information-theoretic predictive factor analysis method with application to transdiagnostic psychometric and neurocognitive data

开发应用于跨诊断心理测量和神经认知数据的信息论预测因素分析方法

• 批准号: 2587468
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2587468
• 关键词: Developing; information; theoretic; predictive; factor

Statistics & applied probabilityFactor analysis/information theory/dimensionality reduction/psychometrics/psychologyLarge scale data sets are increasingly available within the life sciences & medicine, in which many diverse predictive variables are obtained & related to a health-related outcome.Information theory provides a way to directly quantify representational interactions between pairs of predictors.These interactions can take the form of redundancy or synergy.Redundancy quantifies overlapping predictive power within individuals.Synergy indicates that the relationship between 2 predictors within an individual is itself informative about the outcome;you obtain better predictive performance when considering both predictors together than you would if you combined the prediction made from each.This project aims to develop a new information-theoretic factor analysis. Applying recently developed information theoretic tools in a pairwise fashion will allow us to build optimum sets of predictive features by combining synergistic predictors which can be grouped into common predictive factors by clustering redundant features.A cluster of redundant features represents a set of predictors which provide the same predictive information about the outcome across individuals & therefore represents a predictive factor.This methodology will be developed with a large new data set,collected online,to investigate transdiagnostic psychiatric symptom dimensions & their relationship to different aspects of cognition.Traditionally, mental illness has been classified & diagnosed according to formal taxonomic systems,whereby sets of psychopathological symptoms are organized into discrete conditions that correspond to specific psychiatric diagnoses.However,there is increasing evidence that the symptom space is not discrete but dimensional.Importantly,the assumption of the existence of the currently accepted discrete diagnostic categories may be hindering the identification of underlying neural & cognitive substrates & causal mechanisms of symptoms associated with psychopathology.On account of these limitations & concerns regarding the validity & utility of the categorical approach,psychiatric researchers are moving toward a transdiagnostic approach to defining mental health problems.Their aim is to map variations in psychopathology in the general population,to derive valid & psychometrically sound dimensions of mental health & subsequently,aid identification of underlying causes & mechanisms.Heterogeneous application,however,has resulted in a lack of replication and over-enthusiastic interpretation of results & has not yet provided a superior alternative to the current discrete framework. In order to provide the symptom dimensions approach with a robust statistical foundation,this project will collect a large data set to replicate existing results based on factor analysis of psychiatric symptom questionnaires.This will provide a benchmark from a topical & promising research area to which to compare the novel information theoretic approach.We will employ a comprehensive battery of behavioural tasks & psychometric questionnaires across a large online general population sample,to investigate the relationship between transdiagnostic symptom dimensions & neurocognitive task performance.Further, we will apply traditional factor analysis & our novel information theoretic factor analysis to our battery of cognitive tasks to determine if there are common behavioural factors & if and how these relate to the transdiagnostic symptom dimensions.This project therefore has two main aims. 1st-to produce a valuable new data set to investigate transdiagnostic symptom dimensions & their relation to different aspects of cognition, as well as looking for cognitive factors across a range of tasks in a large general population sample. 2nd-to develop a novel information-theoretic factor analysis & compare this to traditional factor analysis in this application.

统计学和应用概率因子分析/信息论/降维/心理测量学/心理学大规模数据集在生命科学和医学中越来越多，其中获得了许多不同的预测变量&与健康相关-相关结果。信息论提供了一种直接量化预测因子对之间的代表性相互作用的方法。这些相互作用可以采取冗余或协同的形式。量化了个体内重叠的预测能力。协同作用表明个体内两个预测因子之间的关系本身就能提供有关结果的信息;当同时考虑两个预测因子时，你会获得比单独考虑两个预测因子时更好的预测性能。本项目旨在开发一种新的信息理论因子分析。以成对的方式应用最近发展的信息理论工具将允许我们通过组合协同预测因子来构建最佳的预测特征集，所述协同预测因子可以通过聚类冗余特征而被分组为共同的预测因子。冗余特征的聚类表示一组预测因子，所述预测因子提供关于个体之间的结果的相同预测信息，因此代表了一个预测因素。这种方法将通过在线收集的大量新数据集来开发，以调查跨诊断精神病症状维度及其与认知不同方面的关系。传统上，精神疾病已经根据正式的分类系统进行分类和诊断，由此精神病理学症状的集合被组织成对应于特定精神病诊断的离散条件。然而，越来越多的证据表明症状空间不是离散的而是多维的。重要的是，假设目前接受的离散诊断类别的存在可能会阻碍与精神病理学相关的症状的潜在神经和认知基质和因果机制的识别。由于这些限制和对分类方法的有效性和实用性的关注，精神病学研究人员正朝着一种跨诊断的方法来定义精神健康问题。2他们的目标是绘制普通人群中精神病理学的变化，得出有效的和心理测量学上合理的精神健康维度，然而，异质性应用导致缺乏复制和对结果的过度热情解释，并且尚未提供比当前离散框架更好的上级替代方案。为了给症状维度方法提供稳健的统计基础，这个项目将收集大量的数据集来复制现有的基于精神症状问卷的因素分析的结果。这将提供一个基准，从一个主题和有前途的研究领域，以比较新的信息理论方法。我们将采用一个全面的电池的行为任务，心理测量问卷在一个大的在线一般人群样本，调查之间的关系transdiagnosis症状维度和神经认知任务的表现。此外，我们将采用传统的因素分析，我们的新的信息理论因素分析，我们的电池认知任务，以确定是否有共同的行为因素，这些是否以及如何与跨诊断症状维度相关。因此，本项目有两个主要目标。第一，产生一个有价值的新数据集，以调查跨诊断症状维度及其与认知不同方面的关系，以及在一个大的一般人群样本中寻找一系列任务中的认知因素。第二，提出一种新的信息论因子分析方法，并与传统的因子分析方法进行比较。