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

统计学和应用概率因素分析/信息论/降维/心理计量学/心理学在生命科学和医学中，越来越多的大规模数据集可用来获得许多不同的预测变量，并与健康相关的结果相关。信息论提供了一种方法来直接量化预测者对之间的代表性交互作用。这些交互作用可以采取冗余或协同的形式。冗余量化个体内部重叠的预测力。协同性表明，个体内部两个预测者之间的关系本身就是关于结果的信息性的；当你同时考虑这两个预测者时，你获得了比你结合从每个预测者中做出的预测更好的预测性能。这个项目的目的是开发一种新的信息论因素分析。两两应用最新发展的信息论工具将使我们能够通过组合协同预测因子来建立最优的预测特征集。通过对冗余特征进行聚类，可以将协同预测因子分组为公共预测因子。冗余特征簇代表了一组预测因子，这些预测符在个体之间提供了关于结果的相同的预测信息，因此代表了预测因子。该方法将利用在线收集的大量新数据集来研究跨诊断精神症状维度及其与认知的不同方面的关系。传统上，精神疾病是根据正式的分类系统来分类和诊断的，从而将精神病理症状集合组织成与特定精神诊断相对应的离散条件。然而，越来越多的证据表明，症状空间不是离散的，而是有维度的。重要的是，目前公认的离散诊断类别的存在可能会阻碍对与精神病理有关的症状的潜在神经和认知基础以及原因机制的识别。由于对分类方法的有效性和实用性的这些限制和担忧，精神病学研究人员正朝着定义心理健康问题的跨诊断方法发展。他们的目标是绘制普通人群中的心理病理学的变异图，得出心理健康的有效和心理测量学方面的维度，从而帮助识别潜在的原因和机制。然而，异质应用这造成了对结果缺乏复制和过度热情的解释，还没有为目前的离散框架提供一个更好的替代办法。为了给症状维度方法提供强有力的统计基础，本项目将收集大量的数据集，以复制基于精神病学症状问卷因素分析的现有结果。这将提供一个基准，用于比较新的信息论方法。我们将使用一组全面的行为任务和心理测量问卷，来自大量的在线一般人群样本，以调查跨诊断症状维度和神经认知任务绩效之间的关系。此外，我们将把传统的因素分析&我们新颖的信息论因素分析应用到我们的认知任务中，以确定是否存在共同的行为因素&这些因素是否与跨诊断症状维度有关，以及这些因素与跨诊断症状维度之间的关系。因此，这个项目有两个主要目标。第一-产生一个有价值的新数据集，以研究跨诊断症状维度及其与认知不同方面的关系，以及在大范围的一般人群样本中寻找一系列任务的认知因素。第二，提出了一种新的信息论因子分析方法，并将其与传统的因子分析方法在该领域的应用进行了比较。

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.