We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

中文翻译：

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静止状态功能磁共振成像的频谱动态因果模型：一项探索性研究，涉及默认模式网络中有效的大脑连通性与遗传学的关系。

我们进行了影像遗传学研究，以探索默认模式网络（DMN）中有效的大脑连通性如何与阿尔茨海默氏病和轻度认知障碍的遗传学相关联。我们开发了对纵向静止状态功能磁共振成像（rs-fMRI）和遗传数据的分析，该数据是从阿尔茨海默氏病神经影像计划（ADNI）数据库进行的319 rs-fMRI扫描共111例受试者获得的。动态因果模型（DCM）适合rs-fMRI扫描，以估计DMN内的有效大脑连通性，并与从疾病中获得的经验疾病约束集中的一组单核苷酸多态性（SNP）相关。样本来自663个仅具有全基因组数据的ADNI受试者。我们将使用频谱DCM估计的纵向有效大脑连通性与使用线性混合效应（LME）模型和标量函数回归（FSR）模型的SNP相关联。在这两种情况下，我们都实现了一个参数自举，以测试SNP系数，并与从渐近零分布获得的p值进行比较。在最初的两个网络中q值阈值为0.1，未发现任何影响。我们报告了具有较高等级的协会的探索模式，这些协会对FSR和LME做出的不同假设都具有稳定性。