Autism Spectrum Disorder Symptoms are Associated with Connectivity Between Large-Scale Neural Networks and Brain Regions Involved in Social Processing

The neurobiology of autism spectrum disorder remains poorly understood. The present study addresses this knowledge gap by examining the relationship between functional brain connectivity and Autism Diagnostic Observation Schedule (ADOS) scores using publicly available data from the Autism Brain Imaging Data Exchange (ABIDE) database (N = 107). This relationship was tested across all brain voxels, without a priori assumptions, using a novel statistical approach. ADOS scores were primarily associated with decreased connectivity to right temporoparietal junction, right anterior insula, and left fusiform gyrus (p < 0.05, corrected). Seven large-scale brain networks influenced these associations. Findings largely encompassed brain regions involved in processing socially relevant information, highlighting the importance of these processes in autism spectrum disorder.

Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies

Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes.

Multiple comparison procedures for neuroimaging genomewide association studies

Recent research in neuroimaging has focused on assessing associations between genetic variants that are measured on a genomewide scale and brain imaging phenotypes. A large number of works in the area apply massively univariate analyses on a genomewide basis to find single nucleotide polymorphisms that influence brain structure. In this paper, we propose using various dimensionality reduction methods on both brain structural MRI scans and genomic data, motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustment method and compare it with two existing false discovery rate (FDR) adjustment methods. The simulation results suggest an increase in power for the proposed method. The real-data analysis suggests that the proposed procedure is able to find associations between genetic variants and brain volume differences that offer potentially new biological insights.