Biology subjects

Gruenewald, S.

Publications and source records attributed to Gruenewald, S..

2 recordsLinked to original sources

bioRxiv2018

Identifying associations in dense connectomes using structured kernel principal component regression

A powerful and computationally efficient multivariate approach is proposed here, called structured kernel principal component regression (sKPCR), for the identification of associations in the voxel-level dense connectome. The method can identify voxel-phenotype associations based on the voxels whole-brain connectivity pattern, which is applicable to detect linear and non-linear signals for both volume-based and surface-based functional magnetic resonance imaging (fMRI) data. For each voxel, our approach first extracts signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations via a general linear model. The method derives its power by appropriately modelling the spatial structure of the data. Simulations based on dense connectome data have shown that our method can accurately control the false-positive rate, and it is more powerful than many state-of-the-art approaches, such as the connectivity-wise general linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). To demonstrate the utility of our approach in real data analysis, we apply these methods to identify voxel-wise difference between schizophrenic patients and healthy controls in two independent resting-state fMRI datasets. The findings of our approach have a better between-sites reproducibility, and a larger proportion of overlap with existing schizophrenia findings. Code for our approach can be downloaded from https://github.com/weikanggong/vBWAS.

neuroscience↗

bioRxiv2016

Statistical testing and power analysis for brain-wide association study

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

neuroscience↗