Send to

Choose Destination

See 1 citation found by title matching your search:

J Neurosci Methods. 2018 Nov 1;309:161-174. doi: 10.1016/j.jneumeth.2018.08.027. Epub 2018 Sep 2.

A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia.

Author information

Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA. Electronic address:
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA.
Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA 70112, USA.
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM 87131, USA.
Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.



Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging.


In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel machine for detecting higher order interactions among biologically relevant multimodal data. Using a semiparametric method on a reproducing kernel Hilbert space, we formulated the proposed method as a standard mixed-effects linear model and derived a score-based variance component statistic to test higher order interactions between multimodal datasets.


The method was evaluated using extensive numerical simulation and real data from the Mind Clinical Imaging Consortium with both schizophrenia patients and healthy controls. Our method identified 13-triplets that included 6 gene-derived SNPs, 10 ROIs, and 6 gene-specific DNA methylations that are correlated with the changes in hippocampal volume, suggesting that these triplets may be important for explaining schizophrenia-related neurodegeneration.


The performance of the proposed method is compared with the following methods: test based on only first and first few principal components followed by multiple regression, and full principal component analysis regression, and the sequence kernel association test.


With strong evidence (p-value ≤0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) is a significant biomarker for schizophrenia patients. This novel method can be applicable to the study of other disease processes, where multimodal data analysis is a common task.


Higher order interaction; Imaging genetics and epigenetics; Kernel machine methods; Multimodal datasets; Schizophrenia

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center