Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data

• Authors: MABS Akhonda, University of Maryland Baltimore County; Yuri Levin-Schwartz, Icahn School of Medicine at Mount Sinai; Vince D Calhoun, Emory University; Tülay Adali, University of Maryland Baltimore County
• Journal Title: SENSORS
• Volume: Volume 22, Number 3
• Publisher: MDPI AG | 2022-02-05
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3390/s22031224
• Subjects: Health Sciences, Public Health; Engineering, Electronics and Electrical; Computer Science
• Keywords: data fusion; fMRI; ICA; IVA; neuroimaging; neuropsychology

Abstract

It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets of data—neuroimaging and non-neuroimaging—that we can understand and explain the evolution of neural and cognitive processes, and predict outcomes for intervention and treatment. Multiple methods for the joint analysis or fusion of multiple neuroimaging datasets or modalities exist; however, methods for the joint analysis of imaging and non-imaging data are still in their infancy. Current approaches for identifying brain networks related to cognitive assessments are still largely based on simple one-to-one correlation analyses and do not use the cross information available across multiple datasets. This work proposes two approaches based on independent vector analysis (IVA) to jointly analyze the imaging datasets and behavioral variables such that multivariate relationships across imaging data and behavioral features can be identified. The simulation results show that our proposed methods provide better accuracy in identifying associations across imaging and behavioral components than current approaches. With functional magnetic resonance imaging (fMRI) task data collected from 138 healthy controls and 109 patients with schizophrenia, results reveal that the central executive network (CEN) estimated in multiple datasets shows a strong correlation with the behavioral variable that measures working memory, a result that is not identified by traditional approaches. Most of the identified fMRI maps also show significant differences in activations across healthy controls and patients potentially providing a useful signature of mental disorders.