JIVE integration of imaging and behavioral data

Abstract

A major goal in neuroscience is to understand the neural pathways underlying human behavior. We introduce the recently developed Joint and Individual Variation Explained (JIVE) method to the neuroscience community to simultaneously analyze imaging and behavioral data from the Human Connectome Project. Motivated by recent computational and theoretical improvements in the JIVE approach, we simultaneously explore the joint and individual variation between and within imaging and behavioral data. In particular, we demonstrate that JIVE is an effective and efficient approach for integrating task fMRI and behavioral variables using three examples: one example where task variation is strong, one where task variation is weak and a reference case where the behavior is not directly related to the image. These examples are provided to visualize the different levels of signal found in the joint variation including working memory regions in the image data and accuracy and response time from the in-task behavioral variables. Joint analysis provides insights not available from conventional single block decomposition methods such as Singular Value Decomposition. Additionally, the joint variation estimated by JIVE appears to more clearly identify the working memory regions than Partial Least Squares (PLS), while Canonical Correlation Analysis (CCA) gives grossly overfit results. The individual variation in JIVE captures the behavior unrelated signals such as a background activation that is spatially homogeneous and activation in the default mode network. The information revealed by this individual variation is not examined in traditional methods such as CCA and PLS. We suggest that JIVE can be used as an alternative to PLS and CCA to improve estimation of the signal common to two or more datasets and reveal novel insights into the signal unique to each dataset.

Brain Age Prediction: Cortical and Subcortical Shape Covariation in the Developing Human Brain

“…Specifically, JIVE decomposes the total variance in data into three terms: joint variation across all seven shape measures, structured variation unique to individual shape measures, and residual noise that should be discarded from analyses. JIVE has been used in cancer studies to identify genetic variants across different data platforms (e.g., genotyping, mRNA and miRNA expression) (Hellton and Thoresen, 2016;O'Connell and Lock, 2016) and to identify the common variance between task-based fMRI connectivity and a wide range of behavioral measures (Yu et al, 2017). We hypothesized that integrative analysis of different cortical and subcortical shape measures can provide a more comprehensive picture of brain cortical and subcortical development from childhood to early adulthood, and thus may generate insights into the relationship between brain maturation and cognitive development.…”

Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality

“…It is important to mention that appropriate feature extraction is a prerequisite to pattern recognition and classification. While few researchers have used similar techniques like canonical correlation analysis (CCA) to extract features that were hierarchically clustered to identify subtypes of depression (Drysdale et al, ), and to delineate positive–negative axis linking various demographic and lifestyle factors (Smith et al, ); others have used alternate methods like partial least square (PLS; Krishnan, Williams, McIntosh, & Abdi, ) and joint and individual variation explained (JIVE; Yu, Risk, Zhang, & Marron, ) to establish the relationship between brain activity and behavioral measure. Although JIVE has been suggested as an improved alternative to PLS and CCA (for details, Yu et al, ), COBE was shown to be better in estimating the common components compared to JIVE (for details, Zhou, Cichocki, et al, ).…”

Maximizing Dissimilarity in Resting State detects Heterogeneous Subtypes in Healthy population associated with High Substance-Use and Problems in Antisocial Personality

“…It is important to mention that appropriate feature extraction is a prerequisite to pattern recognition and classification. While few researchers have used similar techniques like canonical correlation analysis (CCA) to extract features that were hierarchically clustered to identify subtypes of depression (Drysdale et al, 2017), and to delineate positive-negative axis linking various demographic and lifestyle factors (Smith et al, 2015); others have used alternate methods like partial least square (PLS; Krishnan, Williams, McIntosh, Abdi, 2011) and joint and individual variation explained (JIVE; Yu, Risk, Zhang, & Marron, 2017) to establish the relationship between brain activity and behavioral measure. Though JIVE has been suggested as an improved alternative to PLS and CCA (for details, Yu et al, 2017), COBE was shown to be better in estimating the common components compared to JIVE (for details, Zhou et al, 2016a).…”