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

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.

Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data.

BACKGROUND: Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the data and the relationships of multiple datasets, and hence, are attractive for the fusion of medical imaging data. Two important extensions of ICA for multiset fusion are the joint ICA (jICA) and the multiset canonical correlation analysis and joint ICA (MCCA-jICA) techniques. Both approaches assume identical mixing matrices, emphasizing components that are common across the multiple datasets. However, in general, one would expect to have components that are both common across the datasets and distinct to each dataset. NEW METHOD: We propose a general framework, disjoint subspace analysis using ICA (DS-ICA), which identifies and extracts not only the common but also the distinct components across multiple datasets. A key component of the method is the identification of these subspaces and their separation before subsequent analyses, which helps establish better model match and provides flexibility in algorithm and order choice. COMPARISON: We compare DS-ICA with jICA and MCCA-jICA through both simulations and application to multiset functional magnetic resonance imaging (fMRI) task data collected from healthy controls as well as patients with schizophrenia. RESULTS: The results show DS-ICA estimates more components discriminative between healthy controls and patients than jICA and MCCA-jICA, and with higher discriminatory power showing activation differences in meaningful regions. When applied to a classification framework, components estimated by DS-ICA results in higher classification performance for different dataset combinations than the other two methods. CONCLUSION: These results demonstrate that DS-ICA is an effective method for fusion of multiple datasets.

Identification of Subgroup Differences Using IVA: Application to fMRI Data Fusion.

In application to functional magnetic resonance imaging (fMRI) data analysis, a number of data fusion algorithms have shown success in extracting interpretable brain networks that can distinguish two groups such two populations-patients with mental disorder and the healthy controls. However, there are situations where more than two groups exist such as the fusion of multi-task fMRI data. Therefore, in this work we propose the use of IVA to effectively extract information that is able to distinguish across multiple groups when applied to data fusion. The performance of IVA is investigated using a simulated fMRI-like data. The simulation results illustrate that IVA with multivariate Laplacian distribution and second-order statistics (IVA-L-SOS) yields better performance compared to joint independent component analysis and IVA with multivariate Gaussian distribution in terms of both estimation accuracy and robustness. When applied to real multi-task fMRI data, IVA-L-SOS successfully extract task-related brain networks that are able to distinguish three tasks.

ICA and IVA for Data Fusion: An Overview and a New Approach Based on Disjoint Subspaces.

Data-driven methods have been very attractive for fusion of both multiset and multimodal data, in particular using matrix factorizations based on independent component analysis (ICA) and its extension to multiple datasets, independent vector analysis (IVA). This is primarily due to the fact that independence enables (essentially) unique decompositions under very general conditions and for a large class of signals, and independent components lend themselves to easier interpretation. In this paper, we first present a framework that provides a common umbrella to previously introduced fusion methods based on ICA and IVA, and allows us to clearly demonstrate the tradeoffs involved in the design of these approaches. This then motivates the introduction of a new approach for fusion, that of disjoint subspaces (DS). We demonstrate the desired performance of DS using ICA through simulations as well as application to real data, for fusion of multi-modal medical imaging data-functional magnetic resonance imaging (fMRI),and electroencephalography (EEG) data collected from a group of healthy controls and patients with schizophrenia performing an auditory oddball task.