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

Levin-Schwartz

Calhoun

2022

Sensors

Self Cite

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.

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Levin-Schwartz

Calhoun

2022

Sensors

Self Cite

“…Many applications involve multiple inherently related datasets, where it is reasonable to assume that latent sources within one dataset are related to some corresponding latent sources in one or more other datasets. This is especially prevalent in datasets acquired by functional magnetic resonance imaging (fMRI) [4,5], a standard neuroimaging tool for measuring brain function and activity due to its non-invasive nature and high resolution. In the study of multi-subject fMRI data, similar and consistent responses across multiple different subjects are indicative of shared functional network activities across those subjects.…”

Section: Introductionmentioning

confidence: 99%

“…For the remainder of the paper, we refer to the collection of latent SCVs that are shared as the "shared subspace" of the data and the remaining SCVs as the "non-shared subspace". Tools for identifying the shared and non-shared subspaces have demonstrated their usefulness in fMRI analysis [4,5,20], allowing for the identification of shared functional network activations, in addition to those that may exhibit more variability across the datasets. The former can be useful for identifying activations that are consistently present across all subjects, whereas the latter can be useful for its discriminatory ability in further analysis (e.g., for diagnosing or classifying subjects).…”

Section: Introductionmentioning

confidence: 99%

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis

Sun

Gabrielson

et al. 2023

Sensors

Self Cite

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data’s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the “shared” subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.

“…Discovering common and distinct features across multiple datasets is a fundamental problem in various disciplines, including the analysis of multi-task/multi-subject fMRI data [1] or multimodal image fusion [2]. The distinct features in each dataset may be generated by variability caused by uncontrolled acquisition conditions [3], or by features unique to each dataset in, e.g., medical data [4].…”

Section: Introductionmentioning

confidence: 99%

Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion

Borsoi

Lehmann

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

et al. 2023

Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data. Coupled matrix and tensor factorization approaches have been useful for flexible data fusion, or decomposition to extract features that can be used in multiple ways. However, existing methods do not directly recover shared and dataset-specific components, which requires post-processing steps involving additional hyperparameter selection. In this paper, we propose a tensor-based framework for multi-task fMRI data fusion, using a partially constrained canonical polyadic (CP) decomposition model. Differently from previous approaches, the proposed method directly recovers shared and dataset-specific components, leading to results that are directly interpretable. A strategy to select a highly reproducible solution to the decomposition is also proposed. We evaluate the proposed methodology on real fMRI data of three tasks, and show that the proposed method finds meaningful components that clearly identify group differences between patients with schizophrenia and healthy controls.