Dynamic functional network connectivity (dFNC) analysis is a widely-used to study associations between dynamic correlations and cognitive abilities. Traditional methods analyze time-varying association of different spatial networks while assuming that the itself stationary. However, there has been very little work focused on voxelwise variability. Exploiting variability across both temporal domains provide more promising direction obtain reliable patterns. for extracting spatio-temporal...

The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study the group-specific as well information populations. A number subspace analysis algorithms have been developed successfully applied to data fusion, however they are limited joint only couple datasets. Since is very promising multi-subject medical imaging well, we focus on this problem propose new method based independent vector (IVA) (IVA-CS) analysis. IVA-CS leverages...

The identification of homogeneous subgroups patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms various mental disorders. functional connectivity profiles obtained from magnetic resonance imaging (fMRI) data have been shown be unique each individual, similar fingerprints; however, their use characterizing a clinically useful way still being studied. In this work,...

Independent component analysis (ICA) has found wide application in a variety of areas, and functional magnetic resonance imaging (fMRI) data been particularly fruitful one. Maximum likelihood provides natural formuiation for ICA allows one to take into account multiple statistical properties the data-forms diversity. While use types diversity additional flexibility, it comes at cost, leading high variability solution space. In this paper, using simulated as well fMRI-like data, we provide...

Abstract Data‐driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data‐driven that based on different forms diversity—statistical properties data—statistical independence for ICA sparsity DL. Despite their popularity, comparative advantage emphasizing one property over another...

Background Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory (WMT) alters brain activity during tasks, but its effects on resting network organization remain unknown. Purpose To test whether WMT affects PWH connectivity resting‐state fMRI (rsfMRI). Study Type Prospective. Population A total of 53 (ages 50.7 ± 1.5 years, two women) and ‐seronegative controls ( SN , ages 49.5 1.6 six...

Identification of homogeneous subgroups subjects plays a key role in the study precision medicine. While there are number approaches based on clustering low-level features such as behavioral variables, work that makes use fully multivariate nature medical imaging data is very limited. Given individual variability brain functional networks obtained from magnetic resonance (fMRI) noted being both significant and consistent like fingerprints, its provides particularly appealing approach to this...

The existence of complementary information across multiple sensors has driven the proliferation multivariate datasets. Exploitation this common information, while minimizing assumptions imposed on data led to popularity data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for fusion data. In many practical applications, important prior about exists incorporating into IVA model is expected yield improved separation performance....

There is a growing need for flexible methods the analysis of large-scale functional magnetic resonance imaging (fMRI) data estimation global signatures that summarize population while preserving individual-specific traits. Independent vector (IVA) data-driven method jointly estimates spatio-temporal patterns from multi-subject fMRI data, and effectively preserves subject variability. However, as we show, IVA performance negatively affected when number datasets components increases especially...

Fusing datasets from different brain signal modalities improves accuracy in finding biomarkers of neuropsychiatric diseases. Several approaches, such as joint independent component analysis (ICA) and vector (IVA), are useful but fall short exploring multiple associations between modalities, especially for the case where one underlying modality might have with others another modality. This relationship is possible since a given subject covariation components We show that consecutive...

Aim: In this work, we propose the novel use of adaptively constrained independent vector analysis (acIVA) to effectively capture temporal and spatial properties dynamic blood-oxygen-level-dependent (BOLD) activity (dBA), efficiently quantify property dBA (sdBA). We also incorporate into study brain dynamics gain insight activity-connectivity co-evolution patterns. Introduction: Studies human using functional magnetic resonance imaging (fMRI) have enabled identification unique network...

Brain signals can be measured using multiple imaging modalities, such as magnetic resonance (MRI)-based techniques. Different modalities convey distinct yet complementary information; thus, their joint analyses provide valuable insight into how the brain functions in both healthy and diseased conditions. Data-driven approaches have proven most useful for multimodal fusion they minimize assumptions imposed on data, there are a number of methods that been developed to uncover relationships...

Dynamic functional connectivity (dFC) analysis enables us to capture the time-varying interactions between brain regions and can lead powerful biomarkers. Most dFC studies are focused on study of temporal dynamics require significant post-processing summarize results analysis. In this paper, we introduce an effective framework that makes use independent vector (IVA) with fractional amplitude low frequency fluctuation (fALFF) features extracted from task magnetic resonance imaging (fMRI)...

Data-driven methods based on independent component analysis (ICA) and its extensions, have been attractive for data fusion as they minimize the assumptions placed data. Two widely used extensions of ICA, joint ICA (jICA) multiset canonical correlation prior to (MCCA-jICA) fuse from different datasets by assuming identical mixing matrices. However, these typically only take common features into account within linked disregarding available distinct features, thus limiting their usefulness. In...

In application to functional magnetic resonance imaging (fMRI) data analysis, a number of fusion algorithms have shown success in extracting interpretable brain networks that can distinguish two groups such populations-patients with mental disorder and the healthy controls. However, there are situations where more than exist as multi-task fMRI data. Therefore, this work we propose use IVA effectively extract information is able across multiple when applied fusion. The performance...

Tensor representations have proven useful for many problems, including data completion. A promising application tensor completion is functional magnetic resonance imaging (fMRI) that has an inherent four-dimensional (4D) structure and prone to missing voxels regions due issues in acquisition. key component of successful a rank estimation. While widely used as convex relaxation the rank, nuclear norm (TNN) imposes strong low-rank constraints on all modes be simultaneously often leads...

The detection of steady state visual evoked potentials (SSVEPs) has been identified as an effective solution for brain computer interface (BCI) systems well neurocognitive investigations visually related tasks. SSVEPs are induced at the same frequency stimuli and can be observed in scalp-based recordings electroencephalogram signals, though they one component buried amongst normal signals complex noise. Variations individual response latencies presence multiple biological artifacts...

Due to its relatively few assumptions, independent component analysis (ICA) has become a widely-used tool for the of functional magnetic resonance imaging (fMRI) data. In application, Infomax, been by far most frequently used ICA algorithm, primarily because it is first algorithm applied fMRI analysis. However, now there are number more flexible algorithms, which can exploit multiple types statistical properties signals with fewer assumptions. this work, we investigate performance Infomax...

Functional magnetic resonance imaging (fMRI) is a powerful, noninvasive tool that has significantly contributed to the understanding of human brain. FMRI data provide sequence whole-brain volumes over time and hence are inherently four dimensional (4D). Missing in fMRI experiments arise from image acquisition limits, susceptibility motion artifacts or during confounding noise removal. Hence, significant brain regions may be excluded data, which can seriously undermine quality subsequent...