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.

Metal mixtures are associated with increased anxiety during pregnancy.

Background: Exposure to low-dose toxic metals in the environment is ubiquitous. Several murine studies have shown metals induce anxiety-like behaviors, and mechanistic research supports that metals disrupt neurotransmitter signaling systems implicated in the pathophysiology of anxiety. In this study, we extend prior research by examining joint exposure to six metals in relation to maternal anxiety symptoms during pregnancy.

Nephrotoxic Metal Mixtures and Preadolescent Kidney Function.

Exposure to metals including lead (Pb), cadmium (Cd), and arsenic (As), may impair kidney function as individual toxicants or in mixtures. However, no single medium is ideal to study multiple metals simultaneously. We hypothesized that multi-media biomarkers (MMBs), integrated indices combining information across biomarkers, are informative of adverse kidney function.

Prenatal metal mixtures and sex-specific infant negative affectivity.

Unlabelled: Prenatal exposure to metals has been associated with a range of adverse neurocognitive outcomes; however, associations with early behavioral development are less well understood. We examined joint exposure to multiple co-occurring metals in relation to infant negative affect, a stable temperamental trait linked to psychopathology among children and adults.

Methods: Analyses included 308 mother-infant pairs enrolled in the PRISM pregnancy cohort.

Exosomal miRNAs in urine associated with children's cardiorenal parameters: a cross-sectional study.

The authors sought to examine associations between urinary exosomal miRNAs (exo-miRs), emerging biomarkers of renal health, and cardiorenal outcomes in early childhood. The authors extracted exo-miRs in urine from 88 healthy Mexican children aged 4-6 years. The authors measured associations between 193 exo-miRs and cardiorenal outcomes: systolic/diastolic blood pressure, estimated glomerular filtration rate and urinary sodium and potassium levels.

Integrated measures of lead and manganese exposure improve estimation of their joint effects on cognition in Italian school-age children.

Every day humans are exposed to mixtures of chemicals, such as lead (Pb) and manganese (Mn). An underappreciated aspect of studying the health effects of mixtures is the role that the exposure biomarker media (blood, hair, etc.) may play in estimating the effects of the mixture.

COVID-19 and treating incarcerated populations for opioid use disorder.

The Franklin County Sheriff's Office (FCSO), in Greenfield, Massachusetts, is among the first jails nationwide to provide correctional populations with access to all three medications to treat opioid use disorder (MOUD, i.e., buprenorphine, methadone, naltrexone).

Sex-specific associations between co-exposure to multiple metals and visuospatial learning in early adolescence.

The predisposition, severity, and progression of many diseases differ between males and females. Sex-related differences in susceptibility to neurotoxicant exposures may provide insight into the cause of the observed discrepancy. Early adolescence, a period of substantial structural and functional brain changes, may present a critical window of vulnerability to environmental exposures.

Multi-media biomarkers: Integrating information to improve lead exposure assessment.

Exposure assessment traditionally relies on biomarkers that measure chemical concentrations in individual biological media (i.e., blood, urine, etc.

Length of gestation and birth weight are associated with indices of combined kidney biomarkers in early childhood.

Infants born prematurely or with low birth weights are more susceptible to kidney dysfunction throughout their lives. Multiple proteins measured in urine are noninvasive biomarkers of subclinical kidney damage, but few studies have examined the joint effects of multiple biomarkers. We conducted an exploratory study of 103 children in the Programing Research in Obesity, Growth, Environment, and Social Stressors (PROGRESS) longitudinal birth cohort, and measured nine proteins selected a priori in banked spot urine samples collected at ages 4-6.

Time-varying associations between prenatal metal mixtures and rapid visual processing in children.

Background: Humans are exposed to mixtures of chemicals across their lifetimes, a concept sometimes called the "exposome." Mixtures likely have temporal "critical windows" of susceptibility like single agents and measuring them repeatedly might help to define such windows. Common approaches to evaluate the effects of chemical mixtures have focused on their effects at a single time point.

Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data.

Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes: subject, time, and channel, while functional magnetic resonance imaging (fMRI) data may be in the form of subject by voxel matrices.

A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.

Background: The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unknown and the alignment of factors across different methods is impractical and imprecise.

New Method: We present a novel method, global difference maps (GDMs), to compare the results of different fMRI analysis techniques on real fMRI data, quantify their relative performances, and highlight the differences between the decompositions visually.

The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics.

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 methods that are based on two different forms of diversity-statistical properties of the data-statistical independence for ICA and sparsity for DL.

Quantifying the Interaction and Contribution of Multiple Datasets in Fusion: Application to the Detection of Schizophrenia.

The extraction of information from multiple sets of data is a problem inherent to many disciplines. This is possible by either analyzing the data sets jointly as in data fusion or separately and then combining as in data integration. However, selecting the optimal method to combine and analyze multiset data is an ever-present challenge.

Sample-poor estimation of order and common signal subspace with application to fusion of medical imaging data.

Due to their data-driven nature, multivariate methods such as canonical correlation analysis (CCA) have proven very useful for fusion of multimodal neurological data. However, being able to determine the degree of similarity between datasets and appropriate order selection are crucial to the success of such techniques. The standard methods for calculating the order of multimodal data focus only on sources with the greatest individual energy and ignore relations across datasets.

The role of diversity in complex ICA algorithms for fMRI analysis.

Background: The widespread use of data-driven methods, such as independent component analysis (ICA), for the analysis of functional magnetic resonance imaging data (fMRI) has enabled deeper understanding of neural function. However, most popular ICA algorithms for fMRI analysis make several simplifying assumptions, thus ignoring sources of statistical information, types of "diversity," and limiting their performance.

New Method: We propose the use of complex entropy rate bound minimization (CERBM) for the analysis of actual fMRI data in its native, complex, domain.

Multi-modal data fusion using source separation: Two effective models based on ICA and IVA and their properties.

Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the datasets, it is highly desirable to minimize the underlying assumptions. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence.