A three domain covariance framework for EEG/MEG data

Differential network knockoff filter with application to brain connectivity analysis

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporate those assumptions and domain knowledge into probabilistic graphical models, and use those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.

Separable covariance models for health care quality measures across years and topics

Public quality reports for Medicare Advantage health plans include 11 measures of patient experiences reported in the annual Consumer Assessment of Healthcare Providers and Systems surveys. Computing summaries at the health plan level (of multiple measures in multiple years) yields an array-structured random variable. To summarize associations among measures and years, we model the variance-covariance matrix governing the plan-level vectors of yearly quality measures as a Kronecker product of an across-measure matrix and an across-year matrix, or a sum of such Kronecker products. This approach extends separable covariance structure to Fay-Herriot models. In addition, we develop linear combinations of Kronecker products similar to principal components for array random variables. To each Kronecker-product term, we apply post hoc analyses suited to the corresponding dimension of the cross-classification: 1-way factor analysis for the across-measure factor and time-series analysis to the across-year factor. These methods draw out key patterns of variation in the quality measures over time and suggest new strategies for reporting quality information to consumers.

Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data

A new method for the analysis and localization of brain activity has been developed, based on multichannel magnetic field recordings, over minutes, superimposed on the MRI of the individual. Here, a high resolution Fourier Transform is obtained over the entire recording period, leading to a detailed multi-frequency spectrum. Further analysis implements a total decomposition of the frequency components into functionally invariant entities, each having an invariant field pattern localizable in recording space. The method, addressed as functional tomography, makes it possible to find the distribution of magnetic field sources in space. Here, the method is applied to the analysis of simulated data, to oscillating signals activating a physical current dipoles phantom, and to recordings of spontaneous brain activity in 10 healthy adults. In the analysis of simulated data, 61 dipoles are localized with 0.7 mm precision. Concerning the physical phantom the method is able to localize three simultaneously activated current dipoles with 1 mm precision. Spatial resolution 3 mm was attained when localizing spontaneous alpha rhythm activity in 10 healthy adults, where the alpha peak was specified for each subject individually. Co-registration of the functional tomograms with each subject's head MRI localized alpha range activity to the occipital and/or posterior parietal brain region. This is the first application of this new functional tomography to human brain activity. The method successfully provides an overall view of brain electrical activity, a detailed spectral description and, combined with MRI, the localization of sources in anatomical brain space.