Monday, Aug 5: 8:30 AM - 10:20 AM
5045
Contributed Papers
Oregon Convention Center
Room: CC-D133
Main Sponsor
Section on Statistics in Imaging
Presentations
Functional MRI is a popular noninvasive technique for mapping brain regions activated by specific brain functions. However, as fMRI measures brain activity indirectly through blood flow, the so-called "brain or vein" problem refers to the difficulty in determining whether measured activation corresponds to (desired) brain tissue or (undesired) large veins, which may be draining blood from neighboring regions. Now, fMRI data consist of both magnitude and phase components (i.e., it is complex-valued), but in the vast majority of statistical analyses, only the magnitude data is utilized. However, while activation in the magnitude component can come from both "brain or vein", previous work has demonstrated that activation in the phase component "discriminates" between the two: phase activation occurs in voxels with large, oriented vessels but not in voxels with small, randomly oriented vessels immediately adjacent to brain tissue. Following this motivation, we have developed a model that allows for activation in magnitude and phase, one more general than those previously proposed.
Keywords
functional MRI
Imaging
In the context of time series, principal component analysis of spectral density matrices can provide valuable, parsimonious information about the behavior of the underlying process. Given a high-dimensional weakly stationary time series, it is of interest to obtain principal components of the spectral density matrices that are interpretable as being sparse in coordinates and localized in frequency. In this talk, we introduce a formulation of this novel problem and an algorithm for estimating the object of interest. In addition, we propose a smoothing procedure that improves estimation of eigenvector trajectories over the frequency range. The method is motivated by and used to understand neurological mechanisms from high-density resting-state EEG in a patient hospitalized for a first psychotic episode and compared with a healthy control individual.
Keywords
Principal component Analysis
Spectral density matrix
High dimensional time series
Sparse Estimation
Spectral association plays a vital role in the study of functional brain connectivity, but traditional measures focus on linear spectral associations found in the bulk of the distribution. In certain studies, such as risk analysis, the interest shifts to connectivity in the tails of the distribution, as this reveals crucial information pertaining to extreme events, e.g., seizures. This motivates us to extend the notion of spectral association into the tail of the periodogram (given a specific frequency band) to study electroencephalogram (EEG) signals of seizure-prone neonates. Existing models are limited to tail of univariate periodogram or the tail associations of filtered series. In this study, we develop a non-stationary extremal dependence model for multivariate time series, that permits different dependence behaviour during different brain phases, i.e., high and low activity. This allows us to identify key tail-frequency connectivity at specific frequency bands that could trigger an outburst of energy, and we discuss these novel scientific findings alongside a comparison of the extremal behaviour of brain signals for ictal and non-ictal patients.
Keywords
extreme value theory
spectral analysis
electroencephalogram (EEG)
spectral clustering
conditional extremes
periodogram
We propose analyses of resting-state fMRI data that use partial location information of regions of interest (ROIs). In particular we consider subject-level regression models to explain intra-subject variability in brain functional connectivity and summarize connectivity parsimoniously, and we investigate approaches based on distributions of connectivity values. We apply our approach to Human Connectome Project data.
Keywords
fMRI
connectivity
connectomics
This research enhances fMRI data analysis by integrating temporal dynamics into spatial random field theory. We developed a new test statistic,, within the time-adaptive Scale Space Gaussian Random Field Model, focusing on signal detection in fMRI data. It captures the global maximum across spatial and temporal dimensions.
Our methodology, employing the Functional Autoregressive (FAR (1)) model, focuses on temporal dependencies and spatial arrangements in data, significantly contributing to neuroimaging studies. We used a simulation approach to estimate the p-value for testing the signal using X_max and understand its advantages in analyzing spatial-temporal patterns in fMRI data.
Keywords
Time-Adaptive Scale Space
Gaussian Random Field Model
fMRI Data Analysis
Functional Autoregressive Model
Statistical Methodology