189 citations · 888 across the 107 of their papers we have counts for
26 papers · 1 filter
Nonlinear Coherence for Vector Time Series: Defining Region-to-Region Functional Brain Connectivity
Paolo Victor Redondo, Raphaël Huser, Hernando Ombao
Alterations in functional brain connectivity characterize neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). As a non-invasive and cost…
Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram
Ziling Ma, Mara Sherlin Talento, Ying Sun +1
Clustering multivariate time series (MTS) is challenging due to non-stationary cross-dependencies, noise contamination, and gradual or overlapping state boundaries. We introduce a…
Spectral Extremal Connectivity of Two-State Seizure Brain Waves
Mara Sherlin D. Talento, Jordan Richards, Marco Pinto-Orellana +2
Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading…
Nonlinear Causality in Time Series Networks: With Application to Motor Imagery vs Execution
Sipan Aslan, Hernando Ombao
Causal interactions in time series networks can be dynamic and nonlinear, making it difficult to identify them using conventional linear causality estimations. We propose a novel a…
Granger Causality in High-Dimensional Networks of Time Series
Sipan Aslan, Hernando Ombao
A novel approach is developed for discovering directed connectivity between specified pairs of nodes in a high-dimensional network (HDN) of brain signals. To accurately identify ca…
Statistics of Extremes for Neuroscience
Paolo V. Redondo, Matheus B. Guerrero, Raphaël Huser +1
This chapter illustrates how tools from univariate and multivariate statistics of extremes can complement classical methods used to study brain signals and enhance the understandin…
Functional-Coefficient Models for Multivariate Time Series in Designed Experiments: with Applications to Brain Signals
Paolo Victor Redondo, Raphael Huser, Hernando Ombao
To study the neurophysiological basis of attention deficit hyperactivity disorder (ADHD), clinicians use electroencephalography (EEG) which record neuronal electrical activity on t…
BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data
Meini Tang, Chee-Ming Ting, Hernando Ombao
Intrinsic connectivity networks (ICNs) are specific dynamic functional brain networks that are consistently found under various conditions including rest and task. Studies have sho…
Time-varying optimization for Spike Inference from Multi-Trial Calcium Recordings
Tong Shen, Kevin Johnston, Gyorgy Lur +3
Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from free…
Change-point detection using spectral PCA for multivariate time series
Shuhao Jiao, Tong Shen, Zhaoxia Yu +1
We propose a two-stage approach Spec PC-CP to identify change points in multivariate time series. In the first stage, we obtain a low-dimensional summary of the high-dimensional ti…
Levels and trends in the sex ratio at birth in seven provinces of Nepal between 1980 and 2016 with probabilistic projections to 2050: a Bayesian modeling approach
Fengqing Chao, Samir KC, Hernando Ombao
The sex ratio at birth (SRB; ratio of male to female births) in Nepal has been reported without imbalance on the national level. However, the national SRB could mask the disparity…
Probabilistic Projection of the Sex Ratio at Birth and Missing Female Births by State and Union Territory in India
Fengqing Chao, Christophe Z. Guilmoto, Samir K. C. +1
The sex ratio at birth (SRB) in India has been reported imbalanced since the 1970s. Previous studies have shown a great variation in the SRB across geographic locations in India ti…
Modeling Brain Connectivity with Graphical Models on Frequency Domain
Xu Gao, Weining Shen, Chee-Ming Ting +3
Multichannel electroencephalograms (EEGs) have been widely used to study cortical connectivity during acquisition of motor skills. In this paper, we introduce copula Gaussian graph…
Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials
Charles Fontaine, Ron D. Frostig, Hernando Ombao
This paper intends to develop tools for characterizing non-linear spectral dependence between spontaneous brain signals. We use parametric copula models (both bivariate and vine mo…
Topological Brain Network Distances
Moo K. Chung, Hyekyoung Lee, Andrey Gritsenko +4
Existing brain network distances are often based on matrix norms. The element-wise differences in the existing matrix norms may fail to capture underlying topological differences.…
Coherence-based Time Series Clustering for Brain Connectivity Visualization
Carolina Euan, Ying Sun, Hernando Ombao
We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels tha…
A Robust Interrupted Time Series Model for Analyzing Complex Healthcare Intervention Data
Maricela Cruz, Miriam Bender, Hernando Ombao
Current health policy calls for greater use of evidence based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and…
Multi-Scale Factor Analysis of High-Dimensional Brain Signals
Chee-Ming Ting, Hernando Ombao, Sh-Hussain Salleh
In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-…
Modeling high dimensional multichannel brain signals
Lechuan Hu, Norbert Fortin, Hernando Ombao
Our goal is to model and measure functional and effective (directional) connectivity in multichannel brain physiological signals (e.g., electroencephalograms, local field potential…
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Chee-Ming Ting, Hernando Ombao, S. Balqis Samdin +1
Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes…
Exploratory Analysis of High Dimensional Time Series with Applications to Multichannel Electroencephalograms
Yuxiao Wang, Chee-Ming Ting, Hernando Ombao
In this paper, we address the the major hurdle of high dimensionality in EEG analysis by extracting the optimal lower dimensional representations. Using our approach, connectivity…
A Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in fMRI Data
Stefano Castruccio, Hernando Ombao, Marc G. Genton
Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different scales is one of the main c…
Spectral Synchronicity in Brain Signals
Carolina Euan, Hernando Ombao, Joaquin Ortega
Brain activity following stimulus presentation and during resting state are often the result of highly coordinated responses of large numbers of neurons both locally and globally.…
An Introduction to Applications of Wavelet Benchmarking with Seasonal Adjustment
Homesh Sayal, John A. D. Aston, Duncan Elliott +1
Prior to adjustment, accounting conditions between national accounts data sets are frequently violated. Benchmarking is the procedure used by economic agencies to make such data se…