Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Moon- Ringo Ho is active.

Publication


Featured researches published by Moon- Ringo Ho.


NeuroImage | 2006

A Bayesian approach to modeling dynamic effective connectivity with fMRI data.

Sourabh Bhattacharya; Moon-Ho Ringo Ho; Sumitra Purkayastha

A state-space modeling approach for examining dynamic relationship between multiple brain regions was proposed in Ho, Ombao and Shumway (Ho, M.R., Ombao, H., Shumway, R., 2005. A State-Space Approach to Modelling Brain Dynamics to Appear in Statistica Sinica). Their approach assumed that the quantity representing the influence of one neuronal system over another, or effective connectivity, is time-invariant. However, more and more empirical evidence suggests that the connectivity between brain areas may be dynamic which calls for temporal modeling of effective connectivity. A Bayesian approach is proposed to solve this problem in this paper. Our approach first decomposes the observed time series into measurement error and the BOLD (blood oxygenation level-dependent) signals. To capture the complexities of the dynamic processes in the brain, region-specific activations are subsequently modeled, as a linear function of the BOLD signals history at other brain regions. The coefficients in these linear functions represent effective connectivity between the regions under consideration. They are further assumed to follow a random walk process so to characterize the dynamic nature of brain connectivity. We also consider the temporal dependence that may be present in the measurement errors. ML-II method (Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis (2nd ed.). Springer, New York) was employed to estimate the hyperparameters in the model and Bayes factor was used to compare among competing models. Statistical inference of the effective connectivity coefficients was based on their posterior distributions and the corresponding Bayesian credible regions (Carlin, B.P., Louis, T.A., 2000. Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman and Hall, Boca Raton). The proposed method was applied to a functional magnetic resonance imaging data set and results support the theory of attentional control network and demonstrate that this network is dynamic in nature.


Computational Statistics & Data Analysis | 2006

Time-dependent frequency domain principal components analysis of multichannel non-stationary signals

Hernando Ombao; Moon-Ho Ringo Ho

High dimensional multi-channel signals often exhibit multi-collinearities. This suggests that such signals can be decomposed into uncorrelated principal components with possibly lower dimension than that of the original signal. A time-localized frequency domain principal components analysis method is proposed for signals that exhibit locally stationary behavior. The first step is to form a mean square consistent estimate of the time-varying spectrum matrix by smoothing the time-localized periodograms using a kernel defined on the frequency axis whose span is selected automatically using a generalized cross-validation procedure that is based on the asymptotic gamma distribution. The eigenvalues of the spectral density estimate are then computed which are the estimated spectra of the principal components. In addition, one may apply a formal statistical procedure for testing whether the weights (components of an eigenvector) at a particular channel change over time. The proposed method can be easily implemented because it only requires the fast Fourier transform (FFT) and eigenvalue-eigenvector decomposition routines. An illustration is presented using a multi-channel brain waves data set recorded during an epileptic seizure.


Fathering: A Journal of Theory, Research, and Practice About Men As Fathers | 2004

Unidimensional Versus Multidimensional Perspectives on Father Involvement

Sarah J. Schoppe-Sullivan; Brent A. McBride; Moon-Ho Ringo Ho


Behaviour Research and Therapy | 2007

Responding to symptoms of depression and anxiety: Emotion regulation, neuroticism, and engagement in risky behaviors

Randy P. Auerbach; John R. Z. Abela; Moon-Ho Ringo Ho


Cognitive Brain Research | 2005

A structural equation modeling analysis of attentional control: an event-related fMRI study.

Kirk I. Erickson; Moon-Ho Ringo Ho; Stanley J. Colcombe; Arthur F. Kramer


Archive | 2006

The State-Space Approach to Modeling Dynamic Processes.

Moon-Ho Ringo Ho; Robert H. Shumway; Hernando Ombao


Journal of Experimental Psychology: Learning, Memory and Cognition | 2005

An alternative perspective on von winterfeldt et al.'s (1997) test of consequence monotonicity

Moon-Ho Ringo Ho; Michel Regenwetter; Reinhard Niederée; Dieter Heyer


Ultrasound in Medicine and Biology | 2004

Excess risk thresholds in ultrasound safety studies: statistical methods for data on occurrence and size of lesions.

Douglas G. Simpson; Moon-Ho Ringo Ho; Yan Yang; Jianhui Zhou; James F. Zachary; William D. O’Brien


Statistics & Probability Letters | 2004

Properties of some bilinear models with periodic regime switching

Abdelouahab Bibi; Moon-Ho Ringo Ho


Archive | 2005

Research report A structural equation modeling analysis of attentional control: an event-related fMRI study

Kirk I. Erickson; Moon-Ho Ringo Ho; Stanley J. Colcombe; Arthur F. Kramer

Collaboration


Dive into the Moon- Ringo Ho's collaboration.

Top Co-Authors

Avatar

Hernando Ombao

University of California

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Stanley J. Colcombe

Nathan Kline Institute for Psychiatric Research

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Yan Yang

Arizona State University

View shared research outputs
Top Co-Authors

Avatar
Researchain Logo
Decentralizing Knowledge