PUBLICATIONS
Selected Papers on Statistical Theory and Methodology (* PhD Student & Trainee)
Books:
H Ombao, M Lindquist, W Thompson, J Aston (2016) Handbook of Neuroimaging Data Analysis CRC Press
Prado R. and West. M. (2010). Time Series: Modeling, Computation, and Inference. CRC Press, Taylor & Francis Group.
Submitted and In Revision:
L Hu, N Fortin, H Ombao (2017) Modeling high dimensional multichannel brain signals
CM Ting, H Ombao, SB Samdin, SH Salleh (2017) Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Cadonna, A., Kottas, A., Prado, R. (2016) Bayesian spectral for multiple spectral densities, University of California, Santa Cruz.
C Gorrostieta, H Ombao, R von Sachs (2016) Time-dependent dual frequency coherency in multivariate non-stationary time series
​
Y Wang, CM Ting, H Ombao (2016) Exploratory Analysis of High Dimensional Time Series with Applications to Multichannel Electroencephalograms
S Castruccio, H Ombao, MG Genton (2016) A multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data
X Gao, B Shahbaba, N Fortin, H Ombao (2016) Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials
C Euan, H Ombao, J Ortega (2016) The Hierarchical Spectral Merger algorithm: A New Time Series Clustering Procedure
Yu. C., Prado R., Rowe D. and Ombao H. (2016) A Bayesian Variable Selection Approach Yields Improved Brain Activation From Complex-Valued fMRI
C Euan, H Ombao, J Ortega (2015) Spectral synchronicity in brain signals
2014 -- present
57. X. Gao, B. Shahbaba, H. Ombao, Modeling Binary Time Series Using Gaussian Processes With Application to Predicting Sleep States, To appear in Journal of Classification. R package code HIBITS can be downloaded here.
56. Cruz M, Binder M. and Ombao H. (2017) A Robust Interrupted Time Series Model for Analyzing Complex Healthcare To appear in Statistics in Medicine -- Access Shiny Toolbox and manual here .
55. Warnick, R., Guindani, M., Erhardt, E., Allen, E., Calhoun, V. and Vannucci, M.(2017) A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.Journal of the American Statistical Association. In Press.
54. Cadonna, A., Kottas, A., Prado, R. (2017). Bayesian mixture modeling for spectral density estimation, Statistics & Probability Letters, Volume 125, June 2017, Pages 189–195
53. G Yu, Y Liu (2016) Sparse regression incorporating graphical structure among predictors Journal of the American Statistical Association 111 (514), 707-720
52. SB Samdin, CM Ting, H Ombao, SH Salleh (2017) A unified estimation framework for state-related changes in effective brain connectivity, IEEE Transactions on Biomedical Engineering, 64 (4), 844-858
51. Chiang, S., Guindani, M., Yeh, H.J., Haneef, Z., Stern, J.M. and Vannucci, M. (2017). A Bayesian Vector Autoregressive Model for Multi-Subject Effective Connectivity Inference using Multi-Modal Neuroimaging Data. Human Brain Mapping. Volume 38, Issue 3, Pages 1311–1332, DOI: 10.1002/hbm.23456.
50. H Kang, H Ombao, C Fonnesbeck, Z Ding, VL Morgan (2017) A Bayesian Double Fusion Model for Resting State Brain Connectivity Using Joint Functional and Structural Data Brain Connectivity
49. M Fiecas, H Ombao (2016) Modeling the evolution of dynamic brain processes during an associative learning experiment Journal of the American Statistical Association, 111 (516), 1440-1453
48. Chekouo T, Stingo FC, Guindani M, Do K-A (2016) A Bayesian predictive model for imaging genetics with application to schizophrenia. Annals of Applied Statistics , 10 (3) , 1547-1571.
47. Guhaniyogi, R. and Dunson, D.B. (2016). Compressed Gaussian Process for Manifold Regression. Journal of Machine Learning Research, 17, 1-26
46. G Yu, Y Liu (2016) Sparse regression incorporating graphical structure among predictors Journal of the American Statistical Association 111 (514), 707-720
45. Guhaniyogi, R. and Dunson, D.B. (2016). Bayesian Compressed Regression . Journal of the American Statistical Association, Theory & Methods, 110, 1500-1514
44. Zhang, L., Guindani, M., Versace, F., Engelmann, J.M. and Vannucci, M. (2016). A Spatio-Temporal Nonparametric Bayesian Model of Multi-Subject fMRI Data. Annals of Applied Statistics, 10(2), 638-666.
43. Y Wang, CM Ting, H Ombao (2016) Modeling Effective Connectivity in High-Dimensional Cortical Source Signals, IEEE Journal of Selected Topics in Signal Processing, 10 (7), 1315-1325
42. H Sayal, JAD Aston, D Elliott, H Ombao (2016) An introduction to applications of wavelet benchmarking with seasonal adjustment Journal of the Royal Statistical Society: Series A (Statistics in Society)
41. Chiang S, Cassese A, Guindani M, Vannucci M, Yeh HJ, Haneef Z and Stern, JM (2016).Time-dependence of Graph Theory Metrics in Functional Connectivity Analysis. NeuroImage, 125, 601-615.
40. Z Yu, R Prado, EB Quinlan, SC Cramer, H Ombao (2016) Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach, ,Journal of the American Statistical Association, 111 (514), 549-563
39. B Zhou, DE Moorman, S Behseta, H Ombao, B Shahbaba (2016) A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making Journal of the American Statistical Association, 111 (514), 459-471
38. Sun W, Reich B, Cai T, Guindani M, Schwartzman A (2015) False Discovery Control in Large-Scale Spatial Multiple Testing Journal of the Royal Statistical Society, Series B (Statistical Methodology), 77 (1) , 59-83
37. C Kirch, B Muhsal, H Ombao (2015) Detection of changes in multivariate time series with application to EEG data, Journal of the American Statistical Association, 110 (511), 1197-1216
36. Zhang L, Guindani M, Vannucci M (2015). Bayesian models for functional magnetic resonance imaging data analysis. WIREs Computational Statistics, 7, 21-41.
35. Zhang L, Guindani M, Versace F, Vannucci M (2014) A Spatio-Temporal Nonparametric Bayesian Variable Selection Model of fMRI Data for Clustering Correlated Time Courses. Neuroimage 95 , 162-175.
34. Y Wang, H Ombao, MK Chung (2015) Topological seizure origin detection in electroencephalographic signals, Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 351-354
33. D Ngo, Y Sun, MG Genton, J Wu, R Srinivasan, SC Cramer, H Ombao (2015) An exploratory data analysis of electroencephalograms using the functional boxplots approach Frontiers in neuroscience, 9
32. H Kang, J Blume, H Ombao, D Badre (2015) Simultaneous control of error rates in fMRI data analysis, NeuroImage 123, 102-113
2011 -- 2014
31. Shababa B, Zhou B, Lan S, Ombao H, Moorman D and Behseta S. (2014). A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons. Neural Computation, 26, 2025-2051.
30. *Park, T, Eckley I and Ombao H. (2014). Estimating the time-evolving partial coherence between signals via multivariate locally stationary wavelet processes. IEEE Transactions on Signal Processing, 62, 5250-5250.
29. Macaro C. and Prado R. (2014). Spectral decompositions of multiple time series: A Bayesian nonparametric approach. Psychometrika, 79(1): 105-129.
28. Fiecas M, Ombao H, van Lunen D, Baumgartner R, Coimbra A and Feng D (2013). Quantifying Temporal Correlations: A Test-Retest Evaluation of
Functional Connectivity in Resting-State fMRI. NeuroImage, 65, 231-241.
27. Olhede S and Ombao H. (2013). Covariance of Replicated Modulated Cyclical Time Series. IEEE Transactions on Signal Processing, 61, 1944-1957.
26. *Koestler D, Ombao H and Bender J. (2013). Ensemble-based methods for forecasting census in hospital units. BMC Medical Research Methodology, 13:67, 1-12. D. Koestler recipient of the 2011 ENAR Student Paper Award.
25. Gorrostieta C, Fiecas M, Ombao H, Burke E and Cramer S. (2013). Hierarchical Vector Auto-Regressive Models and Their Applications to Multi-subject Effective Connectivity. Frontiers in Computational Neuroscience, 7: 159, 1-11.
24. Bender J, Koestler D, Ombao H, McCourt M, Alskinis B, Rubin L and Padbury J. (2013). Neonatal Intensive Care Unit: Predictive Models for Length of Stay. The Journal of Perinatology, 33, 147-153.
23. Markova A, Weinstock M, Risica P, Kirtania U, Shaikh W, Ombao H, Chambers C, Kabongo M, Kallail J, Post D. (2013). Effect of a web-based curriculum on primary care practice: Basic Skin Cancer Triage trial. Family Medicine, accepted for publication.
22. Gjelsvik A, Rogers M, Clark M, Ombao H and Rakowski W. (2013). Continuum of Mammography Use among US Women: Classification Tree Analysis. American Journal of Health Behavior, accepted for publication.
21. Prado R. and Lopes H. (2013). Sequential parameter learning and filtering in structured autoregressive models. Statistics and Computing, Vol. 23(1), pp. 43-57.
20. Prado R. (2013) Sequential estimation of mixtures of structured autoregressive models. Computational Statistics and Data Analysis, Vol. 58, pp. 58-70.
19. Datta S., Prado R., and Rodriguez A. (2013) Bayesian factor models in characterizing molecular adaptation. Journal of Applied Statistics, Vol. 40(7), pp. 1402-1424
18. *Gorrostieta C, Ombao H, Bedard P and Sanes J.N. (2012). Investigating Stimulus-Induced Changes in Connectivity Using Mixed Effects Vector Autoregressive Models. NeuroImage, 59, 3347-3355.
17. *Gorrostieta C, Ombao H, Rrado R, Patel S and Eskandar E. (2012). Exploring Dependence Between Brain Signals in a Monkey During Learning. Journal of Time Series Analysis, 33(5), 771-778.
16. Motta, G. and Ombao, H. (2012). Evolutionary Factor Analysis of Replicated Time Series. Biometrics, 68, 825-836.
15. Ombao H. (2012). Discussion: Time–Threshold Maps: Using information from wavelet reconstructions with all threshold values simultaneously. Journal of the Korean Statistical Society, 41, 171-172.
14. Stoffer D and Ombao H. (2012). Editorial: Special Issue on Time Series Analysis in the Biological Sciences. Journal of Time Series Analysis, 33(5), 701-703.
13. Ombao H. (2012). Time Series Analysis of multivariate non-stationary time series using the localised Fourier Library. Handbook of Statistics: Time Series, Elsevier Science.
12. *Kang H, Ombao H, Linkletter C, Long N and Badre D. (2012). Spatio-Spectral Mixed Effects Model for Functional Magnetic Resonance Imaging Data. J Amer Stat Assoc, 107, 568-577. H. KANG Recipient of the 2011 John Van Ryzin Award for Best Paper for ENAR
11. Markova A, Risica P, Shaikh W, Kirtania U, Ombao H and Weinstock M. (2012). Gender in examination and counseling for melanoma in primary care. Archives of Internal Medicine, 6:26, 648-657.
10. Zhu T, Cohen R, Harezlak J, Ombao H, Navia B and Schiffito G. (2012). Patterns of CNS injury in HIV infection after partial immune reconstitution: A DTI tract-based spatial statistics study. Human Brain Mapping, 33, 1384-1392.
9. Datta S., Rodriguez A., and Prado R. (2012). Bayesian semiparametric regression models to characterize molecular evolution. BMC Bioinformatics, Vol. 13: 278.
8. Gorrostieta C., Ombao H., Eskandar E., and Prado R. (2012) Exploring dependence between brain signals in a monkey during learning. Journal of Time Series Analysis.
7. Fiecas, M. and Ombao, H. (2011).The Generalized Shrinkage Estimator for the Analysis ofFunctional Connectivity of Brain Signals. Annals of Applied Statistics, 5, 1102-1125.Recipient of the 2010 Best Student Paper Award, New England Statistics Symposium
6. Bunea F, She Y Ombao H, Gongvatana W, Devlin K and Cohen R. (2011). Penalized Least Squares Regression Methods and Applications to Neuroimaging. NeuroImage, (55), 1519-1527.
5. Verducci J and Ombao H. (2011). Introduction to the special issue on best papers from the SLDM competition. Statistical Analysis and Data Mining, 4: 565-566.
4. Ito H, Matsuo K, Tanaka H, Koestler DC, Ombao H, Fulton J, Shibata A, Fujita M,Sugiyama and Mor V. (2011). Non-filter and filter cigarette consumption and the incidence of lung cancer type in Japan and the United States: Analysis of 30-year data from population-based cancer registries, International Journal of Cancer, 128, 175-1998.
3. Politi MC, Clark MA, Ombao H, Dizon D and Elwyn G. (2011). Communicating uncertainty can lead to less decision satisfaction: A necessary cost of involving patients in shared decision making? Health Expectations, 14, 84-91.
2. Cohen R, de la Monte S, Gongvatana A, Ombao H, Gonzalez B, Devlin K, Navia B and Tashima K. (2011). Plasma cytokine concentrations associated with HIV/hepatitis C coinfection are related to attention, executive and psychomotor functioning, J Neuroimmunology, 233, 204-210.
1. Gongvatana A, Cohen R, Correia S, Devlin K, Miles J, Clark U, Westbrook M, Hana G, Kang H, Ombao H, Navia B, Laidlaw D and Tashima K. (2011). Clinical Contributors to Cerebral White Matter Integrity in HIV-infected Individuals. Journal of Neurovirology, 7, 477-486
Pre-2011 – Statistics (Theory and Methods)
(1.) Stoffer, D. and Ombao, H. (2000). Localized Spectral Envelope, Resenhas, 4, 363-381.
(2.) Ombao, H., Raz, J., von Sachs, R. and Malow, B. (2001). Automatic Statistical Analysis of Bivariate Non-Stationary Time Series, J Amer Stat
Assoc, 96, 543-560.
(3.)Ombao, H., Raz, J., Strawderman, R. and von Sachs, R. (2001). A simple GCV method of span selection for periodogram smoothing,
Biometrika, 88, 1186-1192.
(4.) Ombao, H., Raz, J., von Sachs, R. and Guo, W. (2002). The SLEX Model of Non-Stationary Random Process, Annals Inst Stat Math, 54, 171-200.
(5.) Stoffer, D., Ombao, H. and Tyler, D. (2002). Evolutionary Spectral Envelope: An Approach Using the Tree-Based Adaptive Segmentation,
Annals Inst Stat Math, 54, 201-223.
(6.) Guo, W., Dai, M., Ombao, H. and von Sachs (2003). Smoothing Spline ANOVA For Time-Dependent Spectral Analysis, J Amer Stat Assoc, 98,
643-652.
(7.) *Pasia, J., Hermosilla, A. and Ombao, H. (2004). Genetic Algorithms: Useful Statistical Tools, J Stat Comp Simu, 75, 237-251.
(8.) Ombao, H., Heo, J., Stoffer, D. (2004). Statistical Analysis of Seismic Signals: An Almost Real Time Approach, Time Series Analysis and
Applications to Geophysical Systems (eds. D. Brillinger E. Robinson and F. Schoenberg), New York: Springer Verlag, IMA Series, 139, 53-72.
(9.) *Huang, H., Ombao, H. and Stoffer D. (2004). Classification and Discrimination of Non-Stationary Time Series Using the SLEX Model, J Amer
Stat Assoc, 99, 763-774.
(10.) *Ho, M., Ombao, H. and Shumway, R. (2005). Modelling Brain Dynamics: A State-Space Approach, Statistica Sinica, 15, 407-425.
(11.) *Gamalo M, Ombao H and Jennings R. (2005). Comparing Extent of Activation: A Robust Permutation Approach. NeuroImage, 24(3): 715-
722.
(12.) Ombao, H., von Sachs, R. and Guo, W. (2005). SLEX Analysis of Multivariate Non-Stationary Time Series, J Amer Stat Assoc, 100, 519-531.
(13.) Bunea, F., Ombao, H. and Auguste, A. (2006). Minimax Adaptive Spectral Estimation from an Ensemble of Signals, IEEE Trans Signal Proc, 54,
2865-2873.
(14.) *Ho, M., Shumway, R. and Ombao, H. (2006). State-Space Models for Longitudinal Data With Applications in the Biological and Social
Sciences. In Walls and Shafer (eds). Models for Intensive Longitudinal Data. New York, Ny: Oxford Univ.Press.
(15.) *Shinkareva, S., Ombao, H. and Sutton B. (2006). A Data-Driven Approach to Classification and Discrimination of fMRI Data, NeuroImage,
33, 63-71.
(16.) Ombao, H. and Ho, M. (2006). Time-dependent frequency domain principal components analysis of multi-channel non-stationary signals,
Comp Stat and Data Anal, 50(9), 2339-2360.
(17.) *Choi, H., Ombao, H. and Ray, B. (2007). Sequential Change-point Detection Method in Time Series, Technometrics, 50(1), 40-52.
(18.) Ombao, H. and Van Bellegem (2008). Coherence Analysis: A Linear Filtering Point Of View, IEEE Trans on Signal Proc, 56(6), 2259-2266.
(19.) *Ho, M, Ombao, H, Edgar, C and Miller, G. (2008). Time-Frequency Discriminant Analysis of MEG.
(20.) Ombao, H, Shao, X., Rykhlevskaia, E, Fabiani, M and Gratton, G. (2008). Spatio-Spectral Analysis of Brain Signals, Statistica Sinica, 18, 1465-
1482.
(21.) Shitan, M., Ombao, H. and Ling, K-W. (2009). Spatial Modeling of Peak Frequencies of Brain Signals. Malaysian J Math Sci, 3(1), 13-26.
(22.) Tadjuidje,J, Ombao, H. and Davis, R. (2009). A Class of Switching Regimes Autoregressive Driven Processes with Exogenous Components.J
Time Series Anal, 30, 505 – 533
(23.) Fryzlewicz, P and Ombao, H. (2009). Consistent Classification of Non-Stationary Signals Using Stochastic Wavelet Representations, J Amer
Stat Assoc, 104, 299-312.
(24.) *Gao, B., Ombao, H. and Ho, R. (2010). Cluster Analyis for Non-Stationary Time Series. In Statistical Methods for Modeling Human
Dynamics: An Inter-Disciplinary Dialogu (pp. 85-122),Taylor and Francis.
(25.) Ombao, H. and Prado, R. (2010). A Closer Look at the Two Approaches for Clustering and Classification of Non-Stationary Time Series. In
Statistical Methods for Modeling Human Dynamics: An Inter-Disciplinary Dialogue (pp ), Taylor and Francis.
(26.) *Bohm, H., Ombao, H., von Sachs, R. and Sanes, J.N. (2010). Discrimination and Classification of Multivariate Non-Stationary Signals: The
SLEX-Shrinkage Method. Invited for the Special Issue on Time Series (In Honor of Emmanuel Parzen), Journal of Statistical Planning and Inference, (140), 3754-3763.
(27.) *Freyermuth, J-M., Ombao, H. and von Sachs, R. (2010). Spectral Estimation from Replicated Time Series: An Approach Using the Tree-
Structured Wavelets Mixed Effects Model. Journal of the American Statistical Association, 105, 634-646.
(28.) *Fiecas, M., Ombao, H., Linkletter, C., Thompson, W. and Sanes, J.N. (2010). Functional Connectivity: Shrinkage Estimation and
Randomization Test. NeuroImage, (40), 3005-3014.
(29.) Datta S., Prado R., Rodriguez A., and Escalante A.A. (2010). Characterizing molecular adaptation: A hierarchical approach to assess the
selective influence of amino acid properties. Bioinformatics, doi: 10.1093/bioinformatics/btq532.
(30.) Prado R. (2010) Multi-State Models for Mental Fatigue. In The Handbook of Applied Bayesian Analysis (eds.O'Hagan A. and West M.) Oxford
University Press, pp. 845-874.
(31.) Prado R. (2009). Characterizing Latent Structure in Brain Signals. In Statistical Methods for Modeling Human Dynamics (eds. Chow S.,
Ferrer E. and Hsieh F.), Psychology Press, Taylor and Francis Group, New York, pp. 123-154.
(32.) Ombao H. and Prado R. (2009). A Closer Look at Two Approaches for Analysis and Classification of Nonstationary Time Series. In Statistical
Methods for Modeling Human Dynamics (eds. Chow S., Ferrer E., and Hsieh F.), Psychology Press, Taylor and Francis Group, New York, pp. 155-157.
(33.) Merl D., Prado R. and Escalante A.A. (2008). Assessing the Effect of Selection at the Amino Acid Level in Malaria Antigen Sequences via
Bayesian Generalized Linear Models. Journal of the American Statistical Association, Vol. 103, number 484, pp. 1496-1507.
(34.) Trejo L.J., Knuth K., Prado R., Rosipal R., Kubitz K., Kochavi R., Matthews B. and Zhang Y. (2007). EEG-Based Estimation of Mental Fatigue:
Convergent Evidence for a Three-State Model. In Foundations of Augmented Cognition, LNCS 4565 (eds. Schmorrow D.D. and Reeves L.M.), pp 201-211. Springer-Verlag.
(35.) Merl D. and Prado R. (2007). Detecting Selection in DNA Sequences: Bayesian Modelling and Inference. In Bayesian Statistics 8: Proceedings
of the Eighth Valencia/ISBA World Meeting on Bayesian Statistics.} Editors: Bernardo, J.M., et al. Oxford University Press. pp. 303-324. (Invited Paper With Discussion).
(36.) Prado R., Molina F.J. and Huerta G. (2006). Multivariate Time Series Modeling and Classification Via Hierarchical VAR Mixtures.
Computational Statistics and Data Analysis, Vol. 51 (3), pp. 1445-1442.
(37.) Huerta G. and Prado, R. (2006). Structured Priors for Multivariate Time Series. {\it Journal of Statistical Planning and Inference,} Vol. 136, pp.
3802-3821.
(38.) Prado R. and Huerta G. (2002). Time-Varying Autoregressions with Model Order Uncertainty. Journal of Time Series Analysis, Vol. 23, pp.
599-618. Blackwell Publishers Ltd, Oxford, UK and Boston, USA.
(39.) Prado R., West M. and Krystal A. (2001) Multi-Channel EEG Analyses Via Dynamic Regression Models with Time-Varying Lag/Lead Structure.
Journal of the Royal Statistical Society, Series C (Applied Statistics), Vol. 50, pp. 95-109.
(40.) Prado R., Huerta G. and West M. (2000) Bayesian Time-Varying Autoregressions: Theory, Methods and Applications. Resenhas (Journal of
the Institute of Mathematics and Statistics of the University of S\~ao Paolo), Vol. 4, pp. 405-422. (Invited Paper).
(41.) Krystal A.D., West M., Prado R., Greenside H., Zoldi S., Weiner R. (2000). EEG Effects of ECT: Implications for rTMS. Depression and
Anxiety, Vol. 12, pp. 157-165.
(42.) Krystal A.D., Zoldi S., Prado R. Greenside H. and West M. (1999). The Spatio-Temporal Dynamics of Generalized Tonic-Clonic Seizure EEG
Data: Relevance to the Clinical Practice of Electroconvulsive Therapy. In Nonlinear Dynamics and Brain Functioning (edited by N. Pradhan, P.E. Rapp and R. Sreenivasan Commack), N.Y.: Nova-Science, pp. 401-410.
(43.) Aguilar O., Huerta G., Prado R. and West M. (1999). Bayesian Inference on Latent Structure in Time Series. In {\it Bayesian Statistics VI (eds.
J. O. Berger et al.). Oxford University Press, pp. 3-26. (Invited Paper With Discussion).
(44.) West M., Prado R. and Krystal A.D. (1999). Evaluation and Comparison of EEG Traces: Latent Structure in Non-Stationary Time Series.
Journal of the American Statistical Association, Vol. 94, pp. 1083-1095.
(45.) Krystal A.D., Prado R. and West M. (1999). New Methods of Time Series Analysis of Non-Stationary EEG Data: Eigenstructure
Decompositions of Time-varying Autoregressions. Clinical Neurophysiology, Vol. 110, pp. 2197-2206.
Pre-2011 Interdisciplinary Research
(1.) Buysse D, Hall M, Begley A, Cherry C, Houck P, Land S, Ombao H, Kupfer D and Frank E. (2001). REM Sleep and Treatment Response in
Depression: New Findings Using Power Spectral Analysis, Psychiatry Research, 103, 51-67.
(2.) Fagiolini A, Frank E, Houck P, Mallinger A, Swartz H, Buysse D, Ombao H and Kupfer, D. (2002). Prevalence of Obesity and Weight Change
During Treatment in patients with Bipolar I Disorder, Journal of Clinical Psychiatry, 63(6), 528-534.
(3.) Cranstoun S, Ombao H, von Sachs R, Guo W and Litt B. (2002). Improved Time-FrequencyAnalysis of EEG Signals Using the Auto-SLEX
Method, IEEE Trans in Biomedical Engineering, 49, 988-996.
(4.) Nofzinger E, Buysse D, Miewald J, Meltzer C, Price J, Sembrat R, Ombao H, Reynolds C, Monk T Hall M, Kupfer D and Moore R. (2002). Human
Regional Cerebral Glucose Metabolism During NREM Sleep in Relation to Waking, Brain, 125,1101-1115.
(5.) Moul D, Ombao H, Monk T, Chen Q and Buysse D. (2002). Masking effects of posture and sleep onset on core body temperature: a circadian
rhythm of bedtime temperature drops, Journal of Biological Rhythms, 17, 447-462.
(6.) Julius S, Valentini C, Krause L, Ombao H, Kaciroti N. and Weder A. (2002). A "gender blind" relationship of lean body mass and blood
pressure in the Tecumseh study. American Journal of Hypertension, 15(3), 258-263.
(7.) Raz J, Zheng H, Ombao H and Turetsky B. (2003). Statistical Test for fMRI Based on Experimental Randomization, NeuroImage, 19(2), 226-
232..
(8.) Germain A, Buysse D, Ombao H, Monk T, Kupfer D and Hall M. (2003). Psychophysiological Reactivity and Coping Style Influence the Effect of
Acute Stress on REM During Sleep. Journal of Psychosomatic Medicine, 65, 857-864.
(9.) Hall M, Vasko R, Buysse D, Ombao H, Chen Q, Cashmere D, Kupfer D and Thayer J. (2004). Acute Stress Affects Heart Rate Variability During
Sleep. Journal of Psychosomatic Medicine, 66,
(10.) Buysse D, Nofzinger E, Germaine A, Meltzer C, Wood A, Ombao H, Kupfer D and Moore, R. (2004). Regional Brain Glucose Metabolism
During Morning and Evening Wakefulness in Humans: Preliminary Findings. SLEEP, 27, 1245-1254.
(11.) Politi MC, Clark MA, Ombao H, Légaré F. (2011).The Impact of Physicians’ Reactions to Uncertainty on Patients’ Decision Satisfaction. Journal
of Evaluation in Clinical Practice, 17, 575-578.
(12.) Dickstein DP, Gorrostieta C, Ombao H, Goldberg LD, Brazel AC, Gable CJ, Kelly AMC, Gee DG, Zuo X, Castellanos FX, Milham MP. (2010).
Fronto-temporal spontaneous resting state functional connectivity in pediatric bipolar disorder. Biological Psychiatry, 68(9):839-46.56-62.