METHODOLOGY PAPERS

  1. Shi, R. and Guo, Y. (2017). Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis. Annals of Applied Statistics. 10(4): 1930-1957.An earlier version of the paper was selected for the First-Place winner of the 2015 Student Paper Competition, American Statistical Association (ASA) Statistics in Imaging Section.

  2. Dai, T, and Guo, Y (2017). Predicting Individual Brain Functional Connectivity Using a Bayesian Hierarchical Model. Neuroimage 147(15):772-787. An earlier version of the paper was the Second-Place winner of the 2016 Student Paper Competition, American Statistical Association (ASA) Statistics in Imaging Section.

  3. Rahman AF, Peng, L., Manatunga, AK, Guo, Y. (2017). Nonparametric regression method for broad sense agreement. Journal of Nonparametric Statistics .

  4. Peng L, Manatunga A, Wang M, Guo, Y, Rahman AF.(2016). A general approach to categorizing a continuous scale according to an ordinal outcome. Journal of Statistical Planning and Inference 172:23-25.

  5. Kang, J., Bowman, F.D., Mayberg, H. and Liu, H. (2016). A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. NeuroImage 141:431-441.

  6. Kundu, S., and Kang, J. (2016). Semiparametric Bayes conditional graphical models for imaging genetics applications. Stat 5:322-337.

  7. Cordova, J.S., Gurbani, S. S., Holder, C.A. Olson, J.J., Schreibmann, E., Shi, R., Guo, Y., Shu, H.G., Shim, H., Hadjipanayis, C.G. (2016). Semi-quantitative volumetric and morphological assessment of glioblastoma resection with fluorescence-guided surgery. Molecular Imaging and Biology 18.3: 454-462.

  8. Wang, Y, Kang J, Kemmer, PB and Guo, Y (2016). An efficient and reliable statistical method for estimating functional connectivity in large scale brain networks using partial correlation. Frontiers in Neuroscience. In press.

  9. Kemmer PB, Guo Y, Wang Y. and Pagnoni G. (2015). Network-based characterization of brain functional connectivity in Zen practitioners. Frontiers in Psychology, 6, 603.

  10. Xue, W., Bowman, F.D., Pileggi, A., and Mayer, A.R. (2015) A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity. Frontiers in computational neuroscience.

  11. Zhao, Y.*, Kang, J., and Long, Q. (2015) Bayesian spatial variable selection for ultra-high dimensional neuroimaging data: a multiresolution approach IEEE/ACM Transactions on Computational Biology and Bioinformatics, In press. Student paper award from section on statistical learning and data mining of the ASA.

  12. Wager, T. D., Kang, J., Johnson, T. D., Nichols, T. E., Satapute, A.B., and Feldman Barrette, L. (2015) A Bayesian model of category-specific emotional brain responses. PLOS Computational Biology, In press.

  13. Chen, S., Kang, J. and Wang, G. (2015). An empirical Bayes normalization method for connectivity metrics in resting state fMRI. Frontiers in Neuroscience, 9, 316.

  14. Cordova JS, Schreibmann, E., Hadjipanayis CG., Guo Y, Shu H, Shim H. and Holder C. (2014). Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials. Translational Oncology. 7(1):40-7.

  15. Shim, H., Wei, L., Holder, C., Guo, Y., Hu, X.P., Miller, A.H., Olsen, J.J. (2014). Use of high resolution volumetric MR spectroscopic imaging in assessing treatment response of GBM to an HDAC inhibitor. American Journal of Roentgenology 203.2: W158-W165.

  16. Kang, J., Nichols, T. E., Wager, T. D., and Johnson, T. D. (2014) A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis. Annals of Applied Statistics, 8 (3), 1800–1824.

  17. Xue, W., Kang, J., Bowman, F. D., Guo, J. and Wager, T. (2014) Identifying Functional Co-activation Patterns in Neuroimaging Studies via Poisson Graphical Models, Biometrics, 70, 812--822.

  18. Kang, J., Zhang, N., and Shi, R., (2014) A Bayesian nonparametric model for multivariate spatial binary data with application to a multidrug-resistant tuberculosis (MDR-TB) study. Biometrics, 70, 981--992.

  19. Bai, Y., Kang, J., and Song, P., (2014) Efficient pairwise composite likelihood estimation for spatial-clustered data. Biometrics, 70, 661--670.

  20. Zhao, Y., Kang, J., Yu, T., (2014) A Bayesian nonparametric mixture model for gene and gene-sub network selection, Annals of Applied Statistics, accepted.

  21. Kang, J., Johnson T.D. (2014) A slice sampler for the hierarchical Poisson/gamma random field model, Proceedings for Perspectives on High Dimensional Data Analysis II, Contemporary Mathematics, American Mathematical Society, to appear.

  22. Guo, Y. and Tang Li (2013). A hierarchical probabilistic model for group independent component analysis in fMRI studies. Biometrics. 69(4):970-81.

  23. Kang, J. Yang, Y. (2013) Joint modeling of mixed count and continuous longitudinal data. Analysis of Mixed Data. A.R. de Leon and K. Carriere Chough (Eds). Chapman & Hall/CRC, chapter 4.

  24. Yikai, W., Yueqi, Z. (2013). Graph Construction Based on Re-weighted Sparse Representation for Semi-supervised Learning. JICS. 10(2): 375- 383.

  25. Bowman, F. D., Zhang, L., Derado, G., and Chen, S. (2012). Determining Functional Connectivity using fMRI Data with Diffusion-Based Anatomical Weighting. NeuroImage, 62: 1769-1779.

  26. Derado, G., Bowman, F. D., and Zhang, L. (2012). Predicting Brain Activity using a Bayesian Spatial Model. Statistical Methods in Medical Research (DOI: 10.1177/0962280212448972).

  27. Zhang, L., Agravat, S., Derado, G., Chen, S., McIntosh, B.J., and Bowman, F. D. (2012). BSMac: A MATLAB toolbox Implementing a Bayesian Spatial Model for Brain Activation and Connectivity. Journal of Neuroscience Methods . (DOI:10.1016/j.jneumeth.2011.10.025).

  28. Kang, J., Johnson, T. D., Nichols, T. E., Wager, T. D. (2012). Local mixed-effects fitting for detecting reproductive hormone surge times. Statistics in BioSciences, 4:245-261.

  29. Kang, J., Ye, W., Wang, L., Song, P. (2011). Meta analysis of functional neuroimaging data via Bayesian spatial point processes. Journal of the American Statistical Association, 106(493), 124-134.

  30. Chen, S. and Bowman, F. D. (2011). A Novel Support Vector Classifier for Longitudinal High-dimensional Data and Its Application to Neuroimaging Data. Statistical Analysis and Data Mining. 4(6):604-611.[Winning paper for JSM 2011 Student Paper Competition, ASA Section on Statistical Learning and Data Mining].

  31. Guo Y (2011). A general probabilistic for group independent component analysis and its estimation methods. Biometrics. 67(4):1532-1542.

  32. Caffo, B., Bowman, F. D., Eberly, L., and Bassett, S. S. (2011). A Markov Chain Monte Carlo Based Analysis of a Multilevel Model for Functional MRI Data, Handbook of Markov Chain Monte Carlo: Methods and Applications, edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng, Chapman & Hall.

  33. Kang, J., Johnson, T. D., Nichols, T. E., Wager, T. D. (2011). Meta analysis of functional neuroimaging data via Bayesian spatial point processes. Journal of the American Statistical Association, 106(493), 124-134.

  34. Derado, G., Bowman, F. D., Ely, T., and Kilts, C. (2010). Evaluating Functional Autocorrelation within Spatially Distributed Neural Processing Clusters. Statistics and Its Interface, 3:45-57.

  35. Guo Y. (2010). A weighted cluster kernel PCA prediction model for multi-subject brain imaging data. Statistics And Its Interface. 3:103-111.

  36. Derado, G., Bowman, F. D., and Kilts, C. (2010). Modeling the spatial and temporal dependence in fMRI data. Biometrics, 66:949-957.

  37. Derado, G., Bowman, F. D., Ely, T., and Kilts, C. (2010). Evaluating functional autocorrelation within spatially distributed neural processing clusters. Statistics and Its Interface 3:45-47.

  38. Guo, Y. and Pagnoni, G. (2008). A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage, 42: 1078-1093.

  39. Guo, Y. (2008). Group Independent Component Analysis of Multi-subject fMRI data: Connections and Distinctions between Two Methods. Conference proceeding of International Conference on BioMedical Engineering and Informatics 2008. Hainan, China.

  40. Guo, Y. and Bowman, F. D. (2008). Modeling Dose-Dependent Neural Processing Responses Using Mixed Effects Spline Models. NeuroImage 40.2 (2008): 698-711.

  41. Guo, Y., Bowman, F. D., and Kilts, C. D. (2008). Predicting the Brain Response to Treatment using a Bayesian Hierarchical Model with Application to a Study of Schizophrenia. Human Brain Mapping, 29(9): 1092-1109.

  42. Bowman, F. D., Caffo, B. A, Bassett, S., and Kilts, C. (2008). Bayesian Hierarchical Framework for Spatial Modeling of fMRI Data. NeuroImage 39: 146-156.(Published version: NeuroImage; Preprint: http://www.bepress.com/jhubiostat/paper139; BSMAC Software: See Links tab).

  43. Bowman, F. D., Guo, Y., and Derado, G. (2007). Statistical Approaches to Neuroimaging Data. Neuroimaging Clinics of North America: Imaging of the Mind 17(4): Nov. 2007, 441-458.

  44. Bowman, F. D. (2007). Spatio-Temporal Models for Region of Interest Analyses of Functional Neuroimaging Data, Journal of the American Statistical Association 102(478): 442-453.

  45. Arfanakis, K., Gui, M., Tamhane, A. A., and Carew, J. D. (2007). Investigating the Medial Temporal Lobe in Alzheimer's Disease and Mild Cognitive Impairment, with Turboprop Diffusion Tensor Imaging, MRI-volumetry, and T2-Relaxometry. Brain Imaging and Behavior, 1: 11-21.

  46. Carew, J. D. (2007). dr-ROC Summary Meta-analysis Software Version 2.0. Academic Radiology, 14(6): 767-768.

  47. Bley, T. A., Uhl, M., Carew, J., Markl, M., Schmidt, D., Hans-Hartmut, P., Langer, M., and Wieben, O. (2007). Diagnostic value of high resolution MRI in giant cell arteritis. American Journal of Neuroradiology, 28:1722-1727.

  48. Patel, R., Bowman, F. D., and Rilling, J. K. (2006b). Determining Hierarchical Functional Networks from Auditory Stimuli fMRI. Human Brain Mapping 27:462-470.

  49. Patel, R., Van De Ville, D., and Bowman, F. D. (2006). Determining Significant Connectivity by 4D Spatiotemporal Wavelet Packet Resampling of Functional Neuroimaging Data. NeuroImage 31: 1142-1155.

  50. Patel, R. S., Bowman, F. D., Guo, Y., and Derado, G. (2006). Integrating Support Vector Machines, Supervised Principal Components, and Boosting to Interpret Brain Activity. Pittsburgh Brain Activity Interpretation Competition. the Organization for Human Brain Mapping, 12th Annual Meeting, Florence, Italy.

  51. Patel, R., Bowman, F. D., and Rilling, J. K. (2006a). A Bayesian Approach to Determining Connectivity of the Human Brain. Human Brain Mapping 27: 267-276.

  52. Bowman, F. D. (2005) Spatiotemporal Modeling of Localized Brain Activity. Biostatistics 6(4): 558-575.

  53. Bowman, F. D. and Patel, R. (2004) Identifying Spatial Relationships in Neural Processing Using a Multiple Classification Approach. NeuroImage 23: 260-268.

  54. Bowman, F. D., Patel, R., and Lu, C. (2004) Methods for Detecting Functional Classifications in Neuroimaging Data. Human Brain Mapping 23(2): 109-119.

  55. Bowman, F. D. and Kilts, C. (2003). Modeling Intra-subject Correlation Among Repeated Scans in Positron Emission Tomography (PET) Neuroimaging Data. Human Brain Mapping 20(2): 59-70.

  1. Shi, R. and Guo, Y. (2017). Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis. Annals of Applied Statistics. 10(4): 1930-1957.An earlier version of the paper was selected for the First-Place winner of the 2015 Student Paper Competition, American Statistical Association (ASA) Statistics in Imaging Section.

  2. Dai, T, and Guo, Y (2017). Predicting Individual Brain Functional Connectivity Using a Bayesian Hierarchical Model. Neuroimage 147(15):772-787. An earlier version of the paper was the Second-Place winner of the 2016 Student Paper Competition, American Statistical Association (ASA) Statistics in Imaging Section.

  3. Rahman AF, Peng, L., Manatunga, AK, Guo, Y. (2017). Nonparametric regression method for broad sense agreement. Journal of Nonparametric Statistics .

  4. Peng L, Manatunga A, Wang M, Guo, Y, Rahman AF.(2016). A general approach to categorizing a continuous scale according to an ordinal outcome. Journal of Statistical Planning and Inference 172:23-25.

  5. Kang, J., Bowman, F.D., Mayberg, H. and Liu, H. (2016). A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. NeuroImage 141:431-441.

  6. Kundu, S., and Kang, J. (2016). Semiparametric Bayes conditional graphical models for imaging genetics applications. Stat 5:322-337.

  7. Cordova, J.S., Gurbani, S. S., Holder, C.A. Olson, J.J., Schreibmann, E., Shi, R., Guo, Y., Shu, H.G., Shim, H., Hadjipanayis, C.G. (2016). Semi-quantitative volumetric and morphological assessment of glioblastoma resection with fluorescence-guided surgery. Molecular Imaging and Biology 18.3: 454-462.

  8. Wang, Y, Kang J, Kemmer, PB and Guo, Y (2016). An efficient and reliable statistical method for estimating functional connectivity in large scale brain networks using partial correlation. Frontiers in Neuroscience. In press.

  1. Kemmer PB, Guo Y, Wang Y. and Pagnoni G. (2015). Network-based characterization of brain functional connectivity in Zen practitioners. Frontiers in Psychology, 6, 603.

  2. Xue, W., Bowman, F.D., Pileggi, A., and Mayer, A.R. (2015) A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity. Frontiers in computational neuroscience.

  3. Zhao, Y.*, Kang, J., and Long, Q. (2015) Bayesian spatial variable selection for ultra-high dimensional neuroimaging data: a multiresolution approach IEEE/ACM Transactions on Computational Biology and Bioinformatics, In press. Student paper award from section on statistical learning and data mining of the ASA.

  4. Wager, T. D., Kang, J., Johnson, T. D., Nichols, T. E., Satapute, A.B., and Feldman Barrette, L. (2015) A Bayesian model of category-specific emotional brain responses. PLOS Computational Biology, In press.

  5. Chen, S., Kang, J. and Wang, G. (2015). An empirical Bayes normalization method for connectivity metrics in resting state fMRI. Frontiers in Neuroscience, 9, 316.

  6. Cordova JS, Schreibmann, E., Hadjipanayis CG., Guo Y, Shu H, Shim H. and Holder C. (2014). Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials. Translational Oncology. 7(1):40-7.

  7. Shim, H., Wei, L., Holder, C., Guo, Y., Hu, X.P., Miller, A.H., Olsen, J.J. (2014). Use of high resolution volumetric MR spectroscopic imaging in assessing treatment response of GBM to an HDAC inhibitor. American Journal of Roentgenology 203.2: W158-W165.

  8. Kang, J., Nichols, T. E., Wager, T. D., and Johnson, T. D. (2014) A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis. Annals of Applied Statistics, 8 (3), 1800–1824.

  9. Xue, W., Kang, J., Bowman, F. D., Guo, J. and Wager, T. (2014) Identifying Functional Co-activation Patterns in Neuroimaging Studies via Poisson Graphical Models, Biometrics, 70, 812--822.

  10. Kang, J., Zhang, N., and Shi, R., (2014) A Bayesian nonparametric model for multivariate spatial binary data with application to a multidrug-resistant tuberculosis (MDR-TB) study. Biometrics, 70, 981--992.

  11. Bai, Y., Kang, J., and Song, P., (2014) Efficient pairwise composite likelihood estimation for spatial-clustered data. Biometrics, 70, 661--670.

  12. Zhao, Y., Kang, J., Yu, T., (2014) A Bayesian nonparametric mixture model for gene and gene-sub network selection, Annals of Applied Statistics, accepted.

  13. Kang, J., Johnson T.D. (2014) A slice sampler for the hierarchical Poisson/gamma random field model, Proceedings for Perspectives on High Dimensional Data Analysis II, Contemporary Mathematics, American Mathematical Society, to appear.

  14. Guo, Y. and Tang Li (2013). A hierarchical probabilistic model for group independent component analysis in fMRI studies. Biometrics. 69(4):970-81.

  15. Kang, J. Yang, Y. (2013) Joint modeling of mixed count and continuous longitudinal data. Analysis of Mixed Data. A.R. de Leon and K. Carriere Chough (Eds). Chapman & Hall/CRC, chapter 4.

  16. Yikai, W., Yueqi, Z. (2013). Graph Construction Based on Re-weighted Sparse Representation for Semi-supervised Learning. JICS. 10(2): 375- 383.

  1. Bowman, F. D., Zhang, L., Derado, G., and Chen, S. (2012). Determining Functional Connectivity using fMRI Data with Diffusion-Based Anatomical Weighting. NeuroImage, 62: 1769-1779.

  2. Derado, G., Bowman, F. D., and Zhang, L. (2012). Predicting Brain Activity using a Bayesian Spatial Model. Statistical Methods in Medical Research (DOI: 10.1177/0962280212448972).

  3. Zhang, L., Agravat, S., Derado, G., Chen, S., McIntosh, B.J., and Bowman, F. D. (2012). BSMac: A MATLAB toolbox Implementing a Bayesian Spatial Model for Brain Activation and Connectivity. Journal of Neuroscience Methods . (DOI:10.1016/j.jneumeth.2011.10.025).

  4. Kang, J., Johnson, T. D., Nichols, T. E., Wager, T. D. (2012). Local mixed-effects fitting for detecting reproductive hormone surge times. Statistics in BioSciences, 4:245-261.

  5. Kang, J., Ye, W., Wang, L., Song, P. (2011). Meta analysis of functional neuroimaging data via Bayesian spatial point processes. Journal of the American Statistical Association, 106(493), 124-134.

  6. Chen, S. and Bowman, F. D. (2011). A Novel Support Vector Classifier for Longitudinal High-dimensional Data and Its Application to Neuroimaging Data. Statistical Analysis and Data Mining. 4(6):604-611.[Winning paper for JSM 2011 Student Paper Competition, ASA Section on Statistical Learning and Data Mining].

  7. Guo Y (2011). A general probabilistic for group independent component analysis and its estimation methods. Biometrics. 67(4):1532-1542.

  8. Caffo, B., Bowman, F. D., Eberly, L., and Bassett, S. S. (2011). A Markov Chain Monte Carlo Based Analysis of a Multilevel Model for Functional MRI Data, Handbook of Markov Chain Monte Carlo: Methods and Applications, edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng, Chapman & Hall.

  9. Kang, J., Johnson, T. D., Nichols, T. E., Wager, T. D. (2011). Meta analysis of functional neuroimaging data via Bayesian spatial point processes. Journal of the American Statistical Association, 106(493), 124-134.

  10. Derado, G., Bowman, F. D., Ely, T., and Kilts, C. (2010). Evaluating Functional Autocorrelation within Spatially Distributed Neural Processing Clusters. Statistics and Its Interface, 3:45-57.

  11. Guo Y. (2010). A weighted cluster kernel PCA prediction model for multi-subject brain imaging data. Statistics And Its Interface. 3:103-111.

  12. Derado, G., Bowman, F. D., and Kilts, C. (2010). Modeling the spatial and temporal dependence in fMRI data. Biometrics, 66:949-957.

  13. Derado, G., Bowman, F. D., Ely, T., and Kilts, C. (2010). Evaluating functional autocorrelation within spatially distributed neural processing clusters. Statistics and Its Interface 3:45-47.

  1. Guo, Y. and Pagnoni, G. (2008). A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage, 42: 1078-1093.

  2. Guo, Y. (2008). Group Independent Component Analysis of Multi-subject fMRI data: Connections and Distinctions between Two Methods. Conference proceeding of International Conference on BioMedical Engineering and Informatics 2008. Hainan, China.

  3. Guo, Y. and Bowman, F. D. (2008). Modeling Dose-Dependent Neural Processing Responses Using Mixed Effects Spline Models. NeuroImage 40.2 (2008): 698-711.

  4. Guo, Y., Bowman, F. D., and Kilts, C. D. (2008). Predicting the Brain Response to Treatment using a Bayesian Hierarchical Model with Application to a Study of Schizophrenia. Human Brain Mapping, 29(9): 1092-1109.

  5. Bowman, F. D., Caffo, B. A, Bassett, S., and Kilts, C. (2008). Bayesian Hierarchical Framework for Spatial Modeling of fMRI Data. NeuroImage 39: 146-156.(Published version: NeuroImage; Preprint: http://www.bepress.com/jhubiostat/paper139; BSMAC Software: See Links tab).

  6. Bowman, F. D., Guo, Y., and Derado, G. (2007). Statistical Approaches to Neuroimaging Data. Neuroimaging Clinics of North America: Imaging of the Mind 17(4): Nov. 2007, 441-458.

  7. Bowman, F. D. (2007). Spatio-Temporal Models for Region of Interest Analyses of Functional Neuroimaging Data, Journal of the American Statistical Association 102(478): 442-453.

  8. Arfanakis, K., Gui, M., Tamhane, A. A., and Carew, J. D. (2007). Investigating the Medial Temporal Lobe in Alzheimer's Disease and Mild Cognitive Impairment, with Turboprop Diffusion Tensor Imaging, MRI-volumetry, and T2-Relaxometry. Brain Imaging and Behavior, 1: 11-21.

  9. Carew, J. D. (2007). dr-ROC Summary Meta-analysis Software Version 2.0. Academic Radiology, 14(6): 767-768.

  10. Bley, T. A., Uhl, M., Carew, J., Markl, M., Schmidt, D., Hans-Hartmut, P., Langer, M., and Wieben, O. (2007). Diagnostic value of high resolution MRI in giant cell arteritis. American Journal of Neuroradiology, 28:1722-1727.

  1. Patel, R., Bowman, F. D., and Rilling, J. K. (2006b). Determining Hierarchical Functional Networks from Auditory Stimuli fMRI. Human Brain Mapping 27:462-470.

  2. Patel, R., Van De Ville, D., and Bowman, F. D. (2006). Determining Significant Connectivity by 4D Spatiotemporal Wavelet Packet Resampling of Functional Neuroimaging Data. NeuroImage 31: 1142-1155.

  3. Patel, R. S., Bowman, F. D., Guo, Y., and Derado, G. (2006). Integrating Support Vector Machines, Supervised Principal Components, and Boosting to Interpret Brain Activity. Pittsburgh Brain Activity Interpretation Competition. the Organization for Human Brain Mapping, 12th Annual Meeting, Florence, Italy.

  4. Patel, R., Bowman, F. D., and Rilling, J. K. (2006a). A Bayesian Approach to Determining Connectivity of the Human Brain. Human Brain Mapping 27: 267-276.

  5. Bowman, F. D. (2005) Spatiotemporal Modeling of Localized Brain Activity. Biostatistics 6(4): 558-575.

  6. Bowman, F. D. and Patel, R. (2004) Identifying Spatial Relationships in Neural Processing Using a Multiple Classification Approach. NeuroImage 23: 260-268.

  7. Bowman, F. D., Patel, R., and Lu, C. (2004) Methods for Detecting Functional Classifications in Neuroimaging Data. Human Brain Mapping 23(2): 109-119.

  8. Bowman, F. D. and Kilts, C. (2003). Modeling Intra-subject Correlation Among Repeated Scans in Positron Emission Tomography (PET) Neuroimaging Data. Human Brain Mapping 20(2): 59-70.