功能性核磁共振造影技術(fMRI)透過測量血氧濃度相依對比(BOLD)訊號提供一個非侵入性的方法來研究腦部的活動。因為大腦中的組織和功能構成複雜的網路連結進而可以分離或整合地處理資訊，所以，近年來，fMRI的研究不僅應用於偵測大腦受到刺激時的反應情況，更強調研究大腦在執行某任務時各區域的連結性。實際上，腦部的連結已被用於臨床結果。因此，除了研究腦部受到刺激後的反應情況，探討腦中的連結性也是近年來fMRI研究中重要的方向。為此，我們應用多層次貝式線性模型分析Go-NoGo fMRI實驗資料。此研究目的為偵測當腦部受外在刺激時腦中的反應情況，更探究當在執行任務時感興趣的區域之間的連結性。

Functional magnetic resonance imaging (fMRI) provides a noninvasive way of studying brain activity in vivo by measuring the blood oxygen level dependent (BOLD) signals. More recently, fMRI research has shifted towards studying brain connectivity, demonstrating that the brain is anatomically and functionally organized into complex networks allowing to process the information in segregation and integration. Indeed, connectivity has been used clinical consequences of cerebral disease. So, in addition to the study of brain activity, the investigating the connectivity in the brain becomes one of the most important aims of current fMRI research. To this end, a spatial Bayesian hierarchical model is proposed not only to detect the brain activity resulting from a GoNoGo task affected by the preceding context but also to explore the connectivity of regions of interest (ROIs) activated by this task. To illustrate relevant problems, methods, and techniques, we presented an analysis of a stimulated data and a real event-related fMRI data set.

Aguirre, G. K., Zarahn, E., and D'Esposito, M. (1997). Empirical analyses of BOLD fMRI statistics. II. Spatially smoothed data collected under null-hypothesis and experimental conditions. NeuroImage, 5:199-212.
Barbieri, M. and Berger, J. O. (2004). Optimal predictive model selection. Annals of Statistics, 32:870-897.
Beckmann, F. C., Jenkinson, M., and Smith, S. M. (2003). General multilevel linear modeling for group analysis in FMRI. NeuroImage, 20:1052-1063.
Bowman, F. D., Caffo, B., Bassett, S. S., and Kilts, C. (2008). A Bayesian Hierarchical Framework for Spatial Modeling of fMRI Data. NeuroImage, 39:146-153.
Buxton, R. (2001). The elusive initial dip. Neuroimage, 13:953-958. Chumbley, J. R., Flandin, G., Bach, D. R., Daunizeau, J., Fehr, E., Dolan, R. J., and Friston, K. J. (2012). Learning and generalization under ambiguity: an fMRI study. PLoS Computational Biology, 8:e1002346.
Cocosco, C., Zijdenbos, A., and Evans, A. (2003). A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis, 7:513-527.
Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., and Marchal, G. (1995). Automated multi-modality image registration based on information theory. In Y. Bizais, C. Barillot, and R. Di Paola, editors, Proc. Information Processing in Medical Imaging, Kluwer Academic Publishers, pages 263-274.
Criaud, M. and Boulinguez, P. (2013). Have we been asking the right questions when assessing response inhibition in go/no-go tasks with fMRI? A meta-analysis and critical review. Neuroscience and Biobehavioral Reviews, 37:11-23.
Dale, A., Fischl, B., and Sereno, M. (1999). Cortical surface-based analysis. I. segmentation and surface reconstruction. Neuroimage, 9:179-194.
Detre, J. A. and Floyd, T. (2001). Functional MRI and its applications to the clinical neurosciences. The Neuroscientist, 7:64-79.
Duong, T., Kim, D., and Kim, S. (2000). Spatiotemporal dynamics of the BOLD fMRI signals: Toward mapping submillimeter cortical columns using the early negative response. Magnetic Resonance in Medicine, 44:231-242.
Durston, S., Thomas, K., Worden, M., Yang, Y., and Casey, B. (2002a). The effect of preceding context on inhibition: An event-related fMRI study. NeuroImage, 16:449-453.
Durston, S., Thomas, K., Yang, Y., Ulug, A. M., Zimmerman, R. D., and Casey, B. (2002b). A neural basis for the development of inhibitory control. Developmental
Science, 5:F9-F16.
Durston, S., Tottenham, N. T., Thomas, K., Davidson, M. C., Eigsti, I., Yang, Y., Ulug, A. M., and Casey, B. (2002c). A neural basis for the development of inhibitory control. Developmental Science, 5:F9-F16.
Faro, S. and Mohamed, F. (2006). Functional MRI: Basic Principles and Clinical Applications. Springer.
Flegal, J. M. and Jones, G. L. (2011). In Brooks, S., Gelman, A., Jones, G., and Meng, X.-L., editors, Handbook of Markov Chain Monte Carlo. CRC Press, Boca Raton, FL.
Friston, K., Holmes, A., Worsley, K., Polin, J., C., C. F., and Frackowik, R. (1995). Statistical parametric maps in functional image: A general linear approach. Human
Brain Mapping, 2:189-210.
Friston, K. J., Frith, C. D., Liddle, P. F., and Frackowiak, R. S. (1993). Functional connectivity: the principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow and Metabolism, 13:5-14.
Friston, K. J. and Penny, W. (2003). Posterior probability maps and SPMs. NeuroImage, 19:1240-1249.
Gaal, S. V., Ridderinkhof, K. R., Scholte, H. S., and Lamme, V. A. F. (2011). Unconscious activation of the prefrontal no-go network. The Journal of Neuroscience, 30:4143-4150.
Garavan, H., Ross, T., and Stein, E. (1999). Right hemispheric dominance of inhibitory control: An event-related fMRI study. Proceedings of the National Academy of Sciences, USA, 96:8301-8306.
Garey, L. (2005). Brodman's localisation in the cerebral cortex: the principles of comparative localisation based on cytoarchitectonics. English translation of Vergleichende Lokalisationslehre der Grosshirnrinde by Korbinian Brodman. Springer.
Gelman, A. (1992). Iterative and Non-iterative Simulation Algorithms. Computing Science and Statistics, 24:433-438.
Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721-741.
Hampshire, A., Chamberlain, S. R., Monti, M. M., Duncan, J., and Owen, A. M. (2010). The role of the right inferior frontal gyrus: inhibition and attentional control. NeuroImage, 50:1313-1319.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57:97-109
Holmes, A. and Friston, K. (1998). Generalisability, random effects and population inference. NeuroImage, 7:S754.
Huettel, R., Song, A., and McCarthy, G. (2009a). Functional Magnetic Resonance Imaging, 2nd Edition. Sinauer Associates.
Huettel, R., Song, A. W., and McCarthy, G. (2009b). Functional Magnetic Resonance Imaging, 2nd. Sinauer Associates Inc.
Ide, J. S., Zhang, S., and Li, C. R. (2013). Bayesian network models in brain functional connectivity analysis. International Journal of Approximate Reasoning, In Press.
Jenkinson, M. and Bannister, P. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17:825-841.
Jezzard, P., Matthews, P., and Smith, S. (2001a). Functional MRI: An Introduction to Methods. Oxford Unversity Press.
Jezzard, P., Matthews, P., and Smith, S. (2001b). Functional MRI: An Introduction to Methods. Oxford Unversity Press.
Jones, G., Haran, M., , Caffo, B., and Neath, R. (2006). Fixed-width output analysis for Markov chain Monte Carlo. Journal of the American Statistical Association, 101:1537-1547.
Konishi, S., Nakajima, K., Uchida, I., Kikyo, H., Kameyama, M., and Miyashita, Y. (1999a). Common inhibitory mechanism in human inferior prefrontal cortex revealed by event-related functional MRI. Brain, 122:981-991.
Konishi, S., Nakajima, K., Uchida, I., Sekihara, K., and Miyashita, Y. (1999b). No-go dominant brain activity in human inferior prefrontal cortex revealed by functional magnetic resonance imaging. European Neuroscience Association, 10:1209-1213. Konrad, K. and Eickhoff, S. (2010). Is the ADHD brain wired differently? a review on structural and functional connectivity in attention deficit hyperactivity disorder. Human Brain Mapping, 31:904-916.
Liddle, P. F., Kiehl, K. A., and Smith, A. M. (2001). Event-related fMRI study of response inhibition. Human Brain Mapping, 12:100-109.
Lund, T. E., Madsen, K. H., Sidaros, K., Luo, W. L., and Nicholsd, T. E. (2006). Non-white noise in fMRI: Does modelling have an impact? NeuroImage, 29:54-66.
Makni, S., Idier, J., Vincent, T., Thirion, B., Dehaene-Lambertz, G., and Ciuciuc, P. (2008). A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI. NeuroImage, 41:941-969.
Manly, B. F. J. (1986). Multivariate statistical methods: a primer. London: Chapman & Hall.
Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27:209-220.
Marchini, J. and Presanis, A. (2004). Comparing methods of analyzing fMRI statistical parametric maps. NeuroImage, 22:1203-1213.
Menon, V., Adleman, N. E., White, C. D., Glover, G. H., and Reiss, A. L. (2001). Error-related brain activation during a go/nogo response inhibition task. Human Brain Mapping, 12:131-143.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21:1087-1092.
Mitra, P., Gopalakrishnan, V., and McNamee, R. (2006). Segmentation of fMRI data by maximization of region contrast. Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, page 88.
NessAiver, M. (1996). All You really need to know About MRI Physics. Simply Physics.
Ogawa, S., Lee, T., Nayak, A., and Glynn, P. (1990). Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magnetic Resonance in Medicine, 14:68-78.
Pliszka, S. R., Glahn, D. C., Semrud-Clikeman, M., Franklin, C., R, R. P., Xiong, J., and Liotti, M. (2006). Neuroimaging of inhibitory control areas in children with attention deficit hyperactivity disorder who were treatment naive or in long-term treatment. The American Journal of Psychiatry, 163:1052-1060.
Pluempitiwiriyawej, C., Moura, J. M. F., Wu, Y.-J. L., and Ho, C. (2005). A new active contour scheme for cardiac MR image segmentation. IEEE Transactions on Medical Imaging, 24:593-603.
Salli, E., Visa, A., Aronen, H., Korvenoja, A., and Katila, T. (1999). Proc. of the
2nd international conference on medical image computing and computer assisted intervention - miccai'99. c. taylor, a. colchester (eds.). lecture notes in computer science.
Springer-Verlag, 1679:481-488.
Smith, M. and Fahrmeir, L. (2007). Spatial Bayesian Variable Selection with Application to Functional Magnetic Resonance Imaging. Journal of the American Statistical Association, 102:417-431.
Smith, S. (2003). Preparing fMRI data for statistical analysis. In Jezzard P, Matthews P.M., Smith S.M. (Eds.), Functional MRI: an introduction to methods. Oxford University Press, Oxford, UK.
Smith, S. M. (2002). Fast robust automated brain extraction. human brain mapping. Human Brain Mapping, 17:143-155.
Steele, V. R., Aharoni, E., Munro, G. E., Calhoun, V. D., Nyalakanti, P., Stevens, M. C., Pearlson, G., and Kiehl, K. A. (2013). A large scale (n = 102) functional neuroimaging study of response inhibition in a go/nogo task. Behavioural Brain Research, 256:529-536.
Swann, N. C., Caib, W., Connerc, C. R., Pietersc, T. A., Claffeyb, M. P., Georgeb, J. S., Aronb, A. R., and Tandon, N. (2012). Roles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: Electrophysiological responses and functional and structural connectivity. NeuroImage, 59:2860-2870.
Wager, T. D., Sylvester, C. C., Lacey, S. C., Nee, D. E., Franklin, M., and Jonides, J. (2005). Common and unique components of response inhibition revealed by fMRI. NeuroImage, 27:323-340.
Woolrich, M. W., Behrensa, T. E. J., Beckmanna, C. F., Jenkinsona, M., and Smith, S. M. (2004). Multilevel linear modelling for FMRI group analysis using Bayesian inference. NeuroImage, 21:1732-1747.
Woolrich, M. W., Ripley, B., Brady, J., and Smith, S. M. (2001). Temporal autocorrelation in univariate linear modelling of FMRI data. NeuroImage, 14:1370-1386.
Worsley, K. (1996). The geometry of random images. CHANCE, 9:27-40.
Worsley, K. (2003). Detecting activation in fMRI data. Statistical Methods in Medical Research., 12:401-418.
Worsley, K., Andermann, M., Koulis, T., MacDonald, D., and Evans, A. (1992). Detecting changes in nonisotropic images. Human Brain Mapping, 8:98-101.
Worsley, K. J., Liao, C., Aston, J., Petre, V., Duncan, G., Moralese, F., and Evans, A. (2002). A general statistical analysis for fMRI data. NeuroImage, 15:1-15.
Wu, X., Li, R., Fleisher, A. S., Reiman, E. M., Guan, X., Zhang, Y., Chen, K., and Yao, L. (2011). Altered default mode network connectivity in alzheimer's disease - A resting functional MRI and Bayesian network study. Human Brain Mapping, 32:1868-1881.
Zarahn, E., Aguirre, G. K., and D'Esposito, M. (1997). Empirical analyses of BOLD fMRI statistics. I. Spatially unsmoothed data collected under null-hypothesis conditions. NeuroImage, 5:179-197.