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4 - Fundamentals of Functional Neuroimaging

from Systemic Psychophysiology

Published online by Cambridge University Press:  27 January 2017

John T. Cacioppo
Affiliation:
University of Chicago
Louis G. Tassinary
Affiliation:
Texas A & M University
Gary G. Berntson
Affiliation:
Ohio State University
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Publisher: Cambridge University Press
Print publication year: 2016

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References

Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998). The variability of human, BOLD hemodynamic responses. NeuroImage, 8: 360369.Google Scholar
Amunts, K., Schleicher, A., & Zilles, K. (2007). Cytoarchitecture of the cerebral cortex: more than localization. NeuroImage, 37: 10611065.Google Scholar
Andersson, J. L., Hutton, C., Ashburner, J., Turner, R., & Friston, K. (2001). Modeling geometric deformations in EPI time series. NeuroImage, 13: 903919.CrossRefGoogle ScholarPubMed
Andrews-Hanna, J. R., Reidler, J. S., Huang, C., & Buckner, R. L. (2010). Evidence for the default network’s role in spontaneous cognition. Journal of Neurophysiology, 104: 322335.Google Scholar
Aron, A., Fisher, H., Mashek, D. J., Strong, G., Li, H., & Brown, L. L. (2005). Reward, motivation, and emotion systems associated with early-stage intense romantic love. Journal of Neurophysiology, 94: 327337.Google Scholar
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38: 95113.Google Scholar
Ashburner, J. & Friston, K. J. (2000). Voxel-based morphometry: the methods. NeuroImage, 11: 805821.Google Scholar
Atlas, L. Y., Lindquist, M. A., Bolger, N., & Wager, T. D. (2014). Brain mediators of the effects of noxious heat on pain. Pain, 155: 16321648.CrossRefGoogle ScholarPubMed
Atlas, L. Y., Whittington, R. A., Lindquist, M. A., Wielgosz, J., Sonty, N., & Wager, T. D. (2012). Dissociable influences of opiates and expectations on pain. Journal of Neuroscience, 32: 80538064.Google Scholar
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2013). LME4: Linear mixed-effects models using Eigen and S4. R package version 1.Google Scholar
Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003). General multilevel linear modeling for group analysis in FMRI. NeuroImage, 20: 10521063.Google Scholar
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage, 34: 144155.Google Scholar
Bendriem, B. & Townsend, D. W. (1998). The Theory and Practice of 3D PET. Boston and Dordrecht: Kluwer.CrossRefGoogle Scholar
Benjamini, Y. & Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57: 289300.Google Scholar
Bernstein, M. A., King, K. F., & Zhou, Z. J. (2004). Handbook of MRI Pulse Sequences. Burlington, MA: Elsevier Academic Press.Google Scholar
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex, 19: 27672796.Google Scholar
Birn, R. M., Saad, Z. S., & Bandettini, P. A. (2001). Spatial heterogeneity of the nonlinear dynamics in the FMRI BOLD response. NeuroImage, 14: 817826.CrossRefGoogle ScholarPubMed
Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34: 537541.Google Scholar
Bohning, D. E., Pecheny, A. P., Epstein, C. M., Speer, A. M., Vincent, D. J., Dannels, W., & George, M. S. (1997). Mapping transcranial magnetic stimulation (TMS) fields in vivo with MRI. Neuroreport, 8: 25352538.Google Scholar
Bohning, D. E., Shastri, A., McConnell, K. A., Nahas, Z., Lorberbaum, J. P., Roberts, D. R., Teneback, C., Vincent, D. J., & George, M. S. (1999). A combined TMS/fMRI study of intensity-dependent TMS over motor cortex. Biological Psychiatry, 45: 385394.Google Scholar
Bornhovd, K., Quante, M., Glauche, V., Bromm, B., Weiller, C., & Buchel, C. (2002). Painful stimuli evoke different stimulus-response functions in the amygdala, prefrontal, insula and somatosensory cortex: a single-trial fMRI study. Brain, 125: 13261336.CrossRefGoogle ScholarPubMed
Boubela, R. N., Kalcher, K., Huf, W., Seidel, E. M., Derntl, B., Pezawas, L., … & Moser, E. (2015). fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions. Scientific Reports, 5: 10499.Google Scholar
Boynton, G. M., Engel, S. A., Glover, G. H., & Heeger, D. J. (1996). Linear systems analysis of functional magnetic resonance imaging in human V1. Journal of Neuroscience, 16: 42074221.Google Scholar
Brett, M., Johnsrude, I. S., & Owen, A. M. (2002). The problem of functional localization in the human brain. Nature Reviews Neuroscience, 3: 243249.Google Scholar
Brooks, J. C., Beckmann, C. F., Miller, K. L., Wise, R. G., Porro, C. A., Tracey, I., & Jenkinson, M. (2008). Physiological noise modelling for spinal functional magnetic resonance imaging studies. NeuroImage, 39: 680692.Google Scholar
Brown, A. K., Fujita, M., Fujimura, Y., Liow, J. S., Stabin, M., Ryu, Y. H., … & Innis, R. B. (2007). Radiation dosimetry and biodistribution in monkey and man of 11C-PBR28: a PET radioligand to image inflammation. Journal of Nuclear Medicine, 48: 20722079.Google Scholar
Buchel, C., Bornhovd, K., Quante, M., Glauche, V., Bromm, B., & Weiller, C. (2002), Dissociable neural responses related to pain intensity, stimulus intensity, and stimulus awareness within the anterior cingulate cortex: a parametric single-trial laser functional magnetic resonance imaging study. Journal of Neuroscience, 22: 970976.Google Scholar
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network. Annals of the New York Academy of Sciences, 1124: 138.CrossRefGoogle ScholarPubMed
Buracas, G. T. & Boynton, G. M. (2002). Efficient design of event-related fMRI experiments using M-sequences. NeuroImage, 16: 801813.Google Scholar
Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4: 215222.CrossRefGoogle ScholarPubMed
Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., & Munafo, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14: 365376.Google Scholar
Buxton, R. B. & Frank, L. R. (1997). A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. Journal of Cerebral Blood Flow & Metabolism, 17: 6472.Google Scholar
Buxton, R. B., Frank, L. R., Wong, E. C., Siewert, B., Warach, S., & Edelman, R. R. (1998). A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magnetic Resonance in Medicine, 40: 383396.Google Scholar
Buxton, R. B., Uludag, K., Dubowitz, D. J., & Liu, T. T. (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23: S220S233.Google Scholar
Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45: 1628.Google Scholar
Calhoun, V. D., Miller, R., Pearlson, G., & Adali, T. (2014). The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84: 262274.Google Scholar
Chaimow, D., Yacoub, E., Ugurbil, K., & Shmuel, A. (2011). Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns. NeuroImage, 56: 627642.Google Scholar
Cheng, K., Waggoner, R. A., & Tanaka, K. (2001). Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging. Neuron, 32: 359374.Google Scholar
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18: 192205.Google Scholar
Coltheart, M. (2006). What has functional neuroimaging told us about the mind (so far)? Cortex, 42: 323331.Google Scholar
Constable, R. T. & Spencer, D. D. (1999). Composite image formation in z-shimmed functional MR imaging. Magnetic Resonance in Medicine, 42: 110117.Google Scholar
Cover, T. M. & Thomas, J. A. (1991). Elements of Information Theory. New York: John Wiley.Google Scholar
Cribben, I., Haraldsdottir, R., Atlas, L. Y., Wager, T. D., & Lindquist, M. A. (2012). Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage, 61: 907920.Google Scholar
de Quervain, D. J., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., & Fehr, E. (2004). The neural basis of altruistic punishment. Science, 305: 12541258.Google Scholar
Deckers, R. H., van Gelderen, P., Ries, M., Barret, O., Duyn, J. H., Ikonomidou, V. N., … & de Zwart, J. A. (2006). An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data. NeuroImage, 33: 10721081.Google Scholar
Denis Le Bihan, M. D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: concepts and applications. Journal of Magnetic Resonance Imaging, 13: 534546.Google Scholar
Denny, B. T., Kober, H., Wager, T. D., & Ochsner, K. N. (2012). A meta-analysis of functional neuroimaging studies of self- and other judgments reveals a spatial gradient for mentalizing in medial prefrontal cortex. Journal of Cognitive Neuroscience, 24: 17421752.Google Scholar
Desmond, J. E. & Glover, G. H. (2002). Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses. Journal of Neuroscience Methods, 118: 115128.Google Scholar
Detre, J. A., Zhang, W. G., Roberts, D. A., Silva, A. C., Williams, D. S., Grandis, D. J., … & Leigh, J. S. (1994). Tissue-specific perfusion imaging using arterial spin-labeling. NMR in Biomedicine, 7: 7582.Google Scholar
Devlin, J. T. & Poldrack, R. A. (2007). In praise of tedious anatomy. NeuroImage, 37: 10331041.Google Scholar
Disbrow, E. A., Slutsky, D. A., Roberts, T. P., & Krubitzer, L. A. (2000). Functional MRI at 1.5 tesla: a comparison of the blood oxygenation level-dependent signal and electrophysiology. Proceedings of the National Academy of Sciences of the USA, 97: 97189723.Google Scholar
Doucet, G., Naveau, M., Petit, L., Zago, L., Crivello, F., Jobard, G., … & Joliot, M. (2012). Patterns of hemodynamic low-frequency oscillations in the brain are modulated by the nature of free thought during rest. NeuroImage, 59: 31943200.Google Scholar
Duong, T. Q., Yacoub, E., Adriany, G., Hu, X., Ugurbil, K., Vaughan, J. T., … & Kim, S. G. (2002). High-resolution, spin-echo BOLD, and CBF fMRI at 4 and 7 T. Magnetic Resonance in Medicine, 48: 589593.CrossRefGoogle Scholar
Duvernoy, H. M. (2012). The Human Brain Stem and Cerebellum: Surface, Structure, Vascularization, and Three-Dimensional Sectional Anatomy, with MRI. Dordrecht: Springer Science & Business Media.Google Scholar
Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005). A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage, 25: 13251335.CrossRefGoogle ScholarPubMed
Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302: 290292.Google Scholar
Elster, A. D. (1994). Questions and Answers in Magnetic Resonance Imaging. St. Louis, MO: Mosby.Google Scholar
Ethofer, T., Van De Ville, D., Scherer, K., & Vuilleumier, P. (2009). Decoding of emotional information in voice-sensitive cortices. Current Biology, 19: 10281033.Google Scholar
Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Gunther, M., … & Yacoub, E. (2010). Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One, 5: e15710.CrossRefGoogle ScholarPubMed
Finsterbusch, J., Busch, M. G., & Larson, P. E. Z. (2013). Signal scaling improves the signal-to-noise ratio of measurements with segmented 2D-selective radiofrequency excitations. Magnetic Resonance in Medicine, 70: 14911499.Google Scholar
Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. NeuroImage, 9: 195207.Google Scholar
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the USA, 102: 96739678.Google Scholar
Fox, M. D., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2007). Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron, 56: 171184.Google Scholar
Frey, K. A. (1999). Positron emission tomography. In Siegel, G. J., Agranoff, B. W., Albers, R. W., Fisher, S. K., & Uhler, M. D. (eds.), Basic Neurochemistry, 6th edn. (pp. 11091131). Philadelphia: Lippincott, Williams, & Wilkins.Google Scholar
Friston, K. J. (2009). Modalities, modes, and models in functional neuroimaging. Science, 326: 399403.Google Scholar
Friston, K. J. (2011). Functional and effective connectivity: a review. Brain Connectivity, 1: 1336.Google Scholar
Friston, K. J. (2012). Ten ironic rules for non-statistical reviewers. NeuroImage, 61: 13001310.Google Scholar
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997). Psychophysiological and modulatory interactions in neuroimaging. NeuroImage, 6: 218229.Google Scholar
Friston, K. J., Frith, C. D., Turner, R., & Frackowiak, R. S. (1995). Characterizing evoked hemodynamics with fMRI. NeuroImage, 2: 157165.Google Scholar
Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19: 12731302.CrossRefGoogle ScholarPubMed
Friston, K. J., Mechelli, A., Turner, R., & Price, C. J. (2000). Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage, 12: 466477.Google Scholar
Gianaros, P. J. & Wager, T. D. (2015). Brain–body pathways linking psychological stress and physical health. Current Directions in Psychological Science, 24: 313321.Google Scholar
Glahn, D. C., Paus, T., & Thompson, P. M. (2007a). Imaging genomics: mapping the influence of genetics on brain structure and function. Human Brain Mapping, 28: 461463.Google Scholar
Glahn, D. C., Thompson, P. M., & Blangero, J. (2007b). Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Human Brain Mapping, 28: 488501.Google Scholar
Glover, G. H. & Law, C. S. (2001). Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts. Magnetic Resonance in Medicine, 46:515522.Google Scholar
Glover, G. H., Li, T. Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44: 162167.Google Scholar
Goldman, R. I., Stern, J. M., Engel, J. Jr., & Cohen, M. S. (2000). Acquiring simultaneous EEG and functional MRI. Clinical Neurophysiology, 111: 19741980.Google Scholar
Gonzalez-Castillo, J., Saad, Z. S., Handwerker, D. A., Inati, S. J., Brenowitz, N., & Bandettini, P. A. (2012). Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proceedings of the National Academy of Sciences of the USA, 109: 54875492.Google Scholar
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14: 2136.Google Scholar
Grinband, J., Savitskaya, J., Wager, T. D., Teichert, T., Ferrera, V. P., & Hirsch, J. (2011). The dorsal medial frontal cortex is sensitive to time on task, not response conflict or error likelihood. NeuroImage, 57: 303311.Google Scholar
Haacke, E. M. (1999). Magnetic Resonance Imaging: Physical Principles and Sequence Design. New York: John Wiley.Google Scholar
Haines, D. E. (2000). Neuroanatomy: An Atlas of Structures, Sections, and Systems. Philadelphia: Lippincott Williams & Wilkins.Google Scholar
Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science, 324: 646648.Google Scholar
Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., … & Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72: 404416.Google Scholar
Haynes, J. D. (2015). A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives. Neuron, 87: 257270.Google Scholar
Haynes, J. D., Deichmann, R., & Rees, G. (2005). Eye-specific effects of binocular rivalry in the human lateral geniculate nucleus. Nature, 438: 496499.Google Scholar
Heeger, D. J. & Ress, D. (2002). What does fMRI tell us about neuronal activity? Nature Reviews Neuroscience, 3: 142151.Google Scholar
Henson, R., Shallice, T., & Dolan, R. (2000). Neuroimaging evidence for dissociable forms of repetition priming. Science, 287: 12691272.CrossRefGoogle ScholarPubMed
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the USA, 106: 20352040.CrossRefGoogle ScholarPubMed
Horikawa, T., Tamaki, M., Miyawaki, Y., & Kamitani, Y. (2013). Neural decoding of visual imagery during sleep. Science, 340: 639642.Google Scholar
Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional Magnetic Resonance Imaging. Sunderland, MA: Sinauer Associates.Google Scholar
Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76: 12101224.Google Scholar
Johansen-Berg, H. & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Current Opinion in Neurology, 19: 379385.Google Scholar
Johansen-Berg, H., Behrens, T. E., Robson, M. D., Drobnjak, I., Rushworth, M. F., Brady, J. M., … & Matthews, P. M. (2004). Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proceedings of the National Academy of Sciences of the USA, 101: 1333513340.Google Scholar
Josephs, O. & Henson, R. N. (1999). Event-related functional magnetic resonance imaging: modelling, inference and optimization. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 354: 12151228.Google Scholar
Kamitani, Y. & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8: 679685.Google Scholar
Kao, M. H., Mandal, A., Lazar, N., & Stufken, J. (2009). Multi-objective optimal experimental designs for event-related fMRI studies. NeuroImage, 44: 849856.Google Scholar
Kastner, S. & Ungerleider, L. G. (2000). Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience, 23: 315341.Google Scholar
Kleinschmidt, A., Buchel, C., Zeki, S., & Frackowiak, R. S. (1998). Human brain activity during spontaneously reversing perception of ambiguous figures. Proceedings of the Royal Society of London B: Biological Sciences, 265: 24272433.Google Scholar
Klunk, W. E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D. P., … & Långström, B. (2004). Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Annals of Neurology, 55: 306319.Google Scholar
Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K., & Wager, T. D. (2008). Functional grouping and cortical–subcortical interactions in emotion: a meta-analysis of neuroimaging studies. NeuroImage, 42: 9981031.Google Scholar
Kong, Y., Jenkinson, M., Andersson, J., Tracey, I., & Brooks, J. C. (2012). Assessment of physiological noise modelling methods for functional imaging of the spinal cord. NeuroImage, 60: 15381549.Google Scholar
Kriegeskorte, N., Lindquist, M. A., Nichols, T. E., Poldrack, R. A., & Vul, E. (2010). Everything you never wanted to know about circular analysis, but were afraid to ask. Journal of Cerebral Blood Flow & Metabolism, 30: 15511557.Google Scholar
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., & Baker, C. I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience, 12: 535540.Google Scholar
Kvitsiani, D., Ranade, S., Hangya, B., Taniguchi, H., Huang, J. Z., & Kepecs, A. (2013). Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature, 498: 363366.Google Scholar
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., … & Turner, R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences of the USA, 89: 56755679.Google Scholar
Laufs, H., Daunizeau, J., Carmichael, D. W., & Kleinschmidt, A. (2008). Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging. NeuroImage, 40: 515528.Google Scholar
Leitao, J., Thielscher, A., Tunnerhoff, J., & Noppeney, U. (2015). Concurrent TMS-fMRI reveals interactions between dorsal and ventral attentional systems. Journal of Neuroscience, 35: 1144511457.Google Scholar
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012a). The brain basis of emotion: a meta-analytic review. Behavioral and Brain Sciences, 35: 121143.Google Scholar
Lindquist, M. A., Caffo, B., & Crainiceanu, C. (2013). Ironing out the statistical wrinkles in “ten ironic rules.” NeuroImage, 81: 499502.Google Scholar
Lindquist, M. A., Spicer, J., Asllani, I., & Wager, T. D. (2012b). Estimating and testing variance components in a multi-level GLM. NeuroImage, 59: 490501.Google Scholar
Lindquist, M. A., Waugh, C., & Wager, T. D. (2007). Modeling state-related fMRI activity using change-point theory. NeuroImage, 35: 11251141.Google Scholar
Lindquist, M. A., Zhang, C. H., Glover, G., & Shepp, L. (2008). Rapid three-dimensional functional magnetic resonance imaging of the initial negative BOLD response. Journal of Magnetic Resonance, 191: 100111.Google Scholar
Liu, T. T. (2004). Efficiency, power, and entropy in event-related fMRI with multiple trial types. Part II: design of experiments. NeuroImage, 21: 401413.Google Scholar
Loggia, M. L., Chonde, D. B., Akeju, O., Arabasz, G., Catana, C., Edwards, R. R., … & Hooker, J. M. (2015). Evidence for brain glial activation in chronic pain patients. Brain, 138: 604615.Google Scholar
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453: 869878.Google Scholar
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412: 150157.Google Scholar
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., & Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the USA, 97: 43984403.Google Scholar
Mai, J. K., Paxinos, G., & Voss, T. (2007). Atlas of the Human Brain, 3rd edn. New York: Academic Press.Google Scholar
Menon, R. S. (2002). Postacquisition suppression of large-vessel BOLD signals in high-resolution fMRI. Magnetic Resonance in Medicine, 47: 19.Google Scholar
Miezin, F. M., Maccotta, L., Ollinger, J. M., Petersen, S. E., & Buckner, R. L. (2000). Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. NeuroImage, 11: 735759.Google Scholar
Morawetz, C., Holz, P., Lange, C., Baudewig, J., Weniger, G., Irle, E., & Dechent, P. (2008). Improved functional mapping of the human amygdala using a standard functional magnetic resonance imaging sequence with simple modifications. Magnetic Resonance Imaging, 26: 4553.Google Scholar
Mumford, J. A. & Nichols, T. E. (2008). Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation. NeuroImage, 39: 261268.Google Scholar
Nichols, T. & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical Methods in Medical Research, 12: 419446.Google Scholar
Nichols, T. E. & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15: 125.Google Scholar
Noll, D. C., Fessler, J. A., & Sutton, B. P. (2005). Conjugate phase MRI reconstruction with spatially variant sample density correction. IEEE Transactions on Medical Imaging, 24: 325336.Google Scholar
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10: 424430.Google Scholar
Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006). Self-referential processing in our brain: a meta-analysis of imaging studies on the self. NeuroImage, 31: 440457.Google Scholar
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the USA, 87: 98689872.Google Scholar
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992). Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences of the USA, 89: 59515955.Google Scholar
Paton, J. J., Belova, M. A., Morrison, S. E., & Salzman, C. D. (2006). The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature, 439: 865870.Google Scholar
Paus, T. (2001). Primate anterior cingulate cortex: where motor control, drive and cognition interface. Nature Reviews Neuroscience, 2: 417424.Google Scholar
Petrini, K., Pollick, F. E., Dahl, S., McAleer, P., McKay, L. S., Rocchesso, D., … & Puce, A. (2011). Action expertise reduces brain activity for audiovisual matching actions: an fMRI study with expert drummers. NeuroImage, 56: 14801492.Google Scholar
Phillips, C., Rugg, M. D., & Friston, K. J. (2002). Anatomically informed basis functions for EEG source localization: combining functional and anatomical constraints. NeuroImage, 16: 678695.Google Scholar
Poldrack, R. A. (2011). Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron, 72: 692697.Google Scholar
Price, C. J., Veltman, D. J., Ashburner, J., Josephs, O., & Friston, K. J. (1999). The critical relationship between the timing of stimulus presentation and data acquisition in blocked designs with fMRI. NeuroImage, 10: 3644.Google Scholar
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the USA, 98: 676682.Google Scholar
Rasbash, J. (2002). A User’s Guide to MLwiN. Centre for Multilevel Modelling, University of London.Google Scholar
Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis, 2nd edn. Newbury Park, CA: Sage.Google Scholar
Reiman, E. M., Fusselman, M. J., Fox, P. T., & Raichle, M. E. (1989). Neuroanatomical correlates of anticipatory anxiety. Science, 243: 10711074 [erratum published in Science, 256 (1992): 1696].Google Scholar
Rosen, B. R., Buckner, R. L., & Dale, A. M. (1998). Event-related functional MRI: past, present, and future. Proceedings of the National Academy of Sciences of the USA, 95: 773780.Google Scholar
Ruff, C. C., Blankenburg, F., Bjoertomt, O., Bestmann, S., Freeman, E., Haynes, J. D., … & Driver, J. (2006). Concurrent TMS-fMRI and psychophysics reveal frontal influences on human retinotopic visual cortex. Current Biology, 16: 14791488.Google Scholar
Saad, Z. S., Reynolds, R. C., Argall, B., Japee, S., & Cox, R. W. (2004). SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI. IEEE International Symposium on Biomedical Imaging: Nano to Macro, 1512: 15101513.Google Scholar
Sandler, M. P. (2003). Diagnostic Nuclear Medicine. Philadelphia, PA: Lippincott, Williams & Wilkins.Google Scholar
Sarter, M., Berntson, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience: toward strong inference in attributing function to structure. American Psychologist, 51: 1321.Google Scholar
Schacter, D. L., Buckner, R. L., Koutstaal, W., Dale, A. M., & Rosen, B. R. (1997). Late onset of anterior prefrontal activity during true and false recognition: an event-related fMRI study. NeuroImage, 6: 259269.Google Scholar
Scheibe, C., Ullsperger, M., Sommer, W., & Heekeren, H. R. (2010). Effects of parametrical and trial-to-trial variation in prior probability processing revealed by simultaneous electroencephalogram/functional magnetic resonance imaging. Journal of Neuroscience, 30: 1670916717.Google Scholar
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., … & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27: 23492356.Google Scholar
Setsompop, K., Gagoski, B. A., Polimeni, J. R., Witzel, T., Wedeen, V. J., & Wald, L. L. (2012). Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magnetic Resonance in Medicine, 67: 12101224.Google Scholar
Shulman, R. G. & Rothman, D. L. (1998). Interpreting functional imaging studies in terms of neurotransmitter cycling. Proceedings of the National Academy of Sciences of the USA, 95: 1199311998.Google Scholar
Shulman, R. G., Rothman, D. L., Behar, K. L., & Hyder, F. (2004). Energetic basis of brain activity: implications for neuroimaging. Trends in Neurosciences, 27: 489495.Google Scholar
Sibson, N. R., Dhankhar, A., Mason, G. F., Behar, K. L., Rothman, D. L., & Shulman, R. G. (1997). In vivo 13C NMR measurements of cerebral glutamine synthesis as evidence for glutamate-glutamine cycling. Proceedings of the National Academy of Sciences of the USA, 94: 26992704.Google Scholar
Sinha, R., Lacadie, C., Skudlarski, P., & Wexler, B. E. (2004). Neural circuits underlying emotional distress in humans. Annals of the New York Academy of Sciences, 1032: 254257.Google Scholar
Skudlarski, P., Constable, R. T., & Gore, J. C. (1999). ROC analysis of statistical methods used in functional MRI: individual subjects. NeuroImage, 9: 311329.Google Scholar
Smith, S. M. (2012). The future of FMRI connectivity. NeuroImage, 62: 12571266.Google Scholar
Smith, S. M., Jenkinson, M., Beckmann, C., Miller, K., & Woolrich, M. (2007). Meaningful design and contrast estimability in FMRI. NeuroImage, 34: 127136.Google Scholar
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., … & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23: S208S219.Google Scholar
Sporns, O. (2014). Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience, 17: 652660.Google Scholar
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009). Bayesian model selection for group studies. NeuroImage, 46: 10041017.Google Scholar
Sternberg, S. (2001). Separate modifiability, mental modules, and the use of pure and composite measures to reveal them. Acta Psychologica (Amsterdam), 106: 147246.Google Scholar
Summerfield, C., Greene, M., Wager, T., Egner, T., Hirsch, J., & Mangels, J. (2006). Neocortical connectivity during episodic memory formation. PLoS Biol, 4: e128.Google Scholar
Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M. M., & Egner, T. (2008). Neural repetition suppression reflects fulfilled perceptual expectations. Nature Neuroscience, 11: 10041006.Google Scholar
Sylvester, C. Y., Wager, T. D., Lacey, S. C., Hernandez, L., Nichols, T. E., Smith, E. E., & Jonides, J. (2003). Switching attention and resolving interference: fMRI measures of executive functions. Neuropsychologia, 41: 357370.Google Scholar
Tagliazucchi, E. & Laufs, H. (2014). Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron, 82: 695708.Google Scholar
Talairach, J. & Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the Human Brain. 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Stuttgart and New York: Thieme.Google Scholar
Taylor, J. E. & Worsley, K. J. (2006). Inference for magnitudes and delays of responses in the FIAC data using BRAINSTAT/FMRISTAT. Human Brain Mapping, 27: 434441.Google Scholar
Thompson, P. M., Schwartz, C., Lin, R. T., Khan, A. A., & Toga, A. W. (1996). Three-dimensional statistical analysis of sulcal variability in the human brain. Journal of Neuroscience, 16: 42614274.Google Scholar
Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., … & Drevets, W. (2014). The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8: 153182.Google Scholar
Tye, K. M., Prakash, R., Kim, S.-Y., Fenno, L. E., Grosenick, L., Zarabi, H., … & Deisseroth, K. (2011). Amygdala circuitry mediating reversible and bidirectional control of anxiety. Nature, 471: 358362.Google Scholar
van Ast, V., Spicer, J., Smith, E., Schmer-Galunder, S., Liberzon, I., Abelson, J., & Wager, T. (2014). Brain mechanisms of social threat effects on working memory. Cerebral Cortex (September): bhu206.Google Scholar
Van Essen, D. C. & Dierker, D. L. (2007). Surface-based and probabilistic atlases of primate cerebral cortex. Neuron, 56: 209225.Google Scholar
Van Essen, D. C., Drury, H. A., Dickson, J., Harwell, J., Hanlon, D., & Anderson, C. H. (2001). An integrated software suite for surface-based analyses of cerebral cortex. Journal of the American Medical Informatics Association, 8: 443459.Google Scholar
Vazquez, A. L., Cohen, E. R., Gulani, V., Hernandez-Garcia, L., Zheng, Y., Lee, G. R., … & Noll, D. C. (2006). Vascular dynamics and BOLD fMRI: CBF level effects and analysis considerations. NeuroImage, 32: 16421655.Google Scholar
Vazquez, A. L. & Noll, D. C. (1998). Nonlinear aspects of the BOLD response in functional MRI. NeuroImage, 7: 108118.Google Scholar
Vincent, J. L., Patel, G. H., Fox, M. D., Snyder, A. Z., Baker, J. T., Van Essen, D. C., … & Raichle, M. E. (2007). Intrinsic functional architecture in the anaesthetized monkey brain. Nature, 447: 8386.Google Scholar
Vogt, B. A., Nimchinsky, E. A., Vogt, L. J., & Hof, P. R. (1995). Human cingulate cortex: surface features, flat maps, and cytoarchitecture. Journal of Comparative Neurology, 359: 490506.Google Scholar
Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspectives on Psychological Science, 4: 274290.Google Scholar
Wager, T. D., Atlas, L. Y., Lindquist, M. A., Roy, M., Woo, C. W., & Kross, E. (2013). An fMRI-based neurologic signature of physical pain. New England Journal of Medicine, 368: 13881397.Google Scholar
Wager, T. D., Jonides, J., & Reading, S. (2004a). Neuroimaging studies of shifting attention: a meta-analysis. NeuroImage, 22: 16791693.Google Scholar
Wager, T. D., Jonides, J., Smith, E. E., & Nichols, T. E. (2005b). Toward a taxonomy of attention shifting: individual differences in fMRI during multiple shift types. Cognitive, Affective, & Behavioral Neuroscience, 5: 127143.Google Scholar
Wager, T. D., Lindquist, M., & Kaplan, L. (2007). Meta-analysis of functional neuroimaging data: Current and future directions. Social Cognitive and Affective Neuroscience, 2: 150158.Google Scholar
Wager, T. D. & Nichols, T. E. (2003). Optimization of experimental design in fMRI: a general framework using a genetic algorithm. NeuroImage, 18: 293309.Google Scholar
Wager, T. D., Reading, S., & Jonides, J. (2004b). Neuroimaging studies of shifting attention: a meta-analysis. NeuroImage, 22: 16791693.Google Scholar
Wager, T. D., Vazquez, A, Hernandez, L, & Noll, D. C. (2005a). Accounting for nonlinear BOLD effects in fMRI: parameter estimates and a model for prediction in rapid event-related studies. NeuroImage, 25: 206218.Google Scholar
Wager, T. D., Waugh, C. E., Lindquist, M., Noll, D. C., Fredrickson, B. L., & Taylor, S. F. (2009). Brain mediators of cardiovascular responses to social threat. Part I: Reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity. NeuroImage, 47: 821835.Google Scholar
Waugh, C. E., Hamilton, J. P., & Gotlib, I. H. (2010). The neural temporal dynamics of the intensity of emotional experience. NeuroImage, 49: 16991707.Google Scholar
Wiech, K., Jbabdi, S., Lin, C. S., Andersson, J., & Tracey, I. (2014). Differential structural and resting state connectivity between insular subdivisions and other pain-related brain regions. Pain, 155: 20472055.Google Scholar
Wilson, J. L. & Jezzard, P. (2003). Utilization of an intra-oral diamagnetic passive shim in functional MRI of the inferior frontal cortex. Magnetic Resonance in Medicine, 50: 10891094.Google Scholar
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92: 381397.Google Scholar
Wise, R. G., Rogers, R., Painter, D., Bantick, S., Ploghaus, A., Williams, P., … & Tracey, I. (2002). Combining fMRI with a pharmacokinetic model to determine which brain areas activated by painful stimulation are specifically modulated by remifentanil. NeuroImage, 16: 9991014.Google Scholar
Woo, C. W., Koban, L., Kross, E., Lindquist, M. A., Banich, M. T., Ruzic, L., … & Wager, T. D. (2014a). Separate neural representations for physical pain and social rejection. Nature Communications, 5: 5380.Google Scholar
Woo, C. W., Krishnan, A., & Wager, T. D. (2014b). Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. NeuroImage, 91: 412419.Google Scholar
Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004). Multilevel linear modelling for FMRI group analysis using Bayesian inference. NeuroImage, 21: 17321747.Google Scholar
Worsley, K. J. & Friston, K. J. (1995). Analysis of fMRI time-series revisited – again. NeuroImage, 2: 173181.Google Scholar
Worsley, K. J., Taylor, J. E., Tomaiuolo, F., & Lerch, J. (2004). Unified univariate and multivariate random field theory. NeuroImage, 23: S189S195.Google Scholar
Yacubian, J., Sommer, T., Schroeder, K., Glascher, J., Kalisch, R., Leuenberger, B., … & Buchel, C. (2007). Gene–gene interaction associated with neural reward sensitivity. Proceedings of the National Academy of Sciences of the USA, 104: 81258130.Google Scholar
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8: 665670.Google Scholar
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106: 11251165.Google Scholar
Zarahn, E. & Slifstein, M. (2001). A reference effect approach for power analysis in fMRI. NeuroImage, 14: 768779.Google Scholar

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