<html> tags cannot be used outside of normal pages.

FMRIpapers

From The CNBC Wiki
Jump to navigation Jump to search

Adaptation

Drucker, D., Kerr, W., & Aguirre, K. (2009). Distinguishing conjoint and independent neural tuning for stimulus features with fMRI adaptation.
     Journal of Neurophysiology, 101, 3310-3324. [1]

Grill-Spector, K., Henson, R., & Martin, A. (2006). Repetition and the brain: neural models of stimulus-specific effects.
     Trends in Cognitive Sciences, 10(1),  14-23. [2]


BOLD Signal and HRF

Boynton, G., Engel, S., Glover, G., & Heeger, D. (1996). Linear systems analysis of functional magnetic resonance imaging in human V1.
     The Journal of Neuroscience, 16(13), 4207-4221. [3]

Buxton, R. (2001). The elusive initial dip. Neuroimage, 13, 953-958. [4]

Buxton, R., Uludag, K., Dubowitz, D., & Liu, T. (2004). Modeling the hemodynamic response to brain activation. Neuroimage,, 23, S220-S233. [5]

Heeger, D., & Hees, D. (2002). What does fMRI tell us about neuronal activity? Nature Reviews: Neuroscience, 3, 142-151. [6]

Janz,C., Schmitt, C., Speck, O., & Hennig, J. (2000). Comparison of the hemodynamic response to different visual stimuli in single-event and
     block stimulation fMRI experiments.Journal of magnetic resonance imaging, 12,  708-714. [7]

Logothetis, N., & Wandell, B. (2004). Interpreting the BOLD signal. Annual Review Physiology, 66, 735-769. [8]

Neumann, J., Lohmann, G., Zysset, S., & Yves von Carmon, D. (2003). Within-subject variability of BOLD response dynamics. Neuroimage, 19, 784-796. [9]

Robinson, P., Drysdale, P., Van der Merwe, H., Kyriakou, E., Rigozzi, M.K., Germanoska, B., & Rennie, C.J. (2006). BOLD responses to stimuli: dependence on
       frequency, stimulus form, amplitude, and repetition rate.  Neuroimage, 31, 585-599. [10]

Yacoub, E., Shmuel, A., Pfeuffer, J., Van de Moortele, P., Adriany, G., Ugurbil, K., & Hu, X. (2001). Investigation of the initial dip
     in fMRI at 7 Tesla. NMR in Biomedicine, 14, 408-412. [11]

Yesilyurt, B., Ugurbil, K., & Uludag, K. (2008). Dynamics and nonlinearities of the BOLD response at very short stimulus durations.
     Magnetic Resonance Imaging, 26, 853-862. [12]


Circularity

Kriegeskorte, N., Simmons, W.K., Bellgowan, P., & Baker, C.I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping.
     Nature Neuroscience, 12(5), 535-540. [13]

Kriegeskorte, N., Lindquist, M.A., Nichols, T.E., Poldrack, R.S., & Vul, E. (2010). Everything you never wanted to know about circular analysis, but were afraid to ask.
       Journal of Cerebral Blood Flow & Metabolism, 1-7. [14]


ExperimentalDesign

Burock, M.A., Buckner, R.L., Woldorff, M.G., Rosen, B.R., & Dale, A.M (1998). Randomized event-related experimental designs allow for extremely rapid presentation
       rates using functional MRI. NeuroReport, 9, 3735-3739. [15]

Dale, A.M., & Buckner, R.L. (1997). Selective averaging of rapidly presented individual trials using fMRI. Human Brain Mapping, 5, 329-340. [16]

Dale, A.M. (1999). Optimal experimental design for event-related fMRI. Human Brain Mapping, 8, 109-114. [17]

Friston, K.J., Zarahn, E., Josephs, O., Henson, R.N.A, &  Dale, A.M. (1999). Stochastic designs in event-related fMRI. Neuroimage, 10, 607-619. [18]

Kriegeskorte, N. (2010). Interpreting brain images – reflections on an adolescent field. Foundational issues in Human Brain Mapping, 1-2. [19]

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, 95, 773-780. [20]

Serences, J. (2004). A comparison of methods for characterizing the event-related BOLD timeseries in rapid fMRI. Neuroimage, 21, 1690-1700. [21]


Localizers

Duncan, K.J., Pattamadilok, C., Knierim, I., & Devlin, J.T. (2009). Consistency and variability in functional localisers. Neuroimage, 46, 1018-1026. [22]

Horner, A.J., & Andrews, T.J. (2009). Linearity of the fMRI response in category-selective regions of human visual cortex. Human Brain Mapping, 30, 2628-2640. [23]

Poldrack, R.A. (2007). Regions of interest analysis for fMRI. Social, Cognitive, and Affective Neuroscience, 2, 67-70. [24]

Saxe, R., Brett, M., & Kanwsher, N. (2006). Divide and conquer: A defense of functional localizers. Neuroimage, 30, 1088-1096. [25]


MVPA

Etzel, J.A., Valchev, N., & Keysers, C. (2010). The impact of certain methodological choices on multivariate analysis of fMRI data
      with support vector machines. NeuroImage, 54(2), 1159-1167. [26]

Formisano, E., De Martino, F., & Valente, G. (2008). Multivariate analysis of fMRI time series: classification and regression of brain responses
       using machine learning. Magnetic Resonance Imaging, 26, 921-934. [27]

Kriegeskorte, N., & Bandettini, P. (2006). The neuuroscientific exploitation of high-resolution functional magnetic resonance imaging. IEEE Explore,  21-24. [28]

Kriegeskorte, N., & Bandettini, P. (2007). Combining the tools: Activation – and information-based fMRI analysis. Neuroimage, 38, 666-668. [29]

Kriegeskorte, N., & Bandettini, P. (2007). Analyzing for information, not activation, to exploit high-resolution fMRI. Neuroimage, 38, 549-662. [30]

Kriegeskorte, N. (2011). Pattern-information analysis: From stimulus decoding to computational-model testing. NeuroImage, 56, 411-421. [31]

Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences, 103(10), 3863-3868. [32]

Ku, S., Gretton, A., Macke, J., & Logothetis, N.K. (2008). Comparison of pattern recognition methods in classifying high-resolution BOLD signals
       obtained at high magnetic field in monkeys. Magnetic Resonance Imaging, 26, 1007-1014. [33]

Kuncheva, L.I., & Rodriguez, J.J. (2010). Classifier ensembles for fMRI data analysis: an experiment. Magnetic Resonance Imaging, 28, 583-593. [34]

Misaki, M., Kim, Y., Bandettini, P., & Kriegeskorte, N. (2010). Comparison of multivariate classifiers and response
      normalizations for pattern-information fMRI. NeuroImage, 53, 103-118. [35]

Mur, M., Bandettini, P., & Kriegeskorte, N. (2009).  Revealing representational content with pattern-information fMRI – an introductory guide.
       Social, Cognitive, and Affective Neuroscience, 4, 101-109. [36]

Op de Beeck, H.P. (2010). Against hyperacuity in brain reading: Spatial smoothing does not hurt multivariate fMRI analyses? NeuroImage, 49, 1943-1948. [37]

Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45, S199-S209. [38]


PreProcessing

Della-Maggiore, V., Chau, W., Peres-Neto, P.R., & McIntosh, A.R. (2002). An empirical comparison of SPM preprocessing parameters
     to the analysis of fMRI data. NeuroImage, 17, 19-28. [39]

Jo, H.J., Lee, J., Kim, J., Shin, Y., Kim, I., Kwon, J., *& Kim, S. (2007). Spatial accuracy of fMRI activation influenced by volume- and surface-based
       spatial smoothing techniques. NeuroImage, 34,  550-564. [40]

Kamitani, Y., & Sawahata, Y. (2010). Spatial smoothing hurts localization but not information: Pitfalls for brain mappers. NeuroImage, 49, 1949-1952. [41]

Sladky, R, Friston, K.J., Trostl, J., Cunnington, R., Moser, E., & Windischberger, C. (2011). Slice-timing effects and their correction in functional MRI. NeuroImage, 58, 588-594. [42]


Slides

fMRI Crash Course Part 1 [43]
fMRI Crash Course Part 2 [44]
1A) fMRI Introduction [45]
1B) fMRI Safety [46]
2) Data Quality and Preprocessing [47]
3) GLM Analysis [48]
4) Spatiotemporal Limits of fMRI [49]
5) Basic Experimental Design [50]
6) Advanced Experimental Design [51]
8A) Localization [52]
8B) Topography [53]
9) Cortical Sulci [54]
10) fMRI Physics and BOLD [55]
How to Lie with fMRI Statistics [56]