A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures

Scientific Data

Volumn 2, Issue , 2015, Pages

  Gorgolewski, Krzysztof J ^a   Mendes, Natacha ^a   Wilfling, Domenica ^a   Wladimirow, Elisabeth ^a   Gauthier, Claudine J ^a,b   Bonnen, Tyler ^a   Ruby, Florence J M ^c   Trampel, Robert ^a   Bazin, Pierre Louis ^a   Cozatl, Roberto ^a   Smallwood, Jonathan ^c   Margulies, Daniel S ^a  

^a MAX PLANCK INSTITUTE FOR HUMAN COGNITIVE AND BRAIN SCIENCES   (Germany)

^b Dept of Electrical and Computer Engineering   (Canada)

^c UNIVERSITY OF YORK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AFFECT; BRAIN; BREATHING; COGNITION; HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; SENSITIVITY AND SPECIFICITY;

AFFECT; BRAIN; COGNITION; HUMANS; MAGNETIC RESONANCE IMAGING; RESPIRATION; SENSITIVITY AND SPECIFICITY;

EID: 84960983700     PISSN: None     EISSN: 20524463     Source Type: Journal    
DOI: 10.1038/sdata.2014.54     Document Type: Article

References (43)

1. Hale, J. R. et al. Comparison of functional connectivity in default mode and sensorimotor networks at 3 and 7T. MAGMA 23, 339-349 (2010).
2. De Martino, F. et al. Whole brain high-resolution functional imaging at ultra high magnetic fields: an application to the analysis of resting state networks. Neuroimage 57, 1031-1044 (2011).
3. Newton, A. T., Rogers, B. P., Gore, J. C. & Morgan, V. L. Improving measurement of functional connectivity through decreasing partial volume effects at 7 T. Neuroimage 59, 2511-2517 (2012).
4. Bianciardi, M. et al. Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. Magn. Reson. Imaging 27, 1019-1029 (2009).
5. Lenglet, C. et al. Comprehensive in vivo mapping of the human basal ganglia and thalamic connectome in individuals using 7T MRI. PLoS ONE 7, e29153 (2012).
6. Raemaekers, M. et al. Patterns of resting state connectivity in human primary visual cortical areas: a 7T fMRI study. Neuroimage 84, 911-921 (2014).
7. Yang, X., Holmes, M. J., Newton, A. T., Morgan, V. L. & Landman, B. A. A comparison of distributional considerations with statistical analysis of resting state fMRI at 3T and 7T. Proc. Soc. Photo. Opt. Instrum. Eng. 8314, 831416 (2012).
8. Yang, X., Kang, H., Newton, A. T. & Landman, B. A. Evaluation of statistical inference on empirical resting state fMRI. IEEE Trans. Biomed. Eng. 61, 1091-1099 (2014).
9. Shehzad, Z. et al. The resting brain: unconstrained yet reliable. Cereb. Cortex 19, 2209-2229 (2009).
10. Zhang, H. et al. Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy. Neuroimage 55, 607-615 (2011).
11. Birn, R. M. et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83C, 550-558 (2013).
12. Braun, U. et al. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage 59, 1404-1412 (2012).
13. Gorgolewski, K. J., Storkey, A. J., Bastin, M. E., Whittle, I. & Pernet, C. Single subject fMRI test-retest reliability metrics and confounding factors. Neuroimage 69, 231-243 (2013).
14. Bennett, C. M. & Miller, M. B. How reliable are the results from functional magnetic resonance imaging? Ann. N. Y. Acad. Sci. 1191, 133-155 (2010).
15. Yan, C.-G. et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183-201 (2013).
16. Kucyi, A. & Davis, K. D. Dynamic functional connectivity of the default mode network tracks daydreaming. Neuroimage 100, 471-480 (2014).
17. Allen, E. A. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 1-14 (2012).
18. Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L. & Breakspear, M. Time-resolved resting-state brain networks. Proc. Natl Acad. Sci. USA 111, 10341-10346 (2014).
19. Gorgolewski, K. J. et al. A correspondence between individual differences in the brain's intrinsic functional architecture and the content and form of self-generated thoughts. PLoS ONE 9, e97176 (2014).
20. Shrout, P. E. & Fleiss, J. L. Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86, 420-428 (1979).
21. Hanke, M. et al. A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Sci. Data 1, 140003 (2014).
22. Zuo, X. N. et al. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1, 140049 (2014).
23. Shalev, L., Ben-Simon, A., Mevorach, C., Cohen, Y. & Tsal, Y. Conjunctive Continuous Performance Task (CCPT) - a pure measure of sustained attention. Neuropsychologia 49, 2584-2591 (2011).
24. Mathôt, S., Schreij, D. & Theeuwes, J. OpenSesame: an open-source, graphical experiment builder for the social sciences. Behav. Res. Methods 44, 314-324 (2012).
25. Peirce, J. W. PsychoPy - Psychophysics software in Python. J. Neurosci. Methods 162, 8-13 (2007).
26. Peirce, J. W. Generating Stimuli for Neuroscience Using PsychoPy. Front. Neuroinform. 2, 10 (2008).
27. Watson, D. & Clark, L. A. The PANAS-X manual for the positive and negative affect schedule - expanded form http://ir.uiowa. edu/psychology-pubs/11/ (1999).
28. Grühn, D., Kotter-Grühn, D. & Röcke, C. Discrete affects across the adult lifespan: Evidence for multidimensionality and multidirectionality of affective experiences in young, middle-aged and older adults. J. Res. Pers. 44, 492-500 (2010).
29. Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49, 1271-1281 (2010).
30. Pruessmann, K. P., Weiger, M., Scheidegger, M. B. & Boesiger, P. SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952-962 (1999).
31. Heidemann, R. M., Ivanov, D. & Trampel, R. Isotropic submillimeter fMRI in the human brain at 7 T: Combining reduced field of view imaging and partially parallel acquisitions. Magn. Reson. Med. 68, 1506-1516 (2012).
32. Pfeuffer, J. et al. Zoomed functional imaging in the human brain at 7 Tesla with simultaneous high spatial and high temporal resolution. Neuroimage 17, 272-286 (2002).
33. Andrews-Hanna, J. R., Reidler, J. S., Huang, C. & Buckner, R. L. Evidence for the default network's role in spontaneous cognition. J. Neurophysiol. 104, 322-335 (2010).
34. Setsompop, K. et al. Improving diffusion MRI using simultaneous multi-slice echo planar imaging. Neuroimage 63, 569-580 (2012).
35. Scott, A. et al. COINS: An innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front. Neuroinform 5, 33 (2011).
36. Langille, M. G. I. & Eisen, J. A. BioTorrents: a file sharing service for scientific data. PLoS ONE 5, e10071 (2010).
37. Marcus, D. S., Olsen, T. R., Ramaratnam, M. & Buckner, R. L. The extensible neuroimaging archive toolkit. Neuroinformatics 5, 11-33 (2007).
38. Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162-173 (1996).
39. Farzinfar, M. et al. Entropy based DTI quality control via regional orientation distribution. Proc. IEEE Int. Symp. Biomed. Imaging 22-26 (2012).
40. Atkinson, D., Hill, D. L., Stoyle, P. N., Summers, P. E. & Keevil, S. F. Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Trans. Med. Imaging 16, 903-910 (1997).
41. Giannelli, M., Diciotti, S. & Tessa, C. Characterization of Nyquist ghost in EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing. J. Appl. Clin. Med. Phys. 11, 170-180 (2010).
42. Bazin, P.-L. et al. A computational framework for ultra-high resolution cortical segmentation at 7Tesla. Neuroimage 93, 201-209 (2014).
43. Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform 5, 13 (2011).