The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution

NeuroImage - Tập 194 - Trang 228-243 - 2019
Michael Lührs1,2,3, Bruno Riemenschneider4, Judith Eck1,2,3, Amaia Benitez Andonegui1,2, Benedikt A. Poser1,2, Armin Heinecke1,2,3, Florian Krause1,2,5, Fabrizio Esposito1,2,6, Bettina Sorger1,2, Jürgen Hennig4, Rainer Goebel1,2,3,7
1Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, the Netherlands
2Maastricht Brain Imaging Center, Maastricht, The Netherlands
3Brain Innovation B.V., Research Department, Maastricht, the Netherlands
4Dept. of Radiology, Medical Physics, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
5Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
6Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno, Baronissi (SA), Italy
7Netherlands Institute for Neuroscience (NIN), Amsterdam, the Netherlands

Tài liệu tham khảo

Akin, 2017, Enhanced subject-specific resting-state network detection and extraction with fast fMRI, Hum. Brain Mapp., 38, 817, 10.1002/hbm.23420 Assländer, 2013, Single shot whole brain imaging using spherical stack of spirals trajectories, Neuroimage, 73, 59, 10.1016/j.neuroimage.2013.01.065 Barker, 2013, Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS, Biomed. Opt. Express, 4, 1366, 10.1364/BOE.4.001366 Barker, 2016, Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy, Neurophotonics, 3, 10.1117/1.NPh.3.3.031410 Barth, 2016, Simultaneous multislice (SMS) imaging techniques, Magn. Reson. Med., 75, 63, 10.1002/mrm.25897 Bastos, 2016, A tutorial review of functional connectivity analysis methods and their interpretational pitfalls, Front. Syst. Neurosci., 9, 175, 10.3389/fnsys.2015.00175 Bollmann, 2018, Serial correlations in single-subject fMRI with sub-second TR, Neuroimage, 166, 152, 10.1016/j.neuroimage.2017.10.043 Boynton, 1996, Linear systems analysis of functional magnetic resonance imaging in human V1, J. Neurosci., 16, 4207, 10.1523/JNEUROSCI.16-13-04207.1996 Burg, 1975, Maximum entropy spectral analysis, Astron. Astrophys. Suppl., 15, 383 Canuet, 2015, Neurorehabilitation in stroke: the Role of functional connectivity, Int. J. Neurorehabil., 02, 1, 10.4172/2376-0281.1000172 Chen, 2019, On the analysis of rapidly sampled fMRI data, Neuroimage, 188, 807, 10.1016/j.neuroimage.2019.02.008 Chen, 2015, Evaluation of highly accelerated simultaneous multi-slice EPI for fMRI, Neuroimage, 104, 452, 10.1016/j.neuroimage.2014.10.027 Corbin, 2018, Accurate modeling of temporal correlations in rapidly sampled fMRI time series, Hum. Brain Mapp., 39, 3884, 10.1002/hbm.24218 De Hoon, 1996, Why Yule-Walker should not be used for autoregressive modelling, Ann. Nucl. Energy, 23, 1219, 10.1016/0306-4549(95)00126-3 De Martino, 2007, Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers, Neuroimage, 34, 177, 10.1016/j.neuroimage.2006.08.041 Díez-Cirarda, 2017, Increased brain connectivity and activation after cognitive rehabilitation in Parkinson's disease: a randomized controlled trial, Brain Imag. Behav., 11, 1640, 10.1007/s11682-016-9639-x Eklund, 2011 Fadili, 2003, Analysis of fMRI time series, Human Brain Function, 178, 160 Feinberg, 2010, Multiplexed echo planar imaging for sub-second whole brain fmri and fast diffusion imaging, PLoS One, 5, 10.1371/journal.pone.0015710 Feinberg, 2013, Ultra-fast MRI of the human brain with simultaneous multi-slice imaging, J. Magn. Reson., 229, 90, 10.1016/j.jmr.2013.02.002 Feinberg, 2012, The rapid development of high speed, resolution and precision in fMRI, Neuroimage, 62, 720, 10.1016/j.neuroimage.2012.01.049 Friman, 2005, Resampling fMRI time series, Neuroimage, 25, 859, 10.1016/j.neuroimage.2004.11.046 Friston, 2011, Functional and effective connectivity: a review, Brain Connect., 1, 13, 10.1089/brain.2011.0008 Friston, 2000, To smooth or not to smooth? Bias and efficiency in fMRI time-series analysis, Neuroimage, 12, 196, 10.1006/nimg.2000.0609 Glover, 1999, Deconvolution of impulse response in event-related BOLD fMRI, Neuroimage, 9, 416, 10.1006/nimg.1998.0419 Greve, 2009, Accurate and robust brain image alignment using boundary-based registration, Neuroimage, 48, 63, 10.1016/j.neuroimage.2009.06.060 Gulban, 2016 Hao, 2017, Subject-level reliability analysis of fast fMRI with application to epilepsy, Magn. Reson. Med., 78, 370, 10.1002/mrm.26365 Hennig, 2007, MR-Encephalography: fast multi-channel monitoring of brain physiology with magnetic resonance, Neuroimage, 34, 212, 10.1016/j.neuroimage.2006.08.036 Holmes, 1998, Enhancement of MR images using registration for signal averaging, J. Comput. Assist. Tomogr., 22, 324, 10.1097/00004728-199803000-00032 Hsu, 2017, Simultaneous multi-slice inverse imaging of the human brain, Sci. Rep., 7, 17019, 10.1038/s41598-017-16976-0 Hu, 2012, The story of the initial dip in fMRI, Neuroimage, 62, 1103, 10.1016/j.neuroimage.2012.03.005 Hutchison, 2013, Dynamic functional connectivity: promise, issues, and interpretations, Neuroimage, 80, 360, 10.1016/j.neuroimage.2013.05.079 Hyvärinen, 1999, Fast and robust fixed-point algorithms for independent component analysis, IEEE Trans. Neural Netw., 10, 626, 10.1109/72.761722 Kairov, 2017, Determining the optimal number of independent components for reproducible transcriptomic data analysis, BMC Genomics, 18, 712, 10.1186/s12864-017-4112-9 Kanwisher, 1997, The fusiform face area: a module in human extrastriate cortex specialized for face perception, J. Neurosci., 17, 4302, 10.1523/JNEUROSCI.17-11-04302.1997 Karahanoğlu, 2017, Dynamics of large-scale fMRI networks: deconstruct brain activity to build better models of brain function, Curr. Opin. Biomed. Eng., 3, 28, 10.1016/j.cobme.2017.09.008 Kay, 1981, Spectrum analysis—a modern perspective, Proc. IEEE, 69, 1380, 10.1109/PROC.1981.12184 Klein, 2009, Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration, Neuroimage, 46, 786, 10.1016/j.neuroimage.2008.12.037 Kornbrot, 2014, Spearman's Rho Krause, 2017, Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations, Brain-Computer Interfaces, 4, 87, 10.1080/2326263X.2017.1307096 Lee, 2013, Tracking dynamic resting-state networks at higher frequencies using MR-encephalography, Neuroimage, 65, 216, 10.1016/j.neuroimage.2012.10.015 Lenoski, 2008, On the performance of autocorrelation estimation algorithms for fMRI analysis, IEEE J. Selected Top. Signal Proces., 2, 828, 10.1109/JSTSP.2008.2007819 Leonard, 2017, How much motion is too much motion? Determining motion thresholds by sample size for reproducibility in developmental resting-state MRI, Research Ideas and Outcomes, 3, 10.3897/rio.3.e12569 Lewis, 2016, Fast fMRI can detect oscillatory neural activity in humans, Proc. Natl. Acad. Sci. Unit. States Am., 113, E6679, 10.1073/pnas.1608117113 LI, 2014, Burmanesque mini review, Adv. Synth. Catal., 1 Lin, 2012, Ultrafast inverse imaging techniques for fMRI, Neuroimage, 62, 699, 10.1016/j.neuroimage.2012.01.072 Locascio, 1997, Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging, Hum. Brain Mapp., 5, 168, 10.1002/(SICI)1097-0193(1997)5:3<168::AID-HBM3>3.0.CO;2-1 Lührs, 2017, Automated selection of brain regions for real-time fMRI brain-computer interfaces, J. Neural Eng., 14, 10.1088/1741-2560/14/1/016004 McKeown, 2000, Detection of consistently task-related activations in fMRI data with hybrid independent component analysis, Neuroimage, 11, 24, 10.1006/nimg.1999.0518 McKeown, 2003, Independent component analysis of functional MRI: what is signal and what is noise?, Curr. Opin. Neurobiol., 13, 620, 10.1016/j.conb.2003.09.012 McKeown, 1998, Analysis of fMRI data by blind separation into independent spatial components, Hum. Brain Mapp., 6, 160, 10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1 Moeller, 2010, Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain FMRI, Magn. Reson. Med., 63, 1144, 10.1002/mrm.22361 Monti, 2011, Statistical analysis of fMRI time-series: a critical review of the GLM approach, Front. Hum. Neurosci., 5, 28, 10.3389/fnhum.2011.00028 Murphy, 2007, How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration, Neuroimage, 34, 565, 10.1016/j.neuroimage.2006.09.032 Nicholson, 2017, The neurobiology of emotion regulation in posttraumatic stress disorder: amygdala downregulation via real-time fMRI neurofeedback, Hum. Brain Mapp., 38, 541, 10.1002/hbm.23402 Ochmann, 2017, Does functional connectivity provide a marker for cognitive rehabilitation effects in alzheimer's disease? An interventional study, J. Alzheimer's Dis., 57, 1303, 10.3233/JAD-160773 Peterson, 1999, An fMRI study of stroop word-color interference: evidence for cingulate subregions subserving multiple distributed attentional systems, Biol. Psychiatry, 45, 1237, 10.1016/S0006-3223(99)00056-6 Pfeuffer, 2002, Correction of physiologically induced global off-resonance effects in dynamic echo-planar and spiral functional imaging, Magn. Reson. Med., 47, 344, 10.1002/mrm.10065 Pollock, 1999 Poser, 2018, Pulse sequences and parallel imaging for high spatiotemporal resolution MRI at ultra-high field, Neuroimage, 168, 101, 10.1016/j.neuroimage.2017.04.006 Posse, 2012, Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging, Neuroimage, 61, 115, 10.1016/j.neuroimage.2012.02.059 Posse, 2013, High-Speed real-time resting-state fMRI using multi-slab echo-volumar imaging, Front. Hum. Neurosci., 7, 479, 10.3389/fnhum.2013.00479 Rabrait, 2008, High temporal resolution functional MRI using parallel echo volumar imaging, J. Magn. Reson. Imaging, 27, 744, 10.1002/jmri.21329 Riemenschneider, 2019, Targeted partial reconstruction for real-time fMRI with arbitrary trajectories, Magn. Reson. Med., 81, 1118, 10.1002/mrm.27478 Riemenschneider, 2015, Nonlinear trajectories in real-time fMRI using target volumes, Abstract #2055, 4, 232908 Rogers, 2010, Functional MRI and multivariate autoregressive models, Magn. Reson. Imag., 28, 1058, 10.1016/j.mri.2010.03.002 Sahib, 2018, Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm, PLoS One, 13, 10.1371/journal.pone.0190480 Setsompop, 2012, Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty, Magn. Reson. Med., 67, 1210, 10.1002/mrm.23097 Sorger, 2012, A real-time fMRI-based spelling device immediately enabling robust motor-independent communication, Curr. Biol., 22, 1333, 10.1016/j.cub.2012.05.022 Sulzer, 2013, Real-time fMRI neurofeedback: progress and challenges, Neuroimage, 76, 386, 10.1016/j.neuroimage.2013.03.033 Tak, 2014, Statistical analysis of fNIRS data: a comprehensive review, Neuroimage, 85, 72, 10.1016/j.neuroimage.2013.06.016 Thibault, 2018 Todd, 2015, Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking, Neuroimage, 113, 1, 10.1016/j.neuroimage.2015.03.013 Uga, 2014, Optimizing the general linear model for functional near-infrared spectroscopy: an adaptive hemodynamic response function approach, Neurophotonics, 1, 10.1117/1.NPh.1.1.015004 Welvaert, 2013, On the definition of signal-to-noise ratio and contrast-to-noise ratio for fMRI data, PLoS One, 8, 10.1371/journal.pone.0077089 Yakupov, 2017, False fMRI activation after motion correction, Hum. Brain Mapp., 38, 4497, 10.1002/hbm.23677 Zahneisen, 2011, Three-dimensional MR-encephalography: fast volumetric brain imaging using rosette trajectories, Magn. Reson. Med., 65, 1260, 10.1002/mrm.22711 Zahneisen, 2012, Single shot concentric shells trajectories for ultra fast fMRI, Magn. Reson. Med., 68, 484, 10.1002/mrm.23256 Zaitsev, 2017, Prospective motion correction in functional MRI, Neuroimage, 154, 33, 10.1016/j.neuroimage.2016.11.014 Zilverstand, 2014, Windowed correlation: a suitable tool for providing dynamic fMRI-based functional connectivity neurofeedback on task difficulty, PLoS One, 9, 10.1371/journal.pone.0085929