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