OALib Journal期刊
ISSN: 2333-9721
费用：99美元

投递稿件

查看量	下载量

相关文章
更多...

PLOS ONE 2013

Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox

DOI: 10.1371/journal.pone.0081658

Jo？o R. Sato, Rodrigo Basilio, Fernando F. Paiva, Griselda J. Garrido, Ivanei E. Bramati, Patricia Bado, Fernanda Tovar-Moll, Roland Zahn, Jorge Moll

Full-Text Cite this paper Add to My Lib

Abstract:

The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available.

References

[1]	Miller N (1978) Biofeedback and Visceral. Learning - Annu Rev Psychol 29: 373–404. doi:10.1146/annurev.ps.29.020178.002105.
[2]	Birbaumer N, Ramos Murguialday A, Weber C, Montoya P (2009) Neurofeedback and Brain-Computer Interface. Clinical Applications chapter 8. Int Rev Neurobiol Volume 86: 107–117. doi:10.1016/S0074-7742(09)86008-X. PubMed: 19607994.
[3]	Evans JR, Budzynski TH, Budzynski HK, Abarbanel A (2008) Introduction to Quantitative EEG and Neurofeedback, Second Edition: Advanced Theory and Applications. Academic Press.
[4]	Hammond DC (2005) Neurofeedback Treatment of Depression and Anxiety. J Adult Dev 12: 131–137. doi:10.1007/s10804-005-7029-5.
[5]	Kotchoubey B, Strehl U, Uhlmann C, Holzapfel S, K？nig M et al. (2001) Modification of Slow Cortical Potentials in Patients with Refractory Epilepsy: A Controlled Outcome Study. Epilepsia 42: 406–416. doi:10.1046/j.1528-1157.2001.22200.x. PubMed: 11442161.
[6]	Lubar JF, Swartwood MO, Swartwood JN, O’Donnell PH (1995) Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance. Biofeedback Self Regul 20: 83–99. doi:10.1007/BF01712768. PubMed: 7786929.
[7]	Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113: 767–791. doi:10.1016/S1388-2457(02)00057-3. PubMed: 12048038.
[8]	Sorger B, Reithler J, Dahmen B, Goebel R (2012) A real-time fMRI-based spelling device immediately enabling robust motor-independent communication. Curr Biol 22: 1333–1338. doi:10.1016/j.cub.2012.05.022. PubMed: 22748322.
[9]	Freire MAM, Morya E, Faber J, Santos JR, Guimaraes JS et al. (2011) Comprehensive analysis of tissue preservation and recording quality from chronic multielectrode implants. PLOS ONE 6: e27554. doi:10.1371/journal.pone.0027554. PubMed: 22096594.
[10]	Cox RW, Jesmanowicz A, Hyde JS (1995) Real-Time Functional. Magnetic Resonance Imaging - Magn Reson Med 33: 230–236. doi:10.1002/mrm.1910330213.
[11]	deCharms RC, Maeda F, Glover GH, Ludlow D, Pauly JM et al. (2005) Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci U S A 102: 18626–18631. doi:10.1073/pnas.0505210102. PubMed: 16352728.
[12]	LaConte SM, Peltier SJ, Hu XP (2007) Real-time fMRI using brain-state classification. Hum Brain Mapp 28: 1033–1044. doi:10.1002/hbm.20326. PubMed: 17133383.
[13]	Phan KL, Fitzgerald DA, Gao K, Moore GJ (2004) Real-time fMRI of cortico-limbic brain activity during emotional processing. Neuroreport 15: 527–532. doi:10.1097/00001756-200403010-00029. PubMed: 15094517.
[14]	Posse S, Fitzgerald D, Gao K, Habel U, Rosenberg D et al. (2003) Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness. NeuroImage 18: 760–768. doi:10.1016/S1053-8119(03)00004-1. PubMed: 12667853.
[15]	Weiskopf N, Veit R, Erb M, Mathiak K, Grodd W et al. (2003) Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. NeuroImage 19: 577–586. doi:10.1016/S1053-8119(03)00145-9. PubMed: 12880789.
[16]	Kay KN, Naselaris T, Prenger RJ, Gallant JL (2008) Identifying natural images from human brain activity. Nature 452: 352–355. doi:10.1038/nature06713. PubMed: 18322462.
[17]	Mitchell TM, Shinkareva SV, Carlson A, Chang KM, Malave VL et al. (2008) Predicting human brain activity associated with the meanings of nouns. Science 320: 1191–1195. doi:10.1126/science.1152876. PubMed: 18511683.
[18]	Naselaris T, Prenger RJ, Kay KN, Oliver M, Gallant JL (2009) Bayesian reconstruction of natural images from human brain activity. Neuron 63: 902–915. doi:10.1016/j.neuron.2009.09.006. PubMed: 19778517.
[19]	LaConte SM (2011) Decoding fMRI brain states in real-time. NeuroImage 56: 440–454. doi:10.1016/j.neuroimage.2010.06.052. PubMed: 20600972.
[20]	Sitaram R, Lee S, Ruiz S, Rana M, Veit R et al. (2011) Real-time support vector classification and feedback of multiple emotional brain states. NeuroImage 56: 753–765. doi:10.1016/j.neuroimage.2010.08.007. PubMed: 20692351.
[21]	Shibata K, Watanabe T, Sasaki Y, Kawato M (2011) Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334: 1413–1415. doi:10.1126/science.1212003. PubMed: 22158821.
[22]	Hyman SE (2007) Can neuroscience be integrated into the DSM-V? Nat Rev Neurosci 8: 725–732. doi:10.1038/nrn2218. PubMed: 17704814.
[23]	Ruiz S, Lee S, Soekadar SR, Caria A, Veit R et al. (2013) Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia. Hum Brain Mapp 34: 200–212. doi:10.1002/hbm.21427. PubMed: 22021045.
[24]	Sato JR, Fujita A, Thomaz CE, Martin MdaG. , da GM, Mour？o-Miranda J et al. (2009) Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction. NeuroImage 46: 105–114. doi:10.1016/j.neuroimage.2009.01.032. PubMed: 19457392.
[25]	Haxby JV (2012) Multivariate pattern analysis of fMRI: the early beginnings. NeuroImage 62: 852–855. doi:10.1016/j.neuroimage.2012.03.016. PubMed: 22425670.
[26]	Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) FSL. NeuroImage 62: 782–790. doi:10.1016/j.neuroimage.2011.09.015. PubMed: 21979382.
[27]	Chang C, Lin C (2012) LIBSVM: A library for support vector machine, 2001. Available at . http://www.csie.ntu.edu.tw/~cjlin/libsvm？/.
[28]	Zotev V, Krueger F, Phillips R, Alvarez RP, Simmons WK et al. (2011) Self-regulation of amygdala activation using real-time FMRI neurofeedback. PLOS ONE 6: e24522. doi:10.1371/journal.pone.0024522. PubMed: 21931738.
[29]	Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17: 825-841. doi:10.1006/nimg.2002.1132. PubMed: 12377157.
[30]	Woolrich MW, Ripley BD, Brady M, Smith SM (2001) Temporal autocorrelation in univariate linear modeling of FMRI data. NeuroImage 14: 1370–1386. doi:10.1006/nimg.2001.0931. PubMed: 11707093.
[31]	Poldrack R, Mumford J, Nichols T (2011) Handbook of functional mri data analysis. Cambridge University Press.
[32]	Vapnik V (1998) Statistical learning theory.
[33]	Bishop C (2006) Pattern recognition and machine learning. New York: Springer.
[34]	Mour？o-Miranda J, Bokde AL, Born C (2005) Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 28: 980-995. doi:10.1016/j.neuroimage.2005.06.070. PubMed: 16275139.
[35]	Sato JR, Thomaz CE, Cardoso EF, Fujita A, Martin MdaG. , da GM et al. (2008) Hyperplane navigation: a method to set individual scores in fMRI group datasets. NeuroImage 42: 1473–1480. doi:10.1016/j.neuroimage.2008.06.024. PubMed: 18644242.
[36]	Caria A, Veit R, Sitaram R, Lotze M (2007) Regulation of anterior insular cortex activity using real-time fMRI. NeuroImage 35: 1238-1246. doi:10.1016/j.neuroimage.2007.01.018. PubMed: 17336094.
[37]	Jenkinson M (1999) Measuring transformation error by RMS deviation. Studholme, C, Hill, DLG, Hawkes, DJ.
[38]	Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59: 2142–2154. doi:10.1016/j.neuroimage.2011.10.018. PubMed: 22019881.
[39]	Mayka MA, Corcos DM, Leurgans SE, Vaillancourt DE (2006) Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: a meta-analysis. NeuroImage 31: 1453–1474. doi:10.1016/j.neuroimage.2006.02.004. PubMed: 16571375.
[40]	Sitaram R, Veit R, Stevens B, Caria A, Gerloff C et al. (2012) Acquired control of ventral premotor cortex activity by feedback training: an exploratory real-time FMRI and TMS study. Neurorehabil Neural Repair 26: 256–265. doi:10.1177/1545968311418345. PubMed: 21903976.
[41]	Linden DE, Habes I, Johnston SJ (2012) Real-Time Self-Regulation of Emotion Networks in Patients with Depression. PLOS ONE 7: e38115. doi:10.1371/journal.pone.0038115. PubMed: 22675513.
[42]	Green S, Lambon Ralph MA, Moll J, Deakin JFW, Zahn R (2012) Guilt-selective functional disconnection of anterior temporal and subgenual cortices in major depressive disorder. Arch Gen Psychiatry 69: 1014–1021. doi:10.1001/archgenpsychiatry.2012.135. PubMed: 22638494.
[43]	Haynes J-D, Sakai K, Rees G, Gilbert S, Frith C et al. (2007) Reading hidden intentions in the human brain. Curr Biol 17: 323–328. doi:10.1016/j.cub.2006.11.072. PubMed: 17291759.
[44]	Poldrack RA (2011) Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron 72: 692–697. doi:10.1016/j.neuron.2011.11.001. PubMed: 22153367.
[45]	Sato JR, Gra？a Morais Martin M, Fujita A, Mour？o-Miranda J, Brammer MJ et al. (2009) An fMRI normative database for connectivity networks using one-class support vector machines. Hum Brain Mapp 30: 1068–1076. doi:10.1002/hbm.20569. PubMed: 18412113.
[46]	deCharms RC (2008) Applications of real-time fMRI. Nat Rev Neurosci 9: 720–729. doi:10.1038/nrn2414. PubMed: 18714327.
[47]	Weiskopf N (2012) Real-time fMRI and its application to neurofeedback. NeuroImage 62: 682–692. doi:10.1016/j.neuroimage.2011.10.009. PubMed: 22019880.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133