Functional Magnetic Resonance Neurofeedback

Anterior Cingulate Cortex Training

Control over brain activation and pain learned by using real-time functional MRI.
deCharms RC, Maeda F, Glover GH, Ludlow D, Pauly JM, Soneji D, Gabrieli JD, Mackey SC (2005). Proc Natl Acad Sci U S A. 102, 18626-31.
Subjects demonstrated voluntary control over activation in the rostral ACC, an area critical to pain perception, and these changes in activation are powerful enough to impact severe chronic pain. Single-session effects on pain severity were observed in patients with Complex Regional Pain Syndrome, which is known to be resistant to remediation in its more advanced stages.

(Two years after the above study was published very similar results were obtained with Infra-low frequency neurofeedback training at frequencies down to 0.01Hz, also with patients suffering from Complex Regional Pain Syndrome [Mark P. Jensen Ph.D., Caroline Grierson, R.N., Veronika Tracy-Smith, Ph.D., Stacy C. Bacigalupi, M.A., Siegfried Othmer, Ph.D.(2007) Neurofeedback treatment for pain associated with Complex Regional Pain Syndrome Type I. Journal of Neurotherapy, 11(1), pp 45-53])


Real-time fMRI applied to pain management.
Chapin H, Bagarinao E, Mackey S. (2012). Neurosci Lett, Jun 29;520(2), 174-81.
Anterior cingulate cortex (ACC) activation training altered experiences of pain


Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: a preliminary real-time fMRI study.
Li X, Hartwell KJ, Borckardt J, Prisciandaro JJ, Saladin ME, Morgan PS, Johnson KA, Lematty T, Brady KT, George MS. (2013). Addict Biol, 18(4), 739-48.
Three sessions of ACC-activating neurofeedback reduced self-reported craving in 9 subjects. The reduction in craving correlated with ACC activity reduction. In a separate protocol, smokers were unable to increase mPFC activity during an "increase resistance" approach.


Reduction of cue-induced craving through realtime neurofeedback in nicotine users: the role of region of interest selection and multiple visits.
Hanlon CA, Hartwell KJ, Canterberry M, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Brady KT, George MS. (2013). Psychiatry Res, Jul 30;213(1), 79-81.
Smokers were trained to decrease ventral ACC activity, a region ostensibly involved in craving; and increase activity in the dorsomedial Pre-Frontal Cortex (PFC), associated with "resisting" with successful reduction of craving.


The day-after effect: long term, Hebbian-like restructuring of resting-state fMRI patterns induced by a single epoch of cortical activation.
Harmelech T, Preminger S, Wertman E, Malach R. (2013). J Neurosci, May 29;33(22), 9488-97.
A single 30-min neurofeedback session of dorsal ACC activity altered its functional connectivity, an effect that was observed to be even stronger the following day.


Modulation of subgenual anterior cingulate cortex activity with real-time neurofeedback.
Hamilton JP, Glover GH, Hsu JJ, Johnson RF, Gotlib IH. (2011). Hum Brain Mapp, 32(1), 22-31.
Activity of the subgenual ACC, implicated in affective disorders, can be controlled with the aid of neurofeedback.


Social reinforcement can regulate localized brain activity.
Mathiak KA, Koush Y, Dyck M, Gaber TJ, Alawi E, Zepf FD, Zvyagintsev M, Mathiak K. (2010). Eur Arch Psychiatry Clin Neurosci, 260 Suppl 2, S132-6.
We can alter our ACC activity -- here, using smiling face as feedback device.


Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data.
Weiskopf N, Veit R, Erb M, Mathiak K, Grodd W, Goebel R, Birbaumer N. (2003). Neuroimage, 19, 577-86.
Showed that ACC regulation is possible.

Posterior Cingulate Cortex Training

This training targets the principal posterior hub of the Default Mode Network (DMN)

Functional Alteration of the DMN by Learned Regulation of the PCC Using Real-Time fMRI.
Zhang G, Zhang H, Li X, Zhao X, Yao L, Long Z. (2013). IEEE Trans Neural Syst Rehabil Eng, 21(4), 595-606.
Individuals learned to decrease activity in the posterior cingulate cortex (PCC) using motor imagery strategy, which produces decreased activity in other DMN areas (mPFC/ACC), whereas controls showed increased activity of this region.

Pre-Frontal Cortex Training

Real-time self-regulation of emotion networks in patients with depression.
Linden DE, Habes I, Johnston SJ, Linden S, Tatineni R, Subramanian L, Sorger B, Healy D, Goebel R. (2012). PLoS One, 7(6), e38115.
Eight patients with depression learned to up-regulate brain areas involved in the generation of positive emotions (such as the ventro-lateral prefrontal cortex [vlPFC] and insula) during four neurofeedback sessions, which improved their clinical symptoms, unlike the control group.

Self-regulation of regional cortical activity using real-time fMRI: the right inferior frontal gyrus and linguistic processing.
Rota G, Sitaram R, Veit R, Erb M, Weiskopf N, Dogil G, Birbaumer N (2009). Human Brain Mapping, 30, 1605-14
fMRI-neurofeedback training of BA 45 improved emotional prosody processing but not syntactic processing.

Orbito-Frontal Cortex and Insula Training

Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity.
Scheinost D, Stoica T, Saksa J, Papademetris X, Constable RT, Pittenger C, Hampson M. (2013). Transl Psychiatry, Apr 30;3, e250.
OFC connectivity training showed persistent connectivity changes along with improvements in anxiety.

Regulation of anterior insular cortex activity using real-time fMRI.
Caria A, Veit R, Sitaram R, Lotze M, Weiskopf N, Grodd W, Birbaumer N. (2007). Neuroimage, Apr 15;35(3), 1238-46.
Regulation of anterior insular cortex activity using real-time fMRI.

Neurofeedback: A promising tool for the self-regulation of emotion networks.
Johnston SJ, Boehm SG, Healy D, Goebel R, Linden DE. (2010). Neuroimage, 49, 1066-72.
Demonstrated how to use fMRI-based neurofeedback to train emotion networks.

Training of the Dopamine System

Neurofeedback-mediated self-regulation of the dopaminergic midbrain.
Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R. (2013). Neuroimage, 75C, 176-184.
Substantia Nigra/Ventral Tegmental Area (SN/VTA) can be voluntarily activated by imagery, and further enhanced by neurofeedback.

Neurofeedback-mediated self-regulation of the dopaminergic midbrain.
Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R. (2013). Neuroimage, 83C, 817-825.
Individuals can voluntary control Substantia Nigra/Ventral Tegmental Area (SN/VTA) activity via neurofeedback, which may lead to endogenous control of dopamine.

Clinical Correlates (Not Modulation)

Effortless awareness: using real time neurofeedback to investigate correlates of posterior cingulate cortex activity in meditators' self-report.
Garrison KA, Santoyo JF, Davis JH, Thornhill TA 4th, Kerr CE, Brewer JA. (2013). Front Hum Neurosci, Aug 6;7, 440.
For meditators, undistracted awareness, "effortless doing," or contentment corresponds with PCC deactivation whereas distracted awareness, "controlling" or discontentment correspond with PCC activation.

Abnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach.
Ruiz S, Birbaumer N, Sitaram R. (2013). Front Psychiatry, Mar 22;4, 17.
Reviews hemodynamics-based neuroimaging that supports the abnormal connectivity hypothesis in schizophrenia.

Real-time fMRI links subjective experience with brain activity during focused attention.
Garrison KA, Scheinost D, Worhunsky PD, Elwafi HM, Thornhill TA 4th, Thompson E, Saron C, Desbordes G, Kober H, Hampson M, Gray JR, Constable RT, Papademetris X, Brewer JA. (2013). Neuroimage, Nov 1;81, 110-8.
Examines feasibility of using real-time fMRI (rt-fMRI) to link brain activity with subjective experience.

Anatomical and Functional Correlates of Training Success in Healthy Populations

Neurofeedback Training Induces Changes in White and Gray Matter.
Ghaziri J, Tucholka A, Larue V, Blanchette-Sylvestre M, Reyburn G, Gilbert G, Levesque J, Beauregard M. (2013). Clin EEG Neurosci, Mar 26.
Neurofeedback training (NFT) designed to improve sustained attention induced structural changes in white matter (WM) pathways and gray matter (GM) volume (GMV) in frontal and parietal cortical areas, as shown with DTI.

Mind over chatter: plastic up-regulation of the fMRI salience network directly after EEG neurofeedback.
Ros T, Theberge J, Frewen PA, Kluetsch R, Densmore M, Calhoun VD, Lanius RA. (2013). Neuroimage, Jan 15;65, 324-35.
Compared to sham-feedback, a 30-min session of voluntary reduction of alpha activity at site Pz (using EEG neurofeedback) induced an increase of connectivity within the salience network involved in intrinsic alertness (dorsal anterior cingulate, dACC = Broadman Area 32), which was detectable 30 min after termination of training.

Self-regulation of amygdala activation using real-time FMRI neurofeedback.
Zotev V, Krueger F, Phillips R, Alvarez RP, Simmons WK, Bellgowan P, Drevets WC, Bodurka J. (2011). PLoS One, 6(9), e24522.
Healthy subjects can learn to regulate their amygdala activation using rtfMRI neurofeedback.

The morphology of mid-cingulate cortex predicts frontal-midline theta neurofeedback success.
Enriquez-Geppert S, Huster RJ, Scharfenort R, Mokom ZN, Vosskuhl J, Figge C, Zimmermann J, Herrmann CS (2013). Front Hum Neurosci., 7, 453.
Volume of mid-cingulate cortex and underlying white matter predicts responsiveness to training, while no predictability was found for anatomical correlates of the frontal cortices. These findings suggest a neuroanatomical foundation for learning how to control one's own brain activity (i.e., frontal-midline theta training).

Dynamic reconfiguration of human brain functional networks through neurofeedback.
Haller S, Kopel R, Jhooti P, Haas T, Scharnowski F, Lovblad KO, Scheffler K, Van De Ville D. (2013). Neuroimage, Nov 1;81, 243-52.
Neurofeedback learning is mediated by widespread changes in functional connectivity.

Improving visual perception through neurofeedback.
Scharnowski F, Hutton C, Josephs O, Weiskopf N, Rees G. (2012). J Neurosci, Dec 5;32(49), 17830-41.
Brain training delivered particular perceptual enhancements.

Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery.
Chiew M, LaConte SM, Graham SJ. (2012). Neuroimage, May 15;61(1), 21-31.
Some people are able to activate their motor areas preferentially using neurofeedback, but not all. The utility of neurofeedback for motor rehabilitation following stroke, for instance, is discussed.

Real-time fMRI-based neurofeedback reinforces causality of attention networks.
Lee JH, Kim J, Yoo SS. (2012).Neurosci Res, 72(4), 347-54.
Using Granger causality analysis, connectivity was shown to be reinforced within the task-related network but diminished between this network and the DMN after training.

Anatomical and Functional Correlates of Training Success in Clinical Populations

Parkinson's Disease
Real-time functional magnetic resonance imaging neurofeedback for treatment of Parkinson's disease.
Subramanian L, Hindle JV, Johnston S, Roberts MV, Husain M, Goebel R, Linden D. (2011). J Neurosci, Nov 9;31(45), 16309-17.
Self-modulation of cortico-subcortical motor circuits can be achieved by Parkinson's disease patients through neurofeedback

Schizophrenia
Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia.
Ruiz S, Lee S, Soekadar SR, Caria A, Veit R, Kircher T, Birbaumer N, Sitaram R. (2013). Hum Brain Mapp, 34(1), 200-12.
Nine patients with schizophrenia were trained to regulate hemodynamics of the bilateral anterior insula. Following successful self-regulation, disgust faces were recognized more accurately but happy faces less accurately.

Tinnitus
Real-time fMRI feedback training may improve chronic tinnitus.
Haller S, Birbaumer N, Veit R. (2010). Eur Radiol, 20(3), 696-703.
Auditory activation in tinnitus patients can be decreased with rtfMRI neurofeedback, although only two of six reported symptom improvements.

Theory and Reviews

Improving the neural mechanisms of cognition through the pursuit of happiness.
Subramaniam K, Vinogradov S. (2013). Front Hum Neurosci, Aug 7;7, 452.
This paper reviews how positive mood states modulate cognition during creative problem-solving.

Learned regulation of brain metabolism.
Birbaumer N, Ruiz S, Sitaram R. (2013). Trends Cogn Sci, 17(6), 295-302.
Possible underlying neural mechanism of self-regulation focusing on basal ganglia in skill learning and neurofeedback, and clarify that brain-self-regulation need not be an explicit and conscious process as often mistakenly held.

Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks.
Ruiz S, Buyukturkoglu K, Rana M, Birbaumer N, Sitaram R. (2013). Biol Psychol
Reviews the principles of rtfMRI neurofeedback, its applications, benefits and limitations

Real-time fMRI neurofeedback: progress and challenges.
Sulzer J, Haller S, Scharnowski F, Weiskopf N, Birbaumer N, Blefari ML, Bruehl AB, Cohen LG, DeCharms RC, Gassert R, Goebel R, Herwig U, LaConte S, Linden D, Luft A, Seifritz E, Sitaram R. (2013). Neuroimage, Aug 1;76, 386-99.
Reviews study designs, scientific and clinical applications, rtfMRI learning mechanisms and the future.

The use of functional neuroimaging to evaluate psychological and other non-pharmacological treatments for clinical pain.
Jensen KB, Berna C, Loggia ML, Wasan AD, Edwards RR, Gollub RL. (2012). Neurosci Lett, Jun 29;520(2), 156-64.
Reviews utility of neuroimaging in diagnosis and treatment.

Technical Aspects

Single subject task-related BOLD signal artifact in a real-time fMRI feedback paradigm.
Zhang X, Ross TJ, Salmeron BJ, Yang S, Yang Y, Stein EA. (2011). Hum Brain Mapp, 32(4), 592-600.
Warns how BOLD signal is vulnerable to contamination from non-neuronal sources like eye movement that can also be shaped by the feedback provided.

Intermittent real-time fMRI feedback is superior to continuous presentation for a motor imagery task: a pilot study.
Johnson KA, Hartwell K, LeMatty T, Borckardt J, Morgan PS, Govindarajan K, Brady K, George MS. (2012). J Neuroimaging, 22(1), 58-66.
More individuals were better able to increase fMRI signal activity with intermittent feedback than with continuous feedback.

Real-time automated spectral assessment of the BOLD response for neurofeedback at 3 and 7T.
Koush Y, Elliott MA, Scharnowski F, Mathiak K. (2013). J Neurosci Methods, Sep 15;218(2), 148-60.
Examines dynamics at two background magnetic field strengths.

Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback.
Zotev V, Phillips R, Yuan H, Misaki M, Bodurka J. (2013). Neuroimage, May 11.
Feasibility study of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain.

Recovery of the default mode network after demanding neurofeedback training occurs in spatio-temporally segregated subnetworks.
Van De Ville D, Jhooti P, Haas T, Kopel R, Lovblad KO, Scheffler K, Haller S. (2012). Neuroimage, 63(4), 1775-81.
Feasibility of training networks rather than areas.

A new neuroscientific approach using decoded neurofeedback (DecNef).
Shibata K. (2012). Rinsho Shinkeigaku, 52(11), 1185-7.
Tests causal relationships between neural activation in a target brain area and changes in perception or performance.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.
Zheng W, Ackley ES, Martìnez-Ramen M, Posse S. (2013). Magn Reson Imaging, 31(2), 247-61.
Method of identifying activation patterns from neurofeedback

Semi-blind independent component analysis of fMRI based on real-time fMRI system.
Ma X, Zhang H, Zhao X, Yao L, Long Z. (2013). IEEE Trans Neural Syst Rehabil Eng, 21(3), 416-26.
Technical approach to improve signal

Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation.
Shibata K, Watanabe T, Sasaki Y, Kawato M. (2011). Science, 334, 1413-5.
Induced plasticity changes in visual cortex without awareness.

Quantification of adverse events associated with functional MRI scanning and with real-time fMRI-based training.
Hawkinson JE, Ross AJ, Parthasarathy S, Scott DJ, Laramee EA, Posecion LJ, Rekshan WR, Sheau KE, Njaka ND, Bayley PJ, deCharms RC. (2012). Int J Behav Med, 19(3), 372-81.
They demonstrate the safety of repetitive fMRI scanning paradigms.

Feasibility Studies, Technical Refinements, and other References (without Annotation)

Upregulation of emotion areas through neurofeedback with a focus on positive mood.
Johnston S, Linden DE, Healy D, Goebel R, Habes I, Boehm SG. (2011). Cogn Affect Behav Neurosci, 11(1), 44-51.

Neurofeedback of two motor functions using supervised learning-based real-time functional magnetic resonance imaging.
Papageorgiou TD, Curtis WA, McHenry M, LaConte SM. (2009). Conf Proc IEEE Eng Med Biol Soc, 2009, 5377-80.

Integrated real-time neurofeedback system to raise the frontal lobe activity: design and implementation.
Gil Y, Li G, Lee J. (2009). Conf Proc IEEE Eng Med Biol Soc, 2009, 845-8.

Brain imaging: on the way toward a therapeutic discipline
Schneider F, Backes V, Mathiak K. (2009). Eur Arch Psychiatry Clin Neurosci, 259 Suppl 2, S143-7.

Neuroimaging in psychiatry: from bench to bedside.
Linden DE, Fallgatter AJ. (2009). Front Hum Neurosci, Dec 23;3, 49.

Neurofeedback and brain-computer interface clinical applications.
Birbaumer N, Ramos Murguialday A, Weber C, Montoya P. (2009). Int Rev Neurobiol, 86, 107-17.

An open-source hardware and software system for acquisition and real-time processing of electrophysiology during high field MRI.
Purdon PL, Millan H, Fuller PL, Bonmassar G. (2008). J Neurosci Methods, Nov 15;175(2), 165-86.

A new concept of a unified parameter management, experiment control, and data analysis in fMRI: application to real-time fMRI at 3T and 7T.
Hollmann M, Manch T, Mulla-Osman S, Tempelmann C, Stadler J, Bernarding J. (2008). J Neurosci Methods, Oct 30;175(1), 154-62.

Building virtual reality fMRI paradigms: a framework for presenting immersive virtual environments.
Mueller C, Luehrs M, Baecke S, Adolf D, Luetzkendorf R, Luchtmann M, Bernarding J. (2012). J Neurosci Methods, Aug 15;209(2), 290-8.

Multiecho coarse voxel acquisition for neurofeedback fMRI.
Kuo AY, Chiew M, Tam F, Cunningham C, Graham SJ. (2011). Magn Reson Med, 65(3), 715-24.

[Functional magnetic resonance imaging in psychiatry and psychotherapy].
Derntl B, Habel U, Schneider F. (2010). Nervenarzt, 81(1), 16-23.

Neurofeedback: A promising tool for the self-regulation of emotion networks.
Johnston SJ, Boehm SG, Healy D, Goebel R, Linden DE. (2010). Neuroimage, Jan 1;49(1), 1066-72.

Computing moment-to-moment BOLD activation for real-time neurofeedback.
Hinds O, Ghosh S, Thompson TW, Yoo JJ, Whitfield-Gabrieli S, Triantafyllou C, Gabrieli JD. (2011). Neuroimage, Jan 1;54(1), 361-8.

Decoding fMRI brain states in real-time.
LaConte SM. (2011). Neuroimage, May 15;56(2), 440-54.

Real-time fMRI and its application to neurofeedback.
Weiskopf N. (2012). Neuroimage, Aug 15;62(2), 682-92.

Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback.
Berman BD, Horovitz SG, Venkataraman G, Hallett M. (2012). Neuroimage, Jan 16;59(2), 917-25.

Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI.
Koush Y, Zvyagintsev M, Dyck M, Mathiak KA, Mathiak K. (2012). Neuroimage, Jan 2;59(1), 478-89.

Neurofeedback-mediated self-regulation of the dopaminergic midbrain.
Sulzer J, Sitaram R, Blefari ML, Kollias S, Birbaumer N, Stephan KE, Luft A, Gassert R. (2013). Neuroimage, Mar 1;75C, 176-184.

Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI.
Koush Y, Rosa MJ, Robineau F, Heinen K, W Rieger S, Weiskopf N, Vuilleumier P, Van De Ville D, Scharnowski F. (2013). Neuroimage, Nov 1;81, 422-30.

[Towards a new approach of neurophysiology in clinical psychiatry: functional magnetic resonance imaging neurofeedback applied to emotional dysfunctions].
Micoulaud-Franchi JA, Fakra E, Cermolacce M, Vion-Dury J. (2012). Neurophysiol Clin, 42(3), 79-94.

Low-frequency fluctuation in continuous real-time feedback of finger force: a new paradigm for sustained attention.
Dong ZY, Liu DQ, Wang J, Qing Z, Zang ZX, Yan CG, Zang YF. (2012). Neurosci Bull, 28(4), 456-67.

Another kind of 'BOLD Response': answering multiple-choice questions via online decoded single-trial brain signals.
Sorger B, Dahmen B, Reithler J, Gosseries O, Maudoux A, Laureys S, Goebel R. (2009). Prog Brain Res, 177, 275-92.

Real-time functional magnetic imaging-brain-computer interface and virtual reality promising tools for the treatment of pedophilia.
Renaud P, Joyal C, Stoleru S, Goyette M, Weiskopf N, Birbaumer N. (2011). Prog Brain Res, 192, 263-72.

Sustained Reduction of Nicotine Craving With Real-Time Neurofeedback: Exploring the Role of Severity of Dependence.
Canterberry M, Hanlon CA, Hartwell KJ, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Saladin ME, Brady KT, George MS. (2013). Nicotine Tob Res, Aug 9.

Real-time fMRI: a tool for local brain regulation.
Caria A, Sitaram R, Birbaumer N. (2012). Neuroscientist, 18(5), 487-501.

Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery.
Yoo SS, Lee JH, O'Leary H, Panych LP, Jolesz FA. (2008). Int J Imaging Syst Technol, Jun 13;18(1), 69-78.

Atlas-based multichannel monitoring of functional MRI signals in real-time: automated approach.
Lee JH, O'Leary HM, Park H, Jolesz FA, Yoo SS. (2008). Hum Brain Mapp, 29(2), 157-66.

The effects of neurofeedback training in the cognitive division of the anterior cingulate gyrus.
Cannon R, Lubar J, Congedo M, Thornton K, Towler K, Hutchens T. (2007). Int J Neurosci, 117(3), 337-57.

Annotation: neurofeedback - train your brain to train behaviour.
Heinrich H, Gevensleben H, Strehl U. (2007). J Child Psychol Psychiatry, 48(1), 3-16.

Real-time functional MRI: development and emerging applications.
Bagarinao E, Nakai T, Tanaka Y. (2006). Magn Reson Med Sci, 5(3), 157-65.

Increasing cortical activity in auditory areas through neurofeedback functional magnetic resonance imaging.
Yoo SS, O'Leary HM, Fairneny T, Chen NK, Panych LP, Park H, Jolesz FA. (2006). Neuroreport, Aug 21;17(12), 1273-8.

Functional magnetic resonance imaging investigation of the effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder.
Beauregard M, Levesque J. (2006). Appl Psychophysiol Biofeedback, 31(1), 3-20.

Effect of neurofeedback training on the neural substrates of selective attention in children with attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study.
Levesque J, Beauregard M, Mensour B. (2006). Neurosci Lett, Feb 20;394(3), 216-21.

An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI).
Hinterberger T, Weiskopf N, Veit R, Wilhelm B, Betta E, Birbaumer N. (2004). IEEE Trans Biomed Eng, 51(6), 971-4.

Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI).
Weiskopf N, Mathiak K, Bock SW, Scharnowski F, Veit R, Grodd W, Goebel R, Birbaumer N. (2004). IEEE Trans Biomed Eng, 51(6), 966-70.

Real-time independent component analysis of fMRI time-series.
Esposito F, Seifritz E, Formisano E, Morrone R, Scarabino T, Tedeschi G, Cirillo S, Goebel R, Di Salle F. (2003). Neuroimage, 20(4), 2209-24.

Functional MRI for neurofeedback: feasibility study on a hand motor task.
Yoo SS, Jolesz FA. (2002). Neuroreport, Aug 7;13(11), 1377-81.